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Remote Sensing
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Monitoring Land Use Efficiency and Urban Expansion within the Context...
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Monitoring Land Use Efficiency and Urban Expansion within the Context of the UN 2030 Agenda for Sustainable Development

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

Deadline for manuscript submissions: 24 May 2024 | Viewed by 2661

Special Issue Editors

Dr. Zhixin Qi

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China
Interests: urban remote sensing; land use and land cover change
Dr. Le Yu

E-Mail Website
Guest Editor
Department for Earth System Science, Tsinghua University, Beijing 100084, China
Interests: land use change mapping; climate modelling; cropland mapping; uncertainty analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Fang

E-Mail Website
Guest Editor
Environment Research Institute, Shandong University, Qingdao 266237, China
Interests: forest remote sensng; climate change; wildland fire
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kasturi Devi Kanniah

E-Mail Website
Guest Editor
Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, UTM, Johor Bahru 81310, Malaysia
Interests: remote sensing; land use land cover change mapping; urban land use changes, vegetation processes and atmospheric aerosols
Special Issues, Collections and Topics in MDPI journals
Dr. Brian Alan Johnson

E-Mail Website
Guest Editor
Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies, Kanagawa 240-0115, Japan
Interests: geographic information systems (GIS); remote sensing; spatial modeling; and data mining for urban and environmental analysis and planning; mapping urban land cover (green space, impervious surfaces, etc.); monitoring forest health using fine resolution satellite imagery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The monitoring of land-use efficiency and urban expansion plays a crucial role in urban planning and the sustainable utilization of land resources, thereby contributing to the achievement of the Sustainable Development Goals (SDGs) outlined in the UN 2030 Agenda. Remote sensing technology, with its wide coverage and repetitive observation capabilities, has been extensively employed for monitoring urban areas. In recent years, numerous satellite and aerial remote sensing monitoring systems have been deployed, providing abundant data sources characterized by high spatiotemporal resolution and rich spectral information. By synergistically utilizing these multisource remote sensing data and leveraging cutting-edge methods, we can greatly enhance both the accuracy and frequency of monitoring urban areas, advancing our understanding of land-use efficiency and urban expansion. This holds immense significance for identifying urban development issues, mitigating urban risks and disasters and ensuring the healthy growth of cities, in alignment with the SDGs outlined in the UN 2030 Agenda for sustainable land use and urban development.

This Special Issue aims to collect studies that explore diverse applications of remote sensing data from different sensors and platforms for monitoring land-use efficiency and urban expansion within the context of the UN 2030 Agenda for sustainable development. We welcome contributions that focus on the integration of multisource data, including high-resolution, hyperspectral, SAR and night-time light data, for urban application. While not limited to these, potential topics that articles may address include:

  • Land-use change mapping, modeling and application
  • Assessment of land-use efficiency
  • Urban disaster monitoring
  • Sustainable urban development
  • Multisource remote sensing data fusion
  • Urban heat island and thermal sensing
  • Urban green spaces
  • Environmental conservation
  • Impacts of urban expansion on ecosystem services and natural resources
  • Integrating remote sensing and social media data
  • Greenhouse gas emissions
  • Methods and algorithms in urban applications

Dr. Zhixin Qi
Dr. Le Yu
Dr. Lei Fang
Prof. Dr. Kasturi Devi Kanniah
Dr. Brian Alan Johnson
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

  • urban remote sensing
  • land-use efficiency
  • sustainable development goals
  • multisource remote sensing data
  • urban expansion

Published Papers (2 papers)

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Research

16 pages, 14612 KiB  
Article
Detection of Benggang in Remote Sensing Imagery through Integration of Segmentation Anything Model with Object-Based Classification
by Yixin Hu, Zhixin Qi, Zhexun Zhou and Yan Qin
Remote Sens. 2024, 16(2), 428; https://doi.org/10.3390/rs16020428 - 22 Jan 2024
Viewed by 1298
Abstract
Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a [...] Read more.
Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas. Full article
(This article belongs to the Special Issue Monitoring Land Use Efficiency and Urban Expansion within the Context of the UN 2030 Agenda for Sustainable Development)
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19 pages, 4211 KiB  
Article
Evaluation of the Spatial Distribution of Predictors of Fire Regimes in China from 2003 to 2016
by Jiajia Su, Zhihua Liu, Wenjuan Wang, Kewei Jiao, Yue Yu, Kaili Li, Qiushuang Lü and Tamara L. Fletcher
Remote Sens. 2023, 15(20), 4946; https://doi.org/10.3390/rs15204946 - 13 Oct 2023
Cited by 1 | Viewed by 873
Abstract
Wildfire has extensive and profound impacts on forest structure and function. Therefore, it is important to study the spatial and temporal patterns of forest fire regimes and their drivers in order to better understand the feedbacks between climate change, fire disturbance, and forest [...] Read more.
Wildfire has extensive and profound impacts on forest structure and function. Therefore, it is important to study the spatial and temporal patterns of forest fire regimes and their drivers in order to better understand the feedbacks between climate change, fire disturbance, and forest ecosystems. Based on the Global Fire Atlas dataset, three forest fire regime components (fire occurrence density, burned rate, and median fire size) were extracted for China from 2003 to 2016. Three statistical models (Boosted Regression Tree, Random Forest, and Support Vector Machine) were used to systematically analyze the relationships between patterns of forest fire disturbance and climate, human activities, vegetation, and topography in China, as well as their spatial heterogeneity in different climatic regions. The results indicate that the spatial distribution of forest fires is heterogeneous, and different forest fire regime components are predicted by different factors. At the national level, the distribution of forest fire regimes mainly corresponds to climatic factors, although the relationship between median fire size and predictors is obscure. At the scale of each ecoregion, the main climate predictors of forest fire occurrence density and burned rate change from temperature in the north to temperature and precipitation in the south. Median fire size varies with elevation and temperature in the south. These results demonstrate that the spatial heterogeneity of predictors and scaling effects must be fully considered in the study of forest fire disturbance. Full article
(This article belongs to the Special Issue Monitoring Land Use Efficiency and Urban Expansion within the Context of the UN 2030 Agenda for Sustainable Development)
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