Spatial Data for the Sustainable World: Remote Sensing in Geographical Research

Dear Colleagues,

This Special Issue, titled “Spatial Data for the Sustainable World: Remote Sensing in Geographical Research”, will focus on the integration of spatial data infrastructures, geoinformatics, and remote sensing methods for understanding and advancing sustainable development. In an era of rapid environmental change and expanding urbanization, spatially explicit data are essential for monitoring progress toward the United Nations Sustainable Development Goals (SDGs) and for supporting evidence-based decision-making at multiple spatial and temporal scales. This issue seeks to bring together contributions that highlight both methodological innovations and practical applications of Earth Observation (EO) technologies in sustainable spatial planning, resource management, and environmental resilience. The scope of this Special Issue covers a broad range of topics, including remote sensing data analysis, spatial modeling, cartographic visualization, and geostatistical techniques that enhance our ability to measure, monitor, and communicate sustainability indicators. It welcomes studies that demonstrate how spatial data—derived from satellites, UAVs, LiDAR, or in situ networks—can be integrated into geospatial frameworks and digital platforms to support adaptive management and policy development. In particular, this issue encourages interdisciplinary approaches linking geography, geoinformatics, environmental science, and social sustainability, emphasizing open data, reproducibility, and transparent analytical workflows. The purpose of this issue is to stimulate discussion on how remote sensing and geospatial analytics can contribute to sustainable spatial governance, risk assessment, climate adaptation, and landscape transformation. By bridging technological advances and conceptual frameworks, the Special Issue will demonstrate the pivotal role of spatial data in achieving SDG-related targets and transforming geographical research into actionable insights for a sustainable future. This issue will complement the existing literature by connecting remote sensing-based spatial analysis with applied sustainability research, moving beyond traditional land-cover studies toward integrated, multiscale models of socio-environmental systems. It will provide a platform for the dissemination of innovative data integration methods, visualization techniques, and case studies that showcase the evolving contribution of spatial data science to sustainable development.

Prof. Dr. Robert Župan
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 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.