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Land
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Feature Papers for Land Innovations—Data and Machine Learning: 3rd Edition
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Feature Papers for Land Innovations—Data and Machine Learning: 3rd Edition

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1286

Special Issue Editor

Prof. Dr. Chuanrong Zhang

E-Mail Website
Guest Editor
Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, 215 Glenbrook Rd., Unit 4148, Storrs, CT 06269, USA
Interests: geographic information science (GIS); spatial economics; spatial analysis in health; transportation; environmental science; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue “Feature Papers for Land Innovations—Data and Machine Learning: 3rd Edition” welcomes contributions relating to spatial data science for obtaining, processing, analyzing, harnessing, and visualizing social, economic, environmental, and other data related to land. Particularly, it welcomes geospatial artificial intelligence and machine learning techniques for dealing with spatial big data, including remotely sensed data and social media data. Manuscripts can be theoretical, applied, or review articles. Interdisciplinary manuscripts are particularly welcome.

Prof. Dr. Chuanrong Zhang
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. Land is an international peer-reviewed open access monthly 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 2600 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

  • spatial big data
  • land use and land cover
  • geospatial artificial intelligence
  • machine learning
  • deep learning
  • big data processing
  • big data analysis
  • big data visualization

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Related Special Issues

Published Papers (2 papers)

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Research

22 pages, 24147 KB  
Article
Assessment of Landslide Susceptibility and Risk in Tengchong City, Southwestern China Using Machine Learning and the Analytic Hierarchy Process
by Changwei Linghu, Zhipeng Qian, Weizhe Chen, Jiaren Li, Ke Yang, Shilin Zou, Langlang Yang, Yao Gao, Zhiping Zhu and Qiankai Gao
Land 2025, 14(10), 1966; https://doi.org/10.3390/land14101966 - 29 Sep 2025
Abstract
Southwestern China, characterized by highly undulating terrain and mountainous areas, faces frequent landslide disasters. However, previous studies in this region mostly neglected the role of extreme rainfall in landslide susceptibility assessment and the socio-economic risks threatened by landslides. To address these gaps, this [...] Read more.
Southwestern China, characterized by highly undulating terrain and mountainous areas, faces frequent landslide disasters. However, previous studies in this region mostly neglected the role of extreme rainfall in landslide susceptibility assessment and the socio-economic risks threatened by landslides. To address these gaps, this study integrated 688 recorded landslides for Tengchong City in the southwest of China and 10 influencing factors (topography, lithology, climate, vegetation, and human activities), particularly two extreme precipitation indices of maximum consecutive 5 day precipitation (Rx5day) and maximum length of wet spell (CWD). These influencing factors were selected after ensuring variable independence via multicollinearity analysis. Four machine learning models were then built for landslide susceptibility assessment. The Random Forest model performed the best with an Area Under Curve (AUC) of 0.88 and identified elevation, normalized difference vegetation index (NDVI), lithology, and CWD as the four most important influencing factors. Landslides in Tengchong are concentrated in areas with low NDVI (<0.57), indicating increased vegetation cover might reduce landslide frequency. Landslide risk was further quantified via the Analytic Hierarchy Process (AHP) by integrating multiple socio-economic factors. High-risk zones were pinpointed in central-southern Tengchong (e.g., Heshun and Tuantian townships) due to their high social exposure and vulnerability. Overall, this study highlights extreme rainfall and vegetation as key modifiers of landslide susceptibility and identifies the regions with high landslide risk, which provides targeted scientific support for regional early-warning systems and risk management. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning: 3rd Edition)
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26 pages, 26889 KB  
Article
Spatio-Temporal Changes in Mangroves in Sri Lanka: Landsat Analysis from 1987 to 2022
by Darshana Athukorala, Yuji Murayama, Siri Karunaratne, Rangani Wijenayake, Takehiro Morimoto, S. L. J. Fernando and N. S. K. Herath
Land 2025, 14(9), 1820; https://doi.org/10.3390/land14091820 - 6 Sep 2025
Viewed by 867
Abstract
Mangroves in Sri Lanka provide critical ecosystem services, yet they have undergone significant changes due to anthropogenic and natural drivers. This study presents the first national-scale assessment of mangrove dynamics in Sri Lanka using remote sensing techniques. A total of 4670 Landsat images [...] Read more.
Mangroves in Sri Lanka provide critical ecosystem services, yet they have undergone significant changes due to anthropogenic and natural drivers. This study presents the first national-scale assessment of mangrove dynamics in Sri Lanka using remote sensing techniques. A total of 4670 Landsat images from Landsat 5, 7, 8, and 9 were selected to detect mangrove distribution, changes in extent, and structure and stability patterns from 1987 to 2022. A Random Forest classification model was applied to elucidate the spatial changes in mangrove distribution in Sri Lanka. Using national-scale data enhanced mapping accuracy by incorporating region-specific spectral and ecological characteristics. The average overall accuracy of the maps was over 96.29%. The total extent of mangroves in 2022 was 16,615 ha, representing 0.25% of the total land of Sri Lanka. The results further indicate that, at the national scale, mangrove extent increased from 1989 to 2022, with a net gain of 1988 ha (13.6%), suggesting a sustained and continuous recovery of mangroves. Provincial-wise assessments reveal that the Eastern and Northern Provinces showed the largest mangrove extents in Sri Lanka. In contrast, the Colombo, Gampaha, and Kalutara districts in the Western Province showed persistent declines. The top mangrove spatial structure and stability districts were Jaffna, Trincomalee, and Gampaha, while the most degraded mangrove districts were Batticaloa, Colombo, and Kalutara. This study offers critical insights into sustainable mangrove management, policy implementation, and climate resilience strategies in Sri Lanka. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning: 3rd Edition)
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