Expansion Of Built-up Areas Research Articles

Water Balance Assessment, Land Use Land Cover Change and Increasing Water Demand in Three Major Watersheds in Jember, East Java, Indonesia

Jember Regency is the third largest agricultural area in East Java Province. Expansion of built-up and agricultural areas occurred and significantly changed the landscape. Moreover, increasing both types of land cover will increase water consumption. Therefore, there may be changes in the condition of the water balance in the Jember Regency area. This area has three major watersheds: Bedadung, Mayang, and Tanggul. This study evaluated the water balance condition at the three major watersheds in Jember, East Java, Indonesia. The WEAP (Water Evaluation and Planning) model calculated the water balance. Research procedures include (1) inventory of spatial and non-spatial data; (2) setup of the WEAP scheme; (3) Calibration; (4) Validation; and (5) Simulation of water balance model in scenario increasing water demand and land-use change). From 1997 to 2020, the water balance was evaluated using a 10-day interval. The results show that from the November 1st to August 2nd periods, the watershed can supply all demand sites with 100% coverage. However, from August 2nd t to October 3rd periods, the watershed’s ability to supply the water needs decreased. This deficit is due mainly to the uneven rainfall distribution between rainy and dry seasons. Variation in annual rainfall received from 1997 to 2020 also affects the water balance’s surplus and deficit. The fluctuation of land-use change contributed to the surplus and shortage of water balance.
 
 Keywords : Supply; Demand; Water Balance; WEAP; Jember; East Java
 Copyright (c) 2023 Geosfera Indonesia and Department of Geography Education, University of Jember
 This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Changes in the Patterns of Population Distribution and Built-Up Areas of the Rural–Urban Fringe in Post-Socialist Context—A Central European Case Study

The rapid and significant expansion of urban areas is observed worldwide; however, considerable differences are detected within the characteristics of the process. The rural–urban fringe is changing most dynamically from the aspect of land use and this tends to be relevant in the case of post-socialist cities in Central Europe even with a stagnating or decreasing population. Debrecen (Hungary) and its hinterland adequately represent the migration trends of Hungarian cities and the great administrative area provided wide intra-urban suburbanization processes. The current study put the emphasis on the analysis of the spatial pattern of built-up areas and the distribution of residents. In order to discover the processes of the post-socialist transition period, detailed point layers were created to illustrate every built-up parcel in the rural–urban fringe of Debrecen (for the years 1980, 2000, and 2020). The most important characteristics were discovered with the help of GIS methods—Kernel-density, grid pattern analysis of the object density, and analysis of land cover/land use changes using Corine Land Cover Change (CLCC) databases. The dynamic and extended expansion of built-up areas was seen until 2000, in which the outskirts (including hobby gardens) densified spectacularly. The urban sprawl has been less intensive since the millennium and the increase in built-up areas has become more concentrated. As a consequence of the transition period, extended territories—primarily the least dense parts of the rural–urban fringe—are faced with the disappearance of buildings due to agricultural cultivation reasons.

New Insights into Urbanization Based on Global Mapping and Analysis of Human Settlements in the Rural–Urban Continuum

The clear boundary between urban and rural areas is gradually disappearing, and urban and rural areas are two poles of a gradient with many continuous human settlements in between, which is a concept known as the rural–urban continuum. Little is known about the distribution and change trajectories of the various types in the rural–urban continuum across the globe. Therefore, using global land-cover data (FROM-GLC Plus) and global population data (Worldpop) based on the decision-making tree method, this study proposed a method and classification system for global rural–urban continuum mapping and produced the mapping results on a global scale in the Google Earth Engine platform. With the expansion of built-up areas and the increase in population, the global human settlements follow the pattern that develops from wildland to villages (isolated—sparse—dense), and then to towns (sparse—dense), and finally to urban areas (edge—center). From a regional perspective, there are some obvious differences: Africa is dominated by sparse villages; Asia has the highest proportion of densely clustered towns; the proportion of dense villages in Europe is high. Rural–urban continuum mapping and analysis provide a database and new insights into urbanization and differences between urban and rural areas around the world.

