Prof. Dr. Ir. Lilik Budi Prasetyo, M.Sc
Kepala Divisi
E-mail Address
lbprastdp[at]apps.ipb.ac.id
Website
Scopus ID
Google Scholar
Sinta ID
ResearchGate
Lilik Budi Prasetyo is Professor in Landscape Ecology, and Head of Environmental Analysis & Geo-Spatial Modelling at Dept. Forest Resources, Conservation & Ecotourism, at IPB University. He is also coordinator of working group of Lapan-IPB satellite (LAPAN A3). His principal research interests is in the application of Remote Sensing & Geographical Information System for Forest Conservation. His research is often related to landscape ecology especially species distribution model, biodiversity indicator, land use & land cover changes, forest monitoring, carbon sequestration and REDD+. He had been employed by several institution such as Biotrop, GIZ, WWF, TNC, IFAC and some public consultants. As guest researcher, he visited The Tokyo University, Tsukuba University & Vikki Tropical Forest Research Institute of Helsinki University. His working has been presented in seminar/workshop and published in several national & international journals.
- BSc. in Agriculture (Bogor Agricultural University) – Agronomi
- M.Agr. (University of Tsukuba, Japan) – Lingkungan
- Ph.D. (University of Tsukuba, Japan) – Ekologi Lanskap
Application of remote sensing & GIS for Land use, land cover changes, & spatial modelling
| ID | Course Name | Duration | Start Date |
|---|---|---|---|
| KSH637 | Aplikasi SIG untuk Konservasi Biodiversitas | ||
| KSH342 | Analisis Spasial Lingkungan | ||
| KSH444 | Ilmu Hutan Kota |
2021 |
Rizal, Muhammad; Saleh, Muhammad Buce; Prasetyo, Lilik B Biomass Estimation Model For Peat Swamp Forest Ecosystem Using LiDAR (Light Detection And Ranging) Journal Article TELKOMNIKA, 19 (3), 2021, ISSN: 2302-9293. Abstract | Links | BibTeX | Tags: biomass, LiDAR, peat swamp @article{Rizal2021, title = {Biomass Estimation Model For Peat Swamp Forest Ecosystem Using LiDAR (Light Detection And Ranging)}, author = {Muhammad Rizal and Muhammad Buce Saleh and Lilik B Prasetyo}, url = {http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/18152}, doi = {10.12928/telkomnika.v19i3.18152}, issn = {2302-9293}, year = {2021}, date = {2021-06-01}, journal = {TELKOMNIKA}, volume = {19}, number = {3}, abstract = {Peat swamp forest plays avery 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 LIDAR technology, especially in Indonesia. The purpose of this study is to build a biomass estimation model based on LIDAR (Light Detection and Ranging) 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 R2 of 72.16% and RMSE (Root Mean Square Error) of 0.0003% is the best-fitted estimation model (BK). Finally, the biomass value from the models was 244.510 tons/ha.}, keywords = {biomass, LiDAR, peat swamp}, pubstate = {published}, tppubtype = {article} } Peat swamp forest plays avery 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 LIDAR technology, especially in Indonesia. The purpose of this study is to build a biomass estimation model based on LIDAR (Light Detection and Ranging) 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 R2 of 72.16% and RMSE (Root Mean Square Error) of 0.0003% is the best-fitted estimation model (BK). Finally, the biomass value from the models was 244.510 tons/ha. |
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 Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), 10 (4), pp. 559-567, 2020, ISSN: 2460-5824. Abstract | Links | BibTeX | Tags: Covid-19, Kualitas udara, ruang terbuka hujau @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 hujau}, 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 |
Jarulis, ; Solihin, Dedy Duryadi; Mardiastuti, Ani; Prasetyo, Lilik B CHARACTERS OF MITOCHONDRIAL DNA D-LOOP HYPERVARIABLE III FRAGMENTS OF INDONESIAN RHINOCEROS HORNBILL (BUCEROS RHINOCEROS) (AVES: BUCEROTIDAE) Journal Article TREUBIA (A JOURNAL ON ZOOLOGY OF THE INDO-AUSTRALIAN ARCHIPELAGO), 47 (2), pp. 99-110, 2020, ISSN: 2337 -876X. Abstract | Links | BibTeX | Tags: hornbill, rhinoceros @article{Jarulis2020, title = {CHARACTERS OF MITOCHONDRIAL DNA D-LOOP HYPERVARIABLE III FRAGMENTS OF INDONESIAN RHINOCEROS HORNBILL (BUCEROS RHINOCEROS) (AVES: BUCEROTIDAE)}, author = {Jarulis and Dedy Duryadi Solihin and Ani Mardiastuti and Lilik B Prasetyo}, url = {https://e-journal.biologi.lipi.go.id/index.php/treubia/article/view/3971/3261}, doi = {10.14203/treubia.v47i2.3971}, issn = {2337 -876X}, year = {2020}, date = {2020-12-30}, journal = {TREUBIA (A JOURNAL ON ZOOLOGY OF THE INDO-AUSTRALIAN ARCHIPELAGO)}, volume = {47}, number = {2}, pages = {99-110}, abstract = {The rhinoceros hornbill (Buceros rhinoceros) genetic characteristics consist of nucleotide polymorphisms, haplotypes, genetic distances, and relationships which are important for their conservation effort in Indonesia. We sequenced mitochondrial DNA D-loop hypervariable III fragments from five rhinoceros hornbill individuals at Safari Park Indonesia I and Ragunan Zoo, which were isolated using Dneasy® Blood and Tissue Kit Spin-Column Protocol, Qiagen. D-loop fragment replication was done by PCR technique using DLBuce_F (5'-TGGCCTTTCTCCAAGGTCTA-3') and DLBuce_R (5'-TGAAGG AGT TCATGGGCTTAG-3') primer. Thirty SNP sites were found in 788 bp D-loop sequences of five rhinoceros hornbill individuals and each individual had a different haplotype. The average genetic distance between individuals was 3.09% and all individuals were categorized into two groups (Group I: EC6TS, EC1RG, EC2TS and Group II: EC9TS, EC10TS) with a genetic distance of 3.99%. This result indicated that the two groups were distinct subspecies. The genetic distance between Indonesian and Thai rhinoceros hornbills was 10.76%. Five Indonesian rhinoceros hornbill individuals at Safari Park Indonesia I and Ragunan Zoo probably came from different populations, ancestors, and two different islands. This study can be of use for management consideration in captive breeding effort at both zoos. The D-loop sequence obtained is a useful character to distinguish three rhinoceros hornbill subspecies in Indonesia.}, keywords = {hornbill, rhinoceros}, pubstate = {published}, tppubtype = {article} } The rhinoceros hornbill (Buceros rhinoceros) genetic characteristics consist of nucleotide polymorphisms, haplotypes, genetic distances, and relationships which are important for their conservation effort in Indonesia. We sequenced mitochondrial DNA D-loop hypervariable III fragments from five rhinoceros hornbill individuals at Safari Park Indonesia I and Ragunan Zoo, which were isolated using Dneasy® Blood and Tissue Kit Spin-Column Protocol, Qiagen. D-loop fragment replication was done by PCR technique using DLBuce_F (5'-TGGCCTTTCTCCAAGGTCTA-3') and DLBuce_R (5'-TGAAGG AGT TCATGGGCTTAG-3') primer. Thirty SNP sites were found in 788 bp D-loop sequences of five rhinoceros hornbill individuals and each individual had a different haplotype. The average genetic distance between individuals was 3.09% and all individuals were categorized into two groups (Group I: EC6TS, EC1RG, EC2TS and Group II: EC9TS, EC10TS) with a genetic distance of 3.99%. This result indicated that the two groups were distinct subspecies. The genetic distance between Indonesian and Thai rhinoceros hornbills was 10.76%. Five Indonesian rhinoceros hornbill individuals at Safari Park Indonesia I and Ragunan Zoo probably came from different populations, ancestors, and two different islands. This study can be of use for management consideration in captive breeding effort at both zoos. The D-loop sequence obtained is a useful character to distinguish three rhinoceros hornbill subspecies in Indonesia. |
