Land Use Cover Patterns Research Articles

Exploring the dynamics and future projections of land use land cover changes by exploiting geospatial techniques; A case study of the Kabul River Basin

Global land cover change has caused significant environmental degradation and biodiversity loss. It affects ecosystem functions, livelihoods, and climate variation and has drawn substantial attention in recent decades. In the Kabul River Basin (KRB), there are limited studies on the historical Land Use/Land Cover (LULC) pattern, transition, intensity and future perspective. Therefore, this study aims to investigate long-term LULC changes and major drivers of LULC in the KRB over the past thirty years (1990–2020) and then to project the future LULC pattern for the years 2030, 2040 and 2050. Landsat Imageries of (1990–2020) were used as input data by utilizing the Random Forest Classifier algorithm (RF) in the Google Earth Engine (GEE) to classify the LULC. The LULC was then projected for the future, using the Cellular Automata Markov Chain Model (CA-MCM). The results demonstrated drastic LULC changes, controlled primarily by urbanization and agriculture expansion, which expanded from 467 Km2 (0.7 %) to 2312 km2 (3.4 %) and 6528 km2 (9.6 %) to 10812 (15.9 %), between 1990 and 2020. In contrast, bare land decreased from 70606 km2 (82.1 %) to 48212 km2 (70.9 %) between 1990 and 2020. In addition, the study depicts that the expansion in built-up and vegetation areas in the KRB during the study period were at the utilization of bare land. Future LULC predictions indicated that between 2020 and 2050, bare land would trend downward from 48212 km2 (70.9 %) to 46172 km2 (67.9 %), while vegetation and built-up areas would trend upward from 2312 km2 (3.4 %) to 3640 km2 (5.3 %), 10812 km2 (15.9 %) to 11622 km2 (17.1 %), and water bodies and snowcover would slightly vary from 1.2 % to 0.9 % and 7.9 %–9.0 %. In addition, the results of LULC dynamics reveal a significant strong positive correlation between population and built, as well as population and vegetation. Conversely, there is a strong negative correlation between population and bare land. Our results provide precise insights on LULC patterns and trends in the KRB, which could be employed to design a sustainable framework for land use and ecosystem protection.

Dynamics of urban development patterns on thermal distributions and their implications on water spread areas of Vellore, Tamil Nadu, India

Recent satellite maps have reported that India is experiencing extreme heat waves, surpassing even Middle Eastern countries. This study addresses a critical gap in understanding how land use land cover (LULC) changes impact land surface temperature (LST), urban heat intensity (UHI), and water spread area (WSA) in rapidly growing cities such as Vellore and Katpadi over three decades (1997–2024). We used Landsat thermal bands and the support vector machine (SVM) algorithm to investigate LULC and LST patterns, examining the effects of urbanization and water body reduction on local climate dynamics. The LULC results showed an increase in built-up lands from 5.89 to 25.89%, while zooming water areas shrank from 3.15 to 1.02%. LST showed a significant increasing trend, with temperatures for water bodies and vegetation ranging from 17.4°C to 26°C, and for barren and built-up areas from 28°C to 42.6°C. The results of the multivariate analysis revealed a positive correlation between LST and the Normalized Difference Built-up Index (NDBI) and negative correlations between LST and the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Modified Normalized Difference Water Index (MNDWI). Moreover, spatial and time series analyses of WSAs indicated a significant increase in LST. Furthermore, a strong negative correlation was found between WSA and LST, with a 10% decrease in WSA potentially increasing LST by 0.12°C to 0.55°C in surrounding regions. This study offers important contributions to improving land use policy and water resource management in urban areas, while addressing environmental concerns related to rising temperatures. The findings underscore the urgency of mitigating heat impacts and managing water resources in rapidly expanding cities. Our results provide valuable insights for policymakers and practitioners aiming to develop more sustainable, resilient, and livable urban environments.

Urban sprawl impact assessment on the land surface temperature over the green capital of Gujarat using a geospatial approach.

