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Application of Remote Sensing for Sustainable Development of Urban and Rural Areas

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 6585

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Special Issue Editors

Dr. Wei Gao

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Guest Editor

School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: remote sensing geoscience analysis; storm and flood disaster risk assessment; urban heat island mitigation; urban and rural environment

Dr. Yongsheng Li

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Guest Editor

National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, China
Interests: high-precision InSAR deformation inversion algorithms and high-performance InSAR computing technology; promoting the quantitative and operational application of radar remote sensing in the monitoring of geological hazards (earthquakes, landslides, etc.)

Prof. Dr. Lifei Wei

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Guest Editor

Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
Interests: change detection of remote sensing imagery; hyperspectral remote sensing; intelligent agricultural remote sensing
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Special Issue Information

Dear Colleagues,

We would like to invite you to contribute to our upcoming Special Issue of Sustainability titled “Application of Remote Sensing for Sustainable Development of Urban and Rural Areas”.

Urbanization has been an irreversible and accelerating process over the last several decades, and is highly associated with population growth, air pollution, profound changes in landscape patterns and processes, higher temperatures in urban areas than in rural areas, etc. From many perspectives, such anthropic activities give rise to differences between urban and rural areas. Urban and rural areas have attracted many researchers to study their potential sustainable development, including ecological, cultural, political, institutional, social and economic components, among others.

However, sustainable development should be addressed in detail in the context of its theoretical concepts and practical application. Due to large-scale and dynamic observation characteristics, remote sensing technology has been an indispensable tool for the environmental monitoring of sustainable development. New urban and rural sustainable development strategies have to be elaborated and ecologically friendly to improve landscape design and infrastructure planning with a more comprehensive design, leading to a more enjoyable living environment and higher quality of life.

The Special Issue focuses on original research articles and comprehensive reviews regarding the sustainable development of urban and rural areas based on remote sensing technology. In this framework, both specialized and interdisciplinary manuscripts concerning the following topics are welcome, in addition to those addressing other related issues:

Understanding urban land cover with high-resolution remote sensing images;
Change detection and analysis of urban dynamics and sprawl in remote sensing imagery;
Early identification, assessment and response of natural disasters in urban and rural areas;
RS processing algorithm implementation and validation to extract urban and rural features.

Dr. Wei Gao
Dr. Yongsheng Li
Prof. Dr. Lifei Wei
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. Sustainability 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 2400 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

sustainable development
sustainability assessment
rural residential areas
landscape patterns
environmental planning
urban farming and agriculture
sustainable and resilient infrastructure
green infrastructure
high-resolution remote sensing images
micro-climate
urban–rural gradient
natural hazards in urban and rural areas

Published Papers (3 papers)

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23 pages, 8981 KiB

Open AccessArticle

Extraction of Rural Residential Land from Very-High Resolution UAV Images Using a Novel Semantic Segmentation Framework

by Chenggao Sha, Jian Liu, Lan Wang, Bowen Shan, Yaxian Hou and Ailing Wang

Sustainability 2022, 14(19), 12178; https://doi.org/10.3390/su141912178 - 26 Sep 2022

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Abstract

Accurate recognition and extraction of rural residential land (RRL) is significant for scientific planning, utilization, and management of rural land. Very-High Resolution (VHR) Unmanned Aerial Vehicle (UAV) images and deep learning techniques can provide data and methodological support for the target. However, RRL, as a complex land use assemblage, exhibits features of different scales under VHR images, as well as the presence of complex impervious layers and backgrounds such as natural surfaces and tree shadows in rural areas. It still needs further research to determine how to deal with multi-scale features and accurate edge features in such scenarios. In response to the above problems, a novel framework named cascaded dense dilated network (CDD-Net), which combines DenseNet, ASPP, and PointRend, is proposed for RRL extraction from VHR images. The advantages of the proposed framework are as follows: Firstly, DenseNet is used as a feature extraction network, allowing feature reuse and better network design with fewer parameters. Secondly, the ASPP module can better handle multi-scale features. Thirdly, PointRend is added to the model to improve the segmentation accuracy of the edges. The research takes a plain village in China as the research area. Experimental results show that the Precision, Recall, F1 score, and Dice coefficients of our approach are 91.41%, 93.86%, 92.62%, and 0.8359, respectively, higher than other advanced models used for comparison. It is feasible in the task of high-precision extraction of RRL using VHR UAV images. This research could provide technical support for rural land planning, analysis, and formulation of land management policies. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development of Urban and Rural Areas)

