Impact Of Land Use Research Articles

Urban Spatial Dynamics and Geo-informatics Prediction of Karachi from 1990–2050 Using Remote Sensing and CA-ANN Simulation

Rapid urbanization significantly impacts land use and land cover (LULC), leading to various socioeconomic and environmental challenges. Effective monitoring and detection of spatial discrepancies are crucial for urban planners and authorities to manage these changes. This study aims to analyze the spatial dynamics of LULC changes and predict future land use patterns. The specific objectives are to classify historical land use from 1990 to 2020, simulate future land use from 2020 to 2050, and interpret the spatial and temporal results. The study utilized remotely sensed images with the semi-automatic classification plugin (SCP) approach for land use classification from 1990 to 2020. Future land use patterns were simulated using the Modules of Land Use Change Evaluation (MOLUSCE)-based Cellular Automata-Artificial Neural Network (CA-ANN) model. The results were then interpreted to comprehend the dynamics of urban expansion. The conclusions direct a significant increase in built-up and grasslands, with a consistent decline in other land use types. From 1990 to 2020, approximately 423.75 km² and 856.97 km² of land were converted into built-up areas and grasslands, respectively. This was accompanied by a decline in rocky bare and bare soil areas, while the proportions of water bodies and mangroves remained steady. Predictions for 2020 to 2050 suggest an additional increase of 561.93 km² in built-up areas, with a progressive decline in other land use classes. The study emphasizes the critical need for spatial planning policies to address challenges arising from rapid urbanization. By analyzing historical land use changes and predicting future patterns this research offers a comprehensive view of urban growth dynamics. The novel application of these techniques provides valuable insights for urban planners to develop informed strategies for managing expansion and mitigating associated socioeconomic and environmental impacts.

Climate change and the taphonomic signature of Neolithic mounds: the Kur River Basin over five decades of satellite imagery coverage

ABSTRACT This paper discusses the impact of modern land use and climate change on mounded prehistoric sites, considering the evidence from Kur River Basin (southwestern Iran). By applying remote sensing and GIS techniques to historical and recent satellite imagery, we monitored an assemblage of 45 Neolithic mounds in order to map and quantify their taphonomic signatures over the last 53 years. Our results highlight that the deterioration of sites occurred as a result of landscape transformation due to land use and socio-economic dynamics that were exacerbated by current climate change. Increasing demands for fertile land and water led to the over-exploitation of sources, resulting in changing hydrological regimes and ecological systems and loss of the archaeological record. By contextualizing these processes over a historical perspective, our findings contribute to archaeological heritage management efforts.

Spatial–Temporal Divergence and Coupling Analysis of Land Use Change and Ecosystem Service Value in the Yangtze River Delta Urban Agglomeration

Land use changes (LUC) have exacerbated the evolution of ecosystem structure in the urban agglomeration of the Yangtze River Delta (YRDUA), significantly affecting ecosystem service functions and values. Although the impact of land use on ecosystem service value (ESV) has received significant attention, most existing studies explore the relationship between LUC and ESV at the national, provincial, or regional scales. Few studies focus on urban agglomerations, particularly in the YRDUA. Additionally, while many studies analyze the driving factors of ecosystem services and the trade-offs and synergies among them, there is a relative scarcity of research on the coupling coordination relationship between LUC and ESV. In this study, we used the ecosystem service assessment model to dynamically analyze the spatio-temporal changes of land use and ESV in the YRDUA from 2000 to 2020 and evaluated the dynamic relationship between the two using the coupled coordination model system. The results show the following: (1) Land use types within the YRDUA underwent significant changes during the study period, with a notable decrease in farmland and a substantial increase in construction land being the dominant trends. (2) ESV showed upward and downward trends over different periods, with water bodies having the highest value, followed by forested land and farmland, respectively. The ESV of water bodies showed a trend of first increasing and then decreasing. Higher ESVs were mainly concentrated around lakes such as Taihu Lake and coastal areas along the Yangtze River, radiating outward from these central points. (3) Currently, the coupling coordination degree (CCD) between land use intensity and ESV in the YRDUA is mostly reluctant coordinated or in the state of primary coordination. However, with time, the CCD trend has increased. In addition, variations between cities were more pronounced, showing a spatial pattern characterized by higher coupling levels in eastern/northern regions compared to western/southern regions. Our study can provide policy references for improving land planning, optimizing land use structures, and realizing high-quality, sustainable, green, and coordinated development in the YRDUA.

