Research progress and prospects of hyperspectral remote sensing for global wetland from 2010 to 2022
- Vol. 27, Issue 6, Pages: 1281-1299(2023)
Published： 07 June 2023
DOI： 10.11834/jrs.20232620
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摘要
湿地位于陆地和水生生态系统之间的过渡地带，具有维持生态平衡，保护生态多样性、涵养水源等重要作用。高光谱遥感具有波段窄、波段数多和信息丰富等优势，已经成为湿地监测的重要技术手段。本文首先梳理了2010年以来的湿地高光谱中英文期刊论文，分析了作者所属国家/机构、研究热点和关键词等信息。其次，从载荷平台、信息处理、遥感制图和定量反演4个方面，厘清了当前国内外湿地高光谱遥感的研究现状。最后，提出了湿地高光谱遥感亟待解决的问题和未来发展方向。本文研究旨在为高光谱遥感和湿地应用的交叉研究指明方向，对湿地遥感前沿理论和技术发展具有重要意义。
Abstract
Wetlands are transitional zones between terrestrial and aquatic ecosystems and play important roles in maintaining ecological balance
protecting ecological diversity
conserving water sources
and regulating climate. However
traditional field investigations and panchromatic and multispectral remote sensing technologies cannot meet the practical needs of current wetland monitoring. Hyperspectral remote sensing technology has become an important approach for wetland monitoring owing to its advantages of high spectral resolution and rich spectral information. This review summarizes the related literature on the hyperspectral application of wetlands from 2010 to the present.
First
the literature was analyzed using CiteSpace software. Then
the country/institution of authors
international cooperation
keywords
research hotspots
and research trends were clarified. Finally
the feature extraction and dataset processing methods of hyperspectral datasets and their progress in wetland mapping and quantitative inversion were determined.
China and the United States are the top two countries in terms of the number of hyperspectral wetland studies
but only a few international collaborations have been pursued. In addition
the classification of vegetation in wetlands is a hot research topic. Spartina alterniflora
reed
water quality
and soil properties have become the focus of hyperspectral wetland research. Machine learning methods represented by Random Forests (RFs) play an important role in wetland hyperspectral research. However
studies on classification and inversion based on deep learning methods are limited. Furthermore
under the background of global warming
coastal wetlands have received widespread attention from researchers worldwide. For hyperspectral remote sensing sensors
China’s spaceborne hyperspectral platforms have developed rapidly
but foreign countries have dominated ground and near-ground hyperspectral remote sensing platforms
with a spectral coverage range of 350—1000 nm. In terms of hyperspectral information extraction and image processing
studies have mainly focused on traditional feature extraction and classification methods
such as PCA
MNF
RF
decision trees
and spectral angle mappers. The processing and feature extraction of hyperspectral data based on deep learning feature extraction is expected to be an important research direction in the future. Hyperspectral wetland mapping mainly focuses on wetland vegetation
mangroves
and salt marsh vegetation. Nonetheless
the scale of existing research has been limited to small areas
such as nature reserves or national wetland parks
and the mapping algorithm continues to rely on traditional methods
such as RF and support vector machines. More refined tree species identification and mapping from the use of hyperspectral images is a relevant future research direction. Research on hyperspectral wetland quantitative inversion has mostly focused on chlorophyll and aboveground biomass. In the inversion process
the sensitive band is determined using the correlation coefficient between the ground measurement and the hyperspectral band or spectral index. Simple models
such as linear
quadratic polynomial
and logarithmic functions
are subsequently constructed to obtain the estimated biophysical parameters. Deep learning algorithms have good application prospects in hyperspectral band feature selection and inversion estimation models. Moreover
given the complexity of wetland vegetation
small-scale or point-scale parameter inversion is taken as the main research scale. Large-scale hyperspectral wetland quantitative inversion is difficult to implement due to the existence of high wetland heterogeneity. The resolution of hyperspectral images is not high enough
and mixed pixels exist.
The fusion of multisource remote sensing data
such as multispectral-hyperspectral fusion
to improve the resolution or the development of corresponding spectral unmixing algorithms is the future direction of quantitative analysis for hyperspectral remote sensing applications in wetlands.
关键词
高光谱遥感湿地文献分析高光谱载荷平台信息提取红树林盐沼定量反演
Keywords
hyperspectral remote sensingwetlandsliterature analysishyperspectral payload platforminformation extractionmangrove forestsalt marshquantitative inversion
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Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
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State Key Laboratory of Remote Sensing Science, Beijing Normal University
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences
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