Land Use Cover Change Research Articles

Dynamic shifts of functional diversity through climate-resilient strategies and farmland restoration in a mountain protected area

Land-use land-cover (LULC) change contributes to major ecological impacts, particularly in areas undergoing land abandonment, inducing modifications on habitat structure and species distributions. Alternative land-use policies are potential solutions to alleviate the negative impacts of contemporary tendencies of LULC change on biodiversity.This work analyzes these tendencies in the Montesinho Natural Park (Portugal), an area representative of European abandoned mountain rural areas. We built ecological niche models for 226 species of vertebrates (amphibians, reptiles, birds, and mammals) and vascular plants, using a consensus modelling approach available in the R package ‘biomod2’. We projected the models to contemporary (2018) and future (2050) LULC scenarios, under four scenarios aiming to secure relevant ecosystem services and biodiversity conservation for 2050: an afforestation and a rewilding scenario, focused on climate-smart management strategies, and a farmland and an agroforestry recovery scenario, based on re-establishing human traditional activities. We quantified the influences of these scenarios on biodiversity through species habitat suitability changes for 2018–2050. We analyzed how these management strategies could influence indices of functional diversity (functional richness, functional evenness and functional dispersion) within the park.Habitat suitability changes revealed complementary patterns among scenarios. Afforestation and rewilding scenarios benefited more species adapted to habitats with low human influence, such as forests and open woodlands. The highest functional richness and dispersion was predicted for rewilding scenarios, which could improve landscape restoration and provide opportunities for the expansion and recolonization of forest areas by native species. The recovery of traditional farming and agroforestry activities results in the lowest values of functional richness, but these strategies contribute to complex landscape matrices with diversified habitats and resources. Moreover, this strategy could offer opportunities for fire suppression and increase landscape fire resistance. An integrative approach reconciling rewilding initiatives with the recovery of extensive agricultural and agroforestry activities is potentially an harmonious strategy for supporting the provision of ecosystem services while securing biodiversity conservation and functional diversity within the natural park.

Monitoring Changes in Parts of the Guinea-Savannah Woodlands

Land use and land cover (LULC) in Ghana has undergone a lot of changes over the past years emanating from natural and anthropogenic activities. This study is a comprehensive analysis of LULC changes in parts of the Guinea-Savannah through an integrated approach of geospatial procedures. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Landsat ETM+) and 2020 (Landsat ETM+) were analyzed. Built-up area, Agricultural land, Closed savannah vegetation, Open savannah vegetation and Water bodies were LULC categories delineated for Jirapa municipality. The Cellular Automata-Markov (CA-Markov) model was applied to predict the likely changes in LULC in 2030. The study revealed that the most dominant land cover type in the municipality is the Open savannah vegetation as it occupied averagely 45% of the total surface area. Built-up area increased in area coverage by 93% between 1990 and 2020. Agricultural activities, bushfires, deforestation, infrastructural development, and population growth are the main drivers of changes in Agricultural land, Open savannah vegetation, Closed savannah vegetation and Water bodies. The LULC prediction for 2030 showed that the Built-up areas would increase significantly in 2030 leading to a 6% reduction in Agricultural land in 2030.

Monitoring Changes in Parts of the Guinea-Savannah Woodlands

Land use and land cover (LULC) in Ghana has undergone a lot of changes over the past years emanating from natural and anthropogenic activities. This study is a comprehensive analysis of LULC changes in parts of the Guinea-Savannah through an integrated approach of geospatial procedures. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Landsat ETM+) and 2020 (Landsat ETM+) were analyzed. Built-up area, Agricultural land, Closed savannah vegetation, Open savannah vegetation and Water bodies were LULC categories delineated for Jirapa municipality. The Cellular Automata-Markov (CA-Markov) model was applied to predict the likely changes in LULC in 2030. The study revealed that the most dominant land cover type in the municipality is the Open savannah vegetation as it occupied averagely 45% of the total surface area. Built-up area increased in area coverage by 93% between 1990 and 2020. Agricultural activities, bushfires, deforestation, infrastructural development, and population growth are the main drivers of changes in Agricultural land, Open savannah vegetation, Closed savannah vegetation and Water bodies. The LULC prediction for 2030 showed that the Built-up areas would increase significantly in 2030 leading to a 6% reduction in Agricultural land in 2030.