Surface and Subsurface Water Impacts of Forestry and Grassland Land Use in Paired Watersheds: Electrical Resistivity Tomography and Water Balance Analysis

Surface and Subsurface Water Impacts of Forestry and Grassland Land Use in Paired Watersheds: Electrical Resistivity Tomography and Water Balance Analysis

Investigating the Impact of Land Use and Land Cover Changes on Wetlands Using Satellite Data (Case Study: Jazmorian Wetland)

Investigating the Impact of Land Use and Land Cover Changes on Wetlands Using Satellite Data (Case Study: Jazmorian Wetland)

Impact of land use and land cover change on land surface temperature: Comparative studies in four cities in southwestern Ethiopia

The urban climate has undergone significant changes due to rapid population growth, leading to a decline in vegetation cover and an increase in land surface temperature (LST). This study aims to assess the influence of land use land cover (LULC) changes on LST in four major urban areas in southwestern Ethiopia, namely Jimma, Bonga, Mattu and Nekemte, during the period from 2002 to 2024. To investigate the impact of LULC dynamics on LST, 30 m spatial resolution images from Landsat were utilized, including Thematic Mapper (TM) for the year 2002 and Landsat Operational Land Imager (OLI) and Thermal Infrared (TIRS) for the years 2014 and 2024. Over the past 22 years, the mean LST has increased by 2.81 °C, 2.94 °C, 3.37 °C, and 3.96 °C for Bonga, Nekemte, Mattu, and Jimma, respectively. The increase in LST can be attributed to various factors, but one of the primary reasons is linked to the rapid urbanization and decrease in forest cover. Changes in LULC triggered by rapid urbanization significantly influences LST in major cities. The results highlight the increment of impervious surface and the decline in vegetation cover as key factors contributing to the upward trend in LST. The results indicate that urban centers with less vegetation cover experienced higher LST compared to their surroundings. The results of this study indicate the necessity of effective urban planning to increase vegetation cover through urban greenery and parks to mitigate the increasing trends of LST, which can improve urban thermal comfort levels.

Impacts of Land Use on Runoff and Sediment Dynamics in Tropical Watersheds: A Case Study in Bogowonto Upper Watershed

Land use changes in tropical regions have increased, leading to rising environmental stress in Java, Indonesia. Food shortages have driven land conversion and expansion, which increases peak flows during the rainy season and reduces water storage in the dry season, heightening flood risks. Research on integrated catchment hydrology is crucial. This study examines the relationship between land use, runoff, and sediment in the Bogowonto Upper Watershed using SWAT hydrological modeling. The SWAT model helps understand hydrological processes at the watershed scale and the impact of land use changes on runoff and sediment dynamics. The sensitivity of SWAT model parameters varies in the Bogowonto Upper Watershed. Runoff sensitivity analysis indicates a +62% increase with a 50% change in CN value, showing high sensitivity. A 50% change in vegetation cover results in a +50% model output, indicating moderate sensitivity. Slope, Ksat (saturated hydraulic conductivity), and bulk density are fairly sensitive, while AWC is slightly sensitive. For sediment, a 50% increase in CN value results in a +47% change, and a 50% increase in vegetation cover leads to a +58% model output, showing moderate sensitivity. The model, run from 2014-2019, shows excellent accuracy with NSE of 0.82, RRMSE of 0.43, R² of 0.83, and PBIAS of 9.8%.

The Impact of Agricultural Land Use on Some Chemical Properties of Desert Soils in the Holy Karbala Governorate

The Impact of Agricultural Land Use on Some Chemical Properties of Desert Soils in the Holy Karbala Governorate

Analysis of Spatial and Temporal Variations in Evapotranspiration and Its Driving Factors Based on Multi-Source Remote Sensing Data: A Case Study of the Heihe River Basin

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions.

