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A GIS Spatial Analysis Model for Land Use Change (Volume II)

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A special issue of Geographies (ISSN 2673-7086).

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 27193

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Prof. Dr. Elzbieta Bielecka

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Guest Editor

WAT Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warszawa, Poland
Interests: spatial analysis; mapping; geoinformation; geomatics; geographical analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Małgorzata Luc

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Institute of Geography and Spatial Management, Jagiellonian University, 31-007 Kraków, Poland
Interests: geoinformatics; landscape research; land use change

Special Issue Information

Dear Colleagues,

Land use change is one of the most important types of environmental change, and it is occurring rapidly in all regions around the word. Land use is generally driven by such demographic changes as population growth, migration, as well as economic changes. In general, land use changes include urban sprawl, the conversion of agricultural land, land abandonment, deforestation, and reforestation. The reason for changes in LU/LC varies considerably from region to region, and covers many environmental, economic, political, and social problems. Documenting land use changes, simulating land use changes, and identifying their impact on the environment are becoming more important because the results can be useful for sustainable land management on a local, regional, national, or even global scale. GIS-based spatial analysis and GIS modeling have been widely used to monitor and forecast land use/land cover changes and their impact on the environment and human wellbeing. Geospatial technology also plays a key role in monitoring the achievement of the Sustainable Development Goals, in particular, Goal 11 and land use efficiency (SDG 11.3.1).

This Special Issue of Geographies aims to disseminate state-of-the-art research articles as well as review papers on GIS-based spatial analysis and models for land use/land cover change with the use of Earth observation data (in situ and remote sensing), topographic maps, and any other sources of information on land cover/land use. Contributions related to geography, geology, and geosciences are welcome.

Prof. Dr. Elzbieta Bielecka
Dr. Małgorzata Luc
Guest Editors

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Keywords

change detection of land use and land cover
urban sprawl modeling
deforestation modeling and monitoring
multitemporal spatial analysis
accuracy assessment
spatial relation between land use and population distribution
SDG 11.3.1
data mining and machine learning

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Published Papers (9 papers)

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Research

18 pages, 3099 KiB

Open AccessFeature PaperArticle

Spatial Evaluation of Dengue Transmission and Vector Abundance in the City of Dhaka, Bangladesh

by C. Emdad Haque, Parnali Dhar-Chowdhury, Shakhawat Hossain and David Walker

Geographies 2023, 3(2), 268-285; https://doi.org/10.3390/geographies3020014 - 14 Apr 2023

Cited by 4 | Viewed by 2484

Abstract

In recent years, many urban areas in low and middle income countries have experienced major dengue epidemics, and the city of Dhaka, the capital city of Bangladesh, is one of them. Understanding models based on land cover and land use in urban areas in relation to vector abundance and possible disease transmission can be a major epidemiological tool in identifying disease incidence and prevalence. Demographic and human behavioral factors can also play a role in determining microenvironments for entomological distribution—which is a major risk factor for epidemicity. Data collected from a cross-sectional entomological survey in the city of Dhaka during the monsoon season of 2012 and two serological surveys—one pre-monsoon and another post-monsoon in 2012—were analyzed in this study. A total of 898 households and 1003 containers with water were inspected, and 1380 Ae. aegypti pupae and 4174 larvae were counted in these containers. All Stegomyia indices were found to be the highest in the central business and residential mixed zone. The odds ratios of risk factors for seroprevalence, including sex, age, self-reported febrile illness during the previous six months, and travel during the last six months, were calculated; age distribution was found to be a highly significant risk factor (p = value < 0.0001). The study offers clear patterns of dengue viral transmission, disease dynamics, and their association with critical spatial dimensions. Full article

(This article belongs to the Special Issue A GIS Spatial Analysis Model for Land Use Change (Volume II))

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28 pages, 27679 KiB

Open AccessArticle

Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data

by Giuseppe Mancino, Antonio Falciano, Rodolfo Console and Maria Lucia Trivigno

Geographies 2023, 3(1), 82-109; https://doi.org/10.3390/geographies3010005 - 12 Jan 2023

Cited by 6 | Viewed by 3896

Abstract

The present research aims at verifying whether there are significant differences between Land Use/Land Cover (LULC) classifications performed using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data—abbreviated as L8 and S2. To comprehend the degree of accuracy between these classifications, both L8 and S2 scenes covering the study area located in the Basilicata region (Italy) and acquired within a couple of days in August 2017 were considered. Both images were geometrically and atmospherically corrected and then resampled at 30 m. To identify the ground truth for training and validation, a LULC map and a forest map realized by the Basilicata region were used as references. Then, each point was verified through photo-interpretation using the orthophoto AGEA 2017 (spatial resolution of 20 cm) as a ground truth image and, only in doubtful cases, a direct GPS field survey. MLC and SVM supervised classifications were applied to both types of images and an error matrix was computed using the same reference points (ground truth) to evaluate the classification accuracy of different LULC classes. The contribution of S2′s red-edge bands in improving classifications was also verified. Definitively, ML classifications show better performance than SVM, and Landsat data provide higher accuracy than Sentinel-2. Full article

