A Review of Fusion Framework using Optical Sensors and Synthetic Aperture Radar Imagery to Detect and Map Land Degradation and Sustainable Land Management in the Semi-Arid Regions

IF 3.3 4区 地球科学 Q2 ENVIRONMENTAL SCIENCES
David Sengani, Abel Ramoelo, Emma Archer
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Abstract

This paper examines a feature-level fusion framework for detecting and mapping land degradation (LD) and enabling sustainable land management (SLM) in semi-arid areas using optical sensors and Synthetic Aperture Radar (SAR) satellite data. The objectives of this review were to (i) determine the trends and geographical location of land degradation mapping publications, (ii) to identify and report current challenges pertaining to mapping LD using multiscale remote sensing data, (iii) to recommend a way forward for monitoring LD using multiscale remote sensing data. The study reviewed 78 peer-reviewed research articles published over the past 24 years (1998–2022). Image fusion has the potential to be more useful in various remote sensing applications than individual sensor image data, making it more informative and valuable in the interpretation process. In addition, this review discusses the importance of SAR and optical image fusion, pixel-level techniques, applications, and major classes of quality metrics for objectively assessing fusion performance. The literature review alluded that the SAR and optical image fusion in the detection and mapping of land degradation and enabling sustainable land management has not been fully explored. Advanced techniques such as the fusion of SAR and optical satellite imageries need to be incorporated for the detection and mapping of LD, as well as the promotion of SLM in halting LD in South African drylands and around the world. We conclude that there is scope for further research on the fusion of SAR and optical images, as new micro-wave and optical sensors with higher resolution are introduced on a regular basis. The results of this review contribute to a better understanding of the applications of SAR and optical image fusion in future research in the severely degraded drylands of southern Africa.
基于光学传感器和合成孔径雷达图像的半干旱区土地退化与可持续土地管理融合框架研究进展
本文研究了一个特征级融合框架,用于利用光学传感器和合成孔径雷达(SAR)卫星数据在半干旱区检测和绘制土地退化(LD)并实现可持续土地管理(SLM)。本次审查的目标是:(1)确定土地退化制图出版物的趋势和地理位置,(2)确定和报告使用多比例尺遥感数据绘制土地退化图的当前挑战,(3)建议使用多比例尺遥感数据监测土地退化的前进方向。该研究回顾了过去24年(1998-2022年)发表的78篇同行评议的研究论文。图像融合在各种遥感应用中可能比单独的传感器图像数据更有用,使其在判读过程中更具信息性和价值。此外,本文还讨论了SAR和光学图像融合的重要性、像素级技术、应用以及客观评估融合性能的主要质量指标。文献综述指出,SAR和光学图像融合在土地退化检测和制图以及土地可持续管理中的应用尚未得到充分的探索。需要采用诸如合成SAR和光学卫星图像的融合等先进技术来探测和绘制LD,并在南非旱地和世界各地促进SLM以制止LD。我们认为,随着更高分辨率的新型微波和光学传感器的不断推出,SAR和光学图像的融合还有进一步研究的空间。本文综述的结果有助于更好地理解SAR和光学图像融合在南部非洲严重退化旱地研究中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Geocarto International
Geocarto International ENVIRONMENTAL SCIENCES-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
6.30
自引率
13.20%
发文量
407
审稿时长
>12 weeks
期刊介绍: Geocarto International is a professional academic journal serving the world-wide scientific and user community in the fields of remote sensing, GIS, geoscience and environmental sciences. The journal is designed: to promote multidisciplinary research in and application of remote sensing and GIS in geosciences and environmental sciences; to enhance international exchange of information on new developments and applications in the field of remote sensing and GIS and related disciplines; to foster interest in and understanding of science and applications on remote sensing and GIS technologies; and to encourage the publication of timely papers and research results on remote sensing and GIS applications in geosciences and environmental sciences from the world-wide science community. The journal welcomes contributions on the following: precise, illustrated papers on new developments, technologies and applications of remote sensing; research results in remote sensing, GISciences and related disciplines; Reports on new and innovative applications and projects in these areas; and assessment and evaluation of new remote sensing and GIS equipment, software and hardware.
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