2024
|
Wijayanto, Arif K; Prasetyo, Lilik B; Hudjimartsu, Sahid A; Hongo, Chiharu Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics Journal Article In: Smart Agricultural Technology, vol. 10, iss. March 2025, 2024. @article{nokey,
title = {Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics},
author = {Arif K Wijayanto and Lilik B Prasetyo and Sahid A Hudjimartsu and Chiharu Hongo},
url = {https://doi.org/10.1016/j.atech.2024.100766},
doi = {https://doi.org/10.1016/j.atech.2024.100766},
year = {2024},
date = {2024-12-30},
urldate = {2024-12-30},
journal = {Smart Agricultural Technology},
volume = {10},
issue = {March 2025},
abstract = {This study introduces an innovative method for assessing bacterial leaf blight (BLB) in paddy fields using multispectral UAV (Unmanned Aerial Vehicle) data and patch fragmentation analysis. Unlike traditional pixel-based approaches, which often lack spatial context, our method treats pixels as objects and evaluates their spatial relationships to determine BLB severity. Seven patch fragmentation metrics were derived from binarized vegetation indices to quantify BLB damage scores, carefully selected for their ability to describe the spatial arrangement and connectivity of potentially affected patches. This metric-driven approach captures the scale and intensity of BLB damage, facilitating precise assessment. The method demonstrated high accuracy, achieving an AUC of 0.938 with a 0.5-meter sampling window. This advancement enhances the precision of BLB damage assessment, particularly for applications such as crop insurance.},
keywords = {drone, patch fragmentation, rice},
pubstate = {published},
tppubtype = {article}
}
This study introduces an innovative method for assessing bacterial leaf blight (BLB) in paddy fields using multispectral UAV (Unmanned Aerial Vehicle) data and patch fragmentation analysis. Unlike traditional pixel-based approaches, which often lack spatial context, our method treats pixels as objects and evaluates their spatial relationships to determine BLB severity. Seven patch fragmentation metrics were derived from binarized vegetation indices to quantify BLB damage scores, carefully selected for their ability to describe the spatial arrangement and connectivity of potentially affected patches. This metric-driven approach captures the scale and intensity of BLB damage, facilitating precise assessment. The method demonstrated high accuracy, achieving an AUC of 0.938 with a 0.5-meter sampling window. This advancement enhances the precision of BLB damage assessment, particularly for applications such as crop insurance. |
Wijayanto, Arif K; Prasetyo, Lilik B; Hudjimartsu, Sahid A; Hongo, Chiharu Textural features for BLB disease damage assessment in paddy fields using drone data and machine learning: Enhancing disease detection accuracy Journal Article In: Smart Agricultural Technology, vol. 8, iss. August 2024, 2024. @article{nokey,
title = {Textural features for BLB disease damage assessment in paddy fields using drone data and machine learning: Enhancing disease detection accuracy},
author = {Arif K Wijayanto and Lilik B Prasetyo and Sahid A Hudjimartsu and Chiharu Hongo},
url = {https://doi.org/10.1016/j.atech.2024.100498},
doi = {10.1016/j.atech.2024.100498},
year = {2024},
date = {2024-06-28},
urldate = {2024-06-28},
journal = {Smart Agricultural Technology},
volume = {8},
issue = {August 2024},
abstract = {Detecting Bacterial Leaf Blight (BLB) in paddy fields is a critical challenge in Indonesia, where the disease poses a significant threat to rice production by reducing the photosynthetic ability and ultimately compromising plant productivity. This study explored the effectiveness of using drone-acquired data for textural analysis in paddy fields in West Java, with the aim of improving BLB detection by integrating textural and thermal characteristics. Utilizing advanced machine learning techniques, we combined drone data to assess different levels of damage caused by BLB. The normalized difference texture index, derived from the Haralick textural features, was employed as a key predictor. Our findings demonstrate that the inclusion of textural features markedly enhances disease detection accuracy compared with traditional methods based solely on spectral indices. Specifically, the random forest algorithm, which integrates texture and vegetation indices, achieved an impressive classification accuracy of 0.984. This innovative approach offers a robust, non-invasive solution for detecting BLB, significantly contributing to the protection of crop yields and addressing global food security challenges. This study underscores the potential of advanced remote sensing technologies and machine learning to revolutionize agricultural disease management.},
keywords = {drone, haralick, paddy, rice, textural feature},
pubstate = {published},
tppubtype = {article}
}
Detecting Bacterial Leaf Blight (BLB) in paddy fields is a critical challenge in Indonesia, where the disease poses a significant threat to rice production by reducing the photosynthetic ability and ultimately compromising plant productivity. This study explored the effectiveness of using drone-acquired data for textural analysis in paddy fields in West Java, with the aim of improving BLB detection by integrating textural and thermal characteristics. Utilizing advanced machine learning techniques, we combined drone data to assess different levels of damage caused by BLB. The normalized difference texture index, derived from the Haralick textural features, was employed as a key predictor. Our findings demonstrate that the inclusion of textural features markedly enhances disease detection accuracy compared with traditional methods based solely on spectral indices. Specifically, the random forest algorithm, which integrates texture and vegetation indices, achieved an impressive classification accuracy of 0.984. This innovative approach offers a robust, non-invasive solution for detecting BLB, significantly contributing to the protection of crop yields and addressing global food security challenges. This study underscores the potential of advanced remote sensing technologies and machine learning to revolutionize agricultural disease management. |
2023
|
Wijayanto, Arif K; Junaedi, Ahmad; Sujaswara, Azwar A; Khamid, Miftakhul B. R.; Prasetyo, Lilik B; Hongo, Chiharu; Kuze, Hiroaki Machine Learning for Precise Rice Variety Classification in Tropical Environments Using UAV-Based Multispectral Sensing Journal Article In: AgriEngineering, vol. 5, pp. 2000-2019, 2023, ISSN: 2624-7402. @article{nokey,
title = {Machine Learning for Precise Rice Variety Classification in Tropical Environments Using UAV-Based Multispectral Sensing},
author = {Arif K Wijayanto and Ahmad Junaedi and Azwar A Sujaswara and Miftakhul B.R. Khamid and Lilik B Prasetyo and Chiharu Hongo and Hiroaki Kuze},
url = {http://algm.ipb.ac.id/wp-content/uploads/2025/02/agriengineering-05-00123.pdf},
doi = {10.3390/agriengineering5040123},
issn = {2624-7402},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
journal = {AgriEngineering},
volume = {5},
pages = {2000-2019},
abstract = {An efficient assessment of rice varieties in tropical regions is crucial for selecting cultivars suited to unique environmental conditions. This study explores machine learning algorithms that leverage multispectral sensor data from UAVs to evaluate rice varieties. It focuses on three paddy rice types at different ages (six, nine, and twelve weeks after planting), analyzing data from four spectral bands and vegetation indices using various algorithms for classification. The results show that the neural network (NN) algorithm is superior, achieving an area under the curve value of 0.804. The twelfth week post-planting yielded the most accurate results, with green reflectance the dominant predictor, surpassing the traditional vegetation indices. This study demonstrates the rapid and effective classification of rice varieties using UAV-based multispectral sensors and NN algorithms to enhance agricultural practices and global food security.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
An efficient assessment of rice varieties in tropical regions is crucial for selecting cultivars suited to unique environmental conditions. This study explores machine learning algorithms that leverage multispectral sensor data from UAVs to evaluate rice varieties. It focuses on three paddy rice types at different ages (six, nine, and twelve weeks after planting), analyzing data from four spectral bands and vegetation indices using various algorithms for classification. The results show that the neural network (NN) algorithm is superior, achieving an area under the curve value of 0.804. The twelfth week post-planting yielded the most accurate results, with green reflectance the dominant predictor, surpassing the traditional vegetation indices. This study demonstrates the rapid and effective classification of rice varieties using UAV-based multispectral sensors and NN algorithms to enhance agricultural practices and global food security. |
2022
|
Seminar, Kudang B; Nelwan, Leopol O; Budiastra, I W; Sutawijaya, Arya; Wijayanto, Arif K; Imantho, Harry; Nanda, Muhammad A; Ahamed, Tofael Using Precision Agriculture (PA) Approach to Select Suitable Final Disposal Sites for Energy Generation Journal Article In: Information, vol. 14, iss. 1, 2022. @article{nokey,
title = {Using Precision Agriculture (PA) Approach to Select Suitable Final Disposal Sites for Energy Generation},
author = {Kudang B Seminar and Leopol O Nelwan and I W Budiastra and Arya Sutawijaya and Arif K Wijayanto and Harry Imantho and Muhammad A Nanda and Tofael Ahamed},
url = {https://doi.org/10.3390/info14010008},
doi = {https://doi.org/10.3390/info14010008},
year = {2022},
date = {2022-12-23},
urldate = {2022-12-23},
journal = {Information},
volume = {14},
issue = {1},
abstract = {Severe environmental pollution and disease exposure are caused by poor waste management, specifically in urban areas due to urbanization. Additionally, energy shortage has threatened almost all parts of human life in the world. To overcome this problem, a precision agriculture approach using spatial mapping based on social environmental factors and sustainability principles can be used to find the variability of sites with respect to their suitability for waste disposal and energy generation. Therefore, this study aimed to develop a system for selecting suitable areas for municipal waste disposal and energy generation based on several structured criteria as hierarchical weighted factors. The system prototype was developed and tested in a case study conducted in an Indonesian Megapolitan area. The suitability map produced by the system for waste disposal and energy generation had an accuracy of 84.3%. Furthermore, validation was carried out by ground-checking at 102 location points. A future application of the proposed system is to provide spatial data-based analysis to improve regional planning and policy-making for waste disposal and energy generation in certain areas, particularly in Indonesia.},
keywords = {AHP, renewable energy, waste management},
pubstate = {published},
tppubtype = {article}
}
Severe environmental pollution and disease exposure are caused by poor waste management, specifically in urban areas due to urbanization. Additionally, energy shortage has threatened almost all parts of human life in the world. To overcome this problem, a precision agriculture approach using spatial mapping based on social environmental factors and sustainability principles can be used to find the variability of sites with respect to their suitability for waste disposal and energy generation. Therefore, this study aimed to develop a system for selecting suitable areas for municipal waste disposal and energy generation based on several structured criteria as hierarchical weighted factors. The system prototype was developed and tested in a case study conducted in an Indonesian Megapolitan area. The suitability map produced by the system for waste disposal and energy generation had an accuracy of 84.3%. Furthermore, validation was carried out by ground-checking at 102 location points. A future application of the proposed system is to provide spatial data-based analysis to improve regional planning and policy-making for waste disposal and energy generation in certain areas, particularly in Indonesia. |
Prasetyo, Lilik B; Setiawan, Yudi; Condro, Aryo Adhi; Kustiyo,; Putra, Eriyanto Indra; Hayati, Nur; Wijayanto, Arif K; Ramadhi, Almi; Murdiyarso, Daniel Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches Journal Article In: Forests, vol. 13, no. 6, 2022. @article{Prasetyo2022,
title = {Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches},
author = {Lilik B Prasetyo and Yudi Setiawan and Aryo Adhi Condro and Kustiyo and Eriyanto Indra Putra and Nur Hayati and Arif K Wijayanto and Almi Ramadhi and Daniel Murdiyarso},
url = {https://www.mdpi.com/1999-4907/13/6/828},
doi = {10.3390/f13060828},
year = {2022},
date = {2022-05-25},
journal = {Forests},
volume = {13},
number = {6},
abstract = {In recent decades, catastrophic wildfire episodes within the Sumatran peatland have contributed to a large amount of greenhouse gas emissions. The El-Nino Southern Oscillation (ENSO) modulates the occurrence of fires in Indonesia through prolonged hydrological drought. Thus, assessing peatland vulnerability to fires and understanding the underlying drivers are essential to developing adaptation and mitigation strategies for peatland. Here, we quantify the vulnerability of Sumatran peat to fires under various ENSO conditions (i.e., El-Nino, La-Nina, and Normal phases) using correlative modelling approaches. This study used climatic (i.e., annual precipitation, SPI, and KBDI), biophysical (i.e., below-ground biomass, elevation, slope, and NBR), and proxies to anthropogenic disturbance variables (i.e., access to road, access to forests, access to cities, human modification, and human population) to assess fire vulnerability within Sumatran peatlands. We created an ensemble model based on various machine learning approaches (i.e., random forest, support vector machine, maximum entropy, and boosted regression tree). We found that the ensemble model performed better compared to a single algorithm for depicting fire vulnerability within Sumatran peatlands. The NBR highly contributed to the vulnerability of peatland to fire in Sumatra in all ENSO phases, followed by the anthropogenic variables. We found that the high to very-high peat vulnerability to fire increases during El-Nino conditions with variations in its spatial patterns occurring under different ENSO phases. This study provides spatially explicit information to support the management of peat fires, which will be particularly useful for identifying peatland restoration priorities based on peatland vulnerability to fire maps. Our findings highlight Riau’s peatland as being the area most prone to fires area on Sumatra Island. Therefore, the groundwater level within this area should be intensively monitored to prevent peatland fires. In addition, conserving intact forests within peatland through the moratorium strategy and restoring the degraded peatland ecosystem through canal blocking is also crucial to coping with global climate change.},
keywords = {ENSO, fire, land fire, peat land},
pubstate = {published},
tppubtype = {article}
}