In developing countries, examining land use land cover (LULC) change pattern is crucial to understanding the land surface temperature (LST) effect as urban development lacks coherent policy planning. The variability in LST is often determined by continuously changing LULC patterns. In this study, LULC change effect analysis on LST has been carried out using geometric and radiometric corrected thermal bands of multi-spectral Landsat 7 ETM + and 8 TIRS/OLI satellite imagery over Gandhinagar, Gujarat, in the years 2001 and 2022, respectively. Maximum likelihood classification (MLC) was applied to assess LULC change while an NDVI-based single-channel algorithm was used to retrieve LST using Google Earth Engine (GEE). Results showed a substantial change in built-up (+ 347.08%), barren land (- 50.74%), and vegetation (- 31.66%). With the change in LULC and impervious surfaces, the mean LST has increased by 5.47 ℃. The impact of sparse built-up was seen on vegetation and agriculture as a maximum temperature of > 47 ℃ was noticed in all LULC classes except agriculture, where the temperature reached as high as > 49 ℃ in 2022. Since Gandhinagar is developing a twin-city plan with Ahmedabad, this study could be used as a scientific basis for sustainable urban planning to overcome dynamic LULC change and LST impacts.

Seasonal dynamics of land surface temperature and urban thermal comfort with land use land cover pattern in semi-arid Indian cities: Insights for sustainable Urban Management

The cities in semi-arid and arid regions generally exhibit distinctive land use land cover (LULC) pattern and seasonal variation in urban thermal comfort (UTC) and land surface temperature (LST). This study examines the seasonal variation in LST and UTC and quantify the importance of LULC pattern in influencing LST in eight Indian semi-arid cities using Landsat 8 datasets. Random forest regression (RFR) model has been applied to quantify the influence of LULC in determining LST. The study shows that bare soil and open spaces in outskirts of selected cities have comparatively high LST than the core of cities. The mean LST varies seasonally from 12.84 °C in Udaipur to 26.18 °C in Jaipur in winter and summer. Analysis of UTC shows that autumn and winter seasons have better UTC than spring and summer seasons. RFR analysis shows a robust correlation (R2 > 0.85) between LULC and LST across all cities. Notably, built-up areas, open land, and vegetation cover exert the greatest influence on LST. Study suggests that targeted LULC planning may effectively address UTC problem in semi-arid cities and enhance sustainability. Study may help in understanding the seasonality thermal environment and assist in mitigating UHI and maintain UTC in these cities.

The promotion of sustainable land use planning for the enhancement of ecosystem service capacity: Based on the FLUS-INVEST-RUSLE-CASA model.

Land Use/Land Cover (LULC) is one of the most significant human variables influencing the efficiency of Ecosystem Services (ESs) in terrestrial ecosystems. Theoretical and technical assistance for regional sustainable land use planning and management, as well as ecosystem conservation and restoration, is provided by investigating the influence of changes in the LULC pattern on the efficiency of ESs. This research focuses on the interactions between socioeconomic activities and natural ecological processes in the Three Gorges Reservoir Area (TGRA). We use LULC data from the TGRA for the years 1990, 2000, 2010, and 2020. The study includes the analysis and calculation of the spatiotemporal evolution features of the current LULC pattern and the efficiency of ESs, including their spatiotemporal distribution. Considering the TGRA's national development orientation and guidance, three potential LULC patterns are constructed under various develop-ment scenarios. To calculate the efficiency of ESs, the GeoSOS-FLUS future LULC simulation model is linked, and several methodologies such as INVEST, RUSLE, and CASA are used. The goal is to investigate the influence of future changes in LULC patterns on ESs efficiency. The findings show the following: (1) From 1990 to 2020, the values of water conservation services in the TGRA decreased and subsequently increased. High-value areas are primarily located in the reservoir's centre and eastern sections, whereas low-value areas are mostly found in the western section. Soil conservation service values initially declined and later climbed. The TGRA's carbon storage services have in-creased yearly, from 552.64 g/m2 in 2000 to 615.92 g/m2 in 2020. (2) In the ecological protection scenario, carbon storage and soil erosion increased compared to the ecosystem services in 2020. The ecological system service benefits are greater when compared to the natural development scenario. (3) The four ESs show positive spatial correlations across all three scenarios, and local spatial au-tocorrelation analysis findings demonstrate that carbon storage, water yield, and habitat quality have comparable spatial distributions across all three scenarios. To some extent, high-value areas for water conservation, soil retention, carbon storage, and habitat quality overlap.

