Title :
Airborne LiDAR measurements to estimate tropical peat swamp forest Above Ground Biomass
Author :
Ballhorn, Uwe ; Jubanski, Juilson ; Kronseder, Karin ; Siegert, Florian
Author_Institution :
RSS Remote Sensing Solutions GmbH, Baierbrunn, Germany
Abstract :
We estimated forest Above Ground Biomass (AGB) of tropical peat swamp forests in the Indonesian province of Central Kalimantan through correlating airborne Light Detection And Ranging (LiDAR) data to forest inventory data. Two LiDAR point cloud metrics, the Quadratic Mean Canopy profile Height (QMCH) and the Centroid Height (CH), were correlated to the field derived AGB estimates. The regression models could be improved through the use of the LiDAR point densities as input. The highest coefficient of determination was achieved for CH (R2= 0.88; n= 52). Surveying with a LiDAR point density between 2-4 points per square meter (pt/m2) resulted in the best cost-benefit ratio. It was also shown that impact from logging and the associated AGB losses dating back more than 10 years could still be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat based AGB estimate showed an overestimation of 60.8% in a 3.0 million ha study area.
Keywords :
airborne radar; cost-benefit analysis; geophysical image processing; optical radar; regression analysis; remote sensing by radar; vegetation; vegetation mapping; AGB losses; Central Kalimantan; Centroid Height; Indonesian province; Landsat based AGB estimate; LiDAR point cloud metrics; LiDAR point densities; Quadratic Mean Canopy profile Height; airborne LiDAR measurements; airborne Light Detection And Ranging data; coefficient of determination; cost-benefit ratio; field derived AGB estimates; forest inventory data; multispectral satellite imagery; regression models; tropical peat swamp forest above ground biomass; Accuracy; Biomass; Carbon dioxide; Estimation; Laser radar; Remote sensing; Satellites; Indonesia; LiDAR; REDD; forest biomass; tropical peat swamp forest;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
DOI :
10.1109/IGARSS.2012.6351208