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Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins

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A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 45935

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Special Issue Editor

Dr. Karel Charvat

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Czech Centre for Science and Society, WirelessInfo, Plan4all z.s., K Rybníčku 557, 33012 Horní Bříza, Czech Republic
Interests: remote sensing; ICT; IoT; open data; big data; agriculture; rural development; semantic data; artificial intelligence
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Special Issue Information

Dear Colleagues,

Geospatial data on land use and land cover (LULC) contains information about the bio-physical cover of the Earth's surface and the purposes for which humans exploit it. LULC data are important for many geo-related disciplines, as well as f various specific tasks. Such use involves both physical-geographical (environmental) and socio-economic phenomena. LULC is used for the modeling of human population, urban growth and/or transformation, agriculture, socio-economic activities, or transportation. LULC also affects several environmental characteristics such as hydrological ones, when LULC has an impact on the surface runoff, water quality, soil moisture, and even groundwater accumulation. Another area where LULC data is used is climate modeling at various scales, from urban to regional analysis.

The biggest area is the modeling, analysis, and research, in general, of the LULC itself and its changes over time, which are closely related to the development of the landscape. Sustainable Development Goals on a global scale and the Green Deal Strategy in Europe will require the development of a very high precision digital model of the Earth to monitor and simulate natural and human activity and to develop and test scenarios that would enable more sustainable development and support environmental policies. For this purpose, we will need a digital replica of a living or non-living physical entity—a digital twin—that will give expert and non-expert users high-quality information, services, models, scenarios, forecasts, and visualizations. Detail LULC will be an important part of these models. It will include not only current status but also changes in time and relation to other aspects, for example, climatic change. This Special Issue will collect papers related to existing LULC models. But it will also be focused on modeling and AI tools dealing with LULC. We are interested in papers focused on the use of remote sensing for detection changes, usage of HPC and cloud computing for modeling and analysis of land-use changes, and on paper demonstrations of the use of LULC in different domains.

Dr. Karel Charvat
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.

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Keywords

Land use
Land cover
Digital twins
Sustainable development goals
Modeling
Artificial intelligence

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Published Papers (10 papers)

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Research

20 pages, 30913 KiB

Open AccessArticle

Rockfall Mapping and Monitoring Across the Kalymnos Sport Rock Climbing Sites, Based on Ultra-High-Resolution Remote Sensing Data and Integrated Simulations

by Emmanuel Vassilakis, Aliki Konsolaki, Konstantinos Soukis, Sofia Laskari, Evelina Kotsi, John Lialiaris and Efthymios Lekkas

Land 2024, 13(11), 1873; https://doi.org/10.3390/land13111873 - 9 Nov 2024

Viewed by 595

Abstract

This manuscript presents a multidisciplinary study that proposes a methodology for delineating and categorizing vulnerability at rockfall risk areas to avoid human injuries and infrastructure damage caused by rockfalls. The presented workflow includes (i) classical geological mapping, (ii) the interpretation of high-resolution satellite data for observing the spatial distribution of fallen boulders, (iii) analytical hierarchy processing of spatial information within a Geographical Information System (GIS) platform, (iv) close-range remote sensing campaigns with Unmanned Aerial Systems (UASs), and (v) integrated simulation of rockfall events. This methodology was applied to Kalymnos Island, which belongs to the Dodecanese Islands complex of the southeastern Aegean Sea in Greece. It is characterized by unique geomorphological features, including extensive vertical limestone cliffs that span the island. These cliffs make it one of the world’s most densely concentrated areas for sport climbing. The results highlighted the areas that the local authorities need to focus on and suggested measures for increasing the safety of climbers and infrastructure. Full article

(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)

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25 pages, 22407 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022

by Jose Antonio Sobrino, Sergio Gimeno, Virginia Crisafulli and Álvaro Sobrino-Gómez

