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Computer Vision and Machine Learning Methods for Land Use Land...
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Computer Vision and Machine Learning Methods for Land Use Land Cover Change Modelling and Forecasting

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: 24 March 2025 | Viewed by 951

Special Issue Editors

Dr. Karim Malik

E-Mail Website
Guest Editor
School of the Environment, University of Windsor, Windsor, ON N9B 3P4, Canada
Interests: spatial pattern comparison; landscape similarity analysis; landcover change detection; computer vision; machine/deep learning; remote sensing
Dr. Cameron Proctor

E-Mail Website
Guest Editor
School of the Environment, University of Windsor, Windsor, ON N9B 3P4, Canada
Interests: plant physiology; agriculture; biogeochemical cycles; above and belowground linkages; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Population growth, resource extraction, and natural disturbances are precipitating rapid changes in global land cover dynamics; for example, the spatial expansion of cities is culminating in significant vegetation and habitat loss and/or fragmentation. Forecasting terrestrial change using Earth observation sensor data has been a daunting task; however, with emerging algorithms, new computational tools, and sensor capabilities offered by recent technological advancements, Earth observation and monitoring is taking a more sophisticated dimension with improved accuracy. Accurate and up-to-date knowledge of land use and land cover (LULC) trajectories would enable policy-makers to devise and implement effective and sustainable land management policies.

This Special Issue (SI) aims to attract cutting-edge research with a focus on LULC modelling and forecasting using emerging computer vision, deep learning methods, remote sensing data, and simulation-derived data.

This SI invites original research and review articles that focus on the thematic areas below. Submissions that deploy cellular automata and Bayesian methods to model land-related human and natural disturbances are also highly encouraged in this SI.

(a) Computer vision applications for remote sensing data;

(b) Deep convolutional neural networks for change detection;

(c) Multi-modal data fusion for vegetation change forecasting;

(d) Land use/land cover change characterization using LiDAR data;

(e) Vegetation index-optimized landcover change prediction;

(f) Ecosystems change/productivity forecasting;

(g) Landscape change and/or similarity modelling;

(h) Spatiotemporal analysis of land degradation patterns;

(i) Transformer networks for LULC change prediction;

(j) Hyperspectral remote sensing for vegetation species mapping;

(k) Recurrent neural networks for time analysis of landcover trends;

(l) Long short-term memory (LSTM) networks for forecasting LULC.

Dr. Karim Malik
Dr. Cameron Proctor
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. 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

  • land use/land cover forecasting
  • land cover change simulation
  • vegetation monitoring
  • landscape change
  • remote sensing
  • land management
  • land degradation
  • deep neural networks

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Published Papers (1 paper)

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Research

20 pages, 7341 KiB  
Article
Research on Climate Drivers of Ecosystem Services’ Value Loss Offset in the Qinghai–Tibet Plateau Based on Explainable Deep Learning
by Wenshu Liu, Chang You and Jingbiao Yang
Land 2024, 13(12), 2141; https://doi.org/10.3390/land13122141 - 9 Dec 2024
Viewed by 456
Abstract
As one of the highest and most ecologically vulnerable regions in the world, the Qinghai–Tibet Plateau (QTP) presents significant challenges for the application of existing ecosystem service value (ESV) assessment models due to its extreme climate changes and unique plateau environment. Current models [...] Read more.
As one of the highest and most ecologically vulnerable regions in the world, the Qinghai–Tibet Plateau (QTP) presents significant challenges for the application of existing ecosystem service value (ESV) assessment models due to its extreme climate changes and unique plateau environment. Current models often fail to adequately account for the complex climate variability and topographical features of the QTP, making accurate assessments of ESV loss deviations difficult. To address these challenges, this study focuses on the QTP and employs a modified ESV loss deviation model, integrated with explainable deep learning techniques (LSTM-SHAP), to quantify and analyze ESV loss deviations and their climate drivers from 1990 to 2030. The results show that (1) between 1990 and 2020, the offset index in the eastern QTP consistently remained low, indicating significant deviations. Since 2010, low-value clusters in the western region have significantly increased, reflecting a widening range of ecological damage caused by ESV losses, with no marked improvement from 2020 to 2030. (2) SHAP value analysis identified key climate drivers, including temperature seasonality, diurnal temperature variation, and precipitation patterns, which exhibit nonlinear impacts and threshold effects on ESV loss deviation. (3) In the analysis of nonlinear relationships among key climate drivers, the interaction between diurnal temperature range and precipitation in wet seasons demonstrated significant effects, indicating that the synergistic action of temperature variation and precipitation patterns is critical to ecosystem stability. Furthermore, the complex nonlinear interactions between climate factors exacerbated the volatility of ESV loss deviations, particularly under extreme climate conditions. The 2030 forecast highlights that wet season precipitation and annual rainfall will become key factors driving changes in ESV loss deviation. By combining explainable deep learning methods, this study advances the understanding of the relationship between climate drivers and ecosystem service losses, providing scientific insights for ecosystem protection and sustainable management in the Qinghai–Tibet Plateau. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning Methods for Land Use Land Cover Change Modelling and Forecasting)
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