In recent decades, catastrophic wildfire episodes within the Sumatran peatland have contributed to a large amount of greenhouse gas emissions. The El-Nino Southern Oscillation (ENSO) modulates the occurrence of fires in Indonesia through prolonged hydrological drought. Thus, assessing peatland vulnerability to fires and understanding the underlying drivers are essential to developing adaptation and mitigation strategies for peatland. Here, we quantify the vulnerability of Sumatran peat to fires under various ENSO conditions (i.e., El-Nino, La-Nina, and Normal phases) using correlative modelling approaches. This study used climatic (i.e., annual precipitation, SPI, and KBDI), biophysical (i.e., below-ground biomass, elevation, slope, and NBR), and proxies to anthropogenic disturbance variables (i.e., access to road, access to forests, access to cities, human modification, and human population) to assess fire vulnerability within Sumatran peatlands. We created an ensemble model based on various machine learning approaches (i.e., random forest, support vector machine, maximum entropy, and boosted regression tree). We found that the ensemble model performed better compared to a single algorithm for depicting fire vulnerability within Sumatran peatlands. The NBR highly contributed to the vulnerability of peatland to fire in Sumatra in all ENSO phases, followed by the anthropogenic variables. We found that the high to very-high peat vulnerability to fire increases during El-Nino conditions with variations in its spatial patterns occurring under different ENSO phases. This study provides spatially explicit information to support the management of peat fires, which will be particularly useful for identifying peatland restoration priorities based on peatland vulnerability to fire maps. Our findings highlight Riau’s peatland as being the area most prone to fires area on Sumatra Island. Therefore, the groundwater level within this area should be intensively monitored to prevent peatland fires. In addition, conserving intact forests within peatland through the moratorium strategy and restoring the degraded peatland ecosystem through canal blocking is also crucial to coping with global climate change. |
2021
|
Juniyanti, Lila; Purnomo, Herry; Kartodihardjo, Hariadi; Prasetyo, Lilik Budi Understanding the driving forces and actors of land change due to forestry and agricultural practices in sumatra and kalimantan: A systematic review Journal Article In: Land, vol. 10, no. 5, 2021, ISSN: 2073445X. @article{Juniyanti2021a,
title = {Understanding the driving forces and actors of land change due to forestry and agricultural practices in sumatra and kalimantan: A systematic review},
author = {Lila Juniyanti and Herry Purnomo and Hariadi Kartodihardjo and Lilik Budi Prasetyo},
doi = {10.3390/land10050463},
issn = {2073445X},
year = {2021},
date = {2021-01-01},
journal = {Land},
volume = {10},
number = {5},
abstract = {Indonesia has experienced one of the world's greatest dynamic land changes due to forestry and agricultural practices. Understanding the drivers behind these land changes remains challenging, partly because landscape research is spread across many domains and disciplines. We provide a systematic review of 91 studies that identify the causes and land change actors across Sumatra and Kalimantan. Our review shows that oil palm expansion is the most prominent (65 studies) among multiple direct causes of land change. We determined that property rights are the most prominent issue (31 studies) among the multiple underlying causes of land change. Distinct combinations of mainly economic, institutional, political, and social underlying drivers determine land change, rather than single key drivers. Our review also shows that central and district governments as decision-making actors are prominent (69 studies) among multiple land change actors. Our systematic review indicates knowledge gaps that can be filled by clarifying the identification and role of actors in land change.},
keywords = {direct causes, PRISMA diagram, Tropical deforestation, underlying causes},
pubstate = {published},
tppubtype = {article}
}
Indonesia has experienced one of the world's greatest dynamic land changes due to forestry and agricultural practices. Understanding the drivers behind these land changes remains challenging, partly because landscape research is spread across many domains and disciplines. We provide a systematic review of 91 studies that identify the causes and land change actors across Sumatra and Kalimantan. Our review shows that oil palm expansion is the most prominent (65 studies) among multiple direct causes of land change. We determined that property rights are the most prominent issue (31 studies) among the multiple underlying causes of land change. Distinct combinations of mainly economic, institutional, political, and social underlying drivers determine land change, rather than single key drivers. Our review also shows that central and district governments as decision-making actors are prominent (69 studies) among multiple land change actors. Our systematic review indicates knowledge gaps that can be filled by clarifying the identification and role of actors in land change. |
Rizal, Muhamad; Saleh, M. Buce; Prasetyo, Lilik Budi Biomass estimation model for peat swamp forest ecosystem using light detection and ranging Journal Article In: Telkomnika (Telecommunication Computing Electronics and Control), vol. 19, no. 3, pp. 770–780, 2021, ISSN: 23029293. @article{Rizal2021b,
title = {Biomass estimation model for peat swamp forest ecosystem using light detection and ranging},
author = {Muhamad Rizal and M. Buce Saleh and Lilik Budi Prasetyo},
doi = {10.12928/TELKOMNIKA.v19i3.18152},
issn = {23029293},
year = {2021},
date = {2021-01-01},
journal = {Telkomnika (Telecommunication Computing Electronics and Control)},
volume = {19},
number = {3},
pages = {770–780},
abstract = {Peat swamp forest plays a very important role in absorbing and storing large amounts of terrestrial carbon, both above ground and in the soil. There has been a lot of research on the estimation of the amount of biomass above the ground, but a little on peat swamp ecosystems using light detection and ranging (LiDAR) technology, especially in Indonesia. The purpose of this study is to build a biomass estimation model based on LiDAR data. This technology can obtain information about the structure and characteristics of any vegetation in detail and in real time. Data was obtained from the East Kotawaringin Regency, Central Kalimantan. Biomass field was generated from the available allometry, and Point cloud of LiDAR was extracted into canopy cover (CC), and data on tree height, using the FRCI and local maxima (LM) method, respectively. The CC and tree height data were then used as independent variables in building the regression model. The best-fitted model was obtained after the scoring and ranking of several regression forms such as linear, quadratic, power, exponential and logarithmic. This research concluded that the quadratic regression model, with R2of 72.16% and root mean square error (RMSE) of 0.0003% is the best-fitted estimation model (BK). Finally, the biomass value from the models was 244.510 tons/ha.},
keywords = {Allometry, biomass, canopy cover, LiDAR, Peat Swamp forest},
pubstate = {published},
tppubtype = {article}
}
Peat swamp forest plays a very important role in absorbing and storing large amounts of terrestrial carbon, both above ground and in the soil. There has been a lot of research on the estimation of the amount of biomass above the ground, but a little on peat swamp ecosystems using light detection and ranging (LiDAR) technology, especially in Indonesia. The purpose of this study is to build a biomass estimation model based on LiDAR data. This technology can obtain information about the structure and characteristics of any vegetation in detail and in real time. Data was obtained from the East Kotawaringin Regency, Central Kalimantan. Biomass field was generated from the available allometry, and Point cloud of LiDAR was extracted into canopy cover (CC), and data on tree height, using the FRCI and local maxima (LM) method, respectively. The CC and tree height data were then used as independent variables in building the regression model. The best-fitted model was obtained after the scoring and ranking of several regression forms such as linear, quadratic, power, exponential and logarithmic. This research concluded that the quadratic regression model, with R2of 72.16% and root mean square error (RMSE) of 0.0003% is the best-fitted estimation model (BK). Finally, the biomass value from the models was 244.510 tons/ha. |
Condro, Aryo Adhi; Prasetyo, Lilik Budi; Rushayati, Siti Badriyah; Santikayasa, I. Putu; Iskandar, Entang Measuring Metrics of Climate Change and Its Implication on the Endangered Mammal Conservation in the Leuser Ecosystem Journal Article In: Frontiers in Environmental Science, vol. 9, no. September, pp. 1–9, 2021, ISSN: 2296665X. @article{Condro2021a,
title = {Measuring Metrics of Climate Change and Its Implication on the Endangered Mammal Conservation in the Leuser Ecosystem},
author = {Aryo Adhi Condro and Lilik Budi Prasetyo and Siti Badriyah Rushayati and I. Putu Santikayasa and Entang Iskandar},
doi = {10.3389/fenvs.2021.713837},
issn = {2296665X},
year = {2021},
date = {2021-01-01},
journal = {Frontiers in Environmental Science},
volume = {9},
number = {September},
pages = {1–9},
abstract = {The Leuser Ecosystem is one of the essential landscapes in the world for biodiversity conservation and ecosystem services. However, the Leuser Ecosystem has suffered many threats from anthropogenic activities and changing climate. Climate change is the greatest challenge to global biodiversity conservation. Efforts should be made to elaborate climatic change metrics toward biological conservation practices. Herein, we present several climate change metrics to support conservation management toward mammal species in the Leuser Ecosystem. We used a 30-year climate of mean annual temperature, annual precipitation, and the BIOCLIM data to capture the current climatic conditions. For the future climate (2050), we retrieved three downscaled general circulation models for the business-as-usual scenario of shared socioeconomic pathways (SSP585). We calculated the dissimilarities of the current and 2050 climatic conditions using the standardized Euclidean distance (SED). To capture the probability of climate extremes in each period (i.e., current and future conditions), we calculated the 5th and 95th percentiles of the distributions of monthly temperature and precipitation, respectively, in the current and future conditions. Furthermore, we calculated forward and backward climate velocities based on the mean annual temperature. These metrics can be useful inferences about species conservation. Our results indicate that almost all of the endangered mammals in the Leuser Ecosystem will occur in the area with threats to local populations and sites. Different conservation strategies should be performed in the areas likely to present different threats toward mammal species. Habitat restoration and long-term population monitoring are needed to support conservation in this mega biodiversity region.},
keywords = {biodiversity, climate change, conservation, mammal, tropical landscape},
pubstate = {published},
tppubtype = {article}
}
The Leuser Ecosystem is one of the essential landscapes in the world for biodiversity conservation and ecosystem services. However, the Leuser Ecosystem has suffered many threats from anthropogenic activities and changing climate. Climate change is the greatest challenge to global biodiversity conservation. Efforts should be made to elaborate climatic change metrics toward biological conservation practices. Herein, we present several climate change metrics to support conservation management toward mammal species in the Leuser Ecosystem. We used a 30-year climate of mean annual temperature, annual precipitation, and the BIOCLIM data to capture the current climatic conditions. For the future climate (2050), we retrieved three downscaled general circulation models for the business-as-usual scenario of shared socioeconomic pathways (SSP585). We calculated the dissimilarities of the current and 2050 climatic conditions using the standardized Euclidean distance (SED). To capture the probability of climate extremes in each period (i.e., current and future conditions), we calculated the 5th and 95th percentiles of the distributions of monthly temperature and precipitation, respectively, in the current and future conditions. Furthermore, we calculated forward and backward climate velocities based on the mean annual temperature. These metrics can be useful inferences about species conservation. Our results indicate that almost all of the endangered mammals in the Leuser Ecosystem will occur in the area with threats to local populations and sites. Different conservation strategies should be performed in the areas likely to present different threats toward mammal species. Habitat restoration and long-term population monitoring are needed to support conservation in this mega biodiversity region. |
Suyamto, Desi; Prasetyo, Lilik; Setiawan, Yudi; Wijaya, Arief; Kustiyo, Kustiyo; Kartika, Tatik; Effendi, Hefni; Permatasari, Prita Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution Journal Article In: Geomatics, vol. 1, no. 4, pp. 464–495, 2021, ISSN: 26737418. @article{Suyamto2021,
title = {Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution},
author = {Desi Suyamto and Lilik Prasetyo and Yudi Setiawan and Arief Wijaya and Kustiyo Kustiyo and Tatik Kartika and Hefni Effendi and Prita Permatasari},
doi = {10.3390/geomatics1040027},
issn = {26737418},
year = {2021},
date = {2021-01-01},
journal = {Geomatics},
volume = {1},
number = {4},
pages = {464–495},
abstract = {This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed. |
Haryoko, Tri; O'hara, Mark; Mioduszewska, Berenika; Sutrisno, Hari; Prasetyo, Lilik Budi; Mardiastuti, Ani Implementation of species protection act for the conservation of tanimbar corella, cacatua goffiniana (Finsch, 1863) Journal Article In: Biodiversitas, vol. 22, no. 4, pp. 1733–1740, 2021, ISSN: 20854722. @article{Haryoko2021,
title = {Implementation of species protection act for the conservation of tanimbar corella, cacatua goffiniana (Finsch, 1863)},
author = {Tri Haryoko and Mark O'hara and Berenika Mioduszewska and Hari Sutrisno and Lilik Budi Prasetyo and Ani Mardiastuti},
doi = {10.13057/biodiv/d220417},
issn = {20854722},
year = {2021},
date = {2021-01-01},
journal = {Biodiversitas},
volume = {22},
number = {4},
pages = {1733–1740},
abstract = {Birds are among the most favored pet animals and are nurtured because of their melodious voice, intelligence, and beautiful feathers. Therefore, these animals are usually traded in both local and international markets. Wild bird trades are dominated by species from the order Passeriformes/songbirds and Psittaciformes/parrots. Furthermore, one of the Psittaciformes groups that are in high demand as a pet is the cockatoos. The Goffin's cockatoo or Tanimbar corella/Cacatua goffiniana (Finsch, 1863)is one of seven species of parrots native to Indonesia and has been traded for decades. This endemic bird from the Tanimbar Islands (Maluku) has been protected by the Indonesian government since 1990 and has been on the CITES Appendix I since 1992. Therefore, this study aims to review the harvesting of C. goffiniana and the effect the bird's protection status has on its trade. It was conducted by investigating the legal trade data for 1981-2018, information on illegal trade, and ex-situ conservation of this species. The review on the trade of C. goffiniana was assessed using descriptive analysis. Furthermore, Independent Samples T-Test was used to determine the differences between the number of C. goffiniana traded before and after the bird was listed in Appendix I CITES and protected by Indonesia's law. The results showed that the number of exported Cacatua goffiniana for 38 years to 34 countries was 151,684. Furthermore, the United States of America was the largest importer with a total of 118,356/78.03%. It was discovered that the number of birds legally exported by Indonesia has decreased dramatically since 1990 because these animals are protected by Indonesian law. Consequently, there was a significant difference between the number traded before and after their designation as protected species. The nonparametric correlation between protection status and Appendix I CITES listing to the number of these animals traded was statistically significant. Finally, existing conservation practices involve efforts to restock the population by means of captive breeding programs. However, as conservation agencies have shown little success in breeding these species, the efforts are not sufficient to fulfill the demand. Therefore, illegal trade is still a major threat to the natural populations.},