Kusrini, Mirza Dikari; Khairunnisa, Luna Raftika; Nusantara, Aria; Kartono, Agus Priyono; Prasetyo, Lilik B; Ayuningrum, Novi Tri; Faz, Fata Habiburrahman Diversity of Amphibians and Reptiles in Various Anthropogenic Disturbance Habitats in Nantu Forest, Sulawesi, Indonesia Journal Article Jurnal Manajemen Hutan Tropika, 26 (3), pp. 291-302, 2020, ISSN: 2089-2063. Abstract | Links | BibTeX | Tags: anthropogenic disturbances, biodiversity, herpetofauna, Nantu Wildlife Sanctuary, Sulawesi @article{Kusrini2020, title = {Diversity of Amphibians and Reptiles in Various Anthropogenic Disturbance Habitats in Nantu Forest, Sulawesi, Indonesia}, author = {Mirza Dikari Kusrini and Luna Raftika Khairunnisa and Aria Nusantara and Agus Priyono Kartono and Lilik B Prasetyo and Novi Tri Ayuningrum and Fata Habiburrahman Faz}, url = {http://journal.ipb.ac.id/index.php/jmht/article/view/31437}, doi = {10.7226/jtfm.26.3.291}, issn = {2089-2063}, year = {2020}, date = {2020-12-12}, journal = {Jurnal Manajemen Hutan Tropika}, volume = {26}, number = {3}, pages = {291-302}, abstract = {The Nantu Forest in Gorontalo Province, Sulawesi, Indonesia holds one of the few remaining pristine habitats in the island. The reserve is surrounded by human habituation which provide opportunity to study the impact of forest lost on biodversity. In addition, data on Nantu mostly focused on big mammals, as there is no previous herpetofauna survey at the area. Sampling of amphibian and reptile was conducted in June 2013 and in May–June 2014 using Visual Encounter Survey method, glue traps and transect sampling in seven different sites at the eastern part of Nantu. We categorized four habitat types based on human disturbances: high disturbed habitat (HDH), moderate disturbed habitat (MDH), low disturbed habitat (LDH) and pristine habitat (PH). A total of 680 individual amphibians (4 families; 17 species) and 119 individual reptiles (9 families; 29 species) were recorded. Species richness and species composition for amphibians and reptiles differs according to the level of human disturbances. Low level disturbances habitat demonstrated the highest diversity of amphibians and reptiles, whereas as expected, high distubed habitat showed the lowest diversity. Anthropogenic pressures in forest will decrease species richness of amphibian and reptiles. Although most amphibian and reptiles will be able to persist in low disturbances habitat, forest-dependent species will be lost when pristine forests are disturbed.}, keywords = {anthropogenic disturbances, biodiversity, herpetofauna, Nantu Wildlife Sanctuary, Sulawesi}, pubstate = {published}, tppubtype = {article} } The Nantu Forest in Gorontalo Province, Sulawesi, Indonesia holds one of the few remaining pristine habitats in the island. The reserve is surrounded by human habituation which provide opportunity to study the impact of forest lost on biodversity. In addition, data on Nantu mostly focused on big mammals, as there is no previous herpetofauna survey at the area. Sampling of amphibian and reptile was conducted in June 2013 and in May–June 2014 using Visual Encounter Survey method, glue traps and transect sampling in seven different sites at the eastern part of Nantu. We categorized four habitat types based on human disturbances: high disturbed habitat (HDH), moderate disturbed habitat (MDH), low disturbed habitat (LDH) and pristine habitat (PH). A total of 680 individual amphibians (4 families; 17 species) and 119 individual reptiles (9 families; 29 species) were recorded. Species richness and species composition for amphibians and reptiles differs according to the level of human disturbances. Low level disturbances habitat demonstrated the highest diversity of amphibians and reptiles, whereas as expected, high distubed habitat showed the lowest diversity. Anthropogenic pressures in forest will decrease species richness of amphibian and reptiles. Although most amphibian and reptiles will be able to persist in low disturbances habitat, forest-dependent species will be lost when pristine forests are disturbed. |
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 AES Bioflux, 12 (3), pp. 213-221, 2020, ISSN: 2066-7647. Abstract | Links | BibTeX | Tags: Covid-19, Land Surface Temperature, urban heat island @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. |
Repi, Terri; Masy'ud, Burhanuddin; Mustari, Abdul Haris; Prasetyo, Lilik B Population density, geographical distribution and habitat of Talaud bear cuscus (Ailurops melanotis Thomas, 1898) Journal Article Biodiversitas, 21 (12), pp. 5621-5631, 2020. Abstract | Links | BibTeX | Tags: Ailurops melanotis, conservation, density, distribution, habitat, population @article{Repi2020, title = {Population density, geographical distribution and habitat of Talaud bear cuscus (Ailurops melanotis Thomas, 1898)}, author = {Terri Repi and Burhanuddin Masy'ud and Abdul Haris Mustari and Lilik B Prasetyo}, url = {https://smujo.id/biodiv/article/view/6833}, doi = {10.13057/biodiv/d211207}, year = {2020}, date = {2020-11-06}, journal = {Biodiversitas}, volume = {21}, number = {12}, pages = {5621-5631}, abstract = {The Talaud bear cuscus (Ailurops melanotis) has been reported from Sangihe (the largest island in the Sangihe Island group) and Salibabu (within the Talaud Islands). As an endemic species of Indonesia, this species is rare and there is no certainty regarding its precise geographic distribution or population size. This research aimed to estimate population density and provide the first preliminary data on its geographical distribution, as well as general description of its habitat. Our research shows that A. melanotis occurs on three islands: Salibabu Island, Nusa Island, and Bukide Island, and probably also exists in the Sahandaruman mountain on Sangihe Island. Our population surveys estimate, population density on each island as: Salibabu: 3.69 ± 2.54 ind/km2, with an estimated total population of 28.95 individuals, Nusa Island: was 12.31 ± 2.58 ind/km2, with an estimated population of 19.08 individuals, and Bukide Island: 7.17 ± 1.79/km2, with an estimated population of 10.40 individuals. Information regarding population is a key guiding factor in conservation efforts, where population size is related to extinction risk (threat status) and its geographical distribution, this can help to determine conservation priorities for species or habitats.