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Remote Sensing
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Recent Progress in Remote Sensing of Land Cover Change
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Recent Progress in Remote Sensing of Land Cover Change

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 3752

Special Issue Editors

Dr. Hossein Shafizadeh-Moghadam

E-Mail Website
Guest Editor
Department of Water Engineering and Management, Tarbiat Modares University, Tehran P.O. Box 14115-336, Iran
Interests: land use and land cover; machine learning; remote sensing; water resource management; food security and climate change
Dr. Jay Gao

E-Mail Website
Guest Editor
School of Environment, The University of Auckland, Auckland 1142, New Zealand
Interests: land cover and spatiotemporal analysis; spatial dynamic modelling of land cover change; analysis of land cover change drivers; multi-scale land cover change and carbon storage
Dr. Tingting Xu

E-Mail Website
Guest Editor
School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: spatial modeling and simulation; remote sensing on land use/land cover change

Special Issue Information

Dear Colleagues,

Land cover change (LCC) is a continuous process intertwined with climate change, natural disasters, socio-economic factors, political decisions, increasing populations, and changes in consumption patterns. LCC is a dynamic phenomenon on Earth’s surface; it has a local, regional, and global footprint, and is simultaneously considered a cause and a consequence of environmental change. Monitoring, characterizing, quantifying, and understanding the dynamics of LCC at multiple resolutions and scales is essential for scientists and decision makers. 

While remote sensing plays a crucial role in monitoring the spatiotemporal dynamics of land cover at a range of scales, employing and understanding methods and changes remain challenging. This Special Issue on “Recent Progress in Remote Sensing of Land Cover Change” is specifically designed to present state-of-the-art methods for: the quantification of LCC, the capability assessment of existing products for LCC studies, multi-scale and multi-sensor data for LCC studies, and understanding LCC in large-scale studies.

Authors are encouraged to submit papers on, but not limited to, the following topics:

  • Advanced methods for the quantification of LCC (i.e., deep learning).
  • Large-scale LCC detection.
  • Evaluation and consistency of existing land cover products for LCC studies.
  • Conceptualization of the cycle of LCC in large-scale areas.
  • LCC and sustainable development goals.
  • Spatiotemporal analysis of the pattern of LCC.
  • Methods for analyzing errors in LCC.

Dr. Hossein Shafizadeh-Moghadam
Dr. Jay Gao
Dr. Tingting Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • large-scale land cover change
  • multi-scale and multi-sensor
  • change detection
  • deep learning
  • land cover change model
  • cloud computing

Published Papers (3 papers)