Spatiotemporal patterns and influencing factors of urban ecological space availability in coastal cities of China during rapid urbanization

Urban ecological space (UES) is crucial to the sustainable development of cities and the improvement of human well-being. Rapid urbanization tends to lead to the fragmentation of urban ecological space and functional decline. China's coastal cities are developed regions with high urbanization levels and the unique geographical location makes them have two kinds of ecological spaces, namely marine ecological spaces (MES) and terrestrial ecological spaces (TES). During rapid urbanization, the spatiotemporal changes of the urban ecological space availability (UESA) and its driving factors in China's coastal cities are still unclear. For this, this work selected 53 coastal cities in China as the research samples and applied remote sensing monitoring data of land use to analyze the spatiotemporal patterns of the UESA under different travel modes (walking, public transport, and driving) from 1990 to 2015. Then, the spatial econometric model was applied to reveal the impact of urbanization on urban ecological space availability (UESA). The main research results show: (1) generally, the marine ecological spaces availability (MESA), terrestrial ecological spaces availability (TESA), and marine and terrestrial ecological spaces availability (MTESA) of the coastal cities in China showed an increasing trend from 1990 to 2015. With the increase in travel distance, the MESA, TESA, and MTESA in the coastal cities increased accordingly. (2) however, the spatial pattern of MESA in the coastal cities showed a significant heterogeneity, The hot spots of the MESA mostly appeared in coastal cities from Zhejiang to Fujian, and the cold spots were concentrated in the Liaoning, Hebei, and Shandong. (3) The TESA and MTESA in southern coastal cities were generally higher than that in northern coastal cities. (4) The expansion of the urban built-up area (UBUA) led to the improvement of the UESA in China's coastal cities. GDP and population density were positive factors affecting the TESA and MTESA. Our research can provide scientific planning basis for coordinating marine and terrestrial ecological space and optimizing territorial space planning, which is conducive to improving the life quality of residents and promoting sustainable urban development.

Correlation Analysis Between Urban Heat Island Intensity and Temperature Criticality Value in Denpasar City

The compactness of buildings in Denpasar resulted in the formation of urban heat islands (UHI), which can be evaluated through the Urban Thermal Field Variance Index (UTFVI) and Environment Criticality Index (ECI). ECI is the ratio of land surface temperature to the Normalized Difference Vegetation Index (NDVI). It can be transformed into Temperature Criticality Value (TCV) using air temperature and Index-based Built-up Index (IBI). This study aims to identify the UHI intensity, the impact of land cover changes, and its association with the TCV. The study employs Landsat 8 imagery and field measurements data, and the findings demonstrate that the study area was mainly composed of built-up areas that had grown from 2015 to 2021. TFVI indicates the most intense UHI (>0.02) in the built-up areas, whereas the mean value of NDVI suggested a reduction in vegetation density. The density of built-up areas (IBI) had increased, while vegetation had decreased. TCV in 2015 ranged from -11.15°C.IBI to 6.42°C.IBI; 2018 between -9.96°C.IBI to 6.79°C.IBI; and 2021 between -10.84°C.IBI to 6.87°C.IBI showed that the environment had become increasingly critical from 2015 to 2021. A transect analysis revealed that more vigorous UHI intensity, denser buildings, and a more critical environment were present in urban centers compared to the suburbs. The correlation coefficient (r) between TCV and UTFVI was relatively robust (0.75–0.82), indicating that the growth of UHI intensity was associated with a more critical environment. TCV has the strongest (r=0.99) and strong correlation (r>0,80) with Built-up Index but inverse correlation with NDVI. Therefore, limiting the expansion of built-up areas and increasing vegetation could help to control the environment's criticality.

Future prediction of scenario based land use land cover (LU&LC) using DynaCLUE model for a river basin

Human activities that cause changes to the surface of the Earth lead to alterations in Land Use and Land Cover (LU&LC) which have an impact on biodiversity, ecosystem functioning, and the well-being of humans. In order to comprehend and manage the effects of human activities on the environment, prediction of scenario-based LU&LC in future periods are crucial. Scenario-based predictions of LU&LC provide valuable insights for decision-makers in the sustainable governance of land and water resources. In the present study, the Dynamic Conversion of Land Use and its Effects (DynaCLUE) modelling platform was used to predict future LU&LC for Munneru river basin, India. Using six different user defined scenarios LU&LC change patterns were analyzed in 2030, 2050 and 2080 so as to understand the pressure on the natural resources and to plan sustainable Land Use Planning by preserving the important land use classes. The connection between LU&LC classes and input driving factors was quantified using Binary Logistic Regression (BLR) analysis. The β-coefficient was estimated using LU&LC type as a dependent variable and driving factors as independent variables. The demands of each LU&LC type, spatial policies and constraints, characteristics of each location and land use conversions are used as inputs for prediction of future LU&LC maps. Major conversions in LU&LC observed in this basin from last two decades are the rapid increase in built-up area due to urbanization in the outskirts of cities and towns. The major LU&LC changes projected for the period of 2019–2080 are expansion of built-up area ranging from 42.5% to 88.5%, and a reduction in barren land ranging from 57.3% to 74.5% across all six scenarios in the entire basin. The projected LU&LC maps under different scenarios provide valuable insights that could aid local communities, government agencies, and stakeholders in systematically allocating resources at the local level and in preparing the policies for long-term benefits.