keywords = {Cockatoo, conservation, Harvesting, Protection, Trade},
pubstate = {published},
tppubtype = {article}
}
Birds are among the most favored pet animals and are nurtured because of their melodious voice, intelligence, and beautiful feathers. Therefore, these animals are usually traded in both local and international markets. Wild bird trades are dominated by species from the order Passeriformes/songbirds and Psittaciformes/parrots. Furthermore, one of the Psittaciformes groups that are in high demand as a pet is the cockatoos. The Goffin's cockatoo or Tanimbar corella/Cacatua goffiniana (Finsch, 1863)is one of seven species of parrots native to Indonesia and has been traded for decades. This endemic bird from the Tanimbar Islands (Maluku) has been protected by the Indonesian government since 1990 and has been on the CITES Appendix I since 1992. Therefore, this study aims to review the harvesting of C. goffiniana and the effect the bird's protection status has on its trade. It was conducted by investigating the legal trade data for 1981-2018, information on illegal trade, and ex-situ conservation of this species. The review on the trade of C. goffiniana was assessed using descriptive analysis. Furthermore, Independent Samples T-Test was used to determine the differences between the number of C. goffiniana traded before and after the bird was listed in Appendix I CITES and protected by Indonesia's law. The results showed that the number of exported Cacatua goffiniana for 38 years to 34 countries was 151,684. Furthermore, the United States of America was the largest importer with a total of 118,356/78.03%. It was discovered that the number of birds legally exported by Indonesia has decreased dramatically since 1990 because these animals are protected by Indonesian law. Consequently, there was a significant difference between the number traded before and after their designation as protected species. The nonparametric correlation between protection status and Appendix I CITES listing to the number of these animals traded was statistically significant. Finally, existing conservation practices involve efforts to restock the population by means of captive breeding programs. However, as conservation agencies have shown little success in breeding these species, the efforts are not sufficient to fulfill the demand. Therefore, illegal trade is still a major threat to the natural populations. |
Atmoko, T.; Mardiastuti, A.; Bismark, M.; Prasetyo, L. B.; Iskandar, E. Land cover and Proboscis monkey habitats in Berau Delta, East Kalimantan Journal Article In: IOP Conference Series: Earth and Environmental Science, vol. 739, no. 1, 2021, ISSN: 17551315. @article{Atmoko2021,
title = {Land cover and Proboscis monkey habitats in Berau Delta, East Kalimantan},
author = {T. Atmoko and A. Mardiastuti and M. Bismark and L. B. Prasetyo and E. Iskandar},
doi = {10.1088/1755-1315/739/1/012062},
issn = {17551315},
year = {2021},
date = {2021-01-01},
journal = {IOP Conference Series: Earth and Environmental Science},
volume = {739},
number = {1},
abstract = {The proboscis monkey is an endangered primate endemic to Borneo. Most of their habitats are outside conservation areas and are under threat from conversion to other land uses, such as those found in the Berau Delta. Habitat loss and destruction significantly affect the quality and viability of the proboscis monkey population. This study aims to determine land cover and proboscis monkey habitat types in Berau Delta. Land cover was obtained from the interpretation of the Lansat 8 OLI 2019 satellite image. Vegetation data were collected using a line plot transect method and continued with cluster analysis. The results showed that mangrove forest has the largest coverage (35.92%), followed by secondary forest (17.10%) and riparian forest (12.96%). At least 74 species of woody plants belonging to 61 genera and 37 families in a 2.4 ha observation plot. The range of species diversity index was from 0.80 to 2.88, and; evenness index values range from 0.58 to 0.82. The habitat was categorized into two main clusters: mangrove cluster (Buasin Cape, Guntung Estuary) and riverine cluster (Lati River, Saodang Kecil Island, Batu-Batu, Bebanir Lama). The mangrove forest habitat consists of Rhizophora mucronata, R. apiculata, Bruguiera sp, Sonneratia alba, and Avicennia alba. The riparian habitats are dominated by Sonneratia caseolaris, Vitex pinnata, Cerbera manghas, Brownlowia argentata, Heritiera littoralis, Syzygium lineatum, Nauclea officinalis, Xylocarpus granatum, Syzygium sp.1, and A. alba. The average of total and lower branches height of trees in the Lati River and Basin Cape were higher than in other habitats, forming a continuous canopy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The proboscis monkey is an endangered primate endemic to Borneo. Most of their habitats are outside conservation areas and are under threat from conversion to other land uses, such as those found in the Berau Delta. Habitat loss and destruction significantly affect the quality and viability of the proboscis monkey population. This study aims to determine land cover and proboscis monkey habitat types in Berau Delta. Land cover was obtained from the interpretation of the Lansat 8 OLI 2019 satellite image. Vegetation data were collected using a line plot transect method and continued with cluster analysis. The results showed that mangrove forest has the largest coverage (35.92%), followed by secondary forest (17.10%) and riparian forest (12.96%). At least 74 species of woody plants belonging to 61 genera and 37 families in a 2.4 ha observation plot. The range of species diversity index was from 0.80 to 2.88, and; evenness index values range from 0.58 to 0.82. The habitat was categorized into two main clusters: mangrove cluster (Buasin Cape, Guntung Estuary) and riverine cluster (Lati River, Saodang Kecil Island, Batu-Batu, Bebanir Lama). The mangrove forest habitat consists of Rhizophora mucronata, R. apiculata, Bruguiera sp, Sonneratia alba, and Avicennia alba. The riparian habitats are dominated by Sonneratia caseolaris, Vitex pinnata, Cerbera manghas, Brownlowia argentata, Heritiera littoralis, Syzygium lineatum, Nauclea officinalis, Xylocarpus granatum, Syzygium sp.1, and A. alba. The average of total and lower branches height of trees in the Lati River and Basin Cape were higher than in other habitats, forming a continuous canopy. |
Condro, A. A.; Prasetyo, L. B.; Rushayati, S. B.; Santikayasa, I. P.; Iskandar, E. Redistribution of Sumatran orangutan in the Leuser ecosystem due to dispersal constraints and climate change Journal Article In: IOP Conference Series: Earth and Environmental Science, vol. 771, no. 1, 2021, ISSN: 17551315. @article{Condro2021,
title = {Redistribution of Sumatran orangutan in the Leuser ecosystem due to dispersal constraints and climate change},
author = {A. A. Condro and L. B. Prasetyo and S. B. Rushayati and I. P. Santikayasa and E. Iskandar},
doi = {10.1088/1755-1315/771/1/012006},
issn = {17551315},
year = {2021},
date = {2021-01-01},
journal = {IOP Conference Series: Earth and Environmental Science},
volume = {771},
number = {1},
abstract = {Sumatran orangutan (Pongo abelii) is one of the great apes that lives in Asia. The species' population suffered a significant reduction due to altered habitat and climate shifting; thus, this species is critically endangered (CR) based on The International Union for Conservation of Nature (IUCN) red list. Nowadays, the vast majority of the species only occur in the Leuser ecosystem (LE). The population estimation of Sumatran orangutan towards ground-truthing methods still became a challenge to carry out conservation planning; therefore, the ecological niche modeling (ENM) will be a gan excellent alternative to evaluate this species' population dynamics. Here we present the potential distribution changes of the Sumatran orangutan in the LE under mitigation and business as usual (BAU) scenarios of climate change. This study also conducted the effects of environmental constraint (i.e., deforestation and rivers) on the Sumatran orangutan's future dispersal in LE. We collected the Sumatran orangutan occurrences data from the Global Biodiversity Information Facility (GBIF) and literature reviews of orangutan inventory in the Leuser ecosystem. The ENM and dispersal constraints have been conducted using ENMTML and MigClim R package script-codes, respectively. This study provides novel information regarding future orangutan distribution.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sumatran orangutan (Pongo abelii) is one of the great apes that lives in Asia. The species' population suffered a significant reduction due to altered habitat and climate shifting; thus, this species is critically endangered (CR) based on The International Union for Conservation of Nature (IUCN) red list. Nowadays, the vast majority of the species only occur in the Leuser ecosystem (LE). The population estimation of Sumatran orangutan towards ground-truthing methods still became a challenge to carry out conservation planning; therefore, the ecological niche modeling (ENM) will be a gan excellent alternative to evaluate this species' population dynamics. Here we present the potential distribution changes of the Sumatran orangutan in the LE under mitigation and business as usual (BAU) scenarios of climate change. This study also conducted the effects of environmental constraint (i.e., deforestation and rivers) on the Sumatran orangutan's future dispersal in LE. We collected the Sumatran orangutan occurrences data from the Global Biodiversity Information Facility (GBIF) and literature reviews of orangutan inventory in the Leuser ecosystem. The ENM and dispersal constraints have been conducted using ENMTML and MigClim R package script-codes, respectively. This study provides novel information regarding future orangutan distribution. |
Rohman, M.; Prasetyo, L. B.; Kusrini, M. D. Predicting spatial distribution of Asian Horned Frog (Megophrys montana Kuhl & Van Hasselt 1882) in Java Island using citizen science's data Journal Article In: IOP Conference Series: Earth and Environmental Science, vol. 771, no. 1, 2021, ISSN: 17551315. @article{Rohman2021,
title = {Predicting spatial distribution of Asian Horned Frog (Megophrys montana Kuhl & Van Hasselt 1882) in Java Island using citizen science's data},
author = {M. Rohman and L. B. Prasetyo and M. D. Kusrini},
doi = {10.1088/1755-1315/771/1/012027},
issn = {17551315},
year = {2021},
date = {2021-01-01},
journal = {IOP Conference Series: Earth and Environmental Science},
volume = {771},
number = {1},
abstract = {Citizen science is a tool that has been used globally to gather data on species, recently, this effort is gaining popularity in Indonesia. Asian horned frog (Megophrys montana) is an amphibian endemic to Java with a significant population declining due to forest or habitat losses. The purpose of this study is to analyze the suitability of the habitat and to estimate the potential habitat of Megophrys montana in Java using maximum entropy (maxent). Ninety-four coordinates data from iNaturalist, a citizen science app, were used in modeling along with altitude, slope, rainfall, distance from rivers, Normalized Difference Vegetation Index (NDVI), and land cover categories. Megophrys montana habitat suitability model produced an excellent accuracy with an AUC value of 0.962. Altitude, rainfall, and slope were the most important environmental variables that affect the suitability of the species habitats. The characteristics of Megophrys montana habitat in Java are mountainous forest, wet rainfall, primary or secondary forest with steep slopes, and near to the rivers. West Java and Banten are provinces with the most suitable areas for their habitat, especially within the conservation areas, i.e. Mount Halimun Salak and Mount Gede Pangrango National Park.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Citizen science is a tool that has been used globally to gather data on species, recently, this effort is gaining popularity in Indonesia. Asian horned frog (Megophrys montana) is an amphibian endemic to Java with a significant population declining due to forest or habitat losses. The purpose of this study is to analyze the suitability of the habitat and to estimate the potential habitat of Megophrys montana in Java using maximum entropy (maxent). Ninety-four coordinates data from iNaturalist, a citizen science app, were used in modeling along with altitude, slope, rainfall, distance from rivers, Normalized Difference Vegetation Index (NDVI), and land cover categories. Megophrys montana habitat suitability model produced an excellent accuracy with an AUC value of 0.962. Altitude, rainfall, and slope were the most important environmental variables that affect the suitability of the species habitats. The characteristics of Megophrys montana habitat in Java are mountainous forest, wet rainfall, primary or secondary forest with steep slopes, and near to the rivers. West Java and Banten are provinces with the most suitable areas for their habitat, especially within the conservation areas, i.e. Mount Halimun Salak and Mount Gede Pangrango National Park. |
Juniyanti, Lila; Purnomo, Herry; Kartodihardjo, Hariadi; Prasetyo, Lilik Budi; Suryadi,; Pambudi, Eko Powerful actors and their networks in land use contestation for oil palm and industrial tree plantations in Riau Journal Article In: Forest Policy and Economics, vol. 129, no. May, pp. 102512, 2021, ISSN: 13899341. @article{Juniyanti2021,
title = {Powerful actors and their networks in land use contestation for oil palm and industrial tree plantations in Riau},
author = {Lila Juniyanti and Herry Purnomo and Hariadi Kartodihardjo and Lilik Budi Prasetyo and Suryadi and Eko Pambudi},
url = {https://doi.org/10.1016/j.forpol.2021.102512},
doi = {10.1016/j.forpol.2021.102512},
issn = {13899341},
year = {2021},
date = {2021-01-01},
journal = {Forest Policy and Economics},
volume = {129},
number = {May},
pages = {102512},
publisher = {Elsevier B.V.},
abstract = {Indonesia has experienced one of the world's fastest plantation expansions. Plantation growth is indeed an economic solution to meet the market's needs, but the accompanying environmental damage and social conflict are at odds with sustainability goals. Various actors with interests in land compete with the power they have. The most powerful actors have controlled land use based on their decisions. Accordingly, this paper presents empirical evidence to understand the important role of powerful actors in land-use contestation in oil palm and industrial plantation forests. It focused on analyzing power actors and social networks to help policymakers understand these powerful actors and take steps toward good governance. We conducted a focus group discussion (FGD), field interviews, and observations as well as implemented the actor-centered power (ACP) approach and social networks analysis (SNA). The combination of these two methods aims to improve the ACP approach by explaining how actors form coalitions with one another so that the strongest and most prominent beneficiary actors can be identified. We found that actors at the site level are powerful actors, whereas those with the highest authority in the hierarchy do not have power in land-use control. Village officials are powerful actors, as they are the central figures in the network and mostly use dominant information to weaken other actors. Village officials with strategic positions in the network have the most connections and play a bridging role between actors from different subgroups in the network. Powerful actors who can control the use of natural resources must be involved in determining strategies to improve natural resource governance and implement such a process at the site level.},