Projecting the response of carbon sink potential to land use/land cover change in ecologically fragile regions

Introduction: The carbon storage service of ecosystems in ecologically fragile areas is highly sensitive to regional land use/land cover (LULC) changes. Predicting changes in regional carbon storage under different LULC scenarios is crucial for land use management decisions and exploring carbon sink potential. This study focuses on the Luan River Basin, a typical ecologically fragile area, to analyze the impact of LULC changes on carbon storage.Methods: The PLUS-InVEST model was employed to simulate LULC patterns for the year 2030 under three scenarios: natural development, cropland protection and urban development, and ecological protection. The model projected the future carbon sink potential of the basin under these scenarios.Results: From 2000 to 2020, carbon storage showed a trend of decrease followed by an increase. By 2030, compared to 2020, carbon storage is projected to increase by 16.97% under the ecological protection scenario and decrease by 22.14% under the cropland protection and urban development scenario. The increase in carbon storage was primarily due to the conversion of cropland and grassland to forestland, while the decrease was mainly associated with the conversion of forestland to grassland and cropland, and the transformation of grassland to cropland and construction land. In the potential LULC scenarios of 2030, certain regions within the basin exhibited unstable carbon sink potential, strongly influenced by LULC changes. These areas were predominantly characterized by artificially cultivated forests, shrubs, and agricultural land. Implementing appropriate forest management measures and optimizing agricultural land management practices are essential to enhance carbon sink potential in these regions. Population density, annual average temperature, and DEM (Digital Elevation Model) were the dominant factors driving the spatial variation of carbon sink potential in the Luan River Basin.Discussion: The research results provide a theoretical basis for rational planning of land use and the enhancement of carbon sink potential in ecologically fragile regions.

Spatiotemporal dynamics of land use/land cover (LULC) changes and its impact on land surface temperature: A case study in New Town Kolkata, eastern India

Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis–Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991–2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991–2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities.

Multi-scenario simulation and evaluation of the impacts of land use change on ecosystem service values in the Chishui River Basin of Guizhou Province, China

Changes in land-use patterns are the main driving factors of ecosystem service values (ESVs). The quantitative evaluation of ESVs is significant for the sustainable development of ecologically fragile areas. In this study, the Chishui River Basin (CRB) in China was selected as the study region, and an intensity map (IM) was used to analyze the transformation of land use/land cover (LULC). The modified equivalent coefficient value method was used to evaluate the ESVs based on the 2000, 2010, and 2020 land use datasets. The spatial and temporal distributions of ESV and its response to changes in LULC were analyzed using the Getis-Ord Gi* statistical method and Spearman’s rank correlation analysis. Moreover, spatial and temporal changes in LULC and ESV over the next 30 years in the four development scenarios were simulated using the Markov-PLUS model. The following results were obtained: 1) The farmland and forestland were the dominant LULC categories, and that the LULC pattern dramatically changed in the study area; 2) the total ESV of the CRB decreased significantly from 23.21 billion yuan in 2000 to 22.29 billion yuan in 2020; and 3) the ecological conservation scenario significantly promoted the total ESV, and the town development scenario was more conducive to the urbanization process. Therefore, although the ESV of the CRB showed a decreasing to slowly increasing trend in the context of a series of ecological restoration measures, scientific ecological protection and development policies must be continuously implemented to achieve harmony between ecological service improvement and urbanization development. Our results provide important information for making land-use decisions and facilitating sustainable development in ecologically protected basins.

