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Innovation and Sustainable Development of Remote Sensing Technology—2nd Edition

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 4968

Special Issue Editors

School of Computer Sciences, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing; machine learning; sustainable development
Special Issues, Collections and Topics in MDPI journals
School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
Interests: photoelectric detection; machine vision; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Through the research and application of Earth and humanities–social sciences, we can better understand the Earth system and develop effective sustainable development strategies to contribute to the future of people and the planet. The fundamental elements in the pursuit of Earth science research are image processing and information extraction (such as land cover and land use (LCLU), geological disasters, geological lithology, etc.) by using machine learning and deep learning algorithms, which are gleaned from the advancements in remote sensing technology. Fusion and modeling of Earth and humanities–social data serve as a cornerstone for policy development and scientific research of sustainable development. This Special Issue, titled “Innovation and Sustainable Development of Remote Sensing Technology—2nd Edition”, welcomes original studies on datasets, methodologies, and applications. Topics of interest include but are not limited to the following:

  • Image processing based on machine learning and deep learning algorithms;
  • Information extraction based on machine learning and deep learning algorithms;
  • Remote sensing information extraction and sustainable development;
  • Remote sensing information extraction and urbanization;
  • Remote sensing information extraction and ecological security;
  • Remote sensing information extraction and ecosystem service value;
  • Remote sensing information change detection and driving forces;
  • Remote sensing information extraction and socio-economic system coupling;
  • Remote sensing information extraction and climate change;
  • Remote sensing information extraction and land resource management;
  • Remote sensing information extraction and water resource management;
  • Remote sensing information extraction and spatiotemporal changes;
  • Remote sensing information extraction and industrial development;
  • Remote sensing information extraction and environmental protection;
  • Fusion and modeling of Earth and humanities–social data;
  • Applications of multi-source data for sustainable development.

Dr. Xianju Li
Dr. Pan Zhu
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

  • remote sensing
  • machine learning
  • land cover and land use
  • urbanization
  • ecological security
  • ecosystem service value

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Related Special Issue

Published Papers (2 papers)

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Research

24 pages, 15705 KiB  
Article
Analysis of Spatial and Temporal Evolution of Ecosystem Service Value Based on the Framework of “Risk-Association-Driver”: A Case Study of Panjin City
by Qiaoyu Wang, Li Wang, Qiang Wu and Peng Du
Sustainability 2025, 17(7), 2950; https://doi.org/10.3390/su17072950 - 26 Mar 2025
Cited by 1 | Viewed by 431
Abstract
This study adopts the “Risk-Association-Driver” framework to construct a sustainable security evaluation framework from the three dimensions of ecosystem service value, ecological risk, and drivers, with the aim of providing recommendations for regional ecosystem function enhancement and ecological risk prevention and control and [...] Read more.
This study adopts the “Risk-Association-Driver” framework to construct a sustainable security evaluation framework from the three dimensions of ecosystem service value, ecological risk, and drivers, with the aim of providing recommendations for regional ecosystem function enhancement and ecological risk prevention and control and promoting regional sustainable development. Google Earth Engine was used to extract Panjin City’s land use change data from 2010 to 2023. Furthermore, this paper combines the Equivalent Factor Approach and the Ecological Risk Model to analyze the dynamic changes of ecosystem service value and ecological risk. The spatial and temporal correlations, spatial clustering patterns, and driving variables of ecological risks and ecosystem service values in the research area were explored using GeoDa tools and Geo-detectors. The investigation shows that (1) The ecological risk index showed a gradual declining trend, with an overall decrease of 8.44%, indicating that the regional ecological security issue had improved. From 2010 to 2023, the total value of ecosystem services in Panjin City continued to rise, increasing from 27.923 billion CNY to 30.488 billion CNY; (2) The value of ecosystem services per unit area and the ecological risk index has strong positive hierarchical correlations and some positive spatial correlations; and (3) Land use types and anthropogenic impact indices are important drivers of spatial variation in ecosystem service values and ecological risks in the study area. The framework and methodology of this paper help to understand ecosystem dynamics and identify potential risks, which is significant for promoting sustainable regional development. Full article
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20 pages, 4602 KiB  
Article
Low-Cost Solution for Air Quality Monitoring: Unmanned Aerial System and Data Transmission via LoRa Protocol
by Francisco David Parra-Medina, Manuel Andrés Vélez-Guerrero and Mauro Callejas-Cuervo
Sustainability 2024, 16(22), 10108; https://doi.org/10.3390/su162210108 - 20 Nov 2024
Cited by 2 | Viewed by 4061
Abstract
For both human health and the environment, air pollution is a serious concern. However, the available air quality monitoring networks have important limitations, such as the high implementation costs, limited portability, and considerable operational complexity. In this context, unmanned aerial systems (UASs) are [...] Read more.
For both human health and the environment, air pollution is a serious concern. However, the available air quality monitoring networks have important limitations, such as the high implementation costs, limited portability, and considerable operational complexity. In this context, unmanned aerial systems (UASs) are emerging as a useful technological alternative due to their ability to cover large distances and access areas that are difficult or impossible for humans to reach. This article presents the development of an integrated platform that combines an unmanned aerial system (UAS) with specialized sensors to measure key parameters in relation to air quality, such as carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). In addition, a web application called PTECA is developed to visualize the data gathered by the wireless sensor array in real time. The platform incorporates a system that allows real-time tracking of the UAS route and measurement values during sample collection, employing the LoRa communication protocol. This solution represents a low-cost alternative that mitigates some of the limitations of traditional monitoring networks by offering greater portability and accessibility in terms of data collection. Preliminary tests successfully demonstrate the viability of the proposed system in a controlled airspace using geofencing. Full article
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