keywords = {Interest, Land governance, Patronage, Power analysis, Powerful actor},
pubstate = {published},
tppubtype = {article}
}
Indonesia has experienced one of the world's fastest plantation expansions. Plantation growth is indeed an economic solution to meet the market's needs, but the accompanying environmental damage and social conflict are at odds with sustainability goals. Various actors with interests in land compete with the power they have. The most powerful actors have controlled land use based on their decisions. Accordingly, this paper presents empirical evidence to understand the important role of powerful actors in land-use contestation in oil palm and industrial plantation forests. It focused on analyzing power actors and social networks to help policymakers understand these powerful actors and take steps toward good governance. We conducted a focus group discussion (FGD), field interviews, and observations as well as implemented the actor-centered power (ACP) approach and social networks analysis (SNA). The combination of these two methods aims to improve the ACP approach by explaining how actors form coalitions with one another so that the strongest and most prominent beneficiary actors can be identified. We found that actors at the site level are powerful actors, whereas those with the highest authority in the hierarchy do not have power in land-use control. Village officials are powerful actors, as they are the central figures in the network and mostly use dominant information to weaken other actors. Village officials with strategic positions in the network have the most connections and play a bridging role between actors from different subgroups in the network. Powerful actors who can control the use of natural resources must be involved in determining strategies to improve natural resource governance and implement such a process at the site level. |
Adinugroho, W. C.; Imanuddin, R.; Krisnawati, H.; Syaugi, A.; Santosa, P. B.; Qirom, M. A.; Prasetyo, L. B. Exploring the potential of soil moisture maps using Sentinel Imagery as a Proxy for groundwater levels in peat Journal Article In: IOP Conference Series: Earth and Environmental Science, vol. 874, no. 1, 2021, ISSN: 17551315. @article{Adinugroho2021,
title = {Exploring the potential of soil moisture maps using Sentinel Imagery as a Proxy for groundwater levels in peat},
author = {W. C. Adinugroho and R. Imanuddin and H. Krisnawati and A. Syaugi and P. B. Santosa and M. A. Qirom and L. B. Prasetyo},
doi = {10.1088/1755-1315/874/1/012011},
issn = {17551315},
year = {2021},
date = {2021-01-01},
journal = {IOP Conference Series: Earth and Environmental Science},
volume = {874},
number = {1},
abstract = {Degraded peatlands are extremely vulnerable to the threat of fires and have been a major source of national greenhouse gas emissions. Maintaining a certain level of water in peatlands is an essential measure of disaster vulnerability in peatlands. During the dry season, when the lower part of the peat still retains water, fires only occur on the surface and are relatively easy to extinguish. However, one of the limiting factors in peatland management and its more comprehensive application has been the availability of sufficient and spatially distributed Groundwater Level (GWL) data. This study explores the soil moisture map as a proxy for peat condition indicators that correlate with groundwater level. The case studies conducted at Tumbang Nusa Research Forest and Peat Hydrological Unit of Kahayan Sebangau show that peatland conditions can be estimated through biophysical parameters detectable from remotely-sensed data. Soil Moisture Map (SMM) can be produced with a higher resolution (Sentinel 1 = 10m) using the free and open tools SEPAL based on cloud computing infrastructure. The Support-Vector-Regression machine learning approach is used to estimate soil moisture. There is a correlation between SMM and GWL. However, the response to land cover varies. There is high uncertainty in densely forested areas where the sensors cannot penetrate the canopy. As a result, in its implementation, the SMM can be combined with the vegetation index, which can describe trends of land cover changes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Degraded peatlands are extremely vulnerable to the threat of fires and have been a major source of national greenhouse gas emissions. Maintaining a certain level of water in peatlands is an essential measure of disaster vulnerability in peatlands. During the dry season, when the lower part of the peat still retains water, fires only occur on the surface and are relatively easy to extinguish. However, one of the limiting factors in peatland management and its more comprehensive application has been the availability of sufficient and spatially distributed Groundwater Level (GWL) data. This study explores the soil moisture map as a proxy for peat condition indicators that correlate with groundwater level. The case studies conducted at Tumbang Nusa Research Forest and Peat Hydrological Unit of Kahayan Sebangau show that peatland conditions can be estimated through biophysical parameters detectable from remotely-sensed data. Soil Moisture Map (SMM) can be produced with a higher resolution (Sentinel 1 = 10m) using the free and open tools SEPAL based on cloud computing infrastructure. The Support-Vector-Regression machine learning approach is used to estimate soil moisture. There is a correlation between SMM and GWL. However, the response to land cover varies. There is high uncertainty in densely forested areas where the sensors cannot penetrate the canopy. As a result, in its implementation, the SMM can be combined with the vegetation index, which can describe trends of land cover changes. |
Prayudha, B.; Siregar, V.; Ulumuddin, Y. I.; Suyadi,; Prasetyo, L. B.; Agus, S. B.; Suyarso,; Anggraini, K. The application of Landsat imageries and mangrove vegetation index for monitoring mangrove community in Segara Anakan Lagoon, Cilacap, Central Java Journal Article In: IOP Conference Series: Earth and Environmental Science, vol. 944, no. 1, 2021, ISSN: 17551315. @article{Prayudha2021,
title = {The application of Landsat imageries and mangrove vegetation index for monitoring mangrove community in Segara Anakan Lagoon, Cilacap, Central Java},
author = {B. Prayudha and V. Siregar and Y. I. Ulumuddin and Suyadi and L. B. Prasetyo and S. B. Agus and Suyarso and K. Anggraini},
doi = {10.1088/1755-1315/944/1/012039},
issn = {17551315},
year = {2021},
date = {2021-01-01},
journal = {IOP Conference Series: Earth and Environmental Science},
volume = {944},
number = {1},
abstract = {The only place for estuarine-mangroves in Java Island, Segara Anakan Lagoon, experiences the vast decline of mangrove cover. Satellite remote sensing has a critical role in monitoring that change as it allows to record vast areas over time. However, most studies tend to utilize satellite data to investigate the change of mangrove areas into other land-use types rather than identify the mangrove community's shifting. This study utilized the mangrove vegetation index (MVI) for monitoring the changes of mangrove communities at the life-form level using satellite data. The study used multi-temporal Landsat images as it has historical systematic archive data. The threshold value of the index for each class is defined by referring to the field data. The class referred to the life-form classification consisting of mangrove trees, Nypa, and understorey. The image analysis was conducted using Google Earth Engine (GEE), while R software was used for determining threshold values through statistical analysis. The result shows that the MVI can differentiate between some life forms of mangroves, with the overall accuracy reaching 78.79% and a kappa coefficient of 0.729. Further, the multi-temporal maps showed the decline of mangrove tree areas, which the understorey and Nypa community have replaced.},
keywords = {life-form community, mangrove changes, mangrove vegetation index, remote sensing},
pubstate = {published},
tppubtype = {article}
}
The only place for estuarine-mangroves in Java Island, Segara Anakan Lagoon, experiences the vast decline of mangrove cover. Satellite remote sensing has a critical role in monitoring that change as it allows to record vast areas over time. However, most studies tend to utilize satellite data to investigate the change of mangrove areas into other land-use types rather than identify the mangrove community's shifting. This study utilized the mangrove vegetation index (MVI) for monitoring the changes of mangrove communities at the life-form level using satellite data. The study used multi-temporal Landsat images as it has historical systematic archive data. The threshold value of the index for each class is defined by referring to the field data. The class referred to the life-form classification consisting of mangrove trees, Nypa, and understorey. The image analysis was conducted using Google Earth Engine (GEE), while R software was used for determining threshold values through statistical analysis. The result shows that the MVI can differentiate between some life forms of mangroves, with the overall accuracy reaching 78.79% and a kappa coefficient of 0.729. Further, the multi-temporal maps showed the decline of mangrove tree areas, which the understorey and Nypa community have replaced. |
2020
|
Setiawan, Yudi; Rushayati, Siti Badriyah; Hermawan, Rachmad; Prasetyo, Lilik B; Wijayanto, Arif K The effect of utilization patterns of green open space on the dynamics change of air quality due to the Covid-19 pandemic in Jabodetabek region Journal Article In: Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), vol. 10, no. 4, pp. 559-567, 2020, ISSN: 2460-5824. @article{Setiawan2020,
title = {The effect of utilization patterns of green open space on the dynamics change of air quality due to the Covid-19 pandemic in Jabodetabek region},
author = {Yudi Setiawan and Siti Badriyah Rushayati and Rachmad Hermawan and Lilik B Prasetyo and Arif K Wijayanto},
url = {https://journal.ipb.ac.id/index.php/jpsl/article/view/32550},
doi = {10.29244/jpsl.10.4.559-567},
issn = {2460-5824},
year = {2020},
date = {2020-12-31},
journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management)},
volume = {10},
number = {4},
pages = {559-567},
abstract = {The Covid-19 pandemic has had a global impact on all sectors including the environment. The spread of covid-19 is very much influenced by human activity and mobility. Human activities are also closelyrelated to air pollutant emissions. High concentrations of air pollutants during the Covid-19 pandemic will increase the risk of being exposed to Covid-19. Jakarta and its surroundingarea (known locally as Jabodetabek) havehigh population density. Thesecities are economic and industrial centers. Air pollutant emissions in these cities are very high. High concentrations of air pollutants during the Covid-19 pandemic will increase the risk of being exposed to Covid. To anticipate this problem, the government made a Large-Scale Social Restriction Policy (PSBB). Limited human activities, in addition to having an impact on reducing the risk of humans being exposed to Covid-19 from the droplets released by tested-positive of Covid-19, also have an impact on reducing emissions of air pollutants so that they can reduce the risk of being exposed to Covid-19. Several variables that influence vulnerability and risk to exposure to Covid-19 are the distribution of settlements, roads, economic centers (markets, business centers, industrial centers), and human mobility. In this study, we will also analyze the role of green open space on the risk of exposure to Covid-19. Green open space plays an important role in reducing air pollutants so that it will also affect the risk of being exposed to Covid-19. This study aimedto 1) examine the distribution of air pollutants based on the vulnerability and risk of COVID-19 in Jakarta,Bogor, Depok, Tangerang, and Bekasi (Jabodetabek), and 2) examine the results of the overlay between land cover and vulnerability and risk to Covid-19},
keywords = {Covid-19, Kualitas udara, ruang terbuka hijau},
pubstate = {published},
tppubtype = {article}
}
The Covid-19 pandemic has had a global impact on all sectors including the environment. The spread of covid-19 is very much influenced by human activity and mobility. Human activities are also closelyrelated to air pollutant emissions. High concentrations of air pollutants during the Covid-19 pandemic will increase the risk of being exposed to Covid-19. Jakarta and its surroundingarea (known locally as Jabodetabek) havehigh population density. Thesecities are economic and industrial centers. Air pollutant emissions in these cities are very high. High concentrations of air pollutants during the Covid-19 pandemic will increase the risk of being exposed to Covid. To anticipate this problem, the government made a Large-Scale Social Restriction Policy (PSBB). Limited human activities, in addition to having an impact on reducing the risk of humans being exposed to Covid-19 from the droplets released by tested-positive of Covid-19, also have an impact on reducing emissions of air pollutants so that they can reduce the risk of being exposed to Covid-19. Several variables that influence vulnerability and risk to exposure to Covid-19 are the distribution of settlements, roads, economic centers (markets, business centers, industrial centers), and human mobility. In this study, we will also analyze the role of green open space on the risk of exposure to Covid-19. Green open space plays an important role in reducing air pollutants so that it will also affect the risk of being exposed to Covid-19. This study aimedto 1) examine the distribution of air pollutants based on the vulnerability and risk of COVID-19 in Jakarta,Bogor, Depok, Tangerang, and Bekasi (Jabodetabek), and 2) examine the results of the overlay between land cover and vulnerability and risk to Covid-19 |
Wijayanto, Arif K; Rushayati, Siti Badriyah; Hermawan, Rachmad; Setiawan, Yudi; Prasetyo, Lilik B Jakarta and Surabaya land surface temperature before and during the Covid-19 pandemic Journal Article In: AES Bioflux, vol. 12, no. 3, pp. 213-221, 2020, ISSN: 2066-7647. @article{Wijayanto2020,
title = {Jakarta and Surabaya land surface temperature before and during the Covid-19 pandemic},
author = {Arif K Wijayanto and Siti Badriyah Rushayati and Rachmad Hermawan and Yudi Setiawan and Lilik B Prasetyo},
url = {http://www.aes.bioflux.com.ro/docs/2020.213-221.pdf},
issn = {2066-7647},
year = {2020},
date = {2020-12-02},
journal = {AES Bioflux},
volume = {12},
number = {3},
pages = {213-221},