Spatial-temporal analysis of urban climate dynamics in major Hungarian cities

Increasing heatwaves are making cities and their populations more vulnerable, parallel to urban sprawl and the aging population in Hungary. The increasing number of hot days is predicted to worsen urban climate anomalies at the local scale, which, in parallel with changing land use patterns, may contribute to a significant increase in vulnerability to heatwaves. Local stakeholders and decision-makers need to understand the critical role of spatiotemporal land use—land cover (LULC) patterns and urban climate aspects to address relevant challenges for urban development. The current literature does not contain a synthesis analysis of major Hungarian cities that includes urban climate and sustainability findings hand by hand; therefore, this study aims to analyze LULC patterns, urban hotspots and surface urban heat island effects. In addition, the Normalized Difference Vegetation Index (NDVI) was determined as an important indicator for assessing the health and density of green spaces in major Hungarian cities from 2006 to 2018 using remote sensing data. Our results show that each city experienced significant urban sprawl, while above-average NDVI areas decreased over time. The average increase in the share of built-up areas was 1.3% from 2006 to 2018, while the calculated average decline in agricultural areas was 2%, so the expansion of residential areas and artificial areas is not the only driving force of this shrinking trend in agricultural areas. Furthermore, we found that urban hotspots are generally concentrated in industrial areas and represent new spaces of heat islands on the outskirts of cities. Székesfehérvár has the most intense industrial heat islands, with the largest proportion of urban hotspots (approximately 3.5% of the total area) concentrated in industrial zones. Our study contributes to uncovering inter-urban processes of land use patterns and urban climate issues in major Hungarian cities, moreover revealing sustainability-related issues from a lock-in perspective.

Identification of vulnerable areas using geospatial technologies in the lower Manair River basin of Telangana, Southern India

The present study is undertaken at the lower Manair River basin, the central part of Telangana in southern India, as the Government authorities has done rapid developmental activities over the last seven years demanded identifying change detection in environmental indicators. Geospatial tools, viz., remote sensing and geographic information system, are used to classify and identify land-use/land cover (LULC) changes during 2015–2021 based on the images collected from Landsat 8 OLI/TIRS multispectral and multi-temporal satellite data set. Change detection of environmental indicators such as LULC changes, land surface temperature (LST), and normalized difference vegetation index (NDVI) are crucial parameters that play a vital role in identifying vulnerable areas, which helps in planning and utilizing natural resources. The results revealed that the agricultural cropland filled in the top class with an area of 1124.70 km2 (33.82%) in 2015, amplified to 1442.95 km2 (43.39%) in 2021. The built-up area has marginally raised by 581.45 km2 (17.49%) in the study region. A negative correlation is observed between LST and NDVI. LULC patterns are changing, and its imprint echoed on a surge of LST. It establishes a foundation for evidence-based policymaking and sustainable growth, emphasizing the importance of proactive measures in preserving the delicate balance between development and environmental conservation.

An integrative climate and land cover change detection unveils extensive range contraction in mountain newts