Spatiotemporal dynamics of land use and land cover change around Volcanoes National Park and their implications for biodiversity conservation

ABSTRACT This study comprehensively analyses spatiotemporal land use/land cover (LULC) dynamics within a 10-km buffer zone surrounding Volcanoes National Park (VNP), Rwanda, from 2000 to 2019, integrating biophysical and socio-economic drivers. It aims to quantify LULC change extent, rate, and type around VNP and inside the park; identify socio-economic, demographic, and policy drivers of LULC change; and evaluate the impact of LULC changes on VNP’s biodiversity via habitat fragmentation, connectivity, and species diversity. Employing remote sensing, spatial modelling, and field data, we reveal profound transformations within the park’s periphery. Forests and woodlots have significantly declined, replaced by agriculture, grasslands, and built-up areas, particularly in the 5.1–10 km belt (92.29% forest loss). While reforestation efforts exist, they are outpaced by deforestation, creating a stark deforestation-to-reforestation ratio of 12:1 in the 5.1 10 km belt and 6:1 in the 0–5 km belt. This alarming trend threatens VNP’s biodiversity as resource dependent communities continue to extract firewood, construction materials, and beanpoles from the park. To mitigate these impacts, we propose integrated strategies encompassing strengthened law enforcement, sustainable land management practices, community-based conservation initiatives, and alternative livelihood development.

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.

Long-term shoreline and LULC change computational analysis in part of the east coast of Tamilnadu using geoinformation tools

Long-term shoreline and LULC change computational analysis in part of the east coast of Tamilnadu using geoinformation tools

Land use/land cover (LULC) changes modeling and susceptibility mapping using the binary logistic regression at the territorial level in eastern DR Congo

The Land Use and Land Cover (LULC) changes are commonly used to determine the landscape conditions and have significant impacts on the earth's surface processes. During the last three decades, there has been an acceleration of LULC changes in Eastern DR Congo. However, there is no comprehensive overview of the drivers of these changes at the territorial level in this region, even though the knowledge of these drivers is important for land use planning and spatial modeling of environmental changes. Using the Kalehe Territory as a case study, this work sought to fill this gap by analyzing the drivers of LULC changes during the 1987-2020 period. A mixed approach combining remote sensing, Geographic Information System, and logistic regression modeling was used. The results indicated that the prominent LULC changes in the study area are deforestation, built-up area expansion, cropland expansion, and shrubland expansion. These changes are significantly influenced by biophysical factors (slope, altitude, and soil type), conservation zoning, population dynamics, and accessibility factors at different levels. The occurrence of conservation zones decreases the susceptibility to deforestation, built-up land, and cropland expansions. In contrast, the proximity factors (distance to road, artisanal mining, and locality) increase the susceptibility to LULC changes. These factors can be integrated into spatial models to forecast LULC changes susceptibility in this region. Furthermore, the establishment of future land use management policy at the territorial level in eastern DR Congo should be space-specific as the susceptibility of LULC changes shows a spatial trend.

Land-use land cover changes and their relationship with population and climate in Western Uganda

Sustainable management of land and its ecosystem requires well-documented Land-use and Land Cover changes (LULCCs) that have occurred over time. The objective of this study was to document the LULCCs, determine their relationship with climate and population changes in Western Uganda, and provide evidence to support decision-making in this region. This study utilized satellite images for the years 1992, 2000, 2010, and 2020 obtained from the European Space Agency Climate Change Initiative (ESA CCI), Climatic data from NASA, and Population Data from the Uganda Bureau of Statistics (UBoS). LULCCs and their influencers were characterized in the region, for the period 1992 to 2020. ArcGIS Pro Software Program was used to Filter time series data using Definition Query, while the scatterplot was employed to determine the relationship between changes in climate and population on LULCCs. Results indicate that LULC is dominated by agriculture, covering (66.46%), followed by forests (16.22%), waterbodies (8.0%), grassland, shrubland, wetland, and urban areas at (6.11%), (2.63%), (0.49%) and (0.1%) respectively. From 1992 to 2020, the area under agriculture, forest and urban increased by 0.1%, 0.1%, and 0.08%, respectively, while Grassland, wetland, and shrubland declined by (0.22%), (0.05%), and (0.01%) respectively. LULCCs for areas under wetland, grassland, and urban areas have a stronger relationship with precipitation and population growth. This information can be used by the decision-makers at the local, district, and national levels to better guide land-use practices aimed at sustainable land-use management for the current and future generations.

