Innovative Applications of Multi-Source Remote Sensing Technology in Ecological Environment Quality Assessment and Governance

Dear Colleagues,

With the intensification of global environmental changes and urbanization processes, the assessment of the quality of ecological environments and the optimization of ecological governance have become core tasks to promote sustainable development, maintain ecosystem stability, and safeguard human well-being. Traditional ecological monitoring and governance methods are limited by shortcomings such as discrete spatial coverage, low temporal resolution, and high cost, making it difficult to meet the needs of refined, dynamic, and large-scale ecological management. Multi-source remote sensing technology, integrating satellite, UAV, hyperspectral, and other data sources, has emerged as a pivotal tool in this field, providing spatially explicit, temporally continuous, and cost-effective technical support for ecological environment quality assessment, dynamic monitoring, and governance decision-making.

Multi-source remote sensing technology overcomes the limitations of single-source data and single technical means, enabling the comprehensive capture of ecological elements such as vegetation cover, land use/cover change, underlying surface characteristics, and ecological restoration dynamics. It not only supports the scientific evaluation of the quality of ecological environments at different scales (local, regional, and basin), but also provides accurate insights into the effectiveness of ecological governance measures, forming a complete technical chain from "monitoring–assessment–analysis–governance–verification". In recent years, the integration of multi-source remote sensing with artificial intelligence, machine learning, and ground-based monitoring data has further promoted innovations in theoretical methods and practical applications of ecological environment quality assessment and governance, laying a solid foundation for intelligent and precise ecological management.

This Special Issue aims to gather cutting-edge research findings, innovative methodological explorations, and typical regional practice cases in the field of multi-source remote sensing applied to ecological environment quality assessment and governance. This Special Issue aims to gather cutting-edge research findings, innovative methodological explorations, and typical regional practice cases in the field of multi-source remote sensing applied to the assessment and governance of the quality of ecological environments. We strive to build an academic exchange platform that bridges theoretical research and practical applications, promoting the innovation and popularization of multi-source remote sensing technology in ecological environmental protection. We particularly encourage interdisciplinary submissions that integrate remote sensing science, ecology, environmental science, geospatial analysis, and artificial intelligence, addressing key challenges such as multi-source data fusion, inversion model optimization, governance effectiveness quantification, and the linkage between remote sensing products and ecological governance policies.

Articles may address, but are not limited to, the following topics:

• Integration and optimization of multi-source remote sensing data (satellite, UAV, hyperspectral, etc.) for ecological environment quality evaluation;
• Construction and application of remote sensing inversion indicators (NDVI, ecological environment quality index, etc.) in quality assessments of ecological air, water, and soil;
• Dynamic monitoring and spatiotemporal evolution analysis of regional ecological environment quality based on multi-source remote sensing;
• AI-/machine learning-empowered innovation of remote sensing ecological quality assessment models;
• Multi-source remote sensing for monitoring ecological restoration effects and governance effectiveness evaluations;
• Remote sensing’s inversion of underlying surface characteristics and its guiding role in ecological governance;
• UAV remote sensing applications in refined ecological governance and assessment verification;
• Analysis of the pollutants underlying surface ecological correlation mechanisms based on remote sensing;
• Low-cost applications of open-source remote sensing data in ecological assessment and governance. 

Prof. Dr. Xiuhong Li
Prof. Dr. Rongjin Yang
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 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.

• multi-source remote sensing
• ecological environment quality assessment
• ecological governance
• effectiveness evaluation
• remote sensing inversion
• ecological restoration
• regional practice
• artificial intelligence
• UAV remote sensing
• underlying surface characteristics
• spatiotemporal monitoring