Land 2024, 13(7), 1072; https://doi.org/10.3390/land13071072 - 17 Jul 2024

Viewed by 1585

Abstract

Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four decades. Utilising Landsat 5, 8, and 9 summer images, a Random Forest algorithm renowned for its ability to handle large datasets and complex variables, was employed to produce land cover classifications consisting of five categories: ‘Urban Areas’, ‘Dense Vegetation’, ‘Sparse Vegetation’, ‘Water Bodies’, and Other’. The results were validated through in situ measurements comparing with pre-existing products and utilising a confusion matrix. Over the study period, the urban area practically doubled, increasing from approximately 482 to 940 square kilometres. This expansion was concentrated mainly in the proximity of the already existing urban zone and occurred primarily between 1985 and 1990. The Dense and Sparse Vegetation classes exhibit substantial fluctuations over the years, displaying a subtle trend towards a decrease in their cumulative value. Water bodies and Other classes do not show substantial changes over the years. The Random Forest algorithm showed a high Overall Accuracy (OA) of 95% and Kappa values of 93%, showing good agreement with field measurements (88% OA), ESA World Cover (80% OA), and the Copernicus Global Land Service Land Cover Map (73% OA), confirming the effectiveness of this methodology in generating land cover classifications. Full article

(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)

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24 pages, 16271 KiB

Open AccessArticle

Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS

by Polina Lemenkova

Land 2023, 12(11), 1995; https://doi.org/10.3390/land12111995 - 31 Oct 2023

Cited by 6 | Viewed by 1810

Abstract

This study documents the changes in the Land Use/Land Cover (LULC) in the region of saline lakes in north Tunisia, Sahara Desert. Remote sensing data are a valuable data source in monitoring LULC in lacustrine landscapes, because variations in the extent of lakes are visible from space and can be detected on the images. In this study, changes in LULC of the salt pans of Tunisia were evaluated using a series of 12 Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared (TIRS) images. The images were processed with the Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The study area included four salt lakes of north Tunisia in the two regions of the Gulf of Hammamet and Gulf of Gabès: (1) Sebkhet de Sidi el Hani (Sousse Governorate), (2) Sebkha de Moknine (Mahdia Governorate), (3) Sebkhet El Rharra and (4) Sebkhet en Noual (Sfax). A quantitative estimate of the areal extent analysed in this study is 182 km × 185 km for each Landsat scene in two study areas: Gulf of Hammamet and Gulf of Gabès. The images were analysed for the period 2017–2023 on months February, April and July for each year. Spatio-temporal changes in LULC and their climate–environmental driving forces were analysed. The results were interpreted and the highest changes were detected by accuracy assessment, computing the class separability matrices, evaluating the means and standard deviation for each band and plotting the reject probability maps. Multi-temporal changes in LULC classes are reported for each image. The results demonstrated that changes in salt lakes were determined for winter/spring/summer months as detected changes in water/land/salt/sand/vegetation areas. The accuracy of the classified images was evaluated using pixel rejection probability values, which were filtered out using the ‘r.mapcalc’ module of GRASS GIS. The confidence levels were computed and visualised with a series of maps along with the error matrix and measured convergence level of classified pixels. This paper contributes to the environmental monitoring of Tunisian landscapes and analysis of climate effects on LULC in landscapes of north Africa. Full article

(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)

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20 pages, 6685 KiB

Open AccessArticle

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

by Hamidreza Gharechaee, Aliakbar Nazari Samani, Shahram Khalighi Sigaroodi, Abolfazl Baloochiyan, Maryam Sadat Moosavi, Jason A. Hubbart and Seyed Mohammad Moein Sadeghi

