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
Forest Above-Ground Biomass Inversion Using Optical and SAR Images Based on a Multi-Step Feature Optimized Inversion Model
TL;DR: In this article , an operational and robust method of biomass retrieval using optical and SAR RS data is proposed, where the best feature combinations and KNN models are applied for forest biomass estimation.
Journal ArticleDOI
Improving aboveground biomass estimation of natural forests on the Tibetan Plateau using spaceborne LiDAR and machine learning algorithms
TL;DR: In this paper , an optimized extreme learning machine (ELM) method was proposed to estimate the aboveground biomass (AGB) of natural forests on the eastern Qinghai-Xizang Plateau, China.
Proceedings ArticleDOI
TorchGeo
Adam J Stewart,Caleb Robinson,Isaac A. Corley,Anthony Ortiz,Juan Lavista Ferres,Arindam Banerjee +5 more
TL;DR: TorchGeo as discussed by the authors is a Python library for integrating geospatial data into the PyTorch deep learning ecosystem, which provides data loaders for a variety of benchmark datasets.
Book ChapterDOI
Application of GIS and remote sensing towards forest resource management in mangrove forest of Niger Delta
TL;DR: The mangrove of the Niger Delta is one of the largest and most significant forests in Africa as mentioned in this paper , and it is the best carbon sequester and pollution filter in the African subregion.
Advances in the estimation of forest biomass based on SAR data
TL;DR: SAR后向散射（不同极化方式）、干涉相干性及林化干和气候环境问题有重要意义。
References
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