abstract = {The first incidence of the novel coronavirus or Covid-19 was reported in late 2019, and in the following year, the disease was declared a global pandemic. In Indonesia, the first case was reported in early March, 2020, and ever since, the government has appealed to the public to reduce outdoor activities in order to curtail the spread of the virus. Consequently, many companies and institutions implemented the ‘Work from Home’ (WFH) policy. At the end of April, the provincial government of Jakarta issued large-scale social restrictions, locally called PSBB. These restrictions were later implemented in other cities such as Surabaya. Jakarta was the epicentre of the spread of the virus in Indonesia, followed by Surabaya, the second largest city in the country. Therefore, this study aimed to analyze the Thermal Humidity Index (THI) of both cities, before and during the pandemic. Data were obtained from the MODIS Terra Land Surface Temperature and Emissivity 8-Day Global 1km, from the 1st to 14th May, 2019 (before the pandemic), and during the same period the following year (during the pandemic). Furthermore, data analysis was carried out using Google Earth Engine (GEE), a cloud-based platform for geo-spatial data analysis. The hypothesis in this study was that the social restriction policy caused a difference in the THI before and during the pandemic. Therefore, this hypothesis was proven by the results, as the policy caused a decrease in the THI during the pandemic.},
keywords = {Covid-19, Land Surface Temperature, urban heat island},
pubstate = {published},
tppubtype = {article}
}
The first incidence of the novel coronavirus or Covid-19 was reported in late 2019, and in the following year, the disease was declared a global pandemic. In Indonesia, the first case was reported in early March, 2020, and ever since, the government has appealed to the public to reduce outdoor activities in order to curtail the spread of the virus. Consequently, many companies and institutions implemented the ‘Work from Home’ (WFH) policy. At the end of April, the provincial government of Jakarta issued large-scale social restrictions, locally called PSBB. These restrictions were later implemented in other cities such as Surabaya. Jakarta was the epicentre of the spread of the virus in Indonesia, followed by Surabaya, the second largest city in the country. Therefore, this study aimed to analyze the Thermal Humidity Index (THI) of both cities, before and during the pandemic. Data were obtained from the MODIS Terra Land Surface Temperature and Emissivity 8-Day Global 1km, from the 1st to 14th May, 2019 (before the pandemic), and during the same period the following year (during the pandemic). Furthermore, data analysis was carried out using Google Earth Engine (GEE), a cloud-based platform for geo-spatial data analysis. The hypothesis in this study was that the social restriction policy caused a difference in the THI before and during the pandemic. Therefore, this hypothesis was proven by the results, as the policy caused a decrease in the THI during the pandemic. |
Rahman, Dede Aulia; Setiawan, Yudi; Wijayanto, Arif K; Aziz, Ahmad Abdul; Martiyani, Trisna Rizky Possibility of applying unmanned aerial vehicle and thermal imaging in several canopy cover class for wildlife monitoring – preliminary results Conference vol. 211, E3S Web Conf., 2020, ISSN: 2267-1242. @conference{Rahman2020b,
title = {Possibility of applying unmanned aerial vehicle and thermal imaging in several canopy cover class for wildlife monitoring – preliminary results},
author = {Dede Aulia Rahman and Yudi Setiawan and Arif K Wijayanto and Ahmad Abdul Aziz and Trisna Rizky Martiyani},
url = {https://www.e3s-conferences.org/articles/e3sconf/abs/2020/71/e3sconf_jessd2020_04007/e3sconf_jessd2020_04007.html},
doi = {10.1051/e3sconf/202021104007},
issn = {2267-1242},
year = {2020},
date = {2020-11-25},
volume = {211},
publisher = {E3S Web Conf.},
abstract = {Tropical rainforests are one of the important habitats on earth but are rarely explored because they are difficult to access, making their cryptic animals challenging to monitor. Unmanned aerial vehicle (UAV) with thermal infrared imaging (TIR) technology is gaining entry into wildlife research and monitoring. The researcher tested the possibility of applying DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to wildlife in the five tree density classes in the IPB University Campus. To assess the effectiveness of using drones in detecting wildlife, the researcher measured the optimum flying height, sound level, temperature, and optimum flight time in each canopy cover class. The optimum height for animal detection is <50 m HAGL with a sound level that animals can still tolerate. Wildlife detected had body temperatures around 27 °C and were conspicuous in the thermal infrared imagery at night and early morning when the forest canopy was cool (15–27°C), but were difficult to detect by mid-day. By that time, the direct sunshine had heated up canopy vegetation to over 30°C. Species were difficult to identify from thermal infrared imagery alone but could be recognized from synchronized visual images taken during the daytime.},
keywords = {drone, UAV},
pubstate = {published},
tppubtype = {conference}
}
Tropical rainforests are one of the important habitats on earth but are rarely explored because they are difficult to access, making their cryptic animals challenging to monitor. Unmanned aerial vehicle (UAV) with thermal infrared imaging (TIR) technology is gaining entry into wildlife research and monitoring. The researcher tested the possibility of applying DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to wildlife in the five tree density classes in the IPB University Campus. To assess the effectiveness of using drones in detecting wildlife, the researcher measured the optimum flying height, sound level, temperature, and optimum flight time in each canopy cover class. The optimum height for animal detection is <50 m HAGL with a sound level that animals can still tolerate. Wildlife detected had body temperatures around 27 °C and were conspicuous in the thermal infrared imagery at night and early morning when the forest canopy was cool (15–27°C), but were difficult to detect by mid-day. By that time, the direct sunshine had heated up canopy vegetation to over 30°C. Species were difficult to identify from thermal infrared imagery alone but could be recognized from synchronized visual images taken during the daytime. |
Rahman, Dede Aulia; Setiawan, Yudi; Wijayanto, Arif K; Aziz, Ahmad Abdul; Martiyani, Trisna Rizky An experimental approach to exploring the feasibility of unmanned aerial vehicle and thermal imaging in terrestrial and arboreal mammals research Conference vol. 211, E3S Web Conf., 2020, ISSN: 2267-1242. @conference{Rahman2020,
title = {An experimental approach to exploring the feasibility of unmanned aerial vehicle and thermal imaging in terrestrial and arboreal mammals research},
author = {Dede Aulia Rahman and Yudi Setiawan and Arif K Wijayanto and Ahmad Abdul Aziz and Trisna Rizky Martiyani},
url = {https://www.e3s-conferences.org/articles/e3sconf/abs/2020/71/e3sconf_jessd2020_02010/e3sconf_jessd2020_02010.html},
doi = {10.1051/e3sconf/202021102010},
issn = {2267-1242},
year = {2020},
date = {2020-11-25},
volume = {211},
publisher = {E3S Web Conf.},
abstract = {The visual camouflage of many species living in the dense cover of the tropical rainforest become obstacles to conducting species monitoring. Unmanned aerial vehicles (drones) combined with thermal infrared imaging (TIR) can rapidly scan large areas from above and detect wildlife that has a body temperature that contrasts with its surrounding environment. This research tested the feasibility of DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to detect terrestrial and arboreal mammals in the five tree density classes in the remaining natural environment on the IPB University campus. This study demonstrated that large-size terrestrial mammal thermal signatures are visible in sparse vegetation at daytime and in the area under the canopy at night monitoring. In contrast, arboreal mammals were better detected in at early morning and night. Survey timing highly influenced the results – the best quality thermal images were obtained at sunrise, late evening, and at night. The drones allow safe operation at low altitudes with low levels of disturbance to animals. Both terrestrial and arboreal mammals are well detected and easily identified when the drone is flying at an altitude < 50 m HAGL. Our preliminary results indicated that thermal surveys from drones are a promising method.},
keywords = {drone, UAV},
pubstate = {published},
tppubtype = {conference}
}
The visual camouflage of many species living in the dense cover of the tropical rainforest become obstacles to conducting species monitoring. Unmanned aerial vehicles (drones) combined with thermal infrared imaging (TIR) can rapidly scan large areas from above and detect wildlife that has a body temperature that contrasts with its surrounding environment. This research tested the feasibility of DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to detect terrestrial and arboreal mammals in the five tree density classes in the remaining natural environment on the IPB University campus. This study demonstrated that large-size terrestrial mammal thermal signatures are visible in sparse vegetation at daytime and in the area under the canopy at night monitoring. In contrast, arboreal mammals were better detected in at early morning and night. Survey timing highly influenced the results – the best quality thermal images were obtained at sunrise, late evening, and at night. The drones allow safe operation at low altitudes with low levels of disturbance to animals. Both terrestrial and arboreal mammals are well detected and easily identified when the drone is flying at an altitude < 50 m HAGL. Our preliminary results indicated that thermal surveys from drones are a promising method. |
Condro, Aryo Adhi; Setiawan, Yudi; Prasetyo, Lilik B; Pramulya, Rahmat; Siahaan, Lasriama Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform Journal Article In: Land, vol. 9, no. 10, pp. 377, 2020. @article{Condro2020,
title = {Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform},
author = {Aryo Adhi Condro and Yudi Setiawan and Lilik B Prasetyo and Rahmat Pramulya and Lasriama Siahaan},
url = {https://www.mdpi.com/2073-445X/9/10/377https://algm.ipb.ac.id/wp-content/uploads/2020/11/land-09-00377.pdf},
doi = {10.3390/land9100377},
year = {2020},
date = {2020-10-08},
journal = {Land},
volume = {9},
number = {10},
pages = {377},
abstract = {Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based on remote sensing data using Google Earth Engine. We evaluated a machine learning algorithm—i.e., Random Forest to parameterize how the area in commodity varied in Indonesia. We used various predictors to estimate the productivity of various commodities based on multispectral satellite imageries (36 predictors) at 30-meters spatial resolution. The national commodity map has a relatively high accuracy, with an overall accuracy of about 95% and Kappa coefficient of about 0.90. The results suggest that the oil palm plantation was the highest commodity product that occupied the largest land of Indonesia. However, this study also showed that the land area in rubber, rice paddies, and cacao commodities was underestimated due to its lack of training samples. Improvement in training data collection for each commodity should be done to increase the accuracy of the commodity maps. The commodity data can be viewed online (website can be found in the end of conclusions). This data can further provide significant information related to the agricultural sectors to investigate food provisioning, particularly in Indonesia.},
keywords = {commodity, GEE},
pubstate = {published},
tppubtype = {article}
}
Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based on remote sensing data using Google Earth Engine. We evaluated a machine learning algorithm—i.e., Random Forest to parameterize how the area in commodity varied in Indonesia. We used various predictors to estimate the productivity of various commodities based on multispectral satellite imageries (36 predictors) at 30-meters spatial resolution. The national commodity map has a relatively high accuracy, with an overall accuracy of about 95% and Kappa coefficient of about 0.90. The results suggest that the oil palm plantation was the highest commodity product that occupied the largest land of Indonesia. However, this study also showed that the land area in rubber, rice paddies, and cacao commodities was underestimated due to its lack of training samples. Improvement in training data collection for each commodity should be done to increase the accuracy of the commodity maps. The commodity data can be viewed online (website can be found in the end of conclusions). This data can further provide significant information related to the agricultural sectors to investigate food provisioning, particularly in Indonesia. |
Suyamto, Desi; Condro, Aryo Adhi; Prasetyo, Lilik B; Wijayanto, Arif K Assessing the Agreement between Deforestation Maps of Kalimantan from Various Sources Conference vol. 556, no. 1, IOP Conf. Ser.: Earth Environ. Sci, 2020. @conference{Suyamto2020,
title = {Assessing the Agreement between Deforestation Maps of Kalimantan from Various Sources},
author = {Desi Suyamto and Aryo Adhi Condro and Lilik B Prasetyo and Arif K Wijayanto},
url = {https://iopscience.iop.org/article/10.1088/1755-1315/556/1/012011/pdf},
doi = {10.1088/1755-1315/556/1/012011},
year = {2020},
date = {2020-09-22},
volume = {556},
number = {1},
publisher = {IOP Conf. Ser.: Earth Environ. Sci},
abstract = {Due to its multiscale impacts, deforestation of tropical rainforests had become a global concern. A number of stakeholders comprising government, research agencies, and NGOs; ranging from local to international levels; have developed their own forest monitoring systems for detecting forest loss. However, discrepancies on deforestation reports from various producers often trigger public debates; which mostly degenerate the productivity of efforts in providing salient, legitimate and credible data on deforestation. Thus, we should reconcile the dispute by acknowledging the deforestation data from all sources. This study assessed the agreement between deforestation maps from various sources. In this case, deforestation maps of Kalimantan within 2009-2013 period from 4 sources were used; i.e. deforestation maps from European Space Agency - Climate Change Initiative (ESA-CCI), Forest Watch Indonesia (FWI), Global Forest Watch (GFW), and Indonesian Ministry of Environment and Forestry (MoEF). We found that the inter-rater agreement between deforestation maps were relatively low, as indicated by Cohen's kappa (κ), ranging from slight (κ=0.18 between ESA-CCI and GFW) to fair (0.24 ≤ κ ≤ 0.35 for other pairs of sources); due to omission/commission disagreements (47.82% to 87.58%). It suggests that in order to reconcile the dispute, we should remove the omission disagreement by forming the union of deforestation maps. The results from further analyses proved that the union of deforestation maps increased the agreement to moderate (κ=0.44 between union map and FWI) and even substantial (κ=0.79 between union map and GFW). Findings of this study should support the implementation of one map policy.},
keywords = {deforestation},
pubstate = {published},
tppubtype = {conference}
}
