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Regional aboveground forest biomass estimation using airborne and spaceborne LiDAR fusion with optical data in the Southwest of China

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4 Author(s)
Kebiao Huang ; Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China ; Yong Pang ; Qingtai Shu ; Tian Fu

Laser altimeter systems provide an accurate measurement of canopy height, the vertical structure of vegetation and the aboveground biomass (AGB). Airborne discrete return LiDAR (Light Detection and Ranging) was used operationally in many cases and some regions; spaceborne large footprint LiDAR (ICESat GLAS) has acquired over 250 million LiDAR observations over forest regions globally. The ICESat GLAS data have been used successfully for forest height and biomass in various sites. To estimate aboveground forest biomass in the Southwest of China, products from EOS MODIS and ENVISAT MERIS were used to expand the estimation from GLAS data. Airborne LiDAR data were collected along GLAS orbit to estimate forest height and biomass for each GLAS footprint after training with 81 field measured plots. The R2 are 0.68 and 0.91 for field measured biomass and mean height estimation using airborne LiDAR data. Then the aboveground biomass was estimated from ICESat GLAS data using the equation trained by field data (R2 = 0.47, n=185). EOS MODIS Vegetation Continuous Fields (VCF) product, enhanced vegetation index (EVI) product and ENVISAT MERIS Regional Land Cover product were used to generate 175 forest classes, which included five forest canopy density classes, five vegetation index classes, and seven forest cover types. Then we combined forest aboveground biomass derived from GLAS pulses footprint with 175 forest classes to generate a continuous aboveground forest biomass map of study area. Forest aboveground biomass was minimal at 43 Mg/ha, maximal at 133 Mg/ha, averaged at 78.9 Mg/ha in the study area. The results of predicted aboveground biomass were in agreement on the amount and distribution after comparison with reference data, which showed that the predict model for GLAS successfully captured the distribution of aboveground biomass.

Published in:

2010 18th International Conference on Geoinformatics

Date of Conference:

18-20 June 2010