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 In: TELKOMNIKA, vol. 19, no. 3, 2021, ISSN: 2302-9293. Abstract | Links | BibTeX | Tags: biomass, LiDAR, peat swamp @article{Rizal2021,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. |
Rizal, Muhamad; Saleh, M. Buce; Prasetyo, Lilik Budi Biomass estimation model for peat swamp forest ecosystem using light detection and ranging Journal Article In: Telkomnika (Telecommunication Computing Electronics and Control), vol. 19, no. 3, pp. 770–780, 2021, ISSN: 23029293. Abstract | Links | BibTeX | Tags: Allometry, biomass, canopy cover, LiDAR, Peat Swamp forest @article{Rizal2021b,Peat swamp forest plays a very important role in absorbing and storing large amounts of terrestrial carbon, both above ground and in the soil. There has been a lot of research on the estimation of the amount of biomass above the ground, but a little on peat swamp ecosystems using light detection and ranging (LiDAR) technology, especially in Indonesia. The purpose of this study is to build a biomass estimation model based on LiDAR data. This technology can obtain information about the structure and characteristics of any vegetation in detail and in real time. Data was obtained from the East Kotawaringin Regency, Central Kalimantan. Biomass field was generated from the available allometry, and Point cloud of LiDAR was extracted into canopy cover (CC), and data on tree height, using the FRCI and local maxima (LM) method, respectively. The CC and tree height data were then used as independent variables in building the regression model. The best-fitted model was obtained after the scoring and ranking of several regression forms such as linear, quadratic, power, exponential and logarithmic. This research concluded that the quadratic regression model, with R2of 72.16% and root mean square error (RMSE) of 0.0003% is the best-fitted estimation model (BK). Finally, the biomass value from the models was 244.510 tons/ha. |
Saleh, Muhammad Buce; Dewi, Rosima Wati; Prasetyo, Lilik Budi; Santi, Nitya Ade Canopy cover estimation in lowland forest in South Sumatera, using LiDAR and landsat 8 OLI imagery Journal Article In: Jurnal Manajemen Hutan Tropika, vol. 27, no. 1, pp. 50–58, 2021, ISSN: 20892063. Abstract | Links | BibTeX | Tags: canopy cover, Landsat 8 OLI, LiDAR, Vegetation indices @article{Saleh2021,Canopy cover is one of the most important variables in ecology, hydrology, and forest management, and useful as a basis for defining forests. LiDAR is an active remote sensing method that provides the height information of an object in three-dimensional space. The method allows for the mapping of terrain, canopy height and cover. Its only setback is that it has to be integrated with Landsat to cover a large area. The main objective of this study is to generate the canopy cover estimation model using Landsat 8 OLI and LiDAR. Landsat 8 OLI vegetation indices and LiDAR-derived canopy cover estimation, through First Return Canopy Index (FRCI) method, were used to obtain a regression model. The performance of this model was then assessed using correlation, aggregate deviation, and raster display. Lastly, the best canopy cover estimation was obtained using equation |
2020 |
Irlan,; Saleh, Muhammad Buce; Prasetyo, Lilik B; Setiawan, Yudi Evaluation of Tree Detection and Segmentation Algorithms in Peat Swamp Forest Based on LiDAR Point Clouds Data Journal Article In: Jurnal Manajemen Hutan Tropika, vol. 26, no. 2, pp. 123-132, 2020, ISSN: 2089-2063. Abstract | Links | BibTeX | Tags: LiDAR, peat swamp, segmentation @article{Irlan2020,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 In: Jurnal Manajemen Hutan Tropika, vol. 26, no. 2, pp. 123, 2020, ISSN: 2089-2063. Abstract | Links | BibTeX | Tags: LiDAR, peat swamp, point cloud @article{Irlan2020b,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. |
2019 |
Rudianto, Yoga; Prasetyo, Lilik B; Setiawan, Yudi; Hudjimartsu, Sahid A Canopy cover estimation of agroforestry based on airborne LiDAR and Landsat 8 OLI Conference vol. 11372, SPIE, 2019. Abstract | Links | BibTeX | Tags: agroforestry, canopy cover, Landsat, LiDAR @conference{Rudianto2019,Agroforestry/mixed gardens is a land management system that combines agricultural, livestock production with tree to obtain various products in a sustainable manner so as to increase social, economic and environmental benefits This system can be a form of mitigation and adaptation to global climate change, especially in areas with high population densities, but with less agricultural labor, such as in urban fringe area. Based on the formal definition of forests from the Indonesian Ministry of Environment and Forestry of Indonesia based on canopy cover, agroforestry might be considered as forest, whereas the canopy cover >30%. The research aim to estimate canopy cover base on integration of Lidar and Landsat 8 OLI of agroforestry in the Cidanau watershed. The most suitable equation model is an exponential equation (FRCI = 22.928e (-80.439 * 'RED')), however, some underestimation in high canopy cover ( >70%) and underestimation in low canopy cover (< 60%) should be anticipated. The result showed that agroforestry in some location have canopy cover greater than 30% and therefore it can be considered as a forest. |
Prasetyo, Lilik B; Nursal, Wim I; Setiawan, Yudi; Rudianto, Yoga; Wikantika, Ketut; Irawan, Bambang Canopy cover of mangrove estimation based on airborne LIDAR & Landsat 8 OLI Conference vol. 335, IOP Conf. Ser.: Earth Environ. Sci, 2019. Abstract | Links | BibTeX | Tags: canopy cover, Landsat, LiDAR, mangrove @conference{Prasetyo2019,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. |
Publikasi
Artikel Baru
Pengumuman Terbaru
Tampilkan Profilmu di Website ALGM
August 27, 2022
Sertifikasi Basic Remote Pilot License FASI batch XX
November 20, 2020
Agenda Kegiatan
Pelatihan Penggunaan Webgis Ecosystem dan Aplikasi Android INA-Alert
December 22, 2020
DSI TALK – WEBINAR SERIES
December 19, 2020