Due to its multiscale impacts, deforestation of tropical rainforests had become a global concern. A number of stakeholders comprising government, research agencies, and NGOs; ranging from local to international levels; have developed their own forest monitoring systems for detecting forest loss. However, discrepancies on deforestation reports from various producers often trigger public debates; which mostly degenerate the productivity of efforts in providing salient, legitimate and credible data on deforestation. Thus, we should reconcile the dispute by acknowledging the deforestation data from all sources. This study assessed the agreement between deforestation maps from various sources. In this case, deforestation maps of Kalimantan within 2009-2013 period from 4 sources were used; i.e. deforestation maps from European Space Agency - Climate Change Initiative (ESA-CCI), Forest Watch Indonesia (FWI), Global Forest Watch (GFW), and Indonesian Ministry of Environment and Forestry (MoEF). We found that the inter-rater agreement between deforestation maps were relatively low, as indicated by Cohen's kappa (κ), ranging from slight (κ=0.18 between ESA-CCI and GFW) to fair (0.24 ≤ κ ≤ 0.35 for other pairs of sources); due to omission/commission disagreements (47.82% to 87.58%). It suggests that in order to reconcile the dispute, we should remove the omission disagreement by forming the union of deforestation maps. The results from further analyses proved that the union of deforestation maps increased the agreement to moderate (κ=0.44 between union map and FWI) and even substantial (κ=0.79 between union map and GFW). Findings of this study should support the implementation of one map policy. |
Irlan,; Saleh, Muhammad Buce; Prasetyo, Lilik B; Setiawan, Yudi Evaluation of Tree Detection and Segmentation Algorithms in Peat Swamp Forest Based on LiDAR Point Clouds Data Journal Article In: Jurnal Manajemen Hutan Tropika, vol. 26, no. 2, pp. 123-132, 2020, ISSN: 2089-2063. @article{Irlan2020,
title = {Evaluation of Tree Detection and Segmentation Algorithms in Peat Swamp Forest Based on LiDAR Point Clouds Data},
author = {Irlan and Muhammad Buce Saleh and Lilik B Prasetyo and Yudi Setiawan},
url = {http://journal.ipb.ac.id/index.php/jmht/article/view/30179},
doi = {10.7226/jtfm.26.2.123},
issn = {2089-2063},
year = {2020},
date = {2020-08-13},
journal = {Jurnal Manajemen Hutan Tropika},
volume = {26},
number = {2},
pages = {123-132},
abstract = {Application of LiDAR for tree detection and tree canopy segmentation has been widely used in conifer plantation forest in temperate countries with high accuracy, however its application on tropical natural forest especially peat swamp forest hardly found. The objective of this study was evaluated algorithms of individual tree detection and canopy segmentation used LiDAR data in peat swamp forest. The algorithms included (a) Local Maxima (LM) with various variable window size combined with growing region, (b) LM with various variable window size combined with Voronoi Tessellation, (c) LM with various fixed window size combined with growing region, (d) LM with various fixed window size combined with Voronoi Tessellation, and (e) Tree Relative Distance algorithm. The results show that algorithm with the best accuracy was the Tree Relative Distance algorithm with the highest overall F-score of 0.63. The tree relative distance algorithm also provides the highest accuracy in determining three tree parameters which are position, height and diameter of tree canopy with a RMSE value 1.08 m, 6.45 m and 1.19 m, respectively.},
keywords = {LiDAR, peat swamp, segmentation},
pubstate = {published},
tppubtype = {article}
}
Application of LiDAR for tree detection and tree canopy segmentation has been widely used in conifer plantation forest in temperate countries with high accuracy, however its application on tropical natural forest especially peat swamp forest hardly found. The objective of this study was evaluated algorithms of individual tree detection and canopy segmentation used LiDAR data in peat swamp forest. The algorithms included (a) Local Maxima (LM) with various variable window size combined with growing region, (b) LM with various variable window size combined with Voronoi Tessellation, (c) LM with various fixed window size combined with growing region, (d) LM with various fixed window size combined with Voronoi Tessellation, and (e) Tree Relative Distance algorithm. The results show that algorithm with the best accuracy was the Tree Relative Distance algorithm with the highest overall F-score of 0.63. The tree relative distance algorithm also provides the highest accuracy in determining three tree parameters which are position, height and diameter of tree canopy with a RMSE value 1.08 m, 6.45 m and 1.19 m, respectively. |
Hultera,; Prasetyo, Lilik B; Setiawan, Yudi Spatial Model Of The Deforestation Potential 2020 & 2024 And The Prevention Approach, Kutai Barat District Journal Article In: Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan, vol. 10, no. 2, pp. 294-306, 2020, ISSN: 2086-4639. @article{Hultera2020,
title = {Spatial Model Of The Deforestation Potential 2020 & 2024 And The Prevention Approach, Kutai Barat District},
author = {Hultera and Lilik B Prasetyo and Yudi Setiawan},
url = {https://journal.ipb.ac.id/index.php/jpsl/article/view/29821},
doi = {10.29244/jpsl.10.2.294-306},
issn = {2086-4639},
year = {2020},
date = {2020-07-03},
journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan},
volume = {10},
number = {2},
pages = {294-306},
abstract = {Kutai Barat have high forest cover and high deforestation rates. The study purpose to make spatial model, potential distribution of deforestation 2020 and 2024, analysis of the drivers of deforestation, compile and map the approach to reducing deforestation. Deforestation modeling done using MaxEnt and Zonation software. Deforestation sample data used from land cover maps 2009, 2013 and 2016. Deforestation rates used to estimate potential deforestation 2020 and 2024. The drivers of deforestation analyze from land cover change matrix. Prevention strategy approach by overlaying potential deforestation modeling results with RTRW maps. The model has good performance with AUC value 0.873. The validation show very good accuracy for the prediction of area to be deforested by 94%, the accuracy of the spatial distribution of the model 31%. Environmental variables have the highest contribution to the model is the distance from previous deforestation 37.4%. The potential of deforestation 2020 is 85,908 ha and 171,778 ha 2024. Oil palm, agriculture, rubber, HTI and mining are the driver of deforestation. Social forestry is expected to prevent potential deforestation 120,861 ha. Others expected programs to contribute to the deforestation reduction are community land intensification 30,316 ha and implementation of the HCV in plantation 20,120 ha.},
keywords = {deforestation},
pubstate = {published},
tppubtype = {article}
}
Kutai Barat have high forest cover and high deforestation rates. The study purpose to make spatial model, potential distribution of deforestation 2020 and 2024, analysis of the drivers of deforestation, compile and map the approach to reducing deforestation. Deforestation modeling done using MaxEnt and Zonation software. Deforestation sample data used from land cover maps 2009, 2013 and 2016. Deforestation rates used to estimate potential deforestation 2020 and 2024. The drivers of deforestation analyze from land cover change matrix. Prevention strategy approach by overlaying potential deforestation modeling results with RTRW maps. The model has good performance with AUC value 0.873. The validation show very good accuracy for the prediction of area to be deforested by 94%, the accuracy of the spatial distribution of the model 31%. Environmental variables have the highest contribution to the model is the distance from previous deforestation 37.4%. The potential of deforestation 2020 is 85,908 ha and 171,778 ha 2024. Oil palm, agriculture, rubber, HTI and mining are the driver of deforestation. Social forestry is expected to prevent potential deforestation 120,861 ha. Others expected programs to contribute to the deforestation reduction are community land intensification 30,316 ha and implementation of the HCV in plantation 20,120 ha. |
2019
|
Rudianto, Yoga; Prasetyo, Lilik B; Setiawan, Yudi; Hudjimartsu, Sahid A Canopy cover estimation of agroforestry based on airborne LiDAR and Landsat 8 OLI Conference vol. 11372, SPIE, 2019. @conference{Rudianto2019,
title = {Canopy cover estimation of agroforestry based on airborne LiDAR and Landsat 8 OLI},
author = {Yoga Rudianto and Lilik B Prasetyo and Yudi Setiawan and Sahid A Hudjimartsu},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11372/2541549/Canopy-cover-estimation-of-agroforestry-based-on-airborne-LiDAR-and/10.1117/12.2541549.short},
doi = {10.1117/12.2541549},
year = {2019},
date = {2019-12-28},
volume = {11372},
publisher = {SPIE},
abstract = {Agroforestry/mixed gardens is a land management system that combines agricultural, livestock production with tree to obtain various products in a sustainable manner so as to increase social, economic and environmental benefits This system can be a form of mitigation and adaptation to global climate change, especially in areas with high population densities, but with less agricultural labor, such as in urban fringe area. Based on the formal definition of forests from the Indonesian Ministry of Environment and Forestry of Indonesia based on canopy cover, agroforestry might be considered as forest, whereas the canopy cover >30%. The research aim to estimate canopy cover base on integration of Lidar and Landsat 8 OLI of agroforestry in the Cidanau watershed. The most suitable equation model is an exponential equation (FRCI = 22.928e (-80.439 * 'RED')), however, some underestimation in high canopy cover ( >70%) and underestimation in low canopy cover (< 60%) should be anticipated. The result showed that agroforestry in some location have canopy cover greater than 30% and therefore it can be considered as a forest.},
keywords = {agroforestry, canopy cover, Landsat, LiDAR},
pubstate = {published},
tppubtype = {conference}
}
Agroforestry/mixed gardens is a land management system that combines agricultural, livestock production with tree to obtain various products in a sustainable manner so as to increase social, economic and environmental benefits This system can be a form of mitigation and adaptation to global climate change, especially in areas with high population densities, but with less agricultural labor, such as in urban fringe area. Based on the formal definition of forests from the Indonesian Ministry of Environment and Forestry of Indonesia based on canopy cover, agroforestry might be considered as forest, whereas the canopy cover >30%. The research aim to estimate canopy cover base on integration of Lidar and Landsat 8 OLI of agroforestry in the Cidanau watershed. The most suitable equation model is an exponential equation (FRCI = 22.928e (-80.439 * 'RED')), however, some underestimation in high canopy cover ( >70%) and underestimation in low canopy cover (< 60%) should be anticipated. The result showed that agroforestry in some location have canopy cover greater than 30% and therefore it can be considered as a forest. |
Prasetyo, Lilik B; Nursal, Wim I; Setiawan, Yudi; Rudianto, Yoga; Wikantika, Ketut; Irawan, Bambang Canopy cover of mangrove estimation based on airborne LIDAR & Landsat 8 OLI Conference vol. 335, IOP Conf. Ser.: Earth Environ. Sci, 2019. @conference{Prasetyo2019,
title = {Canopy cover of mangrove estimation based on airborne LIDAR & Landsat 8 OLI},
author = {Lilik B Prasetyo and Wim I Nursal and Yudi Setiawan and Yoga Rudianto and Ketut Wikantika and Bambang Irawan},
url = {https://iopscience.iop.org/article/10.1088/1755-1315/335/1/012029},
doi = {10.1088/1755-1315/335/1/012029},
year = {2019},
date = {2019-10-28},
volume = {335},
publisher = {IOP Conf. Ser.: Earth Environ. Sci},
abstract = {Mangroves are very important ecosystems, because of their economic value and environmental services, including as a habitat for various wildlife species, storing carbon, and protecting land from sea abrasion. Indonesia is known to have large mangrove area and diversity. It is estimated that the area of mangroves in Indonesia in 2015 reached about 3 million hectares, with 15 families, 18 genera, 41 true mangrove species and 116 species of mangrove associations. Unfortunately, the area to continue to decline due to degradation and conversion to other land uses, especially ponds and built up areas. Usually, mangrove degradation assessment is carried out by field survey and relying on Normalized Difference Vegetation Index (NDVI) clustering derived from satellite image data. Field surveys require a large amount of time and cost, meanwhile NDVI clustering is either inaccurate or too rough. Therefore, exploration of another methods are needed. Our result showed that pixel value of Band 5, Band 6, NDVI and PC1 can be used to estimate canopy cover. Regression using quadratic equation is better than linear equations. However, we noticed limitations of optical Landsat 8 OLI data for canopy cover mapping, namely pixel saturation on high canopy cover and high pixel value of bush/shrubs/regrowth that was not always representing high canopy cover.},
keywords = {canopy cover, Landsat, LiDAR, mangrove},
pubstate = {published},
tppubtype = {conference}
}
Mangroves are very important ecosystems, because of their economic value and environmental services, including as a habitat for various wildlife species, storing carbon, and protecting land from sea abrasion. Indonesia is known to have large mangrove area and diversity. It is estimated that the area of mangroves in Indonesia in 2015 reached about 3 million hectares, with 15 families, 18 genera, 41 true mangrove species and 116 species of mangrove associations. Unfortunately, the area to continue to decline due to degradation and conversion to other land uses, especially ponds and built up areas. Usually, mangrove degradation assessment is carried out by field survey and relying on Normalized Difference Vegetation Index (NDVI) clustering derived from satellite image data. Field surveys require a large amount of time and cost, meanwhile NDVI clustering is either inaccurate or too rough. Therefore, exploration of another methods are needed. Our result showed that pixel value of Band 5, Band 6, NDVI and PC1 can be used to estimate canopy cover. Regression using quadratic equation is better than linear equations. However, we noticed limitations of optical Landsat 8 OLI data for canopy cover mapping, namely pixel saturation on high canopy cover and high pixel value of bush/shrubs/regrowth that was not always representing high canopy cover. |
Khairiah, Rahmi Nur; Prasetyo, Lilik B; Setiawan, Yudi Agroforestry tree density estimation based on hemispherical photos & Landsat 8 OLI/TIRS image: A case study at Cidanau watershed, Banten-Indonesia Conference Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 2019. @conference{Khairiah2019,
title = {Agroforestry tree density estimation based on hemispherical photos & Landsat 8 OLI/TIRS image: A case study at Cidanau watershed, Banten-Indonesia},
author = {Rahmi Nur Khairiah and Lilik B Prasetyo and Yudi Setiawan},
url = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W7/33/2019/},
doi = {10.5194/isprs-archives-XLII-3-W7-33-2019},
year = {2019},
date = {2019-03-01},
publisher = {Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.},
abstract = {The Cidanau watershed is the only watershed in Indonesia that implements Payment for Environmental Services (PES) for farmers who can maintain tree/stand density of 500 trees/hectare on their land. Payments are made upon the verification on the field by the project supervisor. This method requires a lot of time and costly, so it is necessary to build more efficient indirect methods, including using satellite imagery or camera data. The aim of this study is to understand Landsat OLI 8 and hemispherical photo can estimate tree density in the farmer’s agroforestry stand. To obtain tree density, the number of trees with diameter more than 10 cm in 50 plots (50 m x 50 m) were counted. Some predictor variables were utilized, such as Leaf Area Index (LAI) based on hemispherical photos, Normalized Difference Vegetation Index (NDVI), Forest Cover Density (FCD), as well as NDVI and FCD which were enhanced with topographic correction. The imagery used was Landsat 8 OLI acquired on July 5, 2015, with Path/Row 123/64. The relationship between tree density and predictor variables was done using linear regression analysis. Prior to regression analysis, normality (Kolmogorov Smirnov/K-S), heteroscedasticity (Glejser test) and auto correlation (Durbin Watson) test were performed. The results of the analysis showed that tree density was estimated better with hemispherical photos-based LAI, with determination coefficient of 80.6%. Meanwhile, estimation using NDVI and FCD has lower determination coefficient. Even though, the use of topographic correction had been able to increase the determination coefficient of the regression relationship between tree density and FCD, from 4.64% to 35.18%.},