Environmental Monitoring of Land Use/ Land Cover by Integrating Remote Sensing and Machine Learning Algorithms

Evaluation of the land use/ land cover (LULC) case over large regions is very important in a variety of domains, including natural resources such as soil, water, etc., and climate change risks and LULC change has emerged as a high anxiety for the environment. Therefore, we tested and compared the performance of three classification algorithms: Support Vector Machines (SVM), Random Trees (RT), and Maximum Likelihood (MaxL) to derive and extract LULC information for the district of Sarayönü/ Konya across five distinct classes: water, plantation, grassland, built-up, and bare land. Two remote sensing indices, the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), were used as supplementary inputs for the classification of LULC. To evaluate the performance of the algorithms, a confusion matrix was employed. The average overall accuracy of support vector machines, random trees, and maximum likelihood algorithms was found 85.60%, 79.20%, and 74.80%, respectively, and 82.00%, 74.00%, and 68.50% for the Kappa coefficient. These results indicate that the support vector machines algorithm outperforms other algorithms in terms of accuracy. As a result of the research, it was determined that classification algorithms integrated with remote sensing in LULC change monitoring/determination could produce accurate classification maps that can be used as base data. This is due to the ability of machine learning algorithms to learn complex patterns, adapt to diverse data, and continuously improve, making them achieve higher accuracy compared to traditional classifiers. Therefore, their use was recommended for decision-makers.

Land use and land cover changes, and woody vegetation status of the Tsimur Gebriel Monastery in Northern Ethiopia

Ethiopian Orthodox churches are significant habitats for endemic and threatened plant species, yet their vegetation status and the land use systems impacting them, are little known. Therefore, this study assessed the land use and land cover changes (LULCC) within a 3 km buffer area and the woody vegetation status of the Tsimur Gebriel Monastery in the Tigray region, Northern Ethiopia. The United States Geological Survey's multi-dated (1986, 1999, and 2018) Landsat imagery was used for LULCC analysis. A supervised classification technique was employed for image classification using a maximum likelihood algorithm. Systematic sampling techniques were used to collect vegetation data (tree species, height, and DBH), using 20 sample plots (20 × 20 m) distanced 100 m apart. The results highlighted that among the five identified LULCC types in the buffer zone of the monastery, the farmland area has expanded from 56 to 78 % at the expense of shrublands between 1986 and 2018. At the monastery, 19 woody tree species from 13 families were identified, with an evenness of 0.5 and a Shannon diversity index of 2.4. The stem density was 336 stems per hectare, and the forest cover was approximately 65 %. Olea europaea was the dominant tree species, while Juniperus procera showed a lack of regeneration at the monastery. Despite the fair natural regeneration, the monastery exhibited lower species diversity, richness, and evenness. However, the monastery remains an important habitat for rare and threatened tree species and may supply seeds for the restoration of degraded lands. Therefore, establishing exclosures in the buffer zone, strengthening stone walls and enrichment planting of degraded tree species should be implemented to ensure the sustainable conservation of valuable tree species.

Assessing forest fragmentation due to land use changes from 1992 to 2023: A spatio-temporal analysis using remote sensing data