}, keywords = {Ailurops melanotis, conservation, density, distribution, habitat, population}, pubstate = {published}, tppubtype = {article} } The Talaud bear cuscus (Ailurops melanotis) has been reported from Sangihe (the largest island in the Sangihe Island group) and Salibabu (within the Talaud Islands). As an endemic species of Indonesia, this species is rare and there is no certainty regarding its precise geographic distribution or population size. This research aimed to estimate population density and provide the first preliminary data on its geographical distribution, as well as general description of its habitat. Our research shows that A. melanotis occurs on three islands: Salibabu Island, Nusa Island, and Bukide Island, and probably also exists in the Sahandaruman mountain on Sangihe Island. Our population surveys estimate, population density on each island as: Salibabu: 3.69 ± 2.54 ind/km2, with an estimated total population of 28.95 individuals, Nusa Island: was 12.31 ± 2.58 ind/km2, with an estimated population of 19.08 individuals, and Bukide Island: 7.17 ± 1.79/km2, with an estimated population of 10.40 individuals. Information regarding population is a key guiding factor in conservation efforts, where population size is related to extinction risk (threat status) and its geographical distribution, this can help to determine conservation priorities for species or habitats. |
Septiana, Wardi; Munawir, Ahmad; Pairah, ; Erlan, Mochamad; Irawan, Yosi; Santosa, Yanto; Prasetyo, Lilik B Distribution and Characteristics of Javan Hawk Eagle Nesting Trees in Gunung Halimun Salak National Park, Indonesia Journal Article Jurnal Biodjati, 5 (2), pp. 182-190, 2020, ISSN: 2541-4208. Abstract | Links | BibTeX | Tags: Gunung Halimun Salak National Park, Javan Hawk Eagle @article{Septiana2020, title = {Distribution and Characteristics of Javan Hawk Eagle Nesting Trees in Gunung Halimun Salak National Park, Indonesia}, author = {Wardi Septiana and Ahmad Munawir and Pairah and Mochamad Erlan and Yosi Irawan and Yanto Santosa and Lilik B Prasetyo}, url = {https://journal.uinsgd.ac.id/index.php/biodjati/article/view/8481}, doi = {10.15575/biodjati.v5i2.8481}, issn = {2541-4208}, year = {2020}, date = {2020-11-01}, journal = {Jurnal Biodjati}, volume = {5}, number = {2}, pages = {182-190}, abstract = {Javan Hawk Eagle is one of the three keys species of the Gunung Halimun Salak National Park and endemic to the island of Java. Protecting the active Javan Hawk Eagle nesting tree is one of the efforts to increase the success rate of Java Hawk Eagle breeding so that information on the distribution and characteris-tics of Javan Hawk Eagle nesting tree is needed. Field exploration was carried out to determine the existence of the Javan Hawk Eagle nest. There were 10 individuals of Javan Hawk Eagle nesting trees which consisted of 5 species namely Rasamala, Huru, Damar, Leng-sar and Manggong with tree architecture models of rauh, massart, scarrone and aubreville, tree height between 26-55 m and height of nests between 18-41m. The Javan Hawk Eagle nesting trees grow in primary, secondary, and plantation forests in a height between 670- 1295 masl, with a steep and very steep slope, the majority of the dis-tance from the river is less than 100 m and the majority of the dis-tance with ecotone is less than 600 m. Javan Hawk Eagle nest on Damar is the first finding at Gunung Halimun Salak National Park. }, keywords = {Gunung Halimun Salak National Park, Javan Hawk Eagle}, pubstate = {published}, tppubtype = {article} } Javan Hawk Eagle is one of the three keys species of the Gunung Halimun Salak National Park and endemic to the island of Java. Protecting the active Javan Hawk Eagle nesting tree is one of the efforts to increase the success rate of Java Hawk Eagle breeding so that information on the distribution and characteris-tics of Javan Hawk Eagle nesting tree is needed. Field exploration was carried out to determine the existence of the Javan Hawk Eagle nest. There were 10 individuals of Javan Hawk Eagle nesting trees which consisted of 5 species namely Rasamala, Huru, Damar, Leng-sar and Manggong with tree architecture models of rauh, massart, scarrone and aubreville, tree height between 26-55 m and height of nests between 18-41m. The Javan Hawk Eagle nesting trees grow in primary, secondary, and plantation forests in a height between 670- 1295 masl, with a steep and very steep slope, the majority of the dis-tance from the river is less than 100 m and the majority of the dis-tance with ecotone is less than 600 m. Javan Hawk Eagle nest on Damar is the first finding at Gunung Halimun Salak National Park. |
Atmoko, Tri; Mardiastuti, Ani; Bismark, M; Prasetyo, Lilik B; Iskandar, Entang Habitat suitability of Proboscis monkey (Nasalis larvatus) in Berau Delta, East Kalimantan, Indonesia Journal Article Biodiversitas, 21 (11), pp. 5155-5163, 2020. Abstract | Links | BibTeX | Tags: Colobinae, MaxEnt, primate conservation, riverine forest, Species Distribution Model @article{Atmoko2020, title = {Habitat suitability of Proboscis monkey (Nasalis larvatus) in Berau Delta, East Kalimantan, Indonesia}, author = {Tri Atmoko and Ani Mardiastuti and M Bismark and Lilik B Prasetyo and Entang Iskandar}, url = {https://smujo.id/biodiv/article/view/6873}, doi = {10.13057/biodiv/d211121}, year = {2020}, date = {2020-10-14}, journal = {Biodiversitas}, volume = {21}, number = {11}, pages = {5155-5163}, abstract = {The proboscis monkey (Nasalis larvatus) is an endemic species to Borneos’ island and is largely confined to mangrove, riverine, and swamp forest. Most of their habitat is outside the conservation due to degraded and habitat converted. Habitat loss is a significant threat to a decreased in the monkey's population. Berau Delta is an unprotected habitat of proboscis monkey, lacking in attention and experiencing a lot of disturbances. This study was conducted on April – August 2019; with aims of the study is to determine Species Distribution Modeling (SDM) for identifying proboscis monkey habitat suitability in Delta Berau, East Kalimantan. The MaxEnt algorithm was used to produce a habitat suitability map based on this species’ occurrence records and environmental predictors. We built the models using 208 points of proboscis monkey presence and 12 environment variables within the study area. Model performance was assessed by examining the area under the curve. The variables most influencing the habitat suitability model were the riverine habitat (60.9%), distance from the pond (16.0%), and distance from the coastline (5.2%). The proboscis monkey suitable habitat is only 9.32% (8,726.58 ha) from 93,631.41 ha total area. The appropriate habitat areas are Sapinang Island, Bungkung Island, Sambuayan Island, Saodang Kecil Island, Besing Island, Lati River, Bebanir Lama, Batu-Batu, and Semanting Bay. We provide some suggestions for the proboscis monkey conservation, which are local protection of uninhabited islands, participatory ecotourism management, and company involvement in protection and management efforts.