Land 2023, 12(4), 843; https://doi.org/10.3390/land12040843 - 7 Apr 2023

Cited by 15 | Viewed by 3282

Abstract

Most published studies identify groundwater extraction as the leading cause of land subsidence (LS). However, the causes of LS are not only attributable to groundwater extraction. Other land-use practices can also affect the occurrence of LS. In this study, radar interferometric techniques and machine learning (ML) models were used for the prediction, susceptibility zoning, and prioritization of influential variables in the occurrence of LS in the Bakhtegan basin. The LS rate was characterized by applying an interferometric synthetic aperture radar (InSAR). The recursive feature elimination (RFE) method was used to detect and select the dominant combination of indicators to prepare an LS susceptibility map. Three ML models, including random forest (RF), k-nearest neighbors (KNN), and classification and regression trees (CART), were used to develop predictive models. All three models had acceptable performance. Among the ML models, the RF model performed the best (i.e., Nash–Sutcliffe efficiency, Kling–Gupta efficiency, correlation coefficient, and percent bias metrics of 0.76, 0.78, 0.88, and 0.70 for validating phase, respectively). The analysis conducted on all three ML model outputs showed that high and very high LS susceptibility classes were located on or near irrigated agricultural land. The results indicate that the leading cause of land LS in the study region is not due to groundwater withdrawals. Instead, the distance from dams and the proximity to anticlines, faults, and mines are the most important identifiers of LS susceptibility. Additionally, the highest probability of LS susceptibility was found at distances less than 18 km from synclines, 6 to 13 km from anticlines, 23 km from dams, and distances less than 20 to more than 144 km from mines. The validated methods presented in this study are reproducible, transferrable, and recommended for mapping LS susceptibility in semiarid and arid climate zones with similar environmental conditions. Full article

(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)

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13 pages, 3580 KiB

Open AccessArticle

Urban Heat Island Mitigation and Urban Green Spaces: Testing a Model in the City of Padova (Italy)

by Paolo Semenzato and Lucia Bortolini

Land 2023, 12(2), 476; https://doi.org/10.3390/land12020476 - 15 Feb 2023

Cited by 18 | Viewed by 4851

Abstract

The urban heat island (UHI) is a critical issue in most urbanised areas. Spatial variation of urban air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and the energy demand for cooling. UHIs can be particularly harmful to human health and there are numerous studies that link mortality and morbidity with extreme thermal events, that can be worsened by UHIs. The temperature difference between city centres and the surrounding countryside, which is accentuated in the summer months and at night, is the result not only of a greater production of anthropogenic heat but is mainly due to the properties of urban surfaces. The use of vegetation, and in particular urban tree planting, is one of possible strategies to contrast the heat island effects. In order to analyse the mitigation effects produced by green spaces in the city of Padova, a municipality in the northeast of Italy, simulations of the air temperature variations and their spatial distribution were carried out using the i-Tree Cool Air model. High-resolution RGBir aerial photos were processed to produce a tree canopy and a permeability map and the model was applied on a 10 m × 10 m grid over the entire city, producing a raster map of the aboveground air temperatures. A particularly hot July day with recorded air temperatures of 35 °C at 3 p.m. and 28 °C at 10 p.m. at a reference weather station was chosen for the test. In the daytime, the results show temperature differences up to almost 10 °C between urban open spaces with impervious cover (squares, streets) and green areas under tree canopy. At night, the simulated air temperatures are only slightly cooler in areas with tree cover than those recorded at the reference station, while urban areas with sealed surfaces maintain air temperatures 4.4 °C higher. The study was aimed at testing the applicability of the model as a tool for predicting air temperatures in relation to land use and canopy cover. The results show that the model can potentially be used to compare different urban forest and urban greening planning scenarios, however, further research is necessary to assess the reliability of the temperature predictions. Full article

(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)

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16 pages, 9307 KiB

Open AccessArticle

An Unsupervised Burned Area Mapping Approach Using Sentinel-2 Images

by Michail Sismanis, Rizos-Theodoros Chadoulis, Ioannis Manakos and Anastasios Drosou