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20 pages, 6547 KiB

Open AccessArticle

Fast InSAR Time-Series Analysis Method in a Full-Resolution SAR Coordinate System: A Case Study of the Yellow River Delta

by Huizhi Duan, Yongsheng Li, Bingquan Li and Hao Li

Sustainability 2022, 14(17), 10597; https://doi.org/10.3390/su141710597 - 25 Aug 2022

Cited by 5 | Viewed by 2198

Abstract

Ground deformation is a major determinant of delta sustainability. Sentinel-1 Terrain Observation by Progressive Scans (TOPS) data are widely used in interferometric synthetic aperture radar (InSAR) applications to monitor ground subsidence. Due to the unparalleled mapping coverage and considerable data volume requirements, high-performance computing resources including graphics processing units (GPUs) are employed in state-of-the-art methodologies. This paper presents a fast InSAR time-series processing approach targeting Sentinel-1 TOPS images to process massive data with higher efficiency and resolution. We employed a GPU-assisted InSAR processing method to accelerate data processing. Statistically homogeneous pixel selection (SHPS) filtering was used to reduce noise and detect features in scenes with minimal image resolution loss. Compared to the commonly used InSAR processing software, the proposed method significantly improved the Sentinel-1 TOPS data processing efficiency. The feasibility of the method was investigated by mapping the surface deformation over the Yellow River Delta using SAR datasets acquired between January 2021 and February 2022. The findings indicate that several events of significant subsidence have occurred in the study area. Combined with the geological environment, underground brine and hydrocarbon extraction as well as sediment consolidation and compaction contribute to land subsidence in the Yellow River Delta. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development of Urban and Rural Areas)

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22 pages, 12977 KiB

Open AccessCase Report

Impact of Land Cover Change on a Typical Mining Region and Its Ecological Environment Quality Evaluation Using Remote Sensing Based Ecological Index (RSEI)

by Huan Tang, Jiawei Fang, Ruijie Xie, Xiuli Ji, Dayong Li and Jing Yuan

Sustainability 2022, 14(19), 12694; https://doi.org/10.3390/su141912694 - 06 Oct 2022

Cited by 20 | Viewed by 2537

Abstract

Ecological environment in mining cities has become an important part of ecological construction. This paper takes Tongling, a mining city, as the research area, and uses Landsat series remote sensing images from 2000 to 2020 as data sources. Using the principal component analysis method and the Remote Sensing Ecological Index (RSEI) integrated with four indexes of greenness, humidity, dryness, and heat, the ecological disturbance of the mining area was evaluated and studied. Meanwhile, the land cover spatiotemporal classification of Tongling city was extracted by the maximum likelihood method. Furthermore, landscape metrics were used, based on the information on open-pit mining areas, to quantitatively analyze the ecological environment quality and its change characteristics in the study area. The results show that (1) RSEI can better characterize the ecological quality of Tongling city, greenness and humidity are positively correlated with it, dryness and heat are negatively correlated with it, and dryness and RSEI have the highest correlation coefficient, indicating that urban expansion will cause ecological environment deterioration to a certain extent. (2) The ecological environment quality of the research area showed a “decline-rising” trend, and the mean value of RSEI decreased from 0.706 to 0.644. Spatially, the areas with poor RSEI are mainly distributed in the central urban area and the open-pit mining area in the south. (3) Land cover change leads to changes in landscape metrics, and most landscape-level metrics are positively or negatively correlated with RSEI. The more concentrated the land cover type distribution is, the smaller the change is, and the more regional RSEI can be improved. (4) The mean value of RESI of the ten open-pit mining areas in Tongling city decreased significantly, with a maximum decrease of 52.73%. Among them, the RESI decline rate in the area around the no.1 open pit mine is 0.034/year. The ecological degradation in Tongling city is attributed to the rapid expansion of built-up areas and the development of the mining industry. The research results can provide a scientific basis for protecting the ecological environment of mining cities. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development of Urban and Rural Areas)

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