}, keywords = {Colobinae, MaxEnt, primate conservation, riverine forest, Species Distribution Model}, pubstate = {published}, tppubtype = {article} } The proboscis monkey (Nasalis larvatus) is an endemic species to Borneos’ island and is largely confined to mangrove, riverine, and swamp forest. Most of their habitat is outside the conservation due to degraded and habitat converted. Habitat loss is a significant threat to a decreased in the monkey's population. Berau Delta is an unprotected habitat of proboscis monkey, lacking in attention and experiencing a lot of disturbances. This study was conducted on April – August 2019; with aims of the study is to determine Species Distribution Modeling (SDM) for identifying proboscis monkey habitat suitability in Delta Berau, East Kalimantan. The MaxEnt algorithm was used to produce a habitat suitability map based on this species’ occurrence records and environmental predictors. We built the models using 208 points of proboscis monkey presence and 12 environment variables within the study area. Model performance was assessed by examining the area under the curve. The variables most influencing the habitat suitability model were the riverine habitat (60.9%), distance from the pond (16.0%), and distance from the coastline (5.2%). The proboscis monkey suitable habitat is only 9.32% (8,726.58 ha) from 93,631.41 ha total area. The appropriate habitat areas are Sapinang Island, Bungkung Island, Sambuayan Island, Saodang Kecil Island, Besing Island, Lati River, Bebanir Lama, Batu-Batu, and Semanting Bay. We provide some suggestions for the proboscis monkey conservation, which are local protection of uninhabited islands, participatory ecotourism management, and company involvement in protection and management efforts. |
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 Land, 9 (10), pp. 377, 2020. Abstract | Links | BibTeX | Tags: commodity, GEE @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. |
Juniyanti, Lila; Prasetyo, Lilik B; Aprianto, Dwi Putra; Purnomo, Herry; Kartodiharjo, Hariadi Perubahan penggunaan dan tutupan lahan, serta faktor penyebabnya di Pulau Bengkalis, Provinsi Riau (periode 1990-2019) Journal Article Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan, 10 (3), pp. 419-435, 2020. Abstract | Links | BibTeX | Tags: direct causes, spatial analysis, time-series, underlying causes @article{Juniyanti2020, title = {Perubahan penggunaan dan tutupan lahan, serta faktor penyebabnya di Pulau Bengkalis, Provinsi Riau (periode 1990-2019)}, author = {Lila Juniyanti and Lilik B Prasetyo and Dwi Putra Aprianto and Herry Purnomo and Hariadi Kartodiharjo}, url = {http://journal.ipb.ac.id/index.php/jpsl/article/view/31164}, doi = {10.29244/jpsl.10.3.419-435}, year = {2020}, date = {2020-10-01}, journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan}, volume = {10}, number = {3}, pages = {419-435}, abstract = {Indonesia is one of the countries with dynamic land cover changes because the country's economy is sourced from land-based resource management. On the other hand, it has negative impacts such as social conflict and environmental damage. This paper observed patterns of land change and explores its driving forces during 1900-2019 on Bengkalis Island, Indonesia to monitor and provide information that can be used as a base for reducing uncontrolled land-use changes in an area. We reviewed previous reports and research, observed land cover conditions in the field, carried out focus group discussions, and deep interviews. We implemented GIS to capture time-series land cover and land-use changes. The results showed that the forest cover has declined sharply since 1990. After 2000, the area of mixed garden was larger than the forest cover. The area of oil palm and forest plantations began to increase. The transmigration policy has triggered masive land clearing on Bengkalis Island. Land clearing by transmigrants and the economic crisis have led to greater land clearing by spontaneous transmigrants.}, keywords = {direct causes, spatial analysis, time-series, underlying causes}, pubstate = {published}, tppubtype = {article} } Indonesia is one of the countries with dynamic land cover changes because the country's economy is sourced from land-based resource management. On the other hand, it has negative impacts such as social conflict and environmental damage. This paper observed patterns of land change and explores its driving forces during 1900-2019 on Bengkalis Island, Indonesia to monitor and provide information that can be used as a base for reducing uncontrolled land-use changes in an area. We reviewed previous reports and research, observed land cover conditions in the field, carried out focus group discussions, and deep interviews. We implemented GIS to capture time-series land cover and land-use changes. The results showed that the forest cover has declined sharply since 1990. After 2000, the area of mixed garden was larger than the forest cover. The area of oil palm and forest plantations began to increase. The transmigration policy has triggered masive land clearing on Bengkalis Island. Land clearing by transmigrants and the economic crisis have led to greater land clearing by spontaneous transmigrants. |
Suyamto, Desi; Condro, Aryo Adhi; Prasetyo, Lilik B; Wijayanto, Arif K Assessing the Agreement between Deforestation Maps of Kalimantan from Various Sources Conference 556 (1), IOP Conf. Ser.: Earth Environ. Sci, 2020. Abstract | Links | BibTeX | Tags: deforestation @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 Jurnal Manajemen Hutan Tropika, 26 (2), pp. 123-132, 2020, ISSN: 2089-2063. Abstract | Links | BibTeX | Tags: LiDAR, peat swamp, segmentation @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. |
Irlan, ; Saleh, Muhammad Buce; Prasetyo, Lilik B Evaluation of Tree Detection and Segmentation Algorithms in Peat Swamp Forest Based on LiDAR Point Clouds Data Journal Article Jurnal Manajemen Hutan Tropika, 26 (2), pp. 123, 2020, ISSN: 2089-2063. Abstract | Links | BibTeX | Tags: LiDAR, peat swamp, point cloud @article{Irlan2020b, 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}, url = {https://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}, 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, point cloud}, 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. |
