Leveraging Deep Learning Models for Targeted Aboveground Biomass Estimation in Specific Regions of Interest

Selvin Samuel Arumai Shiney (), Ramachandran Geetha, Ramasamy Seetharaman and Madhavan Shanmugam
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Selvin Samuel Arumai Shiney: Department of Computer Science and Engineering, S.A. Engineering College, Chennai 600077, Tamil Nadu, India
Ramachandran Geetha: Department of Computer Science and Engineering, S.A. Engineering College, Chennai 600077, Tamil Nadu, India
Ramasamy Seetharaman: Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, Tamil Nadu, India
Madhavan Shanmugam: Institute of Remote Sensing, Department of Civil Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, Tamil Nadu, India

Sustainability, 2024, vol. 16, issue 11, 1-17

Abstract: Over the past three decades, a lot of research has been conducted on remote sensing-based techniques for estimating aboveground biomass (AGB) in forest ecosystems. Due to the complexity of satellite images, the conventional image classification methods have been unable to meet the actual application needs. In our proposed work, the estimation of aboveground biomass has been performed on the basis of a Region of Interest (RoI). Initially, this method is employed to measure the green portions of the areas at the local level. The biomass of the subtropical woods in the areas of India, Indonesia, and Thailand is estimated in this work, using data from Deep Globe LIDAR images. Initially, the satellite images are pre-processed. The ROI method is used to select the green portion of the area. The green portion in the satellite images is segmented using the K-means algorithm and binary classification. An empirical formula is used to calculate the carbon weight. The results obtained show 92% accuracy.

Keywords: neural networks; ROI; aboveground biomass estimation; carbon absorption; deep learning model; LIDAR (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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