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Land
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Machine Learning in Historical Land Use and Land Cover Change...
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Machine Learning in Historical Land Use and Land Cover Change Mapping and Modelling

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: closed (19 June 2023) | Viewed by 6995

Special Issue Editors

Dr. Luka Rumora

E-Mail Website
Guest Editor
Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia
Interests: remote sensing; geoinformation, earth sciences, geographical information system; spatial data analysis; geospatial science; vegetation mapping; land use modelling; geospatial statistics; machine learning; deep learning
Dr. Mario Miler

E-Mail Website
Guest Editor
Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia
Interests: architecture for geographic information systems; spatial databases; machine learning; remote sensing technology; computer vision; spatial data science; traffic and transport science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Damir Medak

E-Mail Website
Guest Editor
Department of Geoinformation Science, Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia
Interests: geoinformation science, systems and services; formal models of spatial and temporal information; object-oriented functional programming for the geoinformation science; spatiotemporal database management and GIS; geodynamics and hazard monitoring; hydrographic surveying and visualisation; applied computer programming

Special Issue Information

Dear Colleagues,

Land use (LU) and land cover change (LC) are very important parts of everyday life since their change can influence quality of life in certain regions, especially urban areas. Today, there are many methods for land use or land cover data acquisition, such as unmanned aerial vehicles (UAV), satellites, aircraft, or even some consumer cameras and mobile phones, depending on the recording location (ground or sky/space), or data usage. However, LU and LC mapping and modeling is complex task due to large amount of historical data and spatial environmental data. One of the European Union’s goals is to reduce greenhouse gas emissions by 40% between 1990 and 2030. For this task, historical data sets are very important since they provide important information about biomass, deforestation and land change in general. Consistent time series with consistent land cover information can be obtained with historical and contemporary data sets.

Due to the lack of historical and contemporary image comparison, the aim of this study is to use historical images and classify them using emerging technologies of data processing. This Special Issue encourages the submission of articles that combine historical and modern data set to achieve consistent time series analysis, as well as advanced studies on principles and methods of LU and LC modeling using machine learning algorithms.

Suggested themes for this Special Issue include, but are not limited to:

  • Historical land use/land cover mapping using aircraft or satellite remote sensing data;
  • Machine learning in land use/land cover change;
  • Characteristics, limitations and perspectives of machine learning in land use and land cover change;
  • Time series analysis;
  • Multi-sensor image data acquisition for land cover change;
  • Pansharpening and multi-sensor image fusion;
  • Land cover mapping and monitoring;
  • UAV land cover mapping

Dr. Luka Rumora
Dr. Mario Miler
Prof.  Dr. Damir Medak
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

  • time series
  • machine learning
  • land use
  • land cover
  • deep learning
  • multi-temporal segmentation and classification
  • optical and SAR imagery
  • change detection
  • geospatial data analysis
  • historical modeling

Published Papers (2 papers)

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Research

17 pages, 6618 KiB  
Article
Application of Explainable Artificial Intelligence (XAI) in Urban Growth Modeling: A Case Study of Seoul Metropolitan Area, Korea
by Minjun Kim, Dongbeom Kim, Daeyong Jin and Geunhan Kim
Land 2023, 12(2), 420; https://doi.org/10.3390/land12020420 - 6 Feb 2023
Cited by 6 | Viewed by 2212
Abstract
Unplanned and rapid urban growth requires the reckless expansion of infrastructure including water, sewage, energy, and transportation facilities, and thus causes environmental problems such as deterioration of old towns, reduction of open spaces, and air pollution. To alleviate and prevent such problems induced [...] Read more.
Unplanned and rapid urban growth requires the reckless expansion of infrastructure including water, sewage, energy, and transportation facilities, and thus causes environmental problems such as deterioration of old towns, reduction of open spaces, and air pollution. To alleviate and prevent such problems induced by urban growth, the accurate prediction and management of urban expansion is crucial. In this context, this study aims at modeling and predicting urban expansion in Seoul metropolitan area (SMA), Korea, using GIS and XAI techniques. To this end, we examined the effects of land-cover, socio-economic, and environmental features in 2007 and 2019, within the optimal radius from a certain raster cell. Then, this study combined the extreme gradient boosting (XGBoost) model and Shapley additive explanations (SHAP) in analyzing urban expansion. The findings of this study suggest urban growth is dominantly affected by land-cover characteristics, followed by topographic attributes. In addition, the existence of water body and high ECVAM grades tend to significantly reduce the possibility of urban expansion. The findings of this study are expected to provide several policy implications in urban and environmental planning fields, particularly for effective and sustainable management of lands. Full article
(This article belongs to the Special Issue Machine Learning in Historical Land Use and Land Cover Change Mapping and Modelling)
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14 pages, 4369 KiB  
Article
Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters
by Saleh Yousefi, Somayeh Mirzaee, Hussein Almohamad, Ahmed Abdullah Al Dughairi, Christopher Gomez, Narges Siamian, Mona Alrasheedi and Hazem Ghassan Abdo
Land 2022, 11(7), 993; https://doi.org/10.3390/land11070993 - 29 Jun 2022
Cited by 17 | Viewed by 4122
Abstract
Land use/cover (LU/LC) classification provides proxies of the natural and social processes related to urban development, providing stakeholders with crucial information. Remotely sensed images combined with supervised classification are common to define land use, but high-performance classifiers remain difficult to achieve, due to [...] Read more.
Land use/cover (LU/LC) classification provides proxies of the natural and social processes related to urban development, providing stakeholders with crucial information. Remotely sensed images combined with supervised classification are common to define land use, but high-performance classifiers remain difficult to achieve, due to the presence of model hyperparameters. Conventional approaches rely on manual adjustment, which is time consuming and often unsatisfying. Therefore, the goal of this study has been to optimize the parameters of the support vector machine (SVM) algorithm for the generation of land use/cover maps from Sentinel-2 satellite imagery in selected humid and arid (three study sites each) climatic regions of Iran. For supervised SVM classification, we optimized two important parameters (gamma in kernel function and penalty parameter) of the LU/LC classification. Using the radial basis function (RBF) of the SVM classification method, we examined seven values for both parameters ranging from 0.001 to 1000. For both climate types, the penalty parameters (PP) showed a direct relationship with overall accuracy (OA). Statistical results confirmed that in humid study regions, LU/LC maps produced with a penalty parameter >100 were more accurate. However, for regions with arid climates, LU/LC maps with a penalty parameter >0.1 were more accurate. Mapping accuracy for both climate types was sensitive to the penalty parameter. In contrast, variations of the gamma values in the kernel function had no effect on the accuracy of the LU/LC maps in either of the climate zones. These new findings on SVM image classification are directly applicable to LU/LC for planning and environmental and natural resource management. Full article
(This article belongs to the Special Issue Machine Learning in Historical Land Use and Land Cover Change Mapping and Modelling)
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