Maulana, Sandhi I; Syaufina, Lailan; Prasetyo, Lilik B; Aidi, M N 528 , IOP Conf. Ser.: Earth Environ. Sci, 2020. Abstract | Links | BibTeX | Tags: Bengkalis, DSS, fire, peat land, Riau @conference{Maulana2020b, title = {A spatial decision support system for peatland fires prediction and prevention in Bengkalis Regency, Indonesia}, author = {Sandhi I Maulana and Lailan Syaufina and Lilik B Prasetyo and M N Aidi}, url = {https://iopscience.iop.org/article/10.1088/1755-1315/528/1/012052/meta}, doi = {10.1088/1755-1315/528/1/012052}, year = {2020}, date = {2020-07-21}, volume = {528}, publisher = {IOP Conf. Ser.: Earth Environ. Sci}, abstract = {A Spatial decision support system (SDSS) is an integrated computer-based system that can be used to support decision makers in addressing spatial problems through iterative approaches with functionality for handling both of spatial and non-spatial databases, analytical modelling capabilities, decision making support, as well as effective data and information presentation utilities. Previously, many studies have proven that this kind of decision support system is also useful in addressing wildfires problems effectively. Considering this technological advancement, this study is primarily aimed to develop a peatland fires management system by implementing the concept of SDSS. Developed system in this study is consisting of two separate sub-system, namely prediction and prevention sub-systems, which are then integrated into one whole working scheme using loose coupling method. Overall, it can be concluded that such integrated prediction and prevention system has various advantages. Firstly, it is useful to establish rapid coordination among involved stakeholders in deciding suitable approaches to prevent peatland fires. Secondly, promoting a more pro-active fire management system that is relied on predict-and-prevent approach. Thirdly, avoiding further delay on fires prevention while minimizing error in resources allocation. Lastly, this kind of decision support system can be rapidly updated following on-going technological and field situation developments.}, keywords = {Bengkalis, DSS, fire, peat land, Riau}, pubstate = {published}, tppubtype = {conference} } A Spatial decision support system (SDSS) is an integrated computer-based system that can be used to support decision makers in addressing spatial problems through iterative approaches with functionality for handling both of spatial and non-spatial databases, analytical modelling capabilities, decision making support, as well as effective data and information presentation utilities. Previously, many studies have proven that this kind of decision support system is also useful in addressing wildfires problems effectively. Considering this technological advancement, this study is primarily aimed to develop a peatland fires management system by implementing the concept of SDSS. Developed system in this study is consisting of two separate sub-system, namely prediction and prevention sub-systems, which are then integrated into one whole working scheme using loose coupling method. Overall, it can be concluded that such integrated prediction and prevention system has various advantages. Firstly, it is useful to establish rapid coordination among involved stakeholders in deciding suitable approaches to prevent peatland fires. Secondly, promoting a more pro-active fire management system that is relied on predict-and-prevent approach. Thirdly, avoiding further delay on fires prevention while minimizing error in resources allocation. Lastly, this kind of decision support system can be rapidly updated following on-going technological and field situation developments. |
Hultera, ; Prasetyo, Lilik B; Setiawan, Yudi Spatial Model Of The Deforestation Potential 2020 & 2024 And The Prevention Approach, Kutai Barat District Journal Article Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan, 10 (2), pp. 294-306, 2020, ISSN: 2086-4639. Abstract | Links | BibTeX | Tags: deforestation @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. |
Kamal, Muhammad; Farda, Nur Mohammad; Jamaluddin, Ilham; Parela, Artha; Wikantika, Ketut; Prasetyo, Lilik B; Irawan, Bambang A preliminary study on machine learning and google earth engine for mangrove mapping Conference 500 , IOP Conf. Ser.: Earth Environ. Sci, 2020. Abstract | Links | BibTeX | Tags: GEE, machine learning, mangrove @conference{Kamal2020, title = {A preliminary study on machine learning and google earth engine for mangrove mapping}, author = {Muhammad Kamal and Nur Mohammad Farda and Ilham Jamaluddin and Artha Parela and Ketut Wikantika and Lilik B Prasetyo and Bambang Irawan}, url = {https://iopscience.iop.org/article/10.1088/1755-1315/500/1/012038/meta}, doi = {10.1088/1755-1315/500/1/012038}, year = {2020}, date = {2020-07-03}, volume = {500}, publisher = {IOP Conf. Ser.: Earth Environ. Sci}, abstract = {The alarming rate of global mangrove forest degradation corroborates the need for providing fast, up-to-date and accurate mangrove maps. Conventional scene by scene image classification approach is inefficient and time consuming. The development of Google Earth Engine (GEE) provides a cloud platform to access and seamlessly process large amount of freely available satellite imagery. The GEE also provides a set of the state-of-the-art classifiers for pixel-based classification that can be used for mangrove mapping. This study is an initial effort which is aimed to combine machine learning and GEE for mapping mangrove extent. We used two Landsat 8 scenes over Agats and Timika Papua area as pilot images for this study; path 102 row 64 (2014/10/19) and path 103 row 63 (2013/05/16). The first image was used to develop local training areas for the machine learning classification, while the second one was used as a test image for GEE on the cloud. A total of 838 points samples were collected representing mangroves (244), non-mangroves (161), water bodies (311), and cloud (122) class. These training areas were used by support vector machine classifier in GEE to classify the first image. The classification result show mangrove objects could be efficiently delineated by this algorithm as confirmed by visual checking. This algorithm was then applied to the second image in GEE to check the consistency of the result. A simultaneous view of both classified images shows a corresponding pattern of mangrove forest, which mean the mangrove object has been consistently delineated by the algorithm.}, keywords = {GEE, machine learning, mangrove}, pubstate = {published}, tppubtype = {conference} } The alarming rate of global mangrove forest degradation corroborates the need for providing fast, up-to-date and accurate mangrove maps. Conventional scene by scene image classification approach is inefficient and time consuming. The development of Google Earth Engine (GEE) provides a cloud platform to access and seamlessly process large amount of freely available satellite imagery. The GEE also provides a set of the state-of-the-art classifiers for pixel-based classification that can be used for mangrove mapping. This study is an initial effort which is aimed to combine machine learning and GEE for mapping mangrove extent. We used two Landsat 8 scenes over Agats and Timika Papua area as pilot images for this study; path 102 row 64 (2014/10/19) and path 103 row 63 (2013/05/16). The first image was used to develop local training areas for the machine learning classification, while the second one was used as a test image for GEE on the cloud. A total of 838 points samples were collected representing mangroves (244), non-mangroves (161), water bodies (311), and cloud (122) class. These training areas were used by support vector machine classifier in GEE to classify the first image. The classification result show mangrove objects could be efficiently delineated by this algorithm as confirmed by visual checking. This algorithm was then applied to the second image in GEE to check the consistency of the result. A simultaneous view of both classified images shows a corresponding pattern of mangrove forest, which mean the mangrove object has been consistently delineated by the algorithm. |