keywords = {agroforestry, CidanauLandsat, hemispherical photos},
pubstate = {published},
tppubtype = {conference}
}
The Cidanau watershed is the only watershed in Indonesia that implements Payment for Environmental Services (PES) for farmers who can maintain tree/stand density of 500 trees/hectare on their land. Payments are made upon the verification on the field by the project supervisor. This method requires a lot of time and costly, so it is necessary to build more efficient indirect methods, including using satellite imagery or camera data. The aim of this study is to understand Landsat OLI 8 and hemispherical photo can estimate tree density in the farmer’s agroforestry stand. To obtain tree density, the number of trees with diameter more than 10 cm in 50 plots (50 m x 50 m) were counted. Some predictor variables were utilized, such as Leaf Area Index (LAI) based on hemispherical photos, Normalized Difference Vegetation Index (NDVI), Forest Cover Density (FCD), as well as NDVI and FCD which were enhanced with topographic correction. The imagery used was Landsat 8 OLI acquired on July 5, 2015, with Path/Row 123/64. The relationship between tree density and predictor variables was done using linear regression analysis. Prior to regression analysis, normality (Kolmogorov Smirnov/K-S), heteroscedasticity (Glejser test) and auto correlation (Durbin Watson) test were performed. The results of the analysis showed that tree density was estimated better with hemispherical photos-based LAI, with determination coefficient of 80.6%. Meanwhile, estimation using NDVI and FCD has lower determination coefficient. Even though, the use of topographic correction had been able to increase the determination coefficient of the regression relationship between tree density and FCD, from 4.64% to 35.18%. |
Sujaswara, Azwar A; Setiawan, Yudi; Prasetyo, Lilik B; Hudjimartsu, Sahid A; Wijayanto, Arif K Utilization of UAV technology for vegetation cover mapping using object based image analysis in restoration area of Gunung Halimun Salak National Park, Indonesia Proceedings Article In: Sixth International Symposium on LAPAN-IPB Satellite, pp. 1137221, International Society for Optics and Photonics 2019. @inproceedings{sujaswara2019utilization,
title = {Utilization of UAV technology for vegetation cover mapping using object based image analysis in restoration area of Gunung Halimun Salak National Park, Indonesia},
author = {Azwar A Sujaswara and Yudi Setiawan and Lilik B Prasetyo and Sahid A Hudjimartsu and Arif K Wijayanto},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11372/1137221/Utilization-of-UAV-technology-for-vegetation-cover-mapping-using-object/10.1117/12.2540566.short},
doi = {10.1117/12.2540566},
year = {2019},
date = {2019-01-01},
booktitle = {Sixth International Symposium on LAPAN-IPB Satellite},
volume = {11372},
pages = {1137221},
organization = {International Society for Optics and Photonics},
abstract = {Halimun Salak Corridor (HSC) is an important area that connects the Mount Halimun and Mount Salak, and has important role of animals movements. As the corridor have become degraded over the last ten years, ecosystem restoration action is required. In order to monitor that restoration program, then, it is necessary to mapping the vegetation cover in the corridor. Unmanned Aerial Vehicle (UAV) technology is an alternative technology that can be used to provide a detail vegetation cover map based on a high resolution image. This research aim to mapping vegetation cover based on a combination of structural characteristics of height and vegetation indices by using Object Based Image Analysis (OBIA) method. Structural characteristics was defined from the canopy height model (CHM) using the Structure from Motion (SfM) method, meanwhile, several spectral indices (NDVI, NDWI, and SAVI) were produced from multispectral images. We applied Object Based Image Analysis (OBIA) to classify vegetation cover based on their structure and spectral characteristics. The results shown that the most dominant vegetation cover is the tree class, which is 70.74 ha (77.31 % of the 91.5 ha mapped area) and accuracy test revealed 73.11% of overall accuracy.},
keywords = {UAV},
pubstate = {published},
tppubtype = {inproceedings}
}
Halimun Salak Corridor (HSC) is an important area that connects the Mount Halimun and Mount Salak, and has important role of animals movements. As the corridor have become degraded over the last ten years, ecosystem restoration action is required. In order to monitor that restoration program, then, it is necessary to mapping the vegetation cover in the corridor. Unmanned Aerial Vehicle (UAV) technology is an alternative technology that can be used to provide a detail vegetation cover map based on a high resolution image. This research aim to mapping vegetation cover based on a combination of structural characteristics of height and vegetation indices by using Object Based Image Analysis (OBIA) method. Structural characteristics was defined from the canopy height model (CHM) using the Structure from Motion (SfM) method, meanwhile, several spectral indices (NDVI, NDWI, and SAVI) were produced from multispectral images. We applied Object Based Image Analysis (OBIA) to classify vegetation cover based on their structure and spectral characteristics. The results shown that the most dominant vegetation cover is the tree class, which is 70.74 ha (77.31 % of the 91.5 ha mapped area) and accuracy test revealed 73.11% of overall accuracy. |
Wijayanto, Arif K; Yusuf, Sri M; Pambudi, Wiwid A The Characteristic of spectral reflectance of LAPAN-IPB (LAPAN-A3) Satellite and Landsat 8 over agricultural area in Probolinggo, East Java Proceedings Article In: IOP Conference Series: Earth and Environmental Science, pp. 012004, IOP Publishing 2019. @inproceedings{wijayanto2019characteristic,
title = {The Characteristic of spectral reflectance of LAPAN-IPB (LAPAN-A3) Satellite and Landsat 8 over agricultural area in Probolinggo, East Java},
author = {Arif K Wijayanto and Sri M Yusuf and Wiwid A Pambudi},
url = {https://iopscience.iop.org/article/10.1088/1755-1315/284/1/012004/meta},
doi = {10.1088/1755-1315/284/1/012004},
year = {2019},
date = {2019-01-01},
booktitle = {IOP Conference Series: Earth and Environmental Science},
volume = {284},
number = {1},
pages = {012004},
organization = {IOP Publishing},
abstract = {LAPAN-IPB Satellite which was developed by the National Agency of Aeronautics and Space (LAPAN) and Landsat 8 have quite equal specification. However, it is important to investigate the difference of characteristic between the two satellites since the Landsat 8 commonly used by Indonesian researcher in the agriculture field for years. The study was done in Probolinggo Regency which is located in East Java, Indonesia – has a large area of agriculture. Satellite data of LAPAN A3/IPB used in the analysis of its spectral characteristic over agricultural area was acquired on September 18, 2018, while the Landsat 8 image data was taken from acquisition date on September 12, 2018. Field data measurement was done by collecting spectral reflectance of some agricultural crops at study area consist of paddy, maize, sugar cane, and onion. Spectral reflectance from the four crops are quietly the same, except for paddy which has the lowest reflectance on peak of green band compared to other crops. Spectral profile of LAPAN-A3/IPB on Blue, Green and Red band are always lower than Landsat 8, while the NIR band is always higher. NDVI from Landsat 8 OLI ranged from -1 to 0.622844, while NDVI from LAPAN-A3/IPB ranged from -1 to 0.461655. NDVI from Landsat is able to differentiate water more clearly than LAPAN-A3/IPB, indicated by low NDVI value. It is concluded that LAPAN-A3/IPB has quite similar spectral characteristic compared to Landsat-8 OLI. Although there is some difference of spectral characteristic from some crops. It is recommended to consider the age or growth stage of each crop.},
keywords = {Landsat, LAPAN, spectral},
pubstate = {published},
tppubtype = {inproceedings}
}
LAPAN-IPB Satellite which was developed by the National Agency of Aeronautics and Space (LAPAN) and Landsat 8 have quite equal specification. However, it is important to investigate the difference of characteristic between the two satellites since the Landsat 8 commonly used by Indonesian researcher in the agriculture field for years. The study was done in Probolinggo Regency which is located in East Java, Indonesia – has a large area of agriculture. Satellite data of LAPAN A3/IPB used in the analysis of its spectral characteristic over agricultural area was acquired on September 18, 2018, while the Landsat 8 image data was taken from acquisition date on September 12, 2018. Field data measurement was done by collecting spectral reflectance of some agricultural crops at study area consist of paddy, maize, sugar cane, and onion. Spectral reflectance from the four crops are quietly the same, except for paddy which has the lowest reflectance on peak of green band compared to other crops. Spectral profile of LAPAN-A3/IPB on Blue, Green and Red band are always lower than Landsat 8, while the NIR band is always higher. NDVI from Landsat 8 OLI ranged from -1 to 0.622844, while NDVI from LAPAN-A3/IPB ranged from -1 to 0.461655. NDVI from Landsat is able to differentiate water more clearly than LAPAN-A3/IPB, indicated by low NDVI value. It is concluded that LAPAN-A3/IPB has quite similar spectral characteristic compared to Landsat-8 OLI. Although there is some difference of spectral characteristic from some crops. It is recommended to consider the age or growth stage of each crop. |
Permatasari, Prita A; Amalo, Luisa F; Wijayanto, Arif K Comparison of urban heat island effect in Jakarta and Surabaya, Indonesia Proceedings Article In: Sixth International Symposium on LAPAN-IPB Satellite, pp. 1137209, International Society for Optics and Photonics International Society for Optics and Photonics, 2019. @inproceedings{permatasari2019comparison,
title = {Comparison of urban heat island effect in Jakarta and Surabaya, Indonesia},
author = {Prita A Permatasari and Luisa F Amalo and Arif K Wijayanto},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11372/1137209/Comparison-of-urban-heat-island-effect-in-Jakarta-and-Surabaya/10.1117/12.2541581.short?SSO=1},
doi = {10.1117/12.2541581},
year = {2019},
date = {2019-01-01},
booktitle = {Sixth International Symposium on LAPAN-IPB Satellite},
volume = {11372},
pages = {1137209},
publisher = {International Society for Optics and Photonics},
organization = {International Society for Optics and Photonics},
abstract = {Urban heat island is a condition when metropolitan area has warmer temperature that surrounding rural area. High population and activity inside the city can be the factors that trigger urban heat island. Indonesia has some large cities with big population. Jakarta and Surabaya are two largest and most populous cities in Indonesia. In this study, the effect of urban heat island in those two cities will be compared using Landsat 8 data in the period of 2018. The correlation between land surface temperature and the normalized difference vegetation index (NDVI) were analyzed to explore the impacts of the green areas on the urban heat island. The result showed the differences of surface temperature between two largest cities in Indonesia in 2018. The result also showed negative correlation between NDVI and surface temperature that indicates that the green area can decrease the effect on the urban heat island.},
keywords = {UHI, urban heat island},
pubstate = {published},
tppubtype = {inproceedings}
}
Urban heat island is a condition when metropolitan area has warmer temperature that surrounding rural area. High population and activity inside the city can be the factors that trigger urban heat island. Indonesia has some large cities with big population. Jakarta and Surabaya are two largest and most populous cities in Indonesia. In this study, the effect of urban heat island in those two cities will be compared using Landsat 8 data in the period of 2018. The correlation between land surface temperature and the normalized difference vegetation index (NDVI) were analyzed to explore the impacts of the green areas on the urban heat island. The result showed the differences of surface temperature between two largest cities in Indonesia in 2018. The result also showed negative correlation between NDVI and surface temperature that indicates that the green area can decrease the effect on the urban heat island. |
2018
|
Setiawan, Yudi; Prasetyo, Lilik B; Pawitan, Hidayat; Liyantono, Liyantono; Syartinilia, Syartinilia; Wijayanto, Arif K; Permatasari, Prita A; Syafrudin, Hadi A; Hakim, Patria R Pemanfaatan Fusi Data Satelit Lapan-a3/IPB dan Landsat 8 Untuk Monitoring Lahan Sawah Journal Article In: Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), vol. 8, no. 1, pp. 67–76, 2018, ISSN: 2460-5824. @article{setiawan2018pemanfaatan,
title = {Pemanfaatan Fusi Data Satelit Lapan-a3/IPB dan Landsat 8 Untuk Monitoring Lahan Sawah},
author = {Yudi Setiawan and Lilik B Prasetyo and Hidayat Pawitan and Liyantono Liyantono and Syartinilia Syartinilia and Arif K Wijayanto and Prita A Permatasari and Hadi A Syafrudin and Patria R Hakim},
url = {https://journal.ipb.ac.id/index.php/jpsl/article/view/19754},
doi = {10.29244/jpsl.8.1.67-76},
issn = {2460-5824},
year = {2018},
date = {2018-01-01},
journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management)},
volume = {8},
number = {1},
pages = {67--76},
abstract = {Increasing of economic development is generally followed by the change of landuse from agriculture to other function. If it occurs in large frequency and amount, it will threaten national food security. Therefore, it is necessary to monitor the agricultural land, especially paddy fields regarding to changes in landuse and global climate. Utilization and development of satellite technology is necessary to provide more accurate and independent database for agricultural land monitoring, especially paddy fields. This study aims to develop a utilization model for LAPAN-IPB satellite (LISAT) and other several satellites data that have been used for paddy field monitoring. This research is conducted through 2 stages: 1) Characterization LISAT satellite data to know spectral variation of paddy field, and 2) Development method of LISAT data fusion with other satellites for paddy field mapping. Based on the research results, the characteristics Red and NIR band in LISAT data imagery have a good correlation with Red and NIR band in LANDSAT 8 OLI data imagery, especially to detect paddy field in the vegetative phase, compared to other bands. Observation and measurement of spectral values using spectroradiometer need to be conducted periodically (starting from first planting season) to know the dynamics of the change related to the growth phase of paddy in paddy field. Pre-processing of image data needs to be conducted to obtain better LISAT data characterization results. Furthermore, it is necessary to develop appropriate algorithms or methods for geometric correction as well as atmospheric correction of LISAT data.},
keywords = {Landsat, LAPAN},