Mechanisms of spring freshet generation in southern Quebec, Canada

Seasonal forecasting of spring floods in snow-covered basins is challenging due to the ambiguity in the driving processes, uncertain estimations of antecedent catchment conditions, and the choice of predictor variables. In this study, we attempt to improve the prediction of spring flow peaks in southern Quebec, Canada, by studying the preconditioning mechanisms of runoff generation and their impact on inter-annual variations in the timing and magnitude of spring peak flow. Historical observations and simulated data from a hydrological and snowmelt model were used to study the antecedent conditions that control flood characteristics in 12 unregulated snow-dominated catchments. Maximum snow accumulation (peak SWE), snowmelt and rainfall volume and intensity, soil moisture, river baseflow, and the air freezing index, a proxy for soil freezing, were estimated during the pre-flood period. Stepwise multiple linear regression was used to identify the most relevant predictors and assess their relative contribution to interannual variability flood variability. Peak SWE was not by itself a strong predictor of spring flood magnitude and timing. The snowmelt rate during the pre-flood period was the most ubiquitous and skillful predictor of spring flood magnitude, followed by the rainfall intensity. The ‘soil memory’ effect, represented here by the simulated soil moisture content and freezing depth, was generally poorly related to flood characteristics. SWE and snowmelt dynamics dominated the interannual variability of flood magnitude in the southern and agricultural basins while rainfall intensity had a stronger influence in the northern, snowier forested basins. More complex machine learning models (random forest and support vector regression) did not perform better than the simple linear models to predict peakflow from antecedent hydroclimatic factors. These results highlight the impact of climate and land cover and use on spring flood generation mechanism and the moderate predictability potential of spring floods based on antecedent hydrological factors.

Assessing the Impacts of Land Use on Water Quality in the Acacias River Basin, Colombia

Surface water resources have played a fundamental role in the development of human societies. Considering that different agricultural and industrial activities are carried out in the Acacias River basin, the main objective of this research was to analyze the influence of land use on the water quality in this area by identifying the main sources that influence river water quality. The methodology consisted of establishing 12 sampling stations with different land uses at three times. The National Sanitation Foundation-Water Quality Index (NSF-WQI) was applied to the obtained water quality evaluation data. The main results showed that the stations associated with urban centers presented a higher concentration in the following variables: fecal coliforms, biochemical oxygen demand (BOD) and phosphates. The principal components analysis revealed a close relation between the parameters of fecal coliforms, phosphates and BOD, and the pollution processes by organic matter, which are probably related to domestic and industrial wastewater discharges, and to detergents in urbanized areas. The parameters with the greatest range of values were total dissolved solids and turbidity. These results coincide with what was observed in the correlation analysis. Finally, nitrates showed higher concentrations at stations 6 and 7, associated with agricultural and industrial influence areas (i.e., oil palm crops in the basin). This study about the Acacias River is, thus, extremely important for the region, and concludes that the river’s self-purifying capacity allows improved water quality in the areas where the predominant land use is not associated with human settlements.

Assessing the Impact of Land Use and Land Cover Changes on Surface Temperature Dynamics Using Google Earth Engine: A Case Study of Tlemcen Municipality, Northwestern Algeria (1989–2019)

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels.

Investigating the Impact of Recent and Future Urbanization on Flooding in an Indian River Catchment

Socioeconomic growth in India has caused massive infrastructure development which has resulted in extensive damage to the natural environment. A consequence of this urbanization has been extensive monsoon flooding in many locations within the country. The impact of recent land use and land cover (LULC) change because of urbanization and a series of future LULC scenarios is assessed for the Meenachil river basin in central Kerala, India. This catchment flows into the Kuttanad administrative area, which has the country’s lowest elevation, an increasing population, and currently suffers from regular flooding. Hydrological modeling using SHETRAN and hydraulic modeling using HEC-RAS predicts that an extreme event will produce a 105% rise in flood depth in 2100 compared to 2005. A scenario that incorporates Nature-based Solutions suggests the rise in flood depth could be reduced by 44%. A catchment response for future development is needed but is hindered by different administrative boundaries within the river basins that flow into the Kuttanad administrative area, and so this study concludes by providing regional-scale planning recommendations that integrate hydrologic components.

Colonization ability and uniformity of resources and environmental factors determine biological homogenization of soil protists in human land-use systems.

