Aboveground Biomass and Dynamics of Forest Attributes using LiDAR Data and Vegetation Model

Abstract:

In recent years, biomass estimation for tropical forests has received much attention because of the fact that regional biomass is considered to be a critical input to climate change. Biomass almost determines the potential carbon emission that could be released to the atmosphere due to deforestation or conservation to non-forest land use. Thus, accurate biomass estimation is necessary for better understating of deforestation impacts on global warming and environmental degradation. In this context, forest stand height inclusion in biomass estimation plays a major role in reducing the uncertainty in the estimation of biomass. The improvement in the accuracy in biomass shall also help in meeting the MRV objectives of REDD+.

Along with the precise estimate of biomass, it is also important to emphasize the role of vegetation models that will most likely become an important tool for assessing the effects of climate change on potential vegetation dynamics and terrestrial carbon storage and for managing terrestrial ecosystem sustainability.

Remote sensing is an efficient way to estimate forest parameters in large area, especially at regional scale where field data is limited. LIDAR (Light Detection And Ranging) provides accurate information on the vertical structure of forests. 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 total biomass estimation 320.17 Mt and canopy heights 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 Dynamic Global Vegetation Model to understand the possible vegetation dynamics in the event of climate change. The vegetation represents a biogeographic regime. Simulations were carried out for 70 years time period. The model produced leaf area index and biomass for each plant functional type and biome for each grid in that region.