(This article belongs to the Special Issue A GIS Spatial Analysis Model for Land Use Change (Volume II))

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22 pages, 4744 KiB

Open AccessArticle

Hydrological Responses to Land Use/Land Cover Changes in Koga Watershed, Upper Blue Nile, Ethiopia

by Habitamu Alesew Ayele, Alemu O. Aga, Liuelsegad Belayneh and Tilahun Wankie Wanjala

Geographies 2023, 3(1), 60-81; https://doi.org/10.3390/geographies3010004 - 10 Jan 2023

Cited by 5 | Viewed by 2841

Abstract

Information on land use and land cover modification and their related problems for the streamflow and sediment yield are crucial for spatial planners and stakeholders to devise suitable catchment resources management plans and strategies. This research sought to assess the changes in land use and land cover (LULC) effects on the streamflow and sediment yield of the Koga watershed. Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI data were used to create the land use and land cover maps. The LULC type identification analysis was performed by using ERDAS Imagine 2015. After the supervised classification, the land use and land cover maps for three distinct years (1991, 2008, and 2018) were generated, and the accuracy of the maps was reviewed. The LULC change analysis results were pointed out, as there was an appreciable LULC change in the study watershed. Agricultural land increased by 14.21% over the research period, whereas grassland decreased by 22.91%. The other LULC classes (built-up area, forest area, water body, and wetland) increased by 0.39%, 6.36%, 4.30%, and 0.46%, respectively. Contrarily, bushland decreased by 2.80%. Human activities were decisive in the significant land use alterations within the catchment. The flow rate of the river basin increased over the rainy season in the years 1991–2008 and declined in the drier months. The watershed’s sediment yield increased from 1991 to 2008 as a result of the extension of its agricultural area. Thus, the findings of this investigation demonstrated that the flow and sediment yield characteristics are changed because of the modifications within the LULC in the catchment. Some downstream and upstream parts of the area are exposed to comparatively high erosion, and the maximum amount of sediment is generated during the rainy season. Full article

(This article belongs to the Special Issue A GIS Spatial Analysis Model for Land Use Change (Volume II))

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23 pages, 15706 KiB

Open AccessArticle

Land Suitability Evaluation of Tea (Camellia sinensis L.) Plantation in Kallar Watershed of Nilgiri Bioreserve, India

by S. Abdul Rahaman and S. Aruchamy

Geographies 2022, 2(4), 701-723; https://doi.org/10.3390/geographies2040043 - 11 Nov 2022

Cited by 3 | Viewed by 3341

Abstract

Nilgiri tea is a vital perennial beverage variety and is in high demand in global markets due to its quality and medicinal value. In recent years, the cultivation of tea plantations has decreased due to the extreme climate and prolonged practice of tea cultivation in the same area, decreasing its taste and quality. In this scenario, land suitability analysis is the best approach to evaluate the bio-physiochemical and ecological parameters of tea plantations. The present study aims to identify and delineate appropriate land best suited for the cultivation of tea within the Kallar watershed using the geographic information system (GIS) and multi-criteria evaluation (MCE) techniques. This study utilises various suitability criteria, such as soil (texture, hydrogen ion concentration, electrical conductivity, depth, base saturation, and drainability), climate (rainfall and temperature), topography (relief and slope), land use, and the normalised difference vegetation index (NDVI), to evaluate the suitability of the land for growing tea plantations based on the Food and Agricultural Organization (FAO) guidelines for rainfed agriculture. The resultant layers were classified into five suitability classes, including high (S1), moderate (S2), and marginal (S3) classes, which occupied 16.7%, 7.08%, and 16.3% of the land, whereas the currently and permanently not suitable (N1 and N2) classes covered about 18.52% and 29.06% of the total geographic area. This study provides sufficient insights to decision-makers and farmers to support them in making more practical and scientific decisions regarding the cultivation of tea plantations that will result in the increased production of quality tea, and prevent and protect human life from harmful diseases. Full article

(This article belongs to the Special Issue A GIS Spatial Analysis Model for Land Use Change (Volume II))

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10 pages, 1923 KiB

Open AccessArticle

Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data

by Gurwinder Singh, Sartajvir Singh, Ganesh Sethi and Vishakha Sood

Geographies 2022, 2(4), 691-700; https://doi.org/10.3390/geographies2040042 - 11 Nov 2022