Humans have substantially transformed the global land surface, resulting in the decline in variation in biotic communities across scales, a phenomenon known as "biological homogenization." However, different biota are affected by biological homogenization to varying degrees, but this variation and the underlying mechanisms remain little studied, particularly in soil systems. To address this topic, we used metabarcoding to investigate the biogeography of soil protists and their prey/hosts (prokaryotes, fungi, and meso- and macrofauna) in three human land-use ecosystem types (farmlands, residential areas, and parks) and natural forest ecosystems across subtropical and temperate regions in China. Our results showed that the degree of community homogenization largely differed between taxa and functional groups of soil protists, and was strongly and positively linked to their colonization ability of human land-use systems. Removal analysis showed that the introduction of widespread, generalist taxa (OTUs, operational taxonomic units) rather than the loss of narrow-ranged, specialist OTUs was the major cause of biological homogenization. This increase in generalist OTUs seemingly alleviated the negative impact of land use on specialist taxa, but carried the risk of losing functional diversity. Finally, homogenization of prey/host biota and environmental conditions were also important drivers of biological homogenization in human land-use systems, with their importance being more pronounced in phagotrophic than parasitic and phototrophic protists. Overall, our study showed that the variation in biological homogenization strongly depends on the colonization ability of taxa in human land-use systems, but is also affected by the homogenization of resources and environmental conditions. Importantly, biological homogenization is not the major cause of the decline in the diversity of soil protists, and conservation and study efforts should target at taxa highly sensitive to local extinction, such as parasites.

Investigating the influence of spatial characteristics on cycling volume: A multi-scale geographic weighted regression approach

Cycling has seen a remarkable rise, signifying a paradigmatic move towards sustainable, eco-friendly, and efficient commuting alternatives in the contemporary urban setting. Cities also encourage this trend by establishing cycle lanes, bike-sharing programs, and incentives for frequent riders. To enhance these motivations from an urbanistic perspective, it is essential to comprehend the influence of urban characteristics on cycling volume and to incorporate this understanding into data-driven decision-making processes.This research examines the Bicification project data from Istanbul with a spatial perspective. Utilising a comprehensive array of spatial big data, the study explores the impact of urban land use, transport services, land morphology, and sociodemographic factors on cycling volume through a Multi-scale Geographically Weighted Regression (MGWR). With an Adj R2 value of 0.68, the model demonstrates a strong relation between cycling volume and several factors, including biking park stations, park and ride points, pier stops, rail stops, transfer points, main roads, elevation, population, industrial facilities, health facilities, sports areas, and residential areas. The findings will serve to develop a data-driven strategic approach to promote cycling in Istanbul.

Models of bee responses to land use and land cover changes in agricultural landscapes - a review and research agenda.

Predictive modelling tools can be used to support the design of agricultural landscapes to promote pollinator biodiversity and pollination services. Despite the proliferation of such modelling tools in recent decades, there remains a gap in synthesising their main characteristics and representation capacities. Here, we reviewed 42 studies that developed non-correlative models to explore the impact of land use and land cover changes on bee populations, and synthesised information about the modelled systems, modelling approaches, and key model characteristics like spatiotemporal extent and resolution. Various modelling approaches are employed to predict the biodiversity of bees and the pollination services they provide, with a prevalence of models focusing on wild populations compared to managed ones. Of these models, landscape indicators and distance decay models are relatively simple, with few parameters. They allow mapping bee visitation probabilities using basic land cover data and considering bee foraging ranges. Conversely, mechanistic or agent-based models delineate, with varying degrees of complexity, a multitude of processes that characterise, among others, the foraging behaviour and population dynamics of bees. The reviewed models collectively encompass 38 ecological, agronomic, and economic processes, producing various outputs including bee abundance, habitat visitation rate, and crop yield. To advance the development of predictive modelling tools aimed at fostering pollinator biodiversity and pollination services in agricultural landscapes, we highlight future avenues for increasing biophysical realism in models predicting the impact of land use and land cover changes on bees. Additionally, we address the challenges associated with balancing model complexity and practical usability.

