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Remote Sensing
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Advancements in Environmental Remote Sensing and GIS
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Advancements in Environmental Remote Sensing and GIS

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 3386

Special Issue Editors

Dr. Mateo Gašparović

E-Mail Website
Guest Editor
Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia
Interests: remote sensing; GIS; photogrammetry; land cover classification; unmanned aerial vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Dorijan Radočaj

E-Mail Website
Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: cropland suitability; land suitability; remote sensing; GIS; predictive mapping; digital soil mapping; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Dino Dobrinić

E-Mail Website
Guest Editor
Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia
Interests: remote sensing; GIS; spatial data analysis; urban vegetation mapping; time-series analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Sergej Baričević

E-Mail Website
Guest Editor
Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia
Interests: geodesy; surveying; innovations in sensor technology; precise measurement; geoinformatics

Special Issue Information

Dear Colleagues,

Rapid advances in environmental remote sensing and geographic information systems (GIS) have transformed our approach to understanding and managing the Earth's resources. These technologies have become indispensable tools for monitoring, assessing, and modeling environmental phenomena, providing unprecedented precision and insight into ecological systems. The integration of remote sensing and GIS enables improved spatial analysis and provides a powerful means to address environmental challenges and promote sustainable management practices. From monitoring deforestation and urban sprawl to assessing the impact of climate change and natural disasters, the combination of these technologies enables comprehensive and dynamic analyses that support decision-making processes across various fields.

We are pleased to invite you to contribute to the Special Issue “Advancements in Environmental Remote Sensing and GIS”, which will present the latest research results and innovations in this dynamic field. This Special Issue seeks to gather contributions that address new methods, applications, and case studies, highlighting the role of remote sensing and GIS in improving our understanding of environmental systems and increasing the effectiveness of environmental management strategies.

Potential topics for this Special Issue include, but are not limited to, the following:

  • Remote sensing applications in environmental monitoring and assessment;
  • Integration of remote sensing data and GIS for ecosystem management;
  • Advances in spatial modeling and analysis for environmental studies;
  • Applications of GIS and remote sensing in climate change research;
  • Land use and land cover change detection using remote sensing and GIS;
  • Applications in disaster management and risk assessment;
  • Innovations in sensor technology and data processing techniques;
  • Case studies demonstrating the practical applications of remote sensing and GIS in environmental management.

We encourage submissions of original research papers, review articles, and case studies that contribute to the growing body of knowledge in the fields of environmental remote sensing and GIS. By bringing together different perspectives and the latest research findings, this Special Issue aims to advance the field and promote interdisciplinary collaboration.

This Special Issue is published in collaboration with the International Conference of Environmental Remote Sensing and GIS (ICERS) and welcomes submissions from conference participants.

We look forward to receiving your valuable contributions!

Dr. Mateo Gašparović
Dr. Dorijan Radočaj
Dr. Dino Dobrinić
Dr. Sergej Baričević
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

  • environmental monitoring
  • spatial analysis
  • geographic information systems (GIS)
  • remote sensing
  • satellite imagery
  • unmanned aerial vehicles (UAVs)
  • ecosystem management
  • climate change assessment
  • land use and land cover change
  • sustainable development
  • conservation and biodiversity
  • global navigation satellite systems (GNSS)
  • disaster management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

21 pages, 45648 KiB  
Article
A Big Data Approach for the Regional-Scale Spatial Pattern Analysis of Amazonian Palm Locations
by Matthew J. Drouillard and Anthony R. Cummings
Remote Sens. 2025, 17(5), 784; https://doi.org/10.3390/rs17050784 - 24 Feb 2025
Viewed by 192
Abstract
Arecaceae (palms) are an important resource for indigenous communities as well as fauna populations across Amazonia. Understanding the spatial patterns and the environmental factors that determine the habitats of palms is of considerable interest to rainforest ecologists. Here, we utilize remotely sensed imagery [...] Read more.
Arecaceae (palms) are an important resource for indigenous communities as well as fauna populations across Amazonia. Understanding the spatial patterns and the environmental factors that determine the habitats of palms is of considerable interest to rainforest ecologists. Here, we utilize remotely sensed imagery in conjunction with topography and soil attribute data and employ a generalized cluster identification algorithm, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), to study the underlying patterns of palms in two areas of Guyana, South America. The results of the HDBSCAN assessment were cross-validated with several point pattern analysis methods commonly used by ecologists (the quadrat test for complete spatial randomness, Morista Index, Ripley’s L-function, and the pair correlation function). A spatial logistic regression model was generated to understand the multivariate environmental influences driving the placement of cluster and outlier palms. Our results showed that palms are strongly clustered in the areas of interest and that the HDBSCAN’s clustering output correlates well with traditional analytical methods. The environmental factors influencing palm clusters or outliers, as determined by logistic regression, exhibit qualitative similarities to those identified in conventional ground-based palm surveys. These findings are promising for prospective research aiming to integrate remote flora identification techniques with traditional data collection studies. Full article
(This article belongs to the Special Issue Advancements in Environmental Remote Sensing and GIS)
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23 pages, 5956 KiB  
Article
The Impact of Urbanization-Induced Land Use Change on Land Surface Temperature
by Afera Halefom, Yan He, Tatsuya Nemoto, Lei Feng, Runkui Li, Venkatesh Raghavan, Guifei Jing, Xianfeng Song and Zheng Duan
Remote Sens. 2024, 16(23), 4502; https://doi.org/10.3390/rs16234502 - 30 Nov 2024
Cited by 1 | Viewed by 2079
Abstract
Rapid urbanization can change local climate by increasing land surface temperature (LST), particularly in metropolitan regions. This study uses two decades of remote sensing data to investigate how urbanization-induced changes in land use/land cover (LULC) affect LST in the Beijing Region, China. By [...] Read more.
Rapid urbanization can change local climate by increasing land surface temperature (LST), particularly in metropolitan regions. This study uses two decades of remote sensing data to investigate how urbanization-induced changes in land use/land cover (LULC) affect LST in the Beijing Region, China. By focusing on the key issue of LST and its contributing variables through buffer zones, we determined how variables influence LST across buffer zones—core, transit, and suburban areas. This approach is crucial for identifying and prioritizing key variables in each zone, enabling targeted, zone-specific measures that can more effectively mitigate LST rise. The main driving variables for the Beijing Region were determined, and the spatial-temporal relationship between LST and driving variables was investigated using a geographically weighted regression (GWR) model. The results demonstrate that the Beijing Region’s LST climbed from 2002 to 2022, with increases of 0.904, 0.768, and 0.248 °C in core, transit, and suburban areas, respectively. The study found that human-induced variables contributed significantly to the increase in LST across core and transit areas. Meanwhile, natural variables in suburban areas predominated and contributed to stabilizing local climates and cooling. Over two decades and in all buffer zones, GWR models slightly outperformed ordinary least squares (OLS) models, suggesting that the LST is highly influenced by its local geographical location, incorporating natural and human-induced variables. The results of this study have substantial implications for designing methods to mitigate LST across the three buffer zones in the Beijing Region. Full article
(This article belongs to the Special Issue Advancements in Environmental Remote Sensing and GIS)
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