Cited by 16 | Viewed by 4323

Abstract

Continuous observation and management of agriculture are essential to estimate crop yield and crop failure. Remote sensing is cost-effective, as well as being an efficient solution to monitor agriculture on a larger scale. With high-resolution satellite datasets, the monitoring and mapping of agricultural land are easier and more effective. Nowadays, the applicability of deep learning is continuously increasing in numerous scientific domains due to the availability of high-end computing facilities. In this study, deep learning (U-Net) has been implemented in the mapping of different agricultural land use types over a part of Punjab, India, using the Sentinel-2 data. As a comparative analysis, a well-known machine learning random forest (RF) has been tested. To assess the agricultural land, the major winter season crop types, i.e., wheat, berseem, mustard, and other vegetation have been considered. In the experimental outcomes, the U-Net deep learning and RF classifiers achieved 97.8% (kappa value: 0.9691) and 96.2% (Kappa value: 0.9469), respectively. Since little information exists on the vegetation cultivated by smallholders in the region, this study is particularly helpful in the assessment of the mustard (Brassica nigra), and berseem (Trifolium alexandrinum) acreage in the region. Deep learning on remote sensing data allows the object-level detection of the earth’s surface imagery. Full article

(This article belongs to the Special Issue A GIS Spatial Analysis Model for Land Use Change (Volume II))

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13 pages, 1691 KiB

Open AccessArticle

Land Use and Water-Quality Joint Dynamics of the Córrego da Formiga, Brazilian Cerrado Headwaters

by Pedro Rogerio Giongo, Ana Paula Aparecida de Oliveira Assis, Marcos Vinícius da Silva, Abelardo Antônio de Assunção Montenegro, José Henrique da Silva Taveira, Adriana Rodolfo da Costa, Patrícia Costa Silva, Angelina Maria Marcomini Giongo, Héliton Pandorfi, Alessandro José Marques Santos, Clarice Backes, Maria Beatriz Ferreira and Jhon Lennon Bezerra da Silva

Geographies 2022, 2(4), 629-641; https://doi.org/10.3390/geographies2040038 - 19 Oct 2022

Cited by 2 | Viewed by 1731

Abstract

The Brazilian Cerrado biome provides relevant ecosystem services for Brazil and South America, being strategic for the planning and management of water resources as well as for agribusiness. The objective was to evaluate the water quality along the course of the Córrego da Formiga in a virgin portion of the Brazilian Cerrado, the relationship of land use with physical-chemical and biological parameters of the water, and the inflow of the tributary. Five water collection points were defined (between the source and mouth) and observed on a quarterly scale in 2015, water samples were collected and analyzed for physical-chemical and biological parameters in the laboratory, and flow measurements were performed at the same point and day of water collection. To identify and quantify land use and land cover (LULC) in the watershed, an image from the Landsat8-OLI satellite was obtained, and other geomorphological data from hypsometry (Topodata-INPE) were obtained to generate the slope, basin delimitation, and contribution area for each water collection point. The LULC percentages for each area of contribution to the water collection points were correlated with the physical-chemical and biological parameters of the water and submitted to multivariate analysis (PLS-DA) for analysis and grouping among the five analyzed points. Changes in water-quality patterns were more pronounced concerning the time when the first and last sampling was performed (rainy period) and may be influenced by the increase in the volume of water in these periods. The stream flow is highly variable over time and between points, with the lowest recorded flow being 0.1 L s⁻¹ (P1) and the highest being 947.80 L s⁻¹ (P5). Córrego da Formiga has class III water quality (CONAMA resolution 357), which characterizes small restrictions on the use of water for multiple uses. The soil cover with native vegetation is just over 12%, while the predominance was of the classes of sugar cane (62.42%) and pasture (19.33%). The PLS-DA analysis allowed separating the water analysis points between P1, P2, P3, and P5, while P4 was superimposed on others. It was also possible to verify that the parameters that weighed the most for this separation of water quality were pH, alkalinity_T, alkalinity_h, calcium, and hardness, all with a tendency to increase concentration from the source (P1) to the mouth (P5). As for water quality, it was also possible to verify that points P2 and P5 presented better water-quality conditions. Full article

(This article belongs to the Special Issue A GIS Spatial Analysis Model for Land Use Change (Volume II))

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16 pages, 4487 KiB

Open AccessArticle

Techniques of Geoprocessing via Cloud in Google Earth Engine Applied to Vegetation Cover and Land Use and Occupation in the Brazilian Semiarid Region

by Jhon Lennon Bezerra da Silva, Daiana Caroline Refati, Ricardo da Cunha Correia Lima, Ailton Alves de Carvalho, Maria Beatriz Ferreira, Héliton Pandorfi and Marcos Vinícius da Silva

