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
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Book ChapterDOI
Rubber Trees and Biomass Estimation Using Remote Sensing Technology
TL;DR: In this paper , the authors investigated the ability and potential of remote sensing for delivering an efficient spatial approach as the primary tool for rubber plantation biomass estimates, specifically above-ground biomass (AGB), the accretion of biomass utilization, the benefit for renewable energy intervention, and the significant role in sequestrating the atmospheric carbon.
Journal ArticleDOI
Prediction of forest aboveground biomass using an integrated approach of space-based parameters, and forest inventory data
Book ChapterDOI
Aboveground Biomass Prediction Model Using Landsat 8 Data: A Test on Possible Approaches for Seasonally Dry Forests of Northern Ethiopia
Journal ArticleDOI
Combination Strategies of Variables with Various Spatial Resolutions Derived from GF-2 Images for Mapping Forest Stock Volume
TL;DR: In this article , the authors investigated the relationship between spatial resolutions of features and the accuracy of mapping forest stock volume (FSV) and proposed combination strategies of variables with various spatial resolutions.
Posted Content
Constructing Forest Biomass Prediction Maps from Radar Backscatter by Sequential Regression with a Conditional Generative Adversarial Network.
TL;DR: In this paper, a conditional generative adversarial network (cGAN) was used to construct above-ground biomass (AGB) prediction maps from synthetic aperture radar (SAR) intensity images.
References
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Random Forests
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High-Resolution Global Maps of 21st-Century Forest Cover Change
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Journal ArticleDOI
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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.