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Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management (Second Edition)

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

Deadline for manuscript submissions: closed (15 May 2025) | Viewed by 5181

Special Issue Editors

Prof. Dr. Dongping Ming

E-Mail Website
Guest Editor
School of Information Engineering, China University of Geosciences (Beijing), Beijing 10083, China
Interests: geoinformatics; remote sensing; resources and environment monitoring; geological hazard monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jagannath Aryal

E-Mail Website
Guest Editor
Faculty of Engineering and Information technology (FEIT), The University of Melbourne, Melbourne, VIC 3010, Australia
Interests: geomatics, remote sensing; Geo-AI; disaster risk management; sustainability and resilience
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kasturi Devi Kanniah

E-Mail Website
Guest Editor
Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Interests: remote sensing; land use land cover change mapping; urban land use changes, vegetation processes and atmospheric aerosols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technology is indispensable for high-level land use, land management, and sustainable infrastructure design. In recent years, multi-source and multi-temporal remote sensing big data, from optical to microwave, from low to very high spatial resolution, and from multispectral to hyperspectral, and LiDAR data are available and can be applied for broader land management. Meanwhile, with the development of artificial intelligence, big data, and cloud computing techniques, efficient and intelligent remote sensing image interpretation in high-level land use, land management, and sustainable infrastructure design has been systematically engineered, and these issues are currently faced with various challenges in terms of data, information, knowledge, modeling, and computing power. Such challenges can be tackled with innovations in image interpretation, information/feature extraction, modeling techniques, and applications to problems concerning our environment.

Under such circumstances, this Special Issue aims at providing knowledge, methodologies, and approaches for scientific research and decision support systems relating to intelligent remote sensing image interpretation in land use, land management, and sustainable infrastructure design.

We invite contributions from colleagues who wish to innovate the discipline of remote sensing for land use, land management, and sustainable infrastructure design.

Prof. Dr. Dongping Ming
Prof. Dr. Jagannath Aryal
Prof. Dr. Kasturi Devi Kanniah
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

  • multi-source data fusion
  • sample database construction
  • object based image analysis
  • GeoAI
  • region/parcel partition on different scales
  • land cover/use classification
  • land cover/use change monitoring/projection/modelling
  • thematic information extraction
  • advanced image processing techniques (machine learning and deep learning)
  • authenticity verification
  • cloud based intelligent remote sensing application
  • GIS for land use/cover management
  • Impact of land use/cover on environment

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

Related Special Issue

Published Papers (4 papers)