The global decline of amphibian populations, driven by anthropogenic activities, is a pressing conservation issue, with salamanders being of particular concern, as these species serve as ecological indicators vulnerable to environmental change. Mountain newts of the Neurergus genus, which are endemic to the Zagros Mountain chain from southeastern Turkey to northern Iraq and southwestern Iran, face a multitude of threats. Among these threats, climate change and land use alterations have been identified as major contributors to the decline of these species. Given the varying spatiotemporal scales at which these factors operate, in this study we aimed to assess the impacts of climate and land use/land cover (LULC) changes (LULCC) on the distribution of the Neurergus genus. We employed MaxEnt model to predict their habitat suitability under current climatic conditions. We projected the predicted model to the future, i.e., 2050, under two climate change scenarios. We then proceeded to map the LULC patterns of the identified suitable habitats for each species using Landsat satellite images, and conducted a hindcast of LULCC within these habitats for three time-slices 1988, 2005, and 2020. Finally, we evaluated the efficiency of current network of protected areas (PAs) and key biodiversity areas (KBAs) in Iran, Iraq, and Turkey to cover suitable habitats of the species. Our results revealed that climate changes would negatively influence all Neurergus species, with southern species in Iran and Iraq, i.e., N. derjugini, N. kaiseri, and N. crocatus exhibiting the greatest range loss. Conversely, LULC change detection indicated that northern species in Turkey, i.e., N. strauchii and N. barani, are more exposed to cropland developments and have experienced greatest habitat changes over the past 30 years. Ultimately, our findings underscore the insufficiency of extant conservation areas in protecting Neurergus habitats and urge the need for comprehensive conservation measures. We recommend promoting less strictly conserved areas, e.g., KBAs, implementing trans-boundary conservation plans, and designating new reserves to ensure long-term preservation of amphibians in the regions.

Combining spatiotemporal interactions of ecosystem services with land patterns and processes can benefit sensible landscape management in dryland regions

The study of land-use patterns and transitions has extended its influence into multiple ecosystem services (ESs) and their relationships, the integration of which can offer an opportunity to enhance the credibility of ES assessments in spatial landscape planning and management. However, it is not clear whether actual trade-offs/synergies are consistent at different spatial and temporal scales and have similar responses to land use/land cover (LULC). In this study, based on ecological zoning, we simulated three typical ESs (soil retention, water yield and carbon sequestration) and their correlations in the Loess Plateau of China. Furthermore, the spatial relationships among ESs and LULC patterns and the temporal relationships among ESs and LULC transfer were discussed to identify possible implications for future land-use management. The results showed that there were obvious trade-offs or synergies at the spatial scale, and most synergetic relationships among the three ESs weakened during 2000–2020. However, unstable values are approximately 0 at the temporal scale, so the relationship using “space-for-time” approach exaggerates the temporal coupling among ESs. Most spatial ES interactions decreased with increasing ratio of forest and grassland, which deserves more attention in ecological restoration. It is indispensable to consider both spatial and temporal variations in ES relationships as LULC changes to achieve sustainable development in multiscale landscape planning.

Land Use/Cover Change Prediction Based on a New Hybrid Logistic-Multicriteria Evaluation-Cellular Automata-Markov Model Taking Hefei, China as an Example

Land use/cover change (LUCC) detection and modeling play an important role in global environmental change research, in particular, policy-making to mitigate climate change, support land spatial planning, and achieve sustainable development. For the time being, a couple of hybrid models, such as cellular automata–Markov (CM), logistic–cellular automata-Markov (LCM), multicriteria evaluation (MCE), and multicriteria evaluation–cellular automata–Markov (MCM), are available. However, their disadvantages lie in either dependence on expert knowledge, ignoring the constraining factors, or without consideration of driving factors. For this purpose, we proposed in this paper a new hybrid model, the logistic–multicriteria evaluation–cellular automata–Markov (LMCM) model, that uses the fully standardized logistic regression coefficients as impact weights of the driving factors to represent their importance on each land use type in order to avoid these defects but is able to better predict the future land use pattern with higher accuracy taking Hefei, China as a study area. Based on field investigation, Landsat images dated 2010, 2015, and 2020, together with digital elevation model (DEM) data, were harnessed for land use/cover (LUC) mapping using a supervised classification approach, which was achieved with high overall accuracy (AC) and reliability (AC > 95%). LUC changes in the periods 2010–2015 and 2015–2020 were hence detected using a post-classification differencing approach. Based on the LUC patterns of the study area in 2010 and 2015, the one of 2020 was simulated by the LMCM, CM, LCM, and MCM models under the same conditions and then compared with the classified LUC map of 2020. The results show that the LMCM model performs better than the other three models with a higher simulation accuracy, i.e., 1.72–5.4%, 2.14–6.63%, and 2.78–9.33% higher than the CM, LCM, and MCM models, respectively. For this reason, we used the LMCM model to simulate and predict the LUC pattern of the study area in 2025. It is expected that the results of the simulation may provide scientific support for spatial planning of territory in Hefei, and the LMCM model can be applied to other areas in China and the world for similar purposes.

