This repository includes the work related to a case study on biomass estimation in Nepal and capacity building materials on Biomass Estimation using Remote Sensing data.
- Case study
Accurate estimation of forest above-ground biomass (AGB) is a crucial factor for sustainable forest management and mitigating climate change. Satellite Remote sensing technology has proved to be an effective method in large scale forest monitoring as well as forest biomass estimation. In this study, focus has been given for a remote sensing approach for estimate the above-ground biomass of the western Tiger Landscape of Nepal using Radar and Optical remote sensing data.
The objective of this study was to investigate the applicability of the RF regression algorithm for radar ,optical, and combination of radar and optical data for predicting the forest biomass and test the performance of the RF regression models. In this process ALOS PALSAR radar backscatter, radar texture parameters, Vegetation indexes form Landsat 5TM optical data, and digital elevation model data were taken into the random forest algorithm to predict the results.
Link for the case study : https://www.researchgate.net/publication/339139739_Estimation_of_Forest_Above-Ground_Biomass_Using_Random_Forest_Algorithm_Based_on_ALOS_PALSAR_and_Landsat_5TM_Imageries
From the case study folder all the scripts used in the study can be accessed.
Methodology Adopted
- Training on biomass estimation using remote sensing data
From the training folder all the script used for the training can be accessed. Biomass Estimation Practical Sessions.pdf will guide the practical session step by step including
- ALOS-2 data pre-processing
- Biomass estmation from LiDAR Data (R Language)
- Biomass estmation from SAR Data (R Language)