ESTIMATION OF FOREST VERTICAL STRUCTURE AND BIOMASS FROM VEGETATION MODEL AND USING LIDAR DATA

English Abstract:

Biomass governs the potential carbon emission that could be released to the atmosphere due to deforestation, and regional biomass changes have been associated with important outcomes in ecosystem functional characteristics and climate change. Forest stand height is one of the major and of late recognised as critical variables in the estimation of biomass which also contributes as an input in the decision making towards sustainable management as well as for assessment of carbon storage. In order to provide dynamic vegetation/biomass as an input to the large-scale bio-geographical models, several methods have been developed using remote sensing data.

Biomass estimation remains a challenging taskin complex forest stand structures and environmental conditions. LiDAR, based global mapping is sensitive throughout the range of biomass.

The GLAS waveform is used to estimate the forest canopy height using waveform derived parameters extracted using signal decomposition techniques.We estimated average tree canopy heights and AGB from GLAS waveform parameters by using a multi-regression linear model in forested area of Madhya Pradesh (area-3,08,245 km²), India. The derived heights from ICESat-GLAS 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. The spatial maps of canopy heights and stand biomass are generated by using random forest algorithm.

These canopy heights and biomass maps were used in vegetation models to predict the changes biophysical/physiological characteristics of forest according to the changing climate. In our study we have used Spatial Explicit Individual Based Dynamic Global Vegetation Model (SEIB-DGVM) to understand the possible vegetation dynamics in the event of climate change. The vegetation represents a biogeographic transition covering temperate-subtropical regime. Simulations were carried out for 70 years time period (based on annual mean climate data for 100 years) at 60 sites. The SEIB model produced leaf area index and biomass for each plant functional type and biome for each grid in that region.

Key Words: Biomass, Canopy height, ICESat-GLAS, DGVM