Sudhana, Sonny A; Sakti, Anjar D; Syahid, Luri N; Prasetyo, Lilik B; Irawan, Bambang; Kamal, Muhammad; Wikantika, Ketut 500 , IOP Conf. Ser.: Earth Environ. Sci, 2020. Abstract | Links | BibTeX | Tags: deforestation, land cover change, mangrove @conference{Sudhana2020, title = {Detecting mangrove deforestation using multi land use land cover change datasets: a comparative analysis in Southeast Asia}, author = {Sonny A Sudhana and Anjar D Sakti and Luri N Syahid and Lilik B Prasetyo and Bambang Irawan and Muhammad Kamal and Ketut Wikantika}, url = {https://iopscience.iop.org/article/10.1088/1755-1315/500/1/012014/meta}, doi = {10.1088/1755-1315/500/1/012014}, year = {2020}, date = {2020-06-01}, volume = {500}, publisher = {IOP Conf. Ser.: Earth Environ. Sci}, abstract = {Mangrove forest grows on tropical coastal areas and has an ecological role for its surrounding environment. Mangrove forest protects the coast from large waves and becomes a habitat for various marine fauna. It stores the highest densities of carbon among any other ecosystem globally. In Southeast Asia, Mangrove forest is highly biodiverse and contributes to the sustainability of the ecosystem. However, based on previous studies, mangrove forests are experiencing deforestation due to high demands of commodities and land use. In this study, we analyzed changes of land cover in Southeast Asia using several global land cover products produced between 2001 and 2012 and their correlation with mangrove deforestation based on Mangrove Forest Watch (CGMFC-21) data. LULC data products applied in this study were ESA CCI LC, MODIS LC, GlobCover. The analysis was carried out by calculating the rate of increase in mangrove deforestation and comparing it with changes in land cover that replaced the mangrove area temporally. The results of this study were land cover classes that replaced mangrove forest areas in the study period. Based on the results it could be concluded that the methods and products used influence the results. There are many sources of data products that might be used for future research, with other methods that are better so that they provide space for future research and development. Our study can be used as a consideration to implement policies that conserve mangrove forest across Southeast Asia.}, keywords = {deforestation, land cover change, mangrove}, pubstate = {published}, tppubtype = {conference} } Mangrove forest grows on tropical coastal areas and has an ecological role for its surrounding environment. Mangrove forest protects the coast from large waves and becomes a habitat for various marine fauna. It stores the highest densities of carbon among any other ecosystem globally. In Southeast Asia, Mangrove forest is highly biodiverse and contributes to the sustainability of the ecosystem. However, based on previous studies, mangrove forests are experiencing deforestation due to high demands of commodities and land use. In this study, we analyzed changes of land cover in Southeast Asia using several global land cover products produced between 2001 and 2012 and their correlation with mangrove deforestation based on Mangrove Forest Watch (CGMFC-21) data. LULC data products applied in this study were ESA CCI LC, MODIS LC, GlobCover. The analysis was carried out by calculating the rate of increase in mangrove deforestation and comparing it with changes in land cover that replaced the mangrove area temporally. The results of this study were land cover classes that replaced mangrove forest areas in the study period. Based on the results it could be concluded that the methods and products used influence the results. There are many sources of data products that might be used for future research, with other methods that are better so that they provide space for future research and development. Our study can be used as a consideration to implement policies that conserve mangrove forest across Southeast Asia. |
Syahidah, Tazkiyatul; Rizali, Akhmad; Prasetyo, Lilik B; Buchori, Damayanti Landscape composition alters parasitoid wasps but not their host diversity in tropical agricultural landscapes Journal Article Biodiversitas, 21 (4), pp. 1702-1706, 2020. Abstract | Links | BibTeX | Tags: parasitoid @article{Syahidah2020, title = {Landscape composition alters parasitoid wasps but not their host diversity in tropical agricultural landscapes}, author = {Tazkiyatul Syahidah and Akhmad Rizali and Lilik B Prasetyo and Damayanti Buchori}, url = {https://smujo.id/biodiv/article/view/4718}, doi = {10.13057/biodiv/d210452}, year = {2020}, date = {2020-03-29}, journal = {Biodiversitas}, volume = {21}, number = {4}, pages = {1702-1706}, abstract = {The diversity of parasitoid wasps and their hosts in an agricultural landscape is affected by crop management and habitat conditions around crop fields. The composition of agricultural landscapes that are dominated by non-crop or natural habitats are assumed to be able to support the presence of parasitoid wasps as biological control of pests. The aim of this study was to investigate the effect of landscape composition on the diversity of parasitoid wasps and their hosts in agricultural landscapes. The research observations were conducted on six fields of long-bean cultivation located in Bogor District, West Java Province, Indonesia. Parasitoid wasps were collected by hand-collecting of their hosts (lepidopteran larvae) within 60 m distance transect to each long-bean field. In total, 17 species of parasitoid wasps and 12 species of lepidopteran larvae were found from all agricultural landscapes. A parasitoid wasp, Microplitis manilae was found in all long-bean fields (except Bantarjaya) and only parasitized the tobacco cutworm (Spodoptera litura). The tomato looper, Chrysodeixis chalcites had the highest associated parasitoids and was also parasitized by Braconidae sp5 which was also a parasitoid of S. litura. Based on the analysis results, the patch numbers of natural habitats had a positive effect on the diversity of parasitoid wasps and had no effect on the diversity of lepidopteran larvae. In conclusion, landscape compositions with patchy natural habitats have an important role to preserve beneficial insects and maintain ecosystem services in tropical agricultural landscapes.