Estimation of vertical forest structure and above ground biomass using ICESat/GLAS data

Forests play an important role in the global carbon cycle. They form a very large and dynamic global carbon stock and also act as a sink. Deforestation and forest degradation results in the loss of the forest biomass/carbon amplifying the negative effects of climate change. Large uncertainty has been reported in estimating the rates of carbon emission in climate change scenarios due to the difficulty in spatially explicit estimation of the carbon stocks and dynamic changes. Several methods have been developed for estimating the carbon stock/above ground biomass (AGB) by using remote sensing data employing optical remote sensing and radar techniques. However, these applications have shown limited success majorly due to signal saturation at high biomass forests. Light Detection and Ranging (LiDAR) derived vertical forest structure has demonstrated its potential in overcoming the signal saturation problem and in improving the accuracy of AGB estimation. Ice, Cloud and Land Elevation Satellite (ICESat) -Geoscience Laser Altimeter System (GLAS) launched in January 2003 is the first space borne full waveform LiDAR sensor. The LiDAR waveform is used to estimate the forest canopy height using waveform derived parameters extracted using signal decomposition techniques. In the present study, we estimated average tree canopy heights and AGB from GLAS waveform parameters by using a multi-regression linear model across different forest types in Madhya Pradesh, India. The ICESat-GLAS derived heights were correlated with field measured tree canopy heights for 60 plots. Results have shown a significant correlation of R²= 74% for top canopy heights and R²=57% for stand biomass.