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
Estimation of aboveground forest biomass in Himalayan region of West Bengal, India using IRS P6 LISS-IV data
Journal ArticleDOI
Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests
TL;DR: In this article, the authors proposed an alternative AGFB estimation procedure that combines multiple geospatial data, such as land cover maps to allocate forested land areas to alternative forest types; uses the light detection and ranging (LiDAR) data to evaluate tree heights; calculates the area-total AGFB using region- and tree-type-specific functions that relate the tree heights to the AGFB.
Journal ArticleDOI
Comparison of Multiple Machine Learning Models for Estimating the Forest Growing Stock in Large-Scale Forests Using Multi-Source Data
TL;DR: In this paper , three machine learning algorithms, GBDT, XGBoost, and CatBoost, were used to estimate forest growing stock and four category features, forest population, dominant tree species, humus thickness, and slope direction, were involved in estimating forest growing stocks.
Journal ArticleDOI
Selection of training areas for remote sensing-based forest above-ground biomass estimation
TL;DR: In this paper, the training area concept was examined in a two-step approach for forest above-ground biomass (AGB) estimation using airborne laser scanning (ALS) and RapidEye satellite data in the eastern area of Finland.
Journal ArticleDOI
A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine
TL;DR: Zhang et al. as discussed by the authors proposed a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China.
References
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Random Forests
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Journal ArticleDOI
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.