Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

Mahendra K Pal,Thorkild M Rasmussen,Praveen Kumar,Akhouri P Krishna

doi:10.3390/ijgi10010029

Abstract

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

Highlights

Forests are ecosystems maintaining bioresources and providing a range of products and services to meet human needs [1,2]
The results show that modified soil-adjusted vegetation index (MSAVI)-based image fraction of vegetation cover (FVC) delivered the highest correlation and lowest mean absolute error (MAE)
Ground FVC using a fraction of land cover by trees and image FVC using vegetation index from the optical remote sensing data were computed

Summary

Introduction

Forests are ecosystems maintaining bioresources and providing a range of products and services to meet human needs [1,2]. Accurate assessment and monitoring of forest AGB for sustainable management and mitigation, and to recognize climate change scenario due to deforestation are important and crucial [1,5,6]. At a regional as well as global level, estimating the biomass of forest vegetation is an important exercise in determining the storage of carbon in the dominant tree component and computing carbon cycling. It becomes deposited in forest biomass (that is, trunks, branches, roots, and leaves), in dead organic matter (litter and deadwood), and in soils. For mitigating CO2 emissions through carbon sequestration, this driver of climate change has drawn considerable attention to forest ecosystems as a viable option [10]

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