The increasing pressures of urban development and agricultural expansion have significant implications for land use and land cover (LULC) dynamics, particularly in ecologically sensitive regions like the Murree and Kotli Sattian tehsils of the Rawalpindi district in Pakistan. This study's primary objective is to assess spatial variations within each LULC category over three decades (1992–2023) using cross-tabulation in ArcGIS to identify changes in LULC and investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0) to classify forest into several classes such as patch, edge, perforated, small core, medium core, and large core. Utilizing remote sensing data from Landsat 5 and Landsat 9 satellites, the research focuses on the temporal dynamics in various land classes including Coniferous Forest (CF), Evergreen Forest (EF), Arable Land (AR), Buildup Area (BU), Barren Land (BA), Water (WA), and Grassland (GL). The Support Vector Machine (SVM) classifier and ArcGIS software were employed for image processing and classification, ensuring accuracy in categorizing different land types. Our results indicate a notable reduction in forested areas, with Coniferous Forest (CF) decreasing from 363.9 km2, constituting 45.0 % of the area in 1992, to 291.5 km2 (36.0 %) in 2023, representing a total decrease of 72.4 km2. Similarly, Evergreen Forests have also seen a significant reduction, from 177.9 km2 (22.0 %) in 1992 to 99.8 km2 (12.3 %) in 2023, a decrease of 78.1 km2. The study investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0), revealing an increase in fragmentation and a decrease in large core forests from 20.3 % of the total area in 1992 to 7.2 % in 2023. Additionally, the patch forest area increased from 2.4 % in 1992 to 5.9 % in 2023, indicating significant fragmentation. Transition matrices and a Sankey diagram illustrate the transitions between different LULC classes, providing a comprehensive view of the dynamics of land-use changes and their implications for ecosystem services. These findings highlight the critical need for robust conservation strategies and effective land management practices. The study contributes to the understanding of LULC dynamics and forest fragmentation in the Himalayan region of Pakistan, offering insights essential for future land management and policymaking in the face of rapid environmental changes.

Future land use and land cover simulations with cellular automata-based artificial neural network: A case study over Delhi megacity (India)

According to United Nations projections, future global urban growth will mostly occur in Asian megacities. In this study, a Cellular Automata based Artificial Neural Network (CA-ANN) model is used to simulate the future land use and land cover (LULC) over Delhi megacity (India). Delhi, projected to become the world's most populated city by 2030, is an example of a data poor city in Asia, having millions of climate vulnerable people. The CA-ANN model of Modules for Land Change Simulation (MOLUSCE), an open-source plugin, is first tested to simulate the LULC for 2009. Based on good validation results-structural similarity (SSIM; 0.8288), overall accuracy (79.78 %), kappa index of agreement (KIA; 77.25 %), and minimum validation overall error (0.0379), the same model set-up is used to carry out LULC simulation for 2030. This model is found to be simple, efficient, and computationally less expensive tool, and can be used to model future LULCs with a minimal set of inputs, a constraint often found in data poor cities. Results show continued increase in built-up area from 38.3 % (2014) to 53.8 % (2030), at the expense of cultivable areas, forests, and wastelands. The study incorporates past and future LULC change trajectories to highlight the changing LULC dynamics of the megacity from 1977 to 2030. Rate of urban sprawl, calculated using compound annual growth rate (CAGR) is projected to be 2.51 % for 2014–2030, substantially higher than the estimates for 2006–2014 (0.62 %). Further, the past and future urban growth patterns for Delhi are found to mimic other big Asian cities. The database generated from the present study has wide applicability for scientific research community, governmental bodies, profit and non-profit organizations for topics concerning-future urban climate research, climate risk and adaption policy frameworks, climate finance budgeting, future town planning, etc.

LULC change detection analysis of Chamarajanagar district, Karnataka state, India using CNN-based deep learning method

The change detection analysis of land use land cover (LULC) is an important task in several fields and applications such as environmental monitoring, urban planning, disaster management, and climate change studies. This study focuses on the use of remote sensing (RS) and geographic information systems (GIS) to identify the changes in Chamarajanagar district, which is located in Karnataka state, South India. This paper mainly focuses on the classification and change detection analysis of LULC in 2011 and 2021 using linear imaging self-scanning sensor-III (LISS-III) satellite images. Traditional methods for LULC classification involve manual interpretation of satellite images, which provides lower accuracy. Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning classification method for LULC classification. The main objective of the research work is to perform an accurate change detection of the Chamarajanagar district using the classified maps of the years 2011 and 2021. The proposed classification method is outperformed, with a classification accuracy of 95.27 % and 94.57 % for LISS-III satellite imagery of the years 2011 and 2021 respectively. Further, change detection analysis has been carried out using classified maps and results show a decline of 3.23 sq. km, 22.7 sq. km, and 3.83 sq. km in the areas covered by vegetation, agricultural land, and forest area, respectively. In other classes, such as built-up, water bodies, and barren land, an increase in land cover was observed by 5.59 sq. km, 1.99 sq. km, and 20.92 sq. km, respectively.

