Sentinel SAR Data and In-Situ-Based High-Resolution Above-Ground Carbon Stocks Estimation Within the Open Forests of Ramgarh District

View Sample PDF

Author(s): Akash Anand (Department of Geoinformatics, Central University of Jharkhand, India)
Copyright: 2020
Pages: 26
Source title: Spatial Information Science for Natural Resource Management
Source Author(s)/Editor(s): Suraj Kumar Singh (Suresh Gyan Vihar University, Jaipur, India), Shruti Kanga (Suresh Gyan Vihar University, Jaipur, India)and Varun Narayan Mishra (Suresh Gyan Vihar University, Jaipur, India)
DOI: 10.4018/978-1-7998-5027-4.ch010

Keywords: Engineering Science Reference / Environment & Agriculture / Natural Resource Management / Natural Resources Management

Purchase

View Sentinel SAR Data and In-Situ-Based High-Resolution Above-Ground Carbon Stocks Estimation Within the Open Forests of Ramgarh District on the publisher's website for pricing and purchasing information.

Abstract

The present study deals with an approach to estimate the above ground biomass (AGB) to assess the total carbon stock of forest cover present in Ramgarh district using remote sensing and GIS techniques. Due to the fact that biomass estimation is one of the most influential biophysical parameters in traditional carbon sequestration techniques, satellite remote sensing plays an important role in AGB and carbon stock estimation. Presently, AGB is estimated using Sentinel1A SAR data in conjunction with in-situ field data, which is conducted in 20 different sites within the forest area. Biomass is calculated for each plot, and a correlation analysis is performed with the backscatter value obtained from SAR data to generate an allometric equation that is used to calculate the AGB and carbon stock for the entire forest cover. Both Polarization VV and VH are correlated with field data in which cross-polarized backscatter value shown a stronger correlation of 0.75 (R2 Value). C-band is proved to be the best band for the estimation of biomass and carbon stock in tropical mixed forests.

Challenges and Opportunities in Implementing AI for Smart Water

Water Quality Monitoring and Control With AI

Real-Time Water Quality Monitoring: Use of AI Sensors and Algorithms for Real-Time Monitoring of Water

The Critical Role of Water Quality: Health Impacts, Contaminants, and Sustainable Solutions for Environmental and Human Well-being

Applying the Water Sensitive City Index in a House of Quality Framework to Enhance Urban Water Governance and Sustainability: Evaluating Smart Water Cities

Ocotlan Diaz-Parra, Jorge A. Ruiz-Vanoye, Eric Simancas-Acevedo, Julio C. Ramos-Fernández, Juan M. Xicotencatl-Pérez, Francisco Marroquín-Gutierrez, Julio C. Salgado-Ramírez, Yaneth Reyes-Hernández. © 2025. 18 pages.

Water Demand Forecast and Efficient Supply Management

IRMA Offers Over 2,500 Full Text Open Access Research Papers for Free Download Click to Start Searching Free IRM Research!

IRMA Sponsors

Encyclopedia of Information Science and Technology, Fourth Edition

The IRMA Community

Research IRM

Sentinel SAR Data and In-Situ-Based High-Resolution Above-Ground Carbon Stocks Estimation Within the Open Forests of Ramgarh District

Purchase

Abstract

Related Content

IRMA Sponsors