pubstate = {published},
tppubtype = {article}
}
Increasing of economic development is generally followed by the change of landuse from agriculture to other function. If it occurs in large frequency and amount, it will threaten national food security. Therefore, it is necessary to monitor the agricultural land, especially paddy fields regarding to changes in landuse and global climate. Utilization and development of satellite technology is necessary to provide more accurate and independent database for agricultural land monitoring, especially paddy fields. This study aims to develop a utilization model for LAPAN-IPB satellite (LISAT) and other several satellites data that have been used for paddy field monitoring. This research is conducted through 2 stages: 1) Characterization LISAT satellite data to know spectral variation of paddy field, and 2) Development method of LISAT data fusion with other satellites for paddy field mapping. Based on the research results, the characteristics Red and NIR band in LISAT data imagery have a good correlation with Red and NIR band in LANDSAT 8 OLI data imagery, especially to detect paddy field in the vegetative phase, compared to other bands. Observation and measurement of spectral values using spectroradiometer need to be conducted periodically (starting from first planting season) to know the dynamics of the change related to the growth phase of paddy in paddy field. Pre-processing of image data needs to be conducted to obtain better LISAT data characterization results. Furthermore, it is necessary to develop appropriate algorithms or methods for geometric correction as well as atmospheric correction of LISAT data. |
Setiawan, Yudi; Prasetyo, Lilik B; Pawitan, Hidayat; Permatasari, Prita A; Suyamto, Desi; Wijayanto, Arif K Identifying Areas Affected By Fires In Sumatra Based On Time Series Of Remotely Sensed Fire Hotspots And Spatial Modeling Journal Article In: Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), vol. 8, no. 3, pp. 420–427, 2018, ISSN: 2460-5824. @article{setiawan2018identifying,
title = {Identifying Areas Affected By Fires In Sumatra Based On Time Series Of Remotely Sensed Fire Hotspots And Spatial Modeling},
author = {Yudi Setiawan and Lilik B Prasetyo and Hidayat Pawitan and Prita A Permatasari and Desi Suyamto and Arif K Wijayanto},
url = {http://journal.ipb.ac.id/index.php/jpsl/article/view/24760},
doi = {10.29244/jpsl.8.3.420-427},
issn = {2460-5824},
year = {2018},
date = {2018-01-01},
journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management)},
volume = {8},
number = {3},
pages = {420--427},
abstract = {Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires. Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments. Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots.},
keywords = {fire, hotspot},
pubstate = {published},
tppubtype = {article}
}
Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires. Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments. Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots. |
2017
|
Suyamto, Desi; Prasetyo, Lilik B; Setiawan, Yudi; Wijayanto, Arif K Combining projective geometry modelling and spectral thresholding for automated cloud shadow masking in Landsat 8 imageries Proceedings Article In: 2017 European Modelling Symposium (EMS), pp. 22–27, IEEE 2017. @inproceedings{suyamto2017combining,
title = {Combining projective geometry modelling and spectral thresholding for automated cloud shadow masking in Landsat 8 imageries},
author = {Desi Suyamto and Lilik B Prasetyo and Yudi Setiawan and Arif K Wijayanto},
url = {https://ieeexplore.ieee.org/abstract/document/8356785},
doi = {10.1109/EMS.2017.15},
year = {2017},
date = {2017-01-01},
booktitle = {2017 European Modelling Symposium (EMS)},
pages = {22--27},
organization = {IEEE},
abstract = {The presence of cloud shadows in satellite imageries decreases the reflectance of the objects under the shades to relatively low intensities, leads to identification errors. Thus, cloud shadows detection is crucial in image processing steps. We integrated solar position modelling, projective geometry modelling, and spectral thresholding to detect cloud shadows in Landsat 8 imageries. We evaluated the algorithm using the window area of Mount Halimun-Salak, Bogor, West Java, Indonesia. The best rate accuracies of cloud shadow detection using the algorithm was obtained at producer's accuracy, user's accuracy and κ of 63.79%, 70.58%, and 0.66, respectively. Possibility of improving the algorithm for correcting the reflectance of the objects under the shades instead of removing is discussed.},
keywords = {cloud, Landsat, spectral},
pubstate = {published},
tppubtype = {inproceedings}
}
The presence of cloud shadows in satellite imageries decreases the reflectance of the objects under the shades to relatively low intensities, leads to identification errors. Thus, cloud shadows detection is crucial in image processing steps. We integrated solar position modelling, projective geometry modelling, and spectral thresholding to detect cloud shadows in Landsat 8 imageries. We evaluated the algorithm using the window area of Mount Halimun-Salak, Bogor, West Java, Indonesia. The best rate accuracies of cloud shadow detection using the algorithm was obtained at producer's accuracy, user's accuracy and κ of 63.79%, 70.58%, and 0.66, respectively. Possibility of improving the algorithm for correcting the reflectance of the objects under the shades instead of removing is discussed. |
Wijayanto, Arif K; Sani, Octo; Kartika, Nadia D; Herdiyeni, Yeni Classification model for forest fire hotspot occurrences prediction using ANFIS algorithm Proceedings Article In: IOP Conference Series: Earth and Environmental Science, pp. 012059, IOP Publishing IOP Publishing, 2017. @inproceedings{wijayanto2017classification,
title = {Classification model for forest fire hotspot occurrences prediction using ANFIS algorithm},
author = {Arif K Wijayanto and Octo Sani and Nadia D Kartika and Yeni Herdiyeni},
url = {https://iopscience.iop.org/article/10.1088/1755-1315/54/1/012059/meta},
doi = {10.1088/1755-1315/54/1/012059},
year = {2017},
date = {2017-01-01},
booktitle = {IOP Conference Series: Earth and Environmental Science},
volume = {54},
number = {1},
pages = {012059},
publisher = {IOP Publishing},
organization = {IOP Publishing},
abstract = {This study proposed the application of data mining technique namely Adaptive Neuro-Fuzzy inference system (ANFIS) on forest fires hotspot data to develop classification models for hotspots occurrence in Central Kalimantan. Hotspot is a point that is indicated as the location of fires. In this study, hotspot distribution is categorized as true alarm and false alarm. ANFIS is a soft computing method in which a given inputoutput data set is expressed in a fuzzy inference system (FIS). The FIS implements a nonlinear mapping from its input space to the output space. The method of this study classified hotspots as target objects by correlating spatial attributes data using three folds in ANFIS algorithm to obtain the best model. The best result obtained from the 3rd fold provided low error for training (error = 0.0093676) and also low error testing result (error = 0.0093676). Attribute of distance to road is the most determining factor that influences the probability of true and false alarm where the level of human activities in this attribute is higher. This classification model can be used to develop early warning system of forest fire.},
keywords = {ANFIS, fire, hotspot},
pubstate = {published},
tppubtype = {inproceedings}
}
This study proposed the application of data mining technique namely Adaptive Neuro-Fuzzy inference system (ANFIS) on forest fires hotspot data to develop classification models for hotspots occurrence in Central Kalimantan. Hotspot is a point that is indicated as the location of fires. In this study, hotspot distribution is categorized as true alarm and false alarm. ANFIS is a soft computing method in which a given inputoutput data set is expressed in a fuzzy inference system (FIS). The FIS implements a nonlinear mapping from its input space to the output space. The method of this study classified hotspots as target objects by correlating spatial attributes data using three folds in ANFIS algorithm to obtain the best model. The best result obtained from the 3rd fold provided low error for training (error = 0.0093676) and also low error testing result (error = 0.0093676). Attribute of distance to road is the most determining factor that influences the probability of true and false alarm where the level of human activities in this attribute is higher. This classification model can be used to develop early warning system of forest fire. |
2016
|
Wijayanto, Arif K; Seminar, Kudang B; Afnan, Rudi Mobile-based Expert System for Selecting Broiler Farm Location Using PostGIS Journal Article In: TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 14, no. 1, pp. 360-367, 2016, ISSN: 23-2-9293. @article{wijayanto2016mobile,
title = {Mobile-based Expert System for Selecting Broiler Farm Location Using PostGIS},
author = {Arif K Wijayanto and Kudang B Seminar and Rudi Afnan},
url = {http://www.journal.uad.ac.id/index.php/TELKOMNIKA/article/view/2903},
doi = {10.12928/telkomnika.v14i1.2903},
issn = {23-2-9293},
year = {2016},
date = {2016-01-01},
journal = {TELKOMNIKA Indonesian Journal of Electrical Engineering},
volume = {14},
number = {1},
pages = {360-367},
abstract = {Massive development of broiler farms has led to many socio-environmental problems. Based on idea that broiler farm must be located at suitable location, an expert system for site selection based on the socio-environmental factors and sustainable principles is urgently needed to cope with this problem. The objective of this research was to develop a mobile-based expert system as a guidance for broiler farmers to choose best location for broiler farm. There were four factors considered in the system: 1) ecology and environmental impact, 2) economic and infrastructure, 3) natural condition, and 4) natural disaster vulnerability, each of which consists of sub-factors. A mobile-based expert system has been developed by using opensource web GIS server and PostgreSQL/PostGIS, and can be installed on Android device. As conclusion, a mobile-based expert system has been developed and can be used to determine suitable location for broiler farm development.},
keywords = {broiler, PostGIS},
pubstate = {published},
tppubtype = {article}
}
Massive development of broiler farms has led to many socio-environmental problems. Based on idea that broiler farm must be located at suitable location, an expert system for site selection based on the socio-environmental factors and sustainable principles is urgently needed to cope with this problem. The objective of this research was to develop a mobile-based expert system as a guidance for broiler farmers to choose best location for broiler farm. There were four factors considered in the system: 1) ecology and environmental impact, 2) economic and infrastructure, 3) natural condition, and 4) natural disaster vulnerability, each of which consists of sub-factors. A mobile-based expert system has been developed by using opensource web GIS server and PostgreSQL/PostGIS, and can be installed on Android device. As conclusion, a mobile-based expert system has been developed and can be used to determine suitable location for broiler farm development. |
2015
|
Wijayanto, Arif K; Seminar, Kudang B; Afnan, Rudi Suitability Mapping for Broiler Closed House Farm Using Analytical Hierarchy Process and Weighted Overlay with Emphasize on Environmental Aspects Journal Article In: International Journal of Poultry Science, vol. 14, no. 10, pp. 577, 2015. @article{wijayanto2015suitability,
title = {Suitability Mapping for Broiler Closed House Farm Using Analytical Hierarchy Process and Weighted Overlay with Emphasize on Environmental Aspects},
author = {Arif K Wijayanto and Kudang B Seminar and Rudi Afnan},
url = {https://scialert.net/abstract/?doi=ijps.2015.577.583},
doi = {10.3923/ijps.2015.577.583},
year = {2015},
date = {2015-01-01},
journal = {International Journal of Poultry Science},
volume = {14},
number = {10},
pages = {577},
publisher = {Asian Network for Scientific Information (ANSINET)},
abstract = {Massive development of broiler farms has led to many socio-environmental problems. A mapping based on the socio-environmental factors and sustainable principles is urgently needed to cope with this problem. The objective of this research was to create a suitability map for broiler farm development in Parung region, Indonesia-as study area. There were four factors considered in the mapping: (1) ecology and environmental impact, (2) economic and infrastructure, (3) natural condition and (4) natural disaster vulnerability, each of which consists of sub-factors. An Analytical Hierarchy Process (AHP) by using pairwise comparison method was applied to determine weight of each factor and sub-factor based on experts’ valuation. From the AHP process, natural condition was considered as the most important factor, followed by ecological and environmental impact factor. By considering weights resulted from the AHP, the spatial analysis and weighted overlay by GIS software were applied in the data processing and suitability map building. Suitability map for broiler farm in Parung region has been created and can be used as guidance for broiler farm development and also for local government as decision support tool to manage the farming area concerning ecology and environment factor.},
keywords = {AHP, broiler},
pubstate = {published},
tppubtype = {article}
}
Massive development of broiler farms has led to many socio-environmental problems. A mapping based on the socio-environmental factors and sustainable principles is urgently needed to cope with this problem. The objective of this research was to create a suitability map for broiler farm development in Parung region, Indonesia-as study area. There were four factors considered in the mapping: (1) ecology and environmental impact, (2) economic and infrastructure, (3) natural condition and (4) natural disaster vulnerability, each of which consists of sub-factors. An Analytical Hierarchy Process (AHP) by using pairwise comparison method was applied to determine weight of each factor and sub-factor based on experts’ valuation. From the AHP process, natural condition was considered as the most important factor, followed by ecological and environmental impact factor. By considering weights resulted from the AHP, the spatial analysis and weighted overlay by GIS software were applied in the data processing and suitability map building. Suitability map for broiler farm in Parung region has been created and can be used as guidance for broiler farm development and also for local government as decision support tool to manage the farming area concerning ecology and environment factor. |
2013
|
Seminar, Kudang B; Afnan, Rudi; Solahudin, Mohamad; Wijayanto, Arif K; Arifin, Moh Z; Fatikhunnada, Alvin DESIGN AND OPTIMIZATION OF AGRO-SCM FOR FOOD AND ENERGY A REMOTE MONITORING SYSTEM OF BROILERS' BEHAVIOR IN A MULTI-AGENT BROILER CLOSED HOUSE SYSTEM Proceedings Article In: THE 3rd INTERNATIONAL CONFERENCE ON ADAPTIVE AND INTELLIGENT AGROINDUSTRY (ICAIA) 2015, 2013. @inproceedings{seminar2013design,
title = {DESIGN AND OPTIMIZATION OF AGRO-SCM FOR FOOD AND ENERGY A REMOTE MONITORING SYSTEM OF BROILERS' BEHAVIOR IN A MULTI-AGENT BROILER CLOSED HOUSE SYSTEM},
author = {Kudang B Seminar and Rudi Afnan and Mohamad Solahudin and Arif K Wijayanto and Moh Z Arifin and Alvin Fatikhunnada},
year = {2013},
date = {2013-01-01},
booktitle = {THE 3rd INTERNATIONAL CONFERENCE ON ADAPTIVE AND INTELLIGENT AGROINDUSTRY (ICAIA) 2015},
keywords = {broiler},
pubstate = {published},
tppubtype = {inproceedings}
}
|
