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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Dissertation
Modelling biomass of the rehabilitation forest around the Buffelsdraai landfill site using remote sensing data, Durban, South Africa.
TL;DR: The BUFFELSDRAAI REHABILITATION PROJECT as discussed by the authors ) is a group of researchers working on the Rehabilitation Project of the University of South Carolina.
Journal ArticleDOI
New Metrics and the Combinations for Estimating Forest Biomass From GLAS Data
TL;DR: In this paper, the authors explored whether a few optimal GLAS metrics could generate accurate AGB estimates, and proposed five metrics and explored their combinations with ten existing ones, and the two to eight most important metrics were then selected to develop AGB models, and their performances were evaluated using field AGB.
Journal ArticleDOI
Enhancing Aboveground Biomass Estimation for Three Pinus Forests in Yunnan, SW China, Using Landsat 8
TL;DR: In this paper , a habitat dataset describing the distribution environment of forests, Landsat 8 OLI image data of spectral reflectance information, as well as a combination of the two datasets were employed to estimate the forest aboveground biomass (AGB) of the three common pine forests (Pinus yunnanensis forests, Pinus densata forests, and Pinus kesiya forests) in Yunnan Province using a parametric model, stepwise linear regression model (SLR), and a non-parametric model such as random forest (RF) and support vector machine (SVM).
Journal ArticleDOI
A Proposed Ensemble Feature Selection Method for Estimating Forest Aboveground Biomass from Multiple Satellite Data
TL;DR: Zhang et al. as discussed by the authors extracted a series of features from Landsat, Phased Array L-band Synthetic Aperture Radar (PALSAR), and climatic and topographical information, and evaluated the performance of four state-of-the-art FS methods in selecting predictive features and improving the estimation accuracy with selected features.
Journal ArticleDOI
Uncertainty Analysis of Remote Sensing Pretreatment for Biomass Estimation on Landsat OLI and Landsat ETM
TL;DR: The results obtained show that random forest (RF) and 10-fold cross validation algorithms provided reliable accuracy for biomass estimation to better understand the uncertainty in pretreatments and to provide a theoretical basis for improving the accuracy of biomass estimation.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
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Journal ArticleDOI
High-Resolution Global Maps of 21st-Century Forest Cover Change
Matthew C. Hansen,Peter Potapov,Rebecca Moore,M. Hancher,Svetlana Turubanova,Alexandra Tyukavina,David Thau,Stephen V. Stehman,Scott J. Goetz,Thomas R. Loveland,Anil Kommareddy,A. Egorov,Louise Chini,Christopher O. Justice,John R. Townshend +14 more
TL;DR: Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally, and boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms.
Journal ArticleDOI
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
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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.