Geographies 2022, 2(4), 593-608; https://doi.org/10.3390/geographies2040036 - 2 Oct 2022

Cited by 3 | Viewed by 2331

Abstract

Thematic maps of land cover and use can assist in the environmental monitoring of semiarid regions, mainly due to the advent of climate change, such as drought, and pressures from anthropic activities, such as the advance of urban areas. The use of geotechnologies is key for its effectiveness and low operating cost. The objective was to evaluate and understand the spatiotemporal dynamics of the loss and gain of land cover and use in a region of the Brazilian semiarid region, and identify annual trends from changing conditions over 36 years (1985 to 2020), using cloud remote sensing techniques in Google Earth Engine (GEE). Thematic maps of land cover and land use from MapBiomas Brazil were used, evaluated by Mann–Kendall trend analysis. The Normalized Difference Vegetation Index (NDVI) was also determined from the digital processing of about 800 orbital images (1985 to 2020) from the Landsat series of satellites. The trend analysis for land cover and use detected, over time, the loss of forest areas and water bodies, followed by the advance of exposed soil areas and urban infrastructure. The modification of native vegetation directly influences water availability, and agricultural activities increase the pressure on water resources, mainly in periods of severe drought. The NDVI detected that the period from 2013 to 2020 was most affected by climatic variability conditions, with extremely low average values. Thematic maps of land cover and use and biophysical indices are essential indicators to mitigate environmental impacts in the Brazilian semiarid region. Full article

(This article belongs to the Special Issue A GIS Spatial Analysis Model for Land Use Change (Volume II))

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22 pages, 4660 KiB

Open AccessArticle

Toward Sustainable Urban Drainage Planning? Geospatial Assessment of Urban Vegetation Density under Socioeconomic Factors for Quito, Ecuador

by René Ulloa-Espíndola, Elisa Lalama-Noboa and Jenny Cuyo-Cuyo

Geographies 2022, 2(3), 397-418; https://doi.org/10.3390/geographies2030025 - 7 Jul 2022

Cited by 1 | Viewed by 2588

Abstract

Natural or anthropogenic urban vegetation is an important resource for urban planning, risk assessment, and sustainable development of a city. Quito is a megadiverse city due to its location and topography, but the socioeconomic diversity generates more contrasting conditions of certain behaviors and habits related to urban infrastructure. The contrasts of vegetation and green spaces in the different sectors of Quito also reflect the diversity of the city. This study examines the effects of socioeconomic conditions on the loss or increase of urban vegetation. The exploratory regression method (spatial) and logit model (non-spatial) were used to explain the socioeconomic effects on urban vegetation density at the level of urban parishes. On the one hand, the Normalized Difference Vegetation Index (NDVI) was calculated as the dependent variable based on the 2021 sentinel images. On the other hand, the independent variables were structured based on the socioeconomic level, the land valuation areas of Quito (AIVAS), and the quality of life index. This article contributes to establishing baseline information that helps structure the conditions, strategies, and investments to design and implement plans and programs for urban drainage, ecosystem benefits, and sustainable development in the city of Quito. Full article

(This article belongs to the Special Issue A GIS Spatial Analysis Model for Land Use Change (Volume II))

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16 pages, 24756 KiB

Open AccessArticle

Constraint-Based Spatial Data Management for Cartographic Representation at Different Scales

by Natalia Blana and Lysandros Tsoulos

Geographies 2022, 2(2), 258-273; https://doi.org/10.3390/geographies2020018 - 24 May 2022

Cited by 2 | Viewed by 1950

Abstract

This article elaborates on map-quality evaluation and assessment as a result of the generalization of geospatial data through the development of a methodology, which incorporates a quality data model including constraints. These constraints are used to guide the generalization process and they operate as requirements in quality controls applied for the quality evaluation and assessment of the resulting cartographic data. The quality model stores the required map specifications compiled as constraints, and provides quality measures along with new techniques for the evaluation and assessment of cartographic data quality. This secures the map composition process in each and every step and for all features involved, at any map scale. The methodology developed results in the creation of a scale-dependent cartographic database that contains exclusively the features to be portrayed on the map, generalized properly according to the map scale. It will reduce cartographers’ need to review each transformation throughout the map-composition process with considerable savings in time and money and, on the other hand, it will secure the quality of the final map. The formulation of the proposed methodology amalgamates generalization theory with the authors’ research in computer-assisted cartography, taking into account the work conducted on the topic by other researchers. In this study, the quality requirements, the measures and the associated techniques together with the results of the application of the proposed methodology for area and line features are described in detail to allow others to replicate and build on the presented results. Full article

(This article belongs to the Special Issue A GIS Spatial Analysis Model for Land Use Change (Volume II))

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