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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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
A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data
TL;DR: In this paper, a new methodology to estimate the biomass (organic matter) of conifer-dominated boreal forests is developed, which aims to estimate biomass of extensive areas where ground data are limited.
Journal ArticleDOI
Effects of uncertainty in model predictions of individual tree volume on large area volume estimates
TL;DR: In this article, the effects of model residual variability and model parameter uncertainty on large area volume estimates and their uncertainties for a study area in northeastern Minnesota, USA were estimated using Monte Carlo simulation approaches.
Journal ArticleDOI
A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China
TL;DR: In this article, artificial neural network (ANN) and multiple linear regression (MLR) were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China.
Journal ArticleDOI
Model-assisted estimation of change in forest biomass over an 11year period in a sample survey supported by airborne LiDAR: A case study with post-stratification to provide ``activity data''
TL;DR: In this article, the authors used a multinomial logistic regression model to predict change categories for every LiDAR grid cell in the area, and areal changes were estimated from the pure field sample and with the support of the Li-AR data applying model-assisted estimators.
Journal ArticleDOI
Uncertainty in ecosystem mapping by remote sensing
Duccio Rocchini,Giles M. Foody,Harini Nagendra,Carlo Ricotta,Madhur Anand,Kate S. He,Valerio Amici,Birgit Kleinschmit,Michael Förster,Sebastian Schmidtlein,Hannes Feilhauer,Anne Ghisla,Markus Metz,Markus Neteler +13 more
TL;DR: This paper will review recent attempts to take explicitly into account uncertainty when mapping ecosystems and suggest that the statistical quantification of uncertainty should be a core part of scientific research using remote sensing.