}, keywords = {parasitoid}, pubstate = {published}, tppubtype = {article} } The diversity of parasitoid wasps and their hosts in an agricultural landscape is affected by crop management and habitat conditions around crop fields. The composition of agricultural landscapes that are dominated by non-crop or natural habitats are assumed to be able to support the presence of parasitoid wasps as biological control of pests. The aim of this study was to investigate the effect of landscape composition on the diversity of parasitoid wasps and their hosts in agricultural landscapes. The research observations were conducted on six fields of long-bean cultivation located in Bogor District, West Java Province, Indonesia. Parasitoid wasps were collected by hand-collecting of their hosts (lepidopteran larvae) within 60 m distance transect to each long-bean field. In total, 17 species of parasitoid wasps and 12 species of lepidopteran larvae were found from all agricultural landscapes. A parasitoid wasp, Microplitis manilae was found in all long-bean fields (except Bantarjaya) and only parasitized the tobacco cutworm (Spodoptera litura). The tomato looper, Chrysodeixis chalcites had the highest associated parasitoids and was also parasitized by Braconidae sp5 which was also a parasitoid of S. litura. Based on the analysis results, the patch numbers of natural habitats had a positive effect on the diversity of parasitoid wasps and had no effect on the diversity of lepidopteran larvae. In conclusion, landscape compositions with patchy natural habitats have an important role to preserve beneficial insects and maintain ecosystem services in tropical agricultural landscapes. |
Putri, Anika; Kusrini, Mirza Dikari; Prasetyo, Lilik B Pemodelan Kesesuaian Habitat Katak Serasah (Leptobrachium hasseltii Tschudi 1838) dengan Sistem Informasi Geografis di Pulau Jawa Journal Article Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan, 10 (1), pp. 12-24, 2020, ISBN: 2086-4639. Abstract | Links | BibTeX | Tags: katak, Leptobrachium hasseltii Tschudi 1838 @article{Putri2020, title = {Pemodelan Kesesuaian Habitat Katak Serasah (Leptobrachium hasseltii Tschudi 1838) dengan Sistem Informasi Geografis di Pulau Jawa}, author = {Anika Putri and Mirza Dikari Kusrini and Lilik B Prasetyo}, url = {http://journal.ipb.ac.id/index.php/jpsl/article/view/21135}, doi = {10.29244/jpsl.10.1.12-24}, isbn = {2086-4639}, year = {2020}, date = {2020-03-20}, journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan}, volume = {10}, number = {1}, pages = {12-24}, abstract = {Hasselt’s litter frogs (Leptobrachium hasseltii Tschudi 1838) is a wide spread species in Java and Sumatra, but there is no specific distribution map for this species. The purpose of this study is to identify the distribution of hasselt’s litter frogs in Java and examine the suitability of it’s using maxent. We used presence data and environment variables consisting of elevation, slope, NDVI (Normalized Difference Vegetation Index), distance from the river, temperature, precipitation, and land cover to evelop the distribution model of this species. Hasselt’s litter frogs in Java depends on forested area with a wide range of elevation (lowland to mountain forests), moderate slope, temperature between 20-21 o C and rainfall over 2500 mm/year. The highest number of frogs are found in secondary forest land cover, as supported by NDVI values between 0.8 to 0.9 and relatively close to the river. Habitat model constructed are robust with AUC (Area Under Curve) value of 0.951. Environmental variables that most affectted habitat for hasselt’s litter frog are land cover, temperature, and slope.}, keywords = {katak, Leptobrachium hasseltii Tschudi 1838}, pubstate = {published}, tppubtype = {article} } Hasselt’s litter frogs (Leptobrachium hasseltii Tschudi 1838) is a wide spread species in Java and Sumatra, but there is no specific distribution map for this species. The purpose of this study is to identify the distribution of hasselt’s litter frogs in Java and examine the suitability of it’s using maxent. We used presence data and environment variables consisting of elevation, slope, NDVI (Normalized Difference Vegetation Index), distance from the river, temperature, precipitation, and land cover to evelop the distribution model of this species. Hasselt’s litter frogs in Java depends on forested area with a wide range of elevation (lowland to mountain forests), moderate slope, temperature between 20-21 o C and rainfall over 2500 mm/year. The highest number of frogs are found in secondary forest land cover, as supported by NDVI values between 0.8 to 0.9 and relatively close to the river. Habitat model constructed are robust with AUC (Area Under Curve) value of 0.951. Environmental variables that most affectted habitat for hasselt’s litter frog are land cover, temperature, and slope. |
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 11372 , SPIE, 2019. Abstract | Links | BibTeX | Tags: agroforestry, canopy cover, Landsat, LiDAR @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. |
Condro, Aryo Adhi; Prasetyo, Lilik B; Rushayati, Siti Badriyah 11372 , SPIE, 2019. Abstract | Links | BibTeX | Tags: orangutan @conference{Condro2019, title = {Short-term projection of Bornean orangutan spatial distribution based on climate and land cover change scenario}, author = {Aryo Adhi Condro and Lilik B Prasetyo and Siti Badriyah Rushayati}, url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11372/113721B/Short-term-projection-of-Bornean-orangutan-spatial-distribution-based-on/10.1117/12.2541633.short}, doi = {10.1117/12.2541633}, year = {2019}, date = {2019-12-28}, volume = {11372}, publisher = {SPIE}, abstract = {Primates, the closest living biological relatives with human, play the important roles in the livelihoods, human-health, and ecosystem services. In the Anthropocene, populations of 75% of primate species are decreasing globally – due to cultivation activities, logging harvesting, hunting, and climate change. In this study, we focus on Bornean orangutan (Pongo pygmaeus) as the global conservation icons. Hence, understanding Bornean orangutan’s distribution dynamics is crucial regarding to conservation and climate mitigation strategies. The objectives of this study are: (1) to predict current and future spatial distribution of orangutan in Borneo using pessimistic climate model and land cover projection as well; (2) to identify spatial dynamics of Bornean orangutan distribution due to climate and land cover change in 2030. Species distribution modelling of baseline and future scenario was performed using logistic regression model. Land cover categories and climate parameters (i.e. annual temperature and precipitation) were used for model predictors. Presence points of observed primate species were retrieved from Ministry of Environment and Forestry Indonesia (MoEF). We used WorldClim v2.0 annual temperature and precipitation data for the baseline and CMIP5 MIROC-ESM model RCP8.5 2030 for the future climate scenario. We performed cellular automata algorithm to retrieve 2030 projected land-use for the future. Distance to road and distance to selected important land covers were used for transition potential modelling of land cover projection. Generally, the prediction shows that suitable habitat of Bornean orangutan will decrease in 2030. However, we found the gain of suitable area of Bornean orangutan. Findings of this study should support the identification of priority conservation area of Bornean orangutan for the future and wildlife corridor management planning.