Land | Special Issue : Land Use Mapping as the Base for Modeling and Earth Oriented Digital Twins

Research

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 2 | Viewed by 1132

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 [...] Read more.

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 - 07 Apr 2023

Cited by 6 | Viewed by 2376

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 [...] Read more.

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 9 | Viewed by 3040

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 [...] Read more.

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 5 | Viewed by 2621

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 [...] Read more.

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 2180

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 [...] Read more.

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 2093

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 [...] Read more.

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 54 | Viewed by 13441

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 [...] Read more.

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 3709

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. [...] Read more.

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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