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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DissertationDOI
Estimating forest variables using airborne lidar measurements in hemi-boreal forests
TL;DR: In this article, field measurements from 450 sample plots, airborne lidar data and spectral images from Aegviidu, Estonia, 15 by 15 km test site were used to analyse options to estimate main forest inventory variables using remote sensing data.
Journal ArticleDOI
Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau
Ke Luo,Yufeng Wei,Jie Du,Liang Liu,Xinrui Luo,Yuehong Shi,Xiangjun Pei,Ningfei Lei,Ci Song,Jingji Li,Xiaolu Tang +10 more
TL;DR: In this article, the authors used Landsat 8 OLI and Sentinel-2B data to estimate subalpine forest AGB using linear regression, and two machine learning approaches with 54 inventory plots.
Dissertation
Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms
TL;DR: Using nanofiltration membranes for the recovery of phosphorous with a second type of technology for the separation of nitrogen and phosphorus is suggest to be a viable process.
Journal ArticleDOI
Relative importance analysis of landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation
Alyssa Endres,Giorgos Mountrakis,Huiran Jin,Wei Zhuang,Ioannis Manakos,John J. Wiley,Colin M. Beier +6 more
TL;DR: In this paper, the aboveground forest biomass estimation is an integral component for climate change, carbon stocks assessment, biodiversity and forest health, specifically NASA's LiDAR (Light Detection And Ranging).
Journal ArticleDOI
Fusing GEDI with earth observation data for large area aboveground biomass mapping
TL;DR: In this paper , a method for biomass mapping using GEDI and Earth Observation data is proposed, where the gradient boosting machine learning framework is applied to predict AGBD and its uncertainty at the resolutions of 100 m and 200 m.
References
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Journal ArticleDOI
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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.