Journal ArticleDOI
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems
TLDR
A survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables fromRemote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure.Abstract:
Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This...read more
Citations
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Journal ArticleDOI
Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data
TL;DR: In this paper , the authors proposed a new conceptual model to estimate forest aboveground biomass (FAGB) using remotely sensed data from multiple sensors, which is based on the principle of estimating FAGB on the ground using allometry, which needs species, diameter at breast height (DBH), and tree height as inputs.
Journal ArticleDOI
Estimation of Forest Aboveground Biomass of Two Major Conifers in Ibaraki Prefecture, Japan, from PALSAR-2 and Sentinel-2 Data
TL;DR: In this paper , the authors used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which was trained with airborne lidar data.
Journal ArticleDOI
Optimal Wavelength Selection on Hyperspectral Data with Fused Lasso for Biomass Estimation of Tropical Rain Forest
TL;DR: This analysis proves efficiency of fused lasso and image texture in biomass estimation of tropical forests by providing higher accuracy with root-mean-square error than other methods; multiple linear analysis, partial least squares regression, and lasso regression.
Journal ArticleDOI
Satellite based integrated approaches to modelling spatial carbon stock and carbon sequestration potential of different land uses of Northeast India
TL;DR: In this article , a combined approach of field inventory and Landsat OLI derived vegetation indices were used in spatial modelling of aboveground biomass and carbon stock in different land uses in Northeast India and relate these estimates with the land use changes.
Journal ArticleDOI
The real potential of current passive satellite data to map aboveground biomass in tropical forests
Nidhi Jha,Nidhi Jha,Nidhi Jha,Nitin K. Tripathi,Nicolas Barbier,Salvatore Gonario Virdis,Wirong Chanthorn,Gaëlle Viennois,Warren Y. Brockelman,Anuttara Nathalang,Sissades Tongsima,Nophea Sasaki,Raphaël Pélissier,Maxime Réjou-Méchain +13 more
TL;DR: In this article, the ability of three medium-to high-resolution passive satellite sensors, Landsat-8 (L8), Sentinel-2B (S2) and Worldview-3 (WV3), to map AGB in a forest landscape of Thailand was compared.
References
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High-Resolution Global Maps of 21st-Century Forest Cover Change
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Journal ArticleDOI
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BookDOI
Assessing the accuracy of remotely sensed data : principles and practices
Russell G. Congalton,Kass Green +1 more
TL;DR: This chapter discusses Accuracy Assessment, which examines the impact of sample design on cost, statistical Validity, and measuring Variability in the context of data collection and analysis.