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
Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar
TL;DR: In this article, the authors compared three national-scale allometric methods (CRM, Jenkins, and the regional models) of the Forest Inventory and Analysis (FIA) program in the U.S. and examined the impacts of using alternative allometry on the fitting statistics of remote sensing-based woody AGB models.
Journal ArticleDOI
Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests
Pablito M. López-Serrano,José Luis Cárdenas Domínguez,José Javier Corral-Rivas,Enrique Jiménez,Carlos A. López-Sánchez,Daniel José Vega-Nieva +5 more
TL;DR: In this paper, support vector regression (SVR) and random forest (RF) were used to predict the aboveground biomass (AGB) of the Sierra Madre Occidental in Mexico.
Journal ArticleDOI
Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods
TL;DR: The improved kNN algorithm with 10 nearest neighbors showed stronger ability of spatial interpolation than other two models, and provided greater potential of accurately generating population and spatially explicit predictions of forest ecosystem AGB in the complicated basin.
Journal ArticleDOI
Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data
TL;DR: This research used multiple Sentinel-2 data to explore AGB estimation of bamboo forests in Zhejiang Province, China by taking into account the unique characteristics of on-year and off-year bamboo forest growth features, and found that the use of very high spatial resolution images that can effectively extract tree density information may improve bamboo AGB modeling and yield new insights.
Journal ArticleDOI
Canopy cover estimation in miombo woodlands of Zambia: Comparison of Landsat 8 OLI versus RapidEye imagery using parametric, nonparametric, and semiparametric methods
James Halperin,Valerie LeMay,Nicholas C. Coops,Louis V. Verchot,Peter L. Marshall,Kyle Lochhead +5 more
TL;DR: In this article, the authors compared the use of Landsat 8 OLI versus RapidEye satellite imagery in four modeling approaches (generalized linear model (GLM), generalized additive model (GAM), k-NN), with and without auxiliary information (e.g., soils characteristics, distance to roads, etc.) to estimate percent canopy cover by pixel for an ~ 1,000,000 ha area in Zambia.
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.