Land Use and Cover Change Detection in Shankharapur Municipality, Kathmandu Using Spectral Indices

The surging increase of the urban population has been accompanied by a sharp increase in urban built-up areas. The growth of population contributing to rapid expansion of built-up area in recent decades has caused a substantial Land Use Land Cover (LULC) change across Nepal and in particular Kathmandu Valley (KV). In this study, a Normalized Difference Vegetative Index (NDVI) and Normalized Difference Built-up Index (NDBI) was applied to three Landsat imagery collected over time (2002, 2013, and 2022) and one Sentinel-2 imagery that provided recent and historical LULC conditions for, Shankharapur municipality that lies in the eastern part of Kathmandu. The three-land use land cover categories were identified and mapped from the value of NDBI and NDBI. We found that over a period of 20 years (from 2002 to 2022), the Shankharapur municipality has lost 14.64% and 25.97% of its forests and sparse vegetation, and increase in the settlement/open land by 5.48% and 226.73% as indicated by NDVI and NDBI for Landsat imagery respectively. The increase in settlement/open land can be summed to the augmented activities like constructing new building and increase forest and land defragmentation including construction of road and land planning to fulfill the demand of influx of people after the earthquake of 2015. The results of NDVI and NDBI from Sentinel-2 imagery also support the increase in settlement/open land and decrease in forest and sparse vegetation. Also, agriculture cover increased by 4.25 sq.km between 2002 to 2013 and increased by 4.85 sq.km. from 2013 to 2022 as indicated by NDVI derived from Landsat imagery. However, the significant amounts of losses of forest and sparse vegetation during 20 years have been absorbed by the expanding urbanized areas and agriculture land as more land is subjected to the built-up and land planning along with the wood-logging as a result of aftermath of the earthquake, 2015, where population has increased by 19.4 % in the span of 10 years from 2011 to 2021 and is never retreating in terms of changing land cover. Therefore, such trends if unchecked can result in loss of biodiversity and ecosystem services associated with deteriorating conditions for human well-being.

The Impact of the Trans-Java Toll Road Development on Spatial Planning in the Northern Region of Java Island: A Study Utilizing NDBI and Google Earth Images

The primary objective of toll roads is to stimulate economic growth. As the pivotal road network on Java Island, the Trans-Java Toll Road has a significant impact on regional development and spatial planning. However, there has been a lack of research focusing on the specific influence of this toll road on spatial planning. Therefore, this study aims to comprehensively examine the spatial and temporal implications of the Trans-Java Toll Road. To achieve this goal, the Normalized Difference Built-Up Index (NDBI) method is employed using satellite imagery. This method is widely recognized for its effectiveness in detecting changes in built-up areas through remote sensing techniques. The calculated NDBI values reveal substantial expansion of built-up areas in the cities and regencies traversed by the Trans-Java Toll Road. Notably, two specific areas with the highest rates of change are investigated: Gresik Regency and Bekasi Regency, where the built-up areas have experienced remarkable growth of over 186.9% and 61.8% respectively over a five-year period. To validate these findings, Google Earth imagery is utilized for two different years, namely 2016 and 2021. The observed changes in these regions are predominantly attributed to the economic growth facilitated by the establishment of industrial areas. Consequently, the expansion of industrial zones and settlements has resulted in a notable reduction in wetland areas and a simultaneous increase in built-up areas.