Land 2023, 12(2), 379; https://doi.org/10.3390/land12020379 - 31 Jan 2023

Cited by 10 | Viewed by 3805

Abstract

The frequency and severity of large, destructive fires have increased in the recent past, with extended impacts on the landscape, the human population, and ecosystems. Earth observations provide a means for the frequent, wide coverage and accurate monitoring of fire impacts. This study describes an unsupervised approach for the mapping of burned areas from Sentinel-2 satellite imagery, which is based on multispectral thresholding, and introduces an adaptive thresholding method. It takes into account the localized variability of the spectral responses in a two-phase approach. The first phase detects areas that are burned with a high probability, while the second phase adaptively adjusts this preliminary mapping by expanding and refining its boundaries. The resulting classification contains two main classes of interest: burned and unburned. The latter is further classified into four (4) fire impact severity classes, according to the Copernicus Emergency Management Service (CEMS) and the NASA United States Geological Survey (USGS)’s widely acknowledged nomenclature examples. Three distinct wildfire events are assessed, which occurred during the summers of 2020 and 2021 in Greece and Portugal. The classification accuracy is calculated by juxtaposing the classification outputs to burned area validation maps created through the photointerpretation of very high-resolution (VHR) satellite imagery. The corresponding CEMS On-Demand Mapping products are also juxtaposed against the validation maps for comparison purposes. The accuracy assessment showcases that the unsupervised approach closely follows the capacity provided by the CEMS maps (e.g., the kappa coefficient—k—of the proposed unsupervised approach is 0.91, 0.83 and 0.83 for the events processed, while the CEMS products achieve a k of 0.94, 0.93 and 0.8, respectively). The proposed approach considers the variability of the affected areas’ spectral response; thus, it generalizes well to different areas, e.g., areas characterized by different land cover types. It seems to offer an effective means of mapping the wildfire-induced changes, which can be further incorporated and used by forest fire management services and further decision support systems complementary to the CEMS maps. Full article

(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)

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15 pages, 6415 KiB

Open AccessArticle

Are Wildfires in the Wildland-Urban Interface Increasing Temperatures? A Land Surface Temperature Assessment in a Semi-Arid Mexican City

by Mariana Ayala-Carrillo, Michelle Farfán, Anahí Cárdenas-Nielsen and Richard Lemoine-Rodríguez

Land 2022, 11(12), 2105; https://doi.org/10.3390/land11122105 - 22 Nov 2022

Cited by 2 | Viewed by 2906

Abstract

High rates of land conversion due to urbanization are causing fragmented and dispersed spatial patterns in the wildland-urban interface (WUI) worldwide. The occurrence of anthropogenic fires in the WUI represents an important environmental and social issue, threatening not only vegetated areas but also periurban inhabitants, as is the case in many Latin American cities. However, research has not focused on the dynamics of the local climate in the WUI. This study analyzes whether wildfires contribute to the increase in land surface temperature (LST) in the WUI of the metropolitan area of the city of Guanajuato (MACG), a semi-arid Mexican city. We estimated the pre- and post-fire LST for 2018–2021. Spatial clusters of high LST were detected using hot spot analysis and examined using ANOVA and Tukey’s post-hoc statistical tests to assess whether LST is related to the spatial distribution of wildfires during our study period. Our results indicate that the areas where the wildfires occurred, and their surroundings, show higher LST. This has negative implications for the local ecosystem and human population, which lacks adequate infrastructure and services to cope with the effects of rising temperatures. This is the first study assessing the increase in LST caused by wildfires in a WUI zone in Mexico. Full article

(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)

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16 pages, 5259 KiB

Open AccessArticle

An Advanced Open Land Use Database as a Resource to Address Destination Earth Challenges

by Michal Kepka, Pavel Hájek, Dmitrij Kožuch, Tomáš Řezník, Tomáš Mildorf, Karel Charvát, Martina Kepka Vichrová and Jan Chytrý