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Research

19 pages, 8689 KiB  
Article
Enhancing Urban Flood Susceptibility Assessment by Capturing the Features of the Urban Environment
by Juwei Tian, Yinyin Chen, Linhan Yang, Dandan Li, Luo Liu, Jiufeng Li and Xianzhe Tang
Remote Sens. 2025, 17(8), 1347; https://doi.org/10.3390/rs17081347 - 10 Apr 2025
Viewed by 348
Abstract
The frequent occurrence of urban floods (UFs) poses significant threats to public safety and the national economy. Accurate estimation of urban flood susceptibility (UFS) and the identification of potential hotspots are critical for effective UF management. However, existing UFS studies often fall short [...] Read more.
The frequent occurrence of urban floods (UFs) poses significant threats to public safety and the national economy. Accurate estimation of urban flood susceptibility (UFS) and the identification of potential hotspots are critical for effective UF management. However, existing UFS studies often fall short due to a limited understanding of UFs’ nature, frequently relying on disaster factors analogous to those used for natural floods while neglecting key urban characteristics, limiting the accuracy of UFS estimates. To address these challenges, we propose a novel framework for UFS assessment. Unlike those studies that focus primarily on topographic and surface characteristics, our approach integrates urban-specific factors that capture the distinctive attributes of the urban environment, including Urban Heat Island Intensity, Urban Rain Island Intensity, Urban Resilience Index, and Impervious Surface Percentage. Guangzhou was selected as the study area, where machine learning methods were employed to calculate UFS, and Shapley Additive Explanation was utilized to quantify the contributions of employed factors. We evaluated the significance of urban factors from three perspectives: classifier performance, map accuracy, and factor importance. The results indicate that (1) urban factors hold significantly greater importance compared to other factors, and (2) the incorporation of urban factors markedly enhances both the performance of the trained classifier and the accuracy of the UFS map. These findings underscore the value of integrating urban factors into UFS assessments, thereby contributing to more precise UF management and supporting sustainable urban development. Full article
(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management (Second Edition))
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23 pages, 9697 KiB  
Article
Carbon Storage Simulation and Land Use Optimization for High-Water-Table Resource-Based Cities Based on the Coupled GMOP-PLUS-InVEST Model
by Zhen Wang, Anya Zhong, Erhu Wei and Chunming Hu
Remote Sens. 2024, 16(23), 4480; https://doi.org/10.3390/rs16234480 - 29 Nov 2024
Cited by 2 | Viewed by 1222
Abstract
Exploring land use evolution and its impact on carbon storage is crucial for mitigating climate change in resource-based cities and promoting green, low-carbon development. This study constructs a GMOP-PLUS-InVEST coupled model and utilizes remote sensing data from five phases of land use from [...] Read more.
Exploring land use evolution and its impact on carbon storage is crucial for mitigating climate change in resource-based cities and promoting green, low-carbon development. This study constructs a GMOP-PLUS-InVEST coupled model and utilizes remote sensing data from five phases of land use from 2000 to 2020. Four scenarios are established to simulate the future patterns of land use and carbon storage changes in Jining City. The results indicate that: (1) from 2000 to 2020, farmland, forest land, and grassland in Jining City show a declining trend; while construction and waters increase, resulting in a reduction of carbon storage from 167.35 × 10⁶ t in 2000 to 159.85 × 10⁶ t in 2020; (2) coal mining significantly influences nearby land utilization types and carbon storage, leading to a decline in nearby carbon reserves; (3) compared to the other three scenarios, the coordinated development scenario exhibits higher land use efficiency and carbon storage, with lower levels of human disturbance; balancing the local economy and environment, and serving as a sustainable pattern of land use for the area. The outcomes of this paper quantitatively reflect the relationship between land use, coal mining, and carbon storage in high-water-level resource-based cities; providing guidance for the local economy, urban development, and ecological environment protection. Full article
(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management (Second Edition))
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15 pages, 12817 KiB  
Article
Aeolian Desertification Dynamics from 1995 to 2020 in Northern China: Classification Using a Random Forest Machine Learning Algorithm Based on Google Earth Engine
by Caixia Zhang, Ningjing Tan and Jinchang Li
Remote Sens. 2024, 16(16), 3100; https://doi.org/10.3390/rs16163100 - 22 Aug 2024
Cited by 3 | Viewed by 1463
Abstract
Machine learning methods have improved in recent years and provide increasingly powerful tools for understanding landscape evolution. In this study, we used the random forest method based on Google Earth Engine to evaluate the desertification dynamics in northern China from 1995 to 2020. [...] Read more.
Machine learning methods have improved in recent years and provide increasingly powerful tools for understanding landscape evolution. In this study, we used the random forest method based on Google Earth Engine to evaluate the desertification dynamics in northern China from 1995 to 2020. We selected Landsat series image bands, remote sensing inversion data, climate baseline data, land use data, and soil type data as variables for majority voting in the random forest method. The method’s average classification accuracy was 91.6% ± 5.8 [mean ± SD], and the average kappa coefficient was 0.68 ± 0.09, suggesting good classification results. The random forest classifier results were consistent with the results of visual interpretation for the spatial distribution of different levels of desertification. From 1995 to 2000, the area of aeolian desertification increased at an average rate of 9977 km2 yr−1, and from 2000 to 2005, from 2005 to 2010, from 2010 to 2015, and from 2015 to 2020, the aeolian desertification decreased at an average rate of 2535, 3462, 1487, and 4537 km2 yr−1, respectively. Full article
(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management (Second Edition))
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22 pages, 6799 KiB  
Article
Detection of Cliff Top Erosion Drivers through Machine Learning Algorithms between Portonovo and Trave Cliffs (Ancona, Italy)
by Nicola Fullin, Michele Fraccaroli, Mirko Francioni, Stefano Fabbri, Angelo Ballaera, Paolo Ciavola and Monica Ghirotti
Remote Sens. 2024, 16(14), 2604; https://doi.org/10.3390/rs16142604 - 16 Jul 2024
Cited by 2 | Viewed by 1624
Abstract
Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired [...] Read more.
Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing/applying machine learning algorithms and/or numerical modeling, in order to identify/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R2 of 0.84), with FS decreasing with slope height. Full article
(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management (Second Edition))
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