Analysis of the Coordination of Built-Up Area Expansion and Population Growth in the Shandong Peninsula Urban Agglomeration

Exploring the coordinated relationship between built-up area expansion and population growth in the urbanization process is important for the planning and sustainable development of cities. The article took Shandong Peninsula urban agglomeration as the study area, which was divided into the Provincial Capital Economic Circle, Jiaodong Economic Circle, and Lunan Economic Circle according to the plan named the Implementation Plan for Implementing the Opinions of the CPC Central Committee and the State Council on Establishing a New Mechanism for More Effective Regional Co-ordinated Development. The people–land coordination relationship in the region was analyzed from the perspective of spatio-temporal integration through indices such as fractal dimension, spatial autocorrelation, population sprawl, and city land expansion and population growth coordination index (CPI) The results showed that (1) the relationship between built-up area expansion and population growth in the Shandong Peninsula urban agglomeration was uncoordinated. The three economic circles mainly changed from rapid population growth to significant expansion of built-up areas. (2) The development patterns of the three economic circles were different. The Provincial Capital and Jiaodong showed a pattern with Jinan and Qingdao as the core, driving the periphery area. Lunan showed a multi-point scattered development; there was no central city, but Linyi City was developing faster. (3) Jiaodong had the biggest changes in terms of population and built-up area. This study provides a scientific reference for the urban planning and sustainable development of the Shandong Peninsula urban agglomeration.

The prevalence of anthropogenic nest materials differs between two distinct populations of migratory birds in Europe

A major impact of human development is the transformation of natural habitats into farming lands and the expansion of built-up areas. Also, plastic pollution is affecting wildlife on a global scale. Discarded plastic is ubiquitous and accessible for birds, which can incorporate them into the nest structure. Here, we describe the differences in type, prevalence, and the amount of anthropogenic nest materials between two populations of terrestrial, mainly farmland bird, the white stork Ciconia ciconia, on a broad geographical scale, from two migratory divides—eastern in Poland and western in Spain (in total 303 nests). In the two populations, we detected significant differences in the incorporation of anthropogenic nest material, as measured by the Human Footprint Index (HFI) and the Impervious Surface Areas (ISA). We found that ISA was positively related to anthropogenic nest material incorporation in the Spanish population, and HFI was positively related to anthropogenic nest material, in contrast to the Polish population, in which the relationships were not significant. Moreover, we showed that the prevalence of nests with anthropogenic nest material was two times higher in Spanish than in the Polish white stork population. This study demonstrates that the behavior of incorporation of the anthropogenic nest material differs between two distinct populations of a single bird species.

Contribution of anthropogenic and hydroclimatic factors on the variation of surface water extent across the contiguous United States

Human pressure and climate variability are significantly threatening freshwater resources, with cascading effects on societies and ecosystems. In this context, it is crucial to understand the anthropogenic and climatic impacts on surface water dynamics. Here, we examine the interaction between the variation of surface water extent and the change in five potential concurrent drivers across river basins of the contiguous United States (CONUS) during the period 1984–2020. In particular, built-up area, population, and irrigated land are regarded as the anthropogenic drivers, while hydroclimatic drivers are represented by precipitation and potential evapotranspiration (PET). We perform statistical analyses in order to quantify the change in the considered variables and then identify significantly different spatial patterns and possible interrelations. Results show that almost 79% (169 out of 204 river basins) of the CONUS experienced an expansion of surface water extent mainly in the continental and temperate climatic regions (mean expansion 158.33 km2). Increasing precipitation is found to be the most widespread driver of the gain in surface water extent, affecting nearly 70% of river basins. The remaining 35 river basins of the CONUS, mostly located in the arid southwestern region of the country, faced a reduction in surface water extent (mean reduction −146.73 km2). The expansion of built-up areas and increasing PET resulted to contribute to the loss of surface water in all the river basins, followed by population growth (in ∼75% of the river basins), decreasing precipitation (in ∼60% of the river basins, all situated in southwestern US), and irrigated land expansion (in ∼55% of the river basins). Our findings shed light on the potential impacts of the variability of anthropogenic and hydroclimatic factors on hydrology and surface water resources, which could support predictive adaptation strategies that ensure water conservation.