Roles of historical land use/cover and nitrogen fertilizer application changes on ammonia emissions in farmland ecosystem from 1990 to 2020 in China

In the past decades, China has witnessed significant changes in its land use/land cover (LULC) pattern. These changes have led to a direct impact on ammonia (NH3) emissions in soil background, and indirectly affected the total nitrogen fertilizer (N-fertilizer) application, crop planting amount and the resulting straw mass through the changes of cropland area. Great changes have also taken place in the amount and structure of fertilizer application in China, which affects the NH3 emissions from farmland ecosystems caused by N-fertilizer application. The aforementioned changes have led to significant alterations in NH3 emissions from China's farmland ecosystems over the past 30 years. The process of these changes remains to be analyzed, and the contributions of LULC changes and N-fertilizer application in this process are yet to be assessed. This study aims to investigate the NH3 emission changes and spatiotemporal variation characteristics from farmland ecosystems during 1990 and 2020 due to the LULC changes. Additionally, the study employs scenario analysis method to discuss the effects of LULC changes and N-fertilizer application changes on NH3 emissions in farmland ecosystems. Results indicate that there is evident spatiotemporal heterogeneity in China's LULC pattern, particularly in eastern China. The southeast region is predominantly characterized by the conversion of cropland into construction land. Moreover, some regions such as Northwest China and Northeast China have experienced the conversion of other land types into cropland, significantly influenced by national development policies. From 1990 to 2020, the national NH3 emissions from farmland ecosystem range from 3294.75 Gg to 4064.20 Gg. NH3 emissions and their interannual variation in farmland ecosystems exhibit significant differences across various regions. The regions with higher contributions to NH3 emissions in farmland ecosystems are East China, Central China, and North China, accounting for 25.32 %–37.26 %, 18.85 %–22.46 % and 11.24 %–18.50 % of the total emissions, respectively. NH3 emissions in each region are influenced by cropland area, N-fertilizer application, and regional development characteristics. Compared to LULC changes, changes in N-fertilizer application have a more pronounced impact on NH3 emission changes in farmland ecosystems. From 1990 to 2020, the contribution (increase or decrease) of N-fertilizer application changes to NH3 emission changes in farmland ecosystems in China ranges from 0.11 % to 16.61 %, while the contribution (increase or decrease) of LULC changes ranges from 0.47 % to 2.38 %. South China demonstrates a unique situation regarding the influence of LULC changes. This region has a relatively small cropland area, and fluctuations in cropland area significantly affect NH3 emissions in farmland ecosystems. The influence of policies is evident. From the changes in cropland area in Northwest China and Northeast China to changes in N-fertilizer application, policy changes have consistently impacted the changes in NH3 emissions in China's farmland ecosystems. From “soft policies” involving encouragement and guidance to “hard policies” encompassing the establishment of necessary targets, the degree of strictness in policy directly affects the timeliness of policies effectiveness. The results of this study indicate that reducing the application of N-fertilizers is the primary approach to reducing NH3 emissions in China's farmland ecosystems. In terms of policy guidance, compared to implementing structural and pathway adjustments, implementing clear total control of fertilizer usage is a timely and effective choice for reducing NH3 emissions.

