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. @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
|
Irlan,; Saleh, Muhammad Buce; Prasetyo, Lilik B; Setiawan, Yudi Evaluation of Tree Detection and Segmentation Algorithms in Peat Swamp Forest Based on LiDAR Point Clouds Data Journal Article In: Jurnal Manajemen Hutan Tropika, vol. 26, no. 2, pp. 123-132, 2020, ISSN: 2089-2063. @article{Irlan2020,
title = {Evaluation of Tree Detection and Segmentation Algorithms in Peat Swamp Forest Based on LiDAR Point Clouds Data},
author = {Irlan and Muhammad Buce Saleh and Lilik B Prasetyo and Yudi Setiawan},
url = {http://journal.ipb.ac.id/index.php/jmht/article/view/30179},
doi = {10.7226/jtfm.26.2.123},
issn = {2089-2063},
year = {2020},
date = {2020-08-13},
journal = {Jurnal Manajemen Hutan Tropika},
volume = {26},
number = {2},
pages = {123-132},
abstract = {Application of LiDAR for tree detection and tree canopy segmentation has been widely used in conifer plantation forest in temperate countries with high accuracy, however its application on tropical natural forest especially peat swamp forest hardly found. The objective of this study was evaluated algorithms of individual tree detection and canopy segmentation used LiDAR data in peat swamp forest. The algorithms included (a) Local Maxima (LM) with various variable window size combined with growing region, (b) LM with various variable window size combined with Voronoi Tessellation, (c) LM with various fixed window size combined with growing region, (d) LM with various fixed window size combined with Voronoi Tessellation, and (e) Tree Relative Distance algorithm. The results show that algorithm with the best accuracy was the Tree Relative Distance algorithm with the highest overall F-score of 0.63. The tree relative distance algorithm also provides the highest accuracy in determining three tree parameters which are position, height and diameter of tree canopy with a RMSE value 1.08 m, 6.45 m and 1.19 m, respectively.},
keywords = {LiDAR, peat swamp, segmentation},
pubstate = {published},
tppubtype = {article}
}
Application of LiDAR for tree detection and tree canopy segmentation has been widely used in conifer plantation forest in temperate countries with high accuracy, however its application on tropical natural forest especially peat swamp forest hardly found. The objective of this study was evaluated algorithms of individual tree detection and canopy segmentation used LiDAR data in peat swamp forest. The algorithms included (a) Local Maxima (LM) with various variable window size combined with growing region, (b) LM with various variable window size combined with Voronoi Tessellation, (c) LM with various fixed window size combined with growing region, (d) LM with various fixed window size combined with Voronoi Tessellation, and (e) Tree Relative Distance algorithm. The results show that algorithm with the best accuracy was the Tree Relative Distance algorithm with the highest overall F-score of 0.63. The tree relative distance algorithm also provides the highest accuracy in determining three tree parameters which are position, height and diameter of tree canopy with a RMSE value 1.08 m, 6.45 m and 1.19 m, respectively. |
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. @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. |