Land 2022, 11(9), 1552; https://doi.org/10.3390/land11091552 - 13 Sep 2022

Cited by 2 | Viewed by 2750

Abstract

Land-use and land-cover (LULC) themes are important for many domains, especially when they process environmental and socio-economic phenomena. The evolution of a land-use database called Open Land Use (OLU) started in 2013 and was continued by adapting many user requirements. The goal of this study was to design a new version of the OLU database that would better fit the gathered user requirements collected by projects using LULC data. A formal definition of the developed data model through Unified Modeling Language (UML) class diagrams, a feature catalogue based on ISO 19110 and SQL scripts for setting up the OLU database, are the key achievements of the presented paper. The presented research provides a multi-scale open database of LULC information supporting the DestinE initiative to develop a very-high-precision digital model of the earth. The novel spatio-temporal thematic approach also lies in modular views of the OLU database. Full article

(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)

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24 pages, 13328 KiB

Open AccessArticle

Spatiotemporal Change Analysis and Prediction of Future Land Use and Land Cover Changes Using QGIS MOLUSCE Plugin and Remote Sensing Big Data: A Case Study of Linyi, China

by Rizwan Muhammad, Wenyin Zhang, Zaheer Abbas, Feng Guo and Luc Gwiazdzinski

Land 2022, 11(3), 419; https://doi.org/10.3390/land11030419 - 14 Mar 2022

Cited by 84 | Viewed by 17811

Abstract

Land use and land cover (LULC) change analysis is a systematic technique that aids in the comprehension of physical and non-physical interaction with the natural habitat and the pursuit of environmental sustainability. Research regarding LULC’s spatiotemporal changing patterns and the simulation of future scenarios offers a complete view of present and future development possibilities. To simulate the spatiotemporal change transition potential and future LULC simulation, we utilized multi-temporal remotely sensed big data from 1990 to 2020 with a 10-year interval. Independent variables (DEM, slope, and distance from roads) and an integrated CA-ANN methodology within the MOLUSCE plugin of QGIS were utilized. The findings reveal that physical and socioeconomic driving variables have a substantial effect on the patterns of the terrain. In the last three decades, the study area had a significant rise in impervious surface from 10.48% to 26.91%, as well as a minor increase in water from 1.30% to 1.67%. As a result, forest cover decreased from 12.60% to 8.74%, green space decreased from 26.34% to 16.57%, and barren land decreased from 49.28% to 46.11%. Additionally, the predictions (2030–2050) support the increasing trend towards impervious surface at the expense of significant quantities of forest and green space. Full article

(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)

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20 pages, 6482 KiB

Open AccessArticle

Mapping of Sicilian Pocket Beaches Land Use/Land Cover with Sentinel-2 Imagery: A Case Study of Messina Province

by Giovanni Randazzo, Maria Cascio, Marco Fontana, Francesco Gregorio, Stefania Lanza and Anselme Muzirafuti

Land 2021, 10(7), 678; https://doi.org/10.3390/land10070678 - 27 Jun 2021

Cited by 27 | Viewed by 4297

Abstract

Pocket beaches (PBs) are among the most attractive tourist sites and economic development contributors in coastal areas; however, they are negatively impacted by the combined effects of climate change and anthropogenic activities. Generally, research on PBs is conducted from the beach towards offshore. Studies on the land use/land cover (LULC) of PBs are limited and currently lacking. Such studies deserve more investigation due to the importance of LULC in PBs’ functioning. In this study, supervised classification methods were investigated for LULC mapping of the PBs located in the province of Messina. Sentinel-2B satellite images were analyzed using maximum likelihood (MaL), minimum distance (MiD), mahalanobis distance (MaD) and spectral angle mapper (SAM) classification methods. The study was conducted mainly in order to determine which classification method would be adequate for small scale Sentinel-2 imagery analysis and provide accurate results for the LULC mapping of PBs. In addition, an occurrence-based filter algorithm in conjunction with OpenStreetMap data and Google Earth imagery was used to extract linear features within 500 m of the inland buffer zone of the PBs. The results demonstrate that information on the biophysical parameters, namely surface cover fractions, of the coastal area can be obtained by conducting LULC mapping on Sentinel-2 images. Full article

(This article belongs to the Special Issue Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins)

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