Assessing the Convective Environment over Irrigated and Nonirrigated Land Use with Land–Atmosphere Coupling Metrics: Results from GRAINEX

Abstract Land-use land-cover change affects weather and climate. This paper quantifies land–atmosphere interactions over irrigated and nonirrigated land uses during the Great Plains Irrigation Experiment (GRAINEX). Three coupling metrics were used to quantify land–atmosphere interactions as they relate to convection. They include the convective triggering potential (CTP), the low-level humidity index (HIlow), and the lifting condensation level (LCL) deficit. These metrics were calculated from the rawinsonde data obtained from the Integrated Sounding Systems (ISSs) for Rogers Farm and York Airport along with soundings launched from the three Doppler on Wheels (DOW) sites. Each metric was categorized by intensive observation period (IOP), cloud cover, and time of day. Results show that with higher CTP, lower HIlow, and lower LCL deficit, conditions were more favorable for convective development over irrigated land use. When metrics were grouped and analyzed by IOP, compared to nonirrigated land use, HIlow was found to be lower for irrigated land use, suggesting favorable conditions for convective development. Furthermore, when metrics were grouped and analyzed by clear and nonclear days, CTP values were higher over irrigated cropland than nonirrigated land use. In addition, compared to nonirrigated land use, the LCL deficit during the peak growing season was lower over irrigated land use, suggesting a favorable condition for convection. It is found that with the transition from the early summer to the mid/peak summer and increased irrigation, the environment became more favorable for convective development over irrigated land use. Finally, it was found that regardless of background atmospheric conditions, irrigated land use provided a favorable environment for convective development.

Land Use Cover Changes and Household Livelihoods Adaptation Strategies in Mbulu and Karatu Districts, Tanzania

The interactions between the human and the biophysical factors at different spatial scales have triggered Land Use Cover Changes (LUCC). The study uses both qualitative (in depth interviews and focus group discussions) and quantitative techniques (household survey and spatial data analysis through the use of Geographical Information Systems (GIS) to acquire information and data for the study. The results have shown that overall LUCC is decelerating in both districts, Mbulu and Karatu across the two-time intervals. In the period between 1987and 2001, cultivation, settlements, wetland, grasslands and bare soils gained at the expense of forest, woodland, bush-land, and water. However, in the period between 2001 and 2015, again cultivation, settlements, wetland and bare soils gained except grassland which joined the categories of losers. The results have also revealed that communities have engaged in long term adaptation strategies to cope with the impacts of land use and land cover changes in the study area. These included agricultural intensification, afforestation programs and engaging in non-farming activities such as bee keeping and tourism. Despite the existing adaptation strategies to LUCC impacts in the study area, still community empowerment for enhancing the strategies is needed. This is due to the fact that, the community has limited options for livelihoods diversification that can absorb shocks and trends attributed to land use/cover change impacts over time and space.

Classification and detection of changes in land use and land cover using satellite images and GIS- near Suez Canal area

Egypt has developed significantly in the vicinity of the Suez Canal and its surrounding towns over the last ten years. Utilizing satellite pictures to identify land use and cover types from thematic maps remains difficult. Select the optimal strategy for successful satellite image classification across various applications. Enhancing classification accuracy requires the most effective algorithm. The effectiveness of a change detection system has not been tested in the community of Abu SUWEIR, located near the Suez Canal region in Ismailia City, Egypt. This project analyzes seven classification algorithms with the use of remotely sensed Planet Scope multispectral pictures to evaluate and monitor changes in land cover in ABU SUWEIR Village from 2016 to 2022. The post-January 25, 2011 revolution period in Egypt saw a transition from anarchy to the commencement of progress and improvement. This analysis demonstrates that the ANN classifier achieved an overall accuracy of 77.33% in 2016 and 72.00% in 2022, with corresponding KAPPA coefficients of 0.66 and 0.58 for each year. Agriculture was the most common land use type according to the ANN categorization, making up over 60.48% of the study area in 2016 and 53.89% in 2022. Bare terrain is the second most common landcover type, accounting for 30.31% in 2022 and 25.91% in 2016. On the contrary, urbanized regions, accounting for 13.61% in 2016 and 15.80% in 2022, are the least categorized.