Spatial and Temporal Evolution of Urban Patterns in the China Greater Bay Area Based on Remote Sensing of Nighttime Lights

With the advancement of urbanization in China, the rapid expansion of cities has reduced the alternative land use and intensified the conflict between people and land, and the adaptation and optimization of urban spatial patterns have become an important work in current urban research. Based on the comprehensive modified the long-term series DMSP-OLS and NPP-VIIRS nighttime lighting data, the spatial analysis method of GIS and urban expansion indicators are used to reveal the spatial and temporal change process of urban built-up areas in the center of gravity of Guangdong-Hong Kong-Macao Greater Bay Area from 1992 to 2020. The results reveal that the built-up area of the urban agglomerations in the study area generally shows an increasing trend year by year, but there are some periods when the area decreases or remains unchanged, while the expansion rate shows a gradual decrease over a long time scale. The compactness of the urban agglomerations in the GBA decreases over time, and the fractal dimension fluctuates but remains unchanged, while the spatial distribution of the urban agglomerations in the GBA shows a "southwest-northeast" direction during 1992-2020, with the center of gravity moving to the northeast gradually. On the side, the urban expansion shows a fluctuation of "diffusion-coalescence-diffusion". The expansion of urban built-up areas is influenced by both natural and socio-economic factors, and the degree of influence varies in different periods.

Assessment of Ecosystem Service Value in Response to LULC Changes Using Geospatial Techniques: A Case Study in the Merbil Wetland of the Brahmaputra Valley, Assam, India

The alteration of land use and land cover caused by human activities on a global scale has had a notable impact on ecosystem services at regional and global levels, which are crucial for the survival and welfare of human beings. Merbil, a small freshwater wetland located in the Brahmaputra basin in Assam, India, is not exempt from this phenomenon. In the present study, we have estimated and shown a spatio-temporal variation of ecosystem service values in response to land use and land cover alteration for the years 1990, 2000, 2010, and 2021, and predicted the same for 2030 and 2040. Supervised classification and the CA-Markov model were used in this study for land-use and land-cover classification and future projection, respectively. The result showed a significant increase in built-up areas, agricultural land, and aquatic plants and a decrease in open water and vegetation during 1990–2040. The study area experienced a substantial rise in ecosystem service values during the observed period (1990–2021) due to the rapid expansion of built-up areas and agricultural and aquatic land. Although the rise of built-up and agricultural land is economically profitable and has increased the study site’s overall ecosystem service values, decreasing the area under open water and vegetation cover may have led to an ecological imbalance in the study site. Hence, we suggest that protecting the natural ecosystem should be a priority in future land-use planning. The study will aid in developing natural resource sustainability management plans and provide useful guidelines for preserving the local ecological balance in small wetlands over the short to medium term.

Spatiotemporal Change Detection Analysis of Land Cover and Land Use using Multi-Temporal Remote Sensed Imagery and Landscape Metrics: A Sub-Catchment Level Case Study of the Bhagirathi-Hugli River, West Bengal, India

To analyse and characterise land use-land cover (LULC) change, remote sensing (RS) data and geographic information system (GIS) approaches are required. The landscape metrics are also extensively used to explain the structure and pattern of the spatial landscape. This case study has been conducted in the area of the Bhagirathi-Hugli River banks in West Bengal. The study area’s satellite image data from various periods (1989-2017) were used to analyse the spatiotemporal variation of LULC change. The LULC fragmentation was analysed using a total of six landscape measures. Landscape metrics such as Number of Patches (NP), Largest Patch Index (LPI), Landscape Shape Index (LSI), Mean Shape Index (MSI), Contagion (CONTAG), and Shannon’s Diversity Index were used to determine the changes over four different periods. The main features of land use change were the continuous expansion of built-up area (9.30 km2), vegetation (189.14 km2), and the decrease in agricultural fallow land and water bodies from 1989 to 2017. The landscape pattern in the study area has also changed significantly. The landscape metrics indicate that the studied region had a highly fragmented landscape during the study period. The proposed technique is easy to use and can address more than one metric while selecting the appropriate setting can prove problematic.

Integrated assessment of the impact of land use changes on groundwater recharge and groundwater level in the Drava floodplain, Hungary

Land-use/land-cover (LULC) change is considered a key human factor influencing groundwater recharge in floodplains. Without accurate estimations, the impact of LULC change on water balance components may be either significantly understated or exaggerated. This paper assesses the impacts of LULC changes from 1990 to 2018 on water balance components and groundwater levels of the Drava floodplain, Hungary, where human interference has led to a critical environmental situation. In this study, a spatially-distributed water balance model (WetSpass-M), and a groundwater flow model (MODFLOW-NWT) were integrated to assess the impacts of LULC changes. The moderate expansion of built-up areas increased surface runoff, while the afforestation of arable land and meadows and the overgrowth of bare mudflats with willow shrubs increased evapotranspiration. As a consequence, total annual groundwater recharge decreased by 5.3 × 107 m3 in the floodplain with an average of 335 mm year−1 and 317 mm year−1 in 2012 and 2018, respectively. Moreover, an average groundwater level decline by 0.1 m is observed in the same period. Declined groundwater recharge, increased runoff, and evapotranspiration exerted a negative effect on water resources in the Drava basin. The approach tested in this paper allows temporal and spatial estimation of hydrological components under the changes of LULC, providing quantitative information for decision-makers and stakeholders to implement efficient and sustainable management of water resources in the Drava floodplain. The provided integrated model is also applicable to regionally.

Monitoring and assessment of urban green space loss and fragmentation using remote sensing data in the four cities of Malawi from 1986 to 2021

Urban green spaces enhance cities' natural beauty, boost biodiversity, and promote the health and happiness of city residents, among other benefits. Despite the benefits of urban green spaces, their spatial configuration and composition are impacted by urban expansion. This study aims to investigate the spatial-temporal fragmentation trends of urban green spaces in four cities in Malawi (Blantyre, Zomba, Lilongwe, and Mzuzu). We, therefore, applied multi-temporal Landsat images to understand trends in urban green spaces from 1986 to 2021. Five landscape metrics, namely: percentage of land area (PLAND), number of patches (NP), patch density, largest patch index (LPI), landscape shape index (LSI), and mean patch size (MPS), were used to quantify the degree of urban green space fragmentation in Fragstats software. The results showed an increase in the NP, PD, and LSI and a decline in the LPI and MPA in the four cities. This is an indication of fragmentation and a reduction in green space. Rapid urbanisation has resulted in the loss and fragmentation of urban green space areas, which has been attributed to the rapid expansion of built-up areas at the expense of green spaces. The present study is the first to incorporate landscape metrics to understand the spatial heterogeneity of the composition and configuration of green spaces in all the cities of Malawi. The findings of this study contribute to an understanding of how urban growth impacts fragmentation and the loss of urban green spaces and offer valuable insights for better spatial planning and management of cities.

Spatial prediction of the urban inter-annual land surface temperature variability: An integrated modeling approach in a rapidly urbanizing semi-arid region

The inter-annual land surface temperature (LST) is a meteorological indicator of urban environments, affecting energy consumption and quality of life. In this study, the annual LST variability (ALSTV) of a rapidly urbanizing region in Iran including Karaj, Shahriar and Mohammad-Shahr cities and their surrounding suburbs was estimated using Landsat-8 images. Pixel-based image classification and object-based segmentation techniques were employed to extract built-up patches and spectrally homogeneous regions. Using the multiple regression analysis, the ALSTV of built-up patches was modeled as a function of their structure and the spatial characteristics of other land-use patches located in their respective segments (as neighborhood factors). A Cellular Automata model was utilized to simulate the expansion of built-up area up to 5% and, therefore, estimate their future ALSTV. The mean ALSTV was highest in urban areas (33.25± 7.41 °C), while the lowest mean value of 24.12± 3.24 °C was in green covers. The ALSTV of built-up patches were positively associated with their area and the percentage and size of neighborhood built-up patches, while negatively with the percentage of neighborhood green patches. To create a thermally comfortable landscape, additional green patch allocation and preventing excessive urban patch growth are required in all large built-up segments.

Impact Assessment of Urban Built-up Area on Groundwater Level of District Faisalabad, Pakistan

This paper attempts to explore the impact of built-up areas on groundwater levels in district Faisalabad. To understand the rate of built-up area expansion and changes in the level of groundwater in the study area, groundwater data has been acquired from the Land Reclamation Department for all tehsils in district Faisalabad. This study was conducted to assess spatial and temporal variation of groundwater level because of the built-up area change. Descriptive statistics (Scatter Plot correlation technique) have been applied to figure out changes in groundwater levels. Furthermore, some built-up area extraction methods have been incorporated that are always found to be effective tools for the assessment of built-up area change. The spatial analysis tool Spline has applied to 79 bore points in all tehsils of district Faisalabad. Results clearly show that a built-up increase of 41 km2 in Faisalabad city is causing 1-foot depletion in groundwater as compared to other tehsils, where a built-up increase is negligible. Such research studies of groundwater changes would assist the planners to adopt effective sustainable measures.

Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia

Land use/land cover change evaluation and prediction using spatiotemporal data are crucial for environmental monitoring and better planning and management of land use. The main objective of this study is to evaluate land use/land cover changes for the time period of 1991–2022 and predict future changes using the CA-ANN model in the Upper Omo–Gibe River basin. Landsat-5 TM for 1991, 1997, and 2004, Landsat-7 ETM+ for 2010, and Landsat-8 (OLI) for 2016 and 2022 were downloaded from the USGS Earth Explorer Data Center. A random forest machine learning algorithm was employed for LULC classification. The LULC classification result was evaluated using an accuracy assessment technique to assure the correctness of the classification method employing the kappa coefficient. Kappa coefficient values of the classification indicate that there was strong agreement between the classified and reference data. Using the MOLUSCE plugin of QGIS and the CA-ANN model, future LULC changes were predicted. Artificial neural network (ANN) and cellular automata (CA) machine learning methods were made available for LULC change modeling and prediction via the QGIS MOLUSCE plugin. Transition potential modeling was computed, and future LULC changes were predicted using the CA-ANN model. An overall accuracy of 86.53% and an overall kappa value of 0.82 were obtained by comparing the actual data of 2022 with the simulated LULC data from the same year. The study findings revealed that between 2022 and 2037, agricultural land (63.09%) and shrubland (5.74%) showed significant increases, and forest (−48.10%) and grassland (−0.31%) decreased. From 2037 to 2052, the built-up area (2.99%) showed a significant increase, and forest and agricultural land (−2.55%) showed a significant decrease. From 2052 to 2067, the projected LULC simulation result showed that agricultural land (3.15%) and built-up area (0.32%) increased, and forest (−1.59%) and shrubland (−0.56%) showed significant decreases. According to the study’s findings, the main drivers of LULC changes are the expansion of built-up areas and agricultural land, which calls for a thorough investigation using additional data and models to give planners and policymakers clear information on LULC changes and their environmental effects.

Spatiotemporal Monitoring of Urban Sprawl in a Coastal City Using GIS-Based Markov Chain and Artificial Neural Network (ANN)

Over the last two decades, globally coastal areas have urbanized rapidly due to various socioeconomic and demographic driving forces. However, urban expansion in towns and cities of the developing world has been characterized by entangled structures and trends exacerbating numerous negative consequences such as pollution, ecological degradation, loss of agricultural land and green areas, and deprived settlements. Substantially, spatial simulation of urban growth and their consequences on coastal areas particularly in Egypt is still very rare. Geospatial modelling coastal urban growth is crucial and has enormous potential for coastal land use transformation and urban sustainability. The key aim of this study was to analyze spatiotemporal changes (2010–2020) and simulate future dynamics (2030 to 2050) of land use/land cover (LULC) in Alexandria Governorate, Egypt. Artificial Neural Network–Multiple Layer Perceptron (ANN-MLP) and Markov Chain techniques were employed within the GIS platform to assess processes of land transitions and predict urban growth trends, patterns and dimensions. The forecasting process was based on three maps of LULC derived from classified Landsat images of 2000, 2010 and 2020. In addition, topographical, demographic, accessibility, proximity factors were generated and developed in the form of raster spatial parameters of urbanization driving forces. The findings revealed that the observed expansion of the built-up area during one decade (2010–2020) was 12,477.51 ha, with a decline in agricultural area (7440.39 ha) and bare land (4904.91 ha). The projected change was forecasted to be 71,544 ha by 2030 and 81,983 ha in 2040 with a total of 35,998 ha increase in the built-up area and residential expansion by 2050. Despite this expected pattern of rapid changes, urban growth will be shaped by the key drivers of proximity to coastline and agricultural land transformation. The analysis indicates that the vertical urban growth will be most likely dominant along the coastal zone due to the lack of vacant lands, whereas the horizontal urban expansion will primarily take place towards the east-northeastern and south-southeastern directions of the city. The present work provides a holistic framework for establishing initial coastal land use plans not only for planners and urban administrators in Alexandria but also for policymakers and coastal municipalities in developing nations.