}, keywords = {orangutan}, pubstate = {published}, tppubtype = {conference} } Primates, the closest living biological relatives with human, play the important roles in the livelihoods, human-health, and ecosystem services. In the Anthropocene, populations of 75% of primate species are decreasing globally – due to cultivation activities, logging harvesting, hunting, and climate change. In this study, we focus on Bornean orangutan (Pongo pygmaeus) as the global conservation icons. Hence, understanding Bornean orangutan’s distribution dynamics is crucial regarding to conservation and climate mitigation strategies. The objectives of this study are: (1) to predict current and future spatial distribution of orangutan in Borneo using pessimistic climate model and land cover projection as well; (2) to identify spatial dynamics of Bornean orangutan distribution due to climate and land cover change in 2030. Species distribution modelling of baseline and future scenario was performed using logistic regression model. Land cover categories and climate parameters (i.e. annual temperature and precipitation) were used for model predictors. Presence points of observed primate species were retrieved from Ministry of Environment and Forestry Indonesia (MoEF). We used WorldClim v2.0 annual temperature and precipitation data for the baseline and CMIP5 MIROC-ESM model RCP8.5 2030 for the future climate scenario. We performed cellular automata algorithm to retrieve 2030 projected land-use for the future. Distance to road and distance to selected important land covers were used for transition potential modelling of land cover projection. Generally, the prediction shows that suitable habitat of Bornean orangutan will decrease in 2030. However, we found the gain of suitable area of Bornean orangutan. Findings of this study should support the identification of priority conservation area of Bornean orangutan for the future and wildlife corridor management planning. |
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 335 , IOP Conf. Ser.: Earth Environ. Sci, 2019. Abstract | Links | BibTeX | Tags: canopy cover, Landsat, LiDAR, mangrove @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 Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 2019. Abstract | Links | BibTeX | Tags: agroforestry, CidanauLandsat, hemispherical photos @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%. |
Arai, Kohei; Hasbi, Wahyudi; Syafrudin, Hadi A; Hakim, Patria Rachman; Salaswati, Sartika; Prasetyo, Lilik B; Setiawan, Yudi Method for Uncertainty Evaluation of Vicarious Calibration of Spaceborne Visible to Near Infrared Radiometers Journal Article International Journal of Advanced Computer Science and Applications, 10 (1), pp. 387-393, 2019. Abstract | Links | BibTeX | Tags: Field experiment, image quality evaluation, vicarious calibration @article{Arai2019, title = {Method for Uncertainty Evaluation of Vicarious Calibration of Spaceborne Visible to Near Infrared Radiometers}, author = {Kohei Arai and Wahyudi Hasbi and A Hadi Syafrudin and Patria Rachman Hakim and Sartika Salaswati and Lilik B Prasetyo and Yudi Setiawan}, url = {https://thesai.org/Publications/ViewPaper?Volume=10&Issue=1&Code=ijacsa&SerialNo=51}, doi = {10.14569/IJACSA.2019.0100151}, year = {2019}, date = {2019-01-01}, journal = {International Journal of Advanced Computer Science and Applications}, volume = {10}, number = {1}, pages = {387-393}, abstract = {A method for uncertainty evaluation of vicarious calibration for solar reflection channels (visible to near infrared) of spaceborne radiometers is proposed. Reflectance based at sensor radiance estimation method for solar reflection channels of radiometers onboard remote sensing satellites is also proposed. One of examples for vicarious calibration of LISA: Line Imager Space Application onboard LISAT: LAPAN-IPB Satellite is described. Through the preliminary analysis, it is found that the proposed uncertainty evaluation method is appropriate. Also, it is found that percent difference between DN: Digital Number derived radiance and estimated TOA: Top of the Atmosphere radiance (at sensor radiance) ranges from 3.5 to 9.6 %. It is also found that the percent difference at shorter wavelength (Blue) is greater than that of longer wavelength (Near Infrared: NIR). In comparison to those facts to those of Terra/ASTER/VNIR, it is natural and reasonable.}, keywords = {Field experiment, image quality evaluation, vicarious calibration}, pubstate = {published}, tppubtype = {article} } A method for uncertainty evaluation of vicarious calibration for solar reflection channels (visible to near infrared) of spaceborne radiometers is proposed. Reflectance based at sensor radiance estimation method for solar reflection channels of radiometers onboard remote sensing satellites is also proposed. One of examples for vicarious calibration of LISA: Line Imager Space Application onboard LISAT: LAPAN-IPB Satellite is described. Through the preliminary analysis, it is found that the proposed uncertainty evaluation method is appropriate. Also, it is found that percent difference between DN: Digital Number derived radiance and estimated TOA: Top of the Atmosphere radiance (at sensor radiance) ranges from 3.5 to 9.6 %. It is also found that the percent difference at shorter wavelength (Blue) is greater than that of longer wavelength (Near Infrared: NIR). In comparison to those facts to those of Terra/ASTER/VNIR, it is natural and reasonable. |
Sujaswara, Azwar A; Setiawan, Yudi; Prasetyo, Lilik B; Hudjimartsu, Sahid A; Wijayanto, Arif K Sixth International Symposium on LAPAN-IPB Satellite, pp. 1137221, International Society for Optics and Photonics 2019. Abstract | Links | BibTeX | Tags: UAV @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. |
2018 |
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 Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), 8 (3), pp. 420–427, 2018, ISSN: 2460-5824. Abstract | Links | BibTeX | Tags: fire, hotspot @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. |
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 Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), 8 (1), pp. 67–76, 2018, ISSN: 2460-5824. Abstract | Links | BibTeX | Tags: Landsat, LAPAN @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. |
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 Inproceedings 2017 European Modelling Symposium (EMS), pp. 22–27, IEEE 2017. Abstract | Links | BibTeX | Tags: cloud, Landsat, spectral @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. |