A Spatio-Temporal Examination of Land Use and Land Cover Changes in Smart Cities of the Delhi–Mumbai Industrial Corridor

This study provides a detailed analysis of land use and land cover (LULC) changes at the district level within the Delhi–Mumbai Industrial Corridor (DMIC) from 2001 to 2021. Using the Indian Meteorological Department’s sub-divisional framework and MODIS data across seven primary LULC classes, the analysis is instrumental in informing infrastructure planning for existing and future smart cities and industrial clusters within the DMIC. The key findings reveal a yearly increase of 3031.40 sq. km. per year in agricultural land, with decreases in shrubland, grassland, and bareland of −1774.72 sq. km. per year, −1119.62 sq. km. per year, and −203.76 sq. km. per year, respectively. On the other hand, forests grew by a modest 148.14 sq. km. per year, while waterbodies and built-up lands saw minor increases of 55.73 sq. km. and 21.48 sq. km. per year. Ecologically Sensitive Areas (ESAs) were evaluated for LULC changes. The smart cities of Pune and Thane serve as excellent examples of balanced urban development and natural growth management. However, the study also highlights the need for further research to investigate LULC impacts on climatic variables, advocating for a regional planning approach in the DMIC.

Predicting the Impacts of Land Use/Cover and Climate Changes on Water and Sediment Flows in the Megech Watershed, Upper Blue Nile Basin

This study assessed the impacts of the land use/cover (LULC) and climate changes on the runoff and sediment flows in the Megech watershed. The Geospatial Water Erosion Prediction Project (GeoWEPP) was used to assess LULC and climate changes’ impact on runoff, soil loss, and sediment yield. The QGIS 2.16.3 plugin module for land use change evaluation (MOLUSCE) tool with the cellular automata artificial neural network (CA-ANN) was used for LULC prediction based on historical data and exploratory maps. Two commonly used representative concentration pathways (RCPs)—4.5 and 8.5—were used for climate projection in the 2030s, 2050s, and 2070s. The LULC prediction analysis showed an expansion of cropland and settlement areas, with the reduction in the forest and rangelands. The climate projections indicated an increase in maximum temperatures and altered precipitation patterns, particularly with increased wet months and reduced dry periods. The average annual soil loss and sediment yield rates were estimated to increase under both the RCP4.5 and RCP8.5 climate scenarios, with a more noticeable increase under RCP8.5. By integrating DEM, soil, land use, and climate data, we evaluated runoff, soil loss, and sediment yield changes on only land use/cover, only climate, and the combined impacts in the watershed. The results revealed that, under all combined scenarios, the sediment yield in the Megech Reservoir was projected to substantially increase by 23.28–41.01%, showing a potential loss of reservoir capacity. This study recommends strong climate adaptation and mitigation measures to alleviate the impact on land and water resources. It is possible to lessen the combined impacts of climate and LULC change through implementing best-management practices and adaptation strategies for the identified scenarios.

Responses of precipitation and water vapor budget on the Chinese Loess Plateau to global land cover change forcing

There have been notable changes in precipitation patterns on the Loess Plateau (LP) of China in recent decades, and numerous attribution studies have focused on sea surface temperature anomalies and atmospheric circulation changes induced by aerosols and greenhouse gases emission. However, the influences of global land use and land cover change (LULCC) as an important forcing factor in the climate system on regional precipitation remains poorly understood. In this study, we quantified the impacts of LULCC on precipitation and the water vapor budget in the LP region, utilizing data from LULCC forcing experiments conducted by the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Although global LULCC forcing exerted a negative effect on long-term mean precipitation on the LP region from 1850 to 2014, the different response characteristics were detected during different time periods. The global LULCC caused a decrease of 14 mm in annual precipitation during the period of 1850–1960. Conversely, from 1961 to 2014, it led to an increase of 6.4 mm, which is largely attributed to the enhanced water vapor transport along the southern boundary and westerly belt of the LP region. Moreover, from the perspective of the net water vapor balance of the entire LP, although LULCC caused net water vapor export during both periods 1850–1960 and 1961–2014, the export during the latter period (0.20 × 104 kg s−1) was smaller than that during the former period (0.28 × 104 kg s−1), indicating that the global expansion of grassland and cropland, along with the continuous rise in the leaf area index from 1961 to 2014, contributed to retaining more water vapor within the LP, which in turn was more favorable for precipitation. These findings provide valuable insights into the reasons behind precipitation variations in the LP region, emphasizing that global vegetation restoration and greening play a significant role in improving precipitation in ecologically fragile areas.

Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach.

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73°C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50°C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990-2000 to 80.86 km2 in the period 2000-2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization.