The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.
|Journal||International Journal of Applied Earth Observation and Geoinformation (JAG)|
|Publication status||Published - 2017|
Rasel, S. M. M., Groen, T. A., Hussin, Y. A., & Diti, I. J. (2017). Proxies for soil organic carbon derived from remote sensing. International Journal of Applied Earth Observation and Geoinformation (JAG), 59, 157-166. https://doi.org/10.1016/j.jag.2017.03.004