Characteristics of an Inorganic Carbon Sink Influenced by Agricultural Activities in the Karst Peak Cluster Depression of Southern China (Guancun)

Land use in karst areas affects soil properties, impacting carbon sinks. Accurate estimation of carbon sink flux in karst areas through zoning and classification is crucial for understanding global carbon cycling and climate change. The peak cluster depression is the largest continuous karst landform region in southern China, with the depressions primarily covered by farmland and influenced by agricultural activities. This study focused on the Guancun Underground River Basin, a typical peak cluster depression basin, where sampling and analysis were conducted during the agricultural period of 2021–2022. Using hydrochemical analysis and isotopic methods, the results indicated that: (1) The primary hydrochemical type in the Guancun Underground River Basin is HCO3-Ca, with hydrochemical composition mainly controlled by carbonate rock weathering. (2) The primary sources of Cl−, SO42−, and NO3− are agricultural activities, with agriculture contributing 0.68 mmol/L to dissolved inorganic carbon (DIC), accounting for about 13.86%, as confirmed by ion concentration analysis and isotope verification. (3) The size of the depression area is proportional to the contribution of agricultural activities to DIC, while also being influenced by dilution effects. A comparison was made regarding the contribution of other land use types to DIC. The impact of land use on DIC in karst processes should not be overlooked, and zoning and classification assessments of carbon sink flux under different influencing factors contribute to carbon peaking and carbon neutrality goals.

The Carbon Emission Implications of Intensive Urban Land Use in Emerging Regions: Insights from Chinese Cities

Intensive urban land use is a strategy to enhance productivity and mitigate environmental challenges in emerging regions, but its relationship with carbon emissions needs further city-level investigation. This study investigates the impact of intensive urban land use on carbon emissions across 153 cities in China, thus employing the STIRPAT model with the ordinary least square (OLS) and geographical weighted regression (GWR) methods. The findings underscore the heterogenous influence of intensive urban land use on carbon emissions across China’s urban landscapes: (1) R&D investment intensity and population density show significant negative association with carbon emissions in general. (2) Capital investment intensity positively affects carbon emissions in low-income cities, R&D investment intensity shows negative effects on carbon emissions in middle-income cities, and population density emerges as a substantial factor in reducing carbon emissions in both middle- and low-income cities. (3) Capital intensity, labor intensity, and R&D investment intensity exert positive effects on emissions in middle China and negative influences in northeastern and southern China, whereas population density shows converse spatial effects. Based on the study’s results, tailored policy implications are provided for urban planning authorities in emerging regions.

Stable isotope analysis in soil prospection reveals the type of historic land-use under contemporary temperate forests in Europe

The determination of δ13C and δ15N values is a common method in archaeological isotope analysis—in studying botanical and human remains, dietary practices, and less typically soils (to understand methods of agricultural cultivation, including fertilization). Stable isotope measurements are also commonly used in ecological studies to distinguish different ecosystems and to trace diachronic processes and biogeochemical mechanisms, however, the application of this method in geochemical prospection, for determining historic land-use impact, remains unexplored. The study at hand focuses on a deserted site of a Cistercian manor, dating from the thirteenth to fifteenth centuries. Isotopic measurements of anthropogenically influenced soils have been compared to approximately 400 archaeobotanical, soil, and sediment samples collected globally. The results reveal the potential of isotope measurements in soil to study the impact of past land use as isotope measurements identify specific types of agricultural activities, distinguishing crop production or grazing. δ13C and δ15N ratios also likely reflect fertilization practices and—in this case—the results indicate the presence of cereal cultivation (C3 cycle plants) and fertilization and that the site of the medieval manor was primarily used for grain production rather than animal husbandry.

The Impact of Land Use on Water Resources in the Gulf Cooperation Council Region

The Gulf Cooperation Council (GCC) countries experience a harsh environment with low precipitation and high evaporation rates. This presents a significant challenge of water scarcity and water quality degradation which is exacerbated by land use practices. Agriculture, urbanization, and industrialization are impacting the management of water resources. This paper examines the state of water resources in the GCC countries and presents the impact of land use practices. A detailed analysis of the relationship between land use and groundwater is conducted with a focus on the Kingdom of Saudi Arabia (KSA), the Sultanate of Oman, and the United Arab Emirates (UAE). The results indicate a drop in agricultural activities, an increase in bare land and urban areas, depletion of groundwater, and water quality degradation. These changes were attributed to excessive agricultural and industrial uses, climate change, and rapid urbanization. The findings show the need for sustainable land use practices and water resources management. Further research addressing the impact of land use on water in Kuwait, Qatar, and Bahrain, and evaluating the effectiveness of policy, is essential.