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Research

19 pages, 17244 KiB  
Article
Comparison and Validation of Multiple Medium- and High-Resolution Land Cover Products in Southwest China
by Xiangyu Ji, Xujun Han, Xiaobo Zhu, Yajun Huang, Zengjing Song, Jinghan Wang, Miaohang Zhou and Xuemei Wang
Remote Sens. 2024, 16(6), 1111; https://doi.org/10.3390/rs16061111 - 21 Mar 2024
Viewed by 561
Abstract
The rapid advancement of remote sensing technology has given rise to numerous global- and regional-scale medium- to high-resolution land cover (LC) datasets, making significant contributions to the exploration of worldwide environmental shifts and the sustainable governance of natural resources. Nonetheless, owing to the [...] Read more.
The rapid advancement of remote sensing technology has given rise to numerous global- and regional-scale medium- to high-resolution land cover (LC) datasets, making significant contributions to the exploration of worldwide environmental shifts and the sustainable governance of natural resources. Nonetheless, owing to the inherent uncertainties embedded within remote sensing imagery, LC datasets inevitably exhibit inaccuracies. In this study, a local accuracy assessment of LC datasets in Southwest China was conducted. The datasets utilized in our analysis include ESA WorldCover, CLCD, Esri Land Cover, CRLC, FROM-GLC10, GLC_FCS30, GlobeLand30, and SinoLC-1. This study employed a sampling approach that combines proportional allocation and stratified random sampling (SRS) to gather sample points and compute confusion matrices to validate eight LC products. The local accuracy of the eight LC maps differs significantly from the overall accuracy provided by the original authors in Southwest China. ESA WorldCover and CLCD demonstrate higher local accuracy than other products in Southwest China, with their overall accuracy (OA) values being 87.1% and 85.48%, respectively. Simultaneously, we computed the area for each LC map based on categories, quantifying uncertainty through the reporting of confidence intervals for both accuracy and area parameters. This study aims to validate and compare eight LC datasets and assess precision and area of diverse spatial resolution datasets for mapping and monitoring across Southwest China. Full article
(This article belongs to the Special Issue Recent Progress in Remote Sensing of Land Cover Change)
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20 pages, 3445 KiB  
Article
Identification and Measurement of Shrinking Cities Based on Integrated Time-Series Nighttime Light Data: An Example of the Yangtze River Economic Belt
by Zhixiong Tan, Siman Xiang, Jiayi Wang and Siying Chen
Remote Sens. 2023, 15(15), 3797; https://doi.org/10.3390/rs15153797 - 30 Jul 2023
Viewed by 1381
Abstract
Urban shrinkage has gradually become an issue of world-concerning social matter. As urbanization progresses, some Chinese cities are experiencing population loss and economic decline. Our study attempts to correct and integrate DMSP/OLS and NPP/VIIRS data to complete the identification and measurement of shrinking [...] Read more.
Urban shrinkage has gradually become an issue of world-concerning social matter. As urbanization progresses, some Chinese cities are experiencing population loss and economic decline. Our study attempts to correct and integrate DMSP/OLS and NPP/VIIRS data to complete the identification and measurement of shrinking cities in China’s Yangtze River Economic Belt (YREB). We identified 36 shrinking cities and 644 shrinking counties on the municipal and county scales. Based on this approach, we established the average urban shrinkage intensity index and the urban shrinkage frequency index, attempting to find out the causes of shrinking cities for different shrinkage characteristics, city types and shrinkage frequencies. The results show that (1) the shrinking cities are mainly concentrated in the Yangtze River Delta city cluster, the midstream city cluster and the Chengdu–Chongqing economic circle. (2) Most shrinking cities have a moderate frequency of shrinking, dominated by low–low clusters. Resource-based, heavy industrial, small and medium-sized cities are more inclined to shrink. (3) The single economic structure, the difficulty of industrial transformation and the lack of linkage among county-level cities are possible reasons for the urban shrinkage in the YREB. Exploring the causes of urban shrinkage from a more micro perspective will be an inevitable task for sustainable development in YREB and even in China. Full article
(This article belongs to the Special Issue Recent Progress in Remote Sensing of Land Cover Change)
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18 pages, 4838 KiB  
Article
Spatiotemporal Evolution of Arid Ecosystems Using Thematic Land Cover Products
by Lili Xu, Tianyu Chen, Baolin Li, Yecheng Yuan and Nandin-Erdene Tsendbazar
Remote Sens. 2023, 15(12), 3178; https://doi.org/10.3390/rs15123178 - 19 Jun 2023
Viewed by 1080
Abstract
The pathway, direction, and potential drivers of the evolution in global arid ecosystems are of importance for maintaining the stability and sustainability of the global ecosystem. Based on the Climate Change Initiative Land Cover dataset (CCILC), in this study, four indicators of land [...] Read more.
The pathway, direction, and potential drivers of the evolution in global arid ecosystems are of importance for maintaining the stability and sustainability of the global ecosystem. Based on the Climate Change Initiative Land Cover dataset (CCILC), in this study, four indicators of land cover change (LCC) were calculated, i.e., regional change intensity (RCI), rate of change in land cover (CR), evolutionary direction index (EDI), and artificial change percentage (ACP), to progressively derive the intensity, rate, evolutionary direction, and anthropogenic interferences of global arid ecosystems. The LCC from 1992 to 2020 and from 28 consecutive pair-years was observed at the global, continental, and country scales to examine spatiotemporal evolution in the Earth’s arid ecosystems. The following main results were obtained: (1) Global arid ecosystems experienced positive evolution despite complex LCCs and anthropogenic interferences. Cautious steps to avoid potential issues caused by rapid urbanization and farmland expansion are necessary. (2) The arid ecosystems in Australia, Central Asia, and southeastern Africa generally improved, as indicated by EDI values, but those in North America were degraded, with 41.1% of LCCs associated with urbanization or farming. The arid ecosystems in South America also deteriorated, but 83.4% of LCCs were in natural land covers. The arid ecosystems in Europe slightly improved with overall equivalent changes in natural and artificial land covers. (3) Global arid ecosystems experienced three phases of change based on RCI values: ‘intense’ (1992–1998), ‘stable’ (1998–2014), and ‘intense’ (2014–2020). In addition, two phases of evolution based on EDI values were observed: ‘deterioration’ (1992–2002) and ‘improvement’ (2002–2020). The ACP values indicated that urbanization and farming activities contributed increasingly less to global dryland change since 1992. These findings provide critical insights into the evolution of global arid ecosystems based on analyses of LCCs and will be beneficial for sustainable development of arid ecosystems worldwide within the context of ongoing climate change. Full article
(This article belongs to the Special Issue Recent Progress in Remote Sensing of Land Cover Change)
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