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
Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)
TL;DR: The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel -2A dataset and the AlOS- 2 PALSar-2 dataset.
Journal ArticleDOI
Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images
Jie Wang,Xiangming Xiao,Rajen Bajgain,Patrick J. Starks,Jean L. Steiner,Russell Doughty,Qing Chang +6 more
TL;DR: In this article, the authors examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA.
Journal ArticleDOI
Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation
TL;DR: The results indicate that pine forest and mixed forest have the highest AGB saturation values and Chinese fir and broadleaf forest have lower saturation values, and bamboo forest and shrub have the lowest saturation values.
Journal ArticleDOI
Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges
TL;DR: This review provides an overview of the techniques that are currently being used to map various attributes of mangrove, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies.
Journal ArticleDOI
Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics
TL;DR: An overview of the definitions of FH is provided, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how to observe FH with RS, and the concept of spectral traits (ST) and spectral trait variations (STV) in the context ofFH monitoring is introduced.
References
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Journal ArticleDOI
Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass
TL;DR: In this paper, a spatial modeling framework that integrates above-ground biomass transects, derived from plot-based field data and small-footprint discrete return LiDAR, with complete wall-to-wall spaceborne L-band and C-band SAR to predict biomass over a larger area.
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Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: A regional‐scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets
TL;DR: In this paper, a method is presented to characterize forest stand heights in a 110,000 km2 region in the eastern United States surrounding the Chesapeake Bay area, driven by a statistical fusion model solely based on remote sensing data.
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Mapping Forest Biomass Using Remote Sensing and National Forest Inventory in China
TL;DR: In this article, Wang et al. used the spatially explicit MODIS Land Cover Type Product (MCD12C1) to quantitatively estimate the spatial distribution of forest biomass in China with a resolution of 0.05°, ~5600 m.
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Airborne laser scanner-assisted estimation of aboveground biomass change in a temperate oak–pine forest
TL;DR: In this article, the authors used repeated airborne laser scanner (ALS) acquisitions and temporally coincident ground observations of forest biomass, for a relatively undisturbed period (2004-2007;?07-04), a contrasting period of disturbance (2007-2009;?09-07), and an integrated period ( 2004-2009,?09 -04).
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Uncertainty analysis in carbon cycle models of forest ecosystems: Research needs and development of a theoretical framework to estimate error propagation
TL;DR: In this paper, the authors present a model-driven decision support system based on different analytical applications to derive optimum and efficient uncertainty analysis pathways for C cycle forest ecosystem models, which can further facilitate the application of uncertainty analysis methods.