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Remote-Sensing Insights for Sustainable Urban Ecosystems

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 319

Special Issue Editor


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Guest Editor
Chinese Academy of Sciences, Beijing, China
Interests: regional environmental remote sensing; data mining; urban sustainable development application

Special Issue Information

Dear Colleagues,

Accelerated global urbanization and intensifying climate change have imposed unprecedented pressures on the structure, function and service capacity of urban ecosystems, manifested as urban heat island intensification, biodiversity decline, ecological degradation and resource allocation imbalance. As a state-of-the-art Earth observation tool, remote sensing enables the large-scale, spatiotemporally continuous and high-resolution dynamic monitoring of urban ecological environments, providing critical technical support for quantifying urban ecosystem patterns, assessing ecological quality and supporting science-based urban sustainability governance. This field is of profound scientific significance and practical value for advancing low-carbon, resilient and eco-friendly urban development.

This Special Issue aims to collect frontier theoretical advances, methodological innovations and practical applications of remote sensing in sustainable urban ecosystem research and to foster interdisciplinary integration across remote-sensing science, urban ecology and environmental geography. It aligns closely with the scope of Remote Sensing, which publishes high-quality research on advanced remote-sensing technologies, algorithms, multi-source data fusion and their interdisciplinary applications in Earth and environmental sciences.

We encourage submissions of both regular research papers and reviews on topics including, but not limited to, the following:

  • urban ecological environment monitoring and assessment,
  • multi-source remote sensing for urban biodiversity and ecosystem services,
  • remote-sensing-based urban ecological risk early warning,
  • AI-driven remote sensing for sustainable urban planning,
  • high-resolution satellite, aerial and UAV data in urban analysis,
  • time series analysis and urban change detection.

Dr. Xiaojing Yao
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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
  • urban sustainability
  • urban resilience
  • urban biodiversity and ecosystem services
  • AI-driven remote sensing
  • smart cities with remote observation

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Published Papers (1 paper)

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Research

23 pages, 3790 KB  
Article
Biodiversity Assessment of Urban Green Space Based on Remote Sensing—A Case Study of Hangzhou Bay Urban Agglomeration
by Jing Li, Bo Tang, Wei He, Sen Yang, Kai Cao, Huiping Chen, Lingbo Ji, Yanying Xu, Ying Li and Shucun Sun
Remote Sens. 2026, 18(12), 1898; https://doi.org/10.3390/rs18121898 (registering DOI) - 9 Jun 2026
Abstract
Rapid urbanization exerts profound pressure on urban biodiversity, yet long-term assessments integrating multi-source remote sensing data remain scarce. Objective: Focusing on the Hangzhou Bay Urban Agglomeration, a rapidly developing region in China’s Yangtze River Delta, this study aims to construct a remote sensing-based [...] Read more.
Rapid urbanization exerts profound pressure on urban biodiversity, yet long-term assessments integrating multi-source remote sensing data remain scarce. Objective: Focusing on the Hangzhou Bay Urban Agglomeration, a rapidly developing region in China’s Yangtze River Delta, this study aims to construct a remote sensing-based Biodiversity Index (BI) and analyze its spatiotemporal evolution and underlying drivers. Six Essential Biodiversity Variables derived from satellite observations (2000–2024) were integrated using Principal Component Analysis. Spatial autocorrelation and Geodetector models were then applied to examine BI dynamics and driving factors. The regional BI declined gradually from 0.80 in 2000 to 0.72 in 2024, with the rate of decline slowing after 2020 and a partial recovery observed in Zhoushan. Marked inter-city heterogeneity exists: Huzhou retains the highest and most stable BI due to extensive forest cover, whereas Jiaxing exhibits the lowest BI and the most pronounced decline, driven by rapid expansion of construction land. Land use/cover (LULC) and fractional vegetation cover (FVC) emerge as the dominant drivers (average q-values of 0.196 and 0.208, respectively), and their interaction explains over 46% of the spatial variance in BI. Road density shows a consistently increasing influence over time. This study demonstrates the utility of remote sensing-based frameworks for monitoring urban biodiversity dynamics and provides actionable insights for evidence-based land use planning and ecological restoration. Full article
(This article belongs to the Special Issue Remote-Sensing Insights for Sustainable Urban Ecosystems)
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