Remote sensing based estimation of carbon stock of bamboo resources of northeast India using machine learning algorithms
Implementing Organization
Indian Institute of Remote Sensing (IIRS), Dehradun
CO-Principal Investigator
Dr. Subrata Nandy
Indian Institute of Remote Sensing (IIRS), Dehradun
Project Overview
Bamboo forests play an important role in the global carbon sink due to their colossal carbon stock. Using the field inventory based methods, it is difficult to map the spatial distribution of carbon stocks of bamboo resources over a large area. Remote sensing, which enables spatial and temporal assessment of land and vegetation, can effectively address this issue. By integrating satellite and field inventory data, the carbon stock of the bamboo forests can be effectively estimated. The spectral and texture variables, derived from satellite data can be related to the field measured carbon using machine learning algorithms for carbon stock estimation. In recent years, machine learning algorithms have been widely used for carbon storage estimation. These algorithms give better estimation of carbon stocks. Hence, the present study will aim to map the spatial distribution of bamboo resources as well the carbon stock of bamboo forests of northeast India.