Simulating the Hydrological Processes under Multiple Land Use/Land Cover and Climate Change Scenarios in the Mahanadi Reservoir Complex, Chhattisgarh, India

Land use/land cover (LULC) and climate are two crucial environmental factors that impact watershed hydrology worldwide. The current study seeks to comprehend how the evolving climate and LULC patterns are impacting the hydrology of the Mahanadi Reservoir catchment. A semi-distributed Soil and Water Assessment Tool (SWAT) model was utilized to simulate various water balance elements. Twelve distinct scenarios were developed by combining three different climatic data periods (1985–1996, 1997–2008, and 2009–2020) with four sets of land use maps (1985, 1995, 2005, and 2014). The SWAT model demonstrated strong performance in simulating monthly stream flows throughout the calibration and validation phases. The study reveals that changes in LULC have a distinct effect on the environment. Specifically, the changes in LULC lead to heightened streamflow and reduced evapotranspiration (ET). These changes are mainly attributed to amplified urbanization and the diminished presence of water bodies, forest cover, and barren land within the Mahanadi Reservoir catchment. The combined impact of climate change and LULC shifts reveals complex interactions. Therefore, the present study offers an understanding of how changes in climate and land use over the past few decades have influenced the hydrological behavior of the Mahanadi Reservoir catchment in Chhattisgarh. The findings of this study have the potential to offer advantages to governmental bodies, policymakers, water resource engineers, and planners seeking effective strategies for water resource management. These strategies would be particularly relevant in the context of climate change and land use/land cover changes in ecological regions similar to those of the Mahanadi Reservoir catchment. In addition, a rational regulatory framework for land use patterns is essential for assisting stakeholders in managing water resources and appropriately developing the entire catchment.

Quantifying irrigation water demand and supply gap using remote sensing and GIS in Multan, Pakistan.

Human interventions and rapid changes in land use adversely affect the adequate distribution of water resources. A research study was conducted to quantify the gap between demand and supply for irrigation water in Multan, Pakistan, which may lead to sustainable water management. Two remotely sensed images (Landsat 8 OLI and Landsat 5 TM) were downloaded for the years 2010 and 2020, and supervised classification method was performed for the selected land use land cover (LULC) classes and basic framework. During the evaluation, the kappa coefficient was found in the ranges of 0.83-0.85, and overall accuracy was found to be more than 80% which indicated a substantial agreement between the classified maps and the ground truth data for both years and seasons. The LULC maps showed that urbanization has increased by 49% during the last decade (2010-2020). Reduction in planting areas for wheat (9%), cotton (24%), and orchards (46%) was observed. An increase in planting areas for rice (92%) and sugarcane (63%) was observed. The changing LULC pattern may be related to variation in water demand and supply for irrigation. The irrigation water demand has decreased by 370.2 Mm3 from 2010 to 2020, due to the reduction in agricultural land and an increase in urbanization. Available irrigation water supply (canals/rainfall) was estimated as 2432 Mm3 for the year 2020 which was 26% less than that of total irrigation water demand (3281 Mm3). The findings also provide the database for sustainable water management and equitable distribution of water in the region.

Mapping, intensities and future prediction of land use/land cover dynamics using google earth engine and CA- artificial neural network model.

Mapping of land use/ land cover (LULC) dynamics has gained significant attention in the past decades. This is due to the role played by LULC change in assessing climate, various ecosystem functions, natural resource activities and livelihoods in general. In Gedaref landscape of Eastern Sudan, there is limited or no knowledge of LULC structure and size, degree of change, transition, intensity and future outlook. Therefore, the aims of the current study were to (1) evaluate LULC changes in the Gedaref state, Sudan for the past thirty years (1988-2018) using Landsat imageries and the random forest classifier, (2) determine the underlying dynamics that caused the changes in the landscape structure using intensity analysis, and (3) predict future LULC outlook for the years 2028 and 2048 using cellular automata-artificial neural network (CA-ANN). The results exhibited drastic LULC dynamics driven mainly by cropland and settlement expansions, which increased by 13.92% and 319.61%, respectively, between 1988 and 2018. In contrast, forest and grassland declined by 56.47% and 56.23%, respectively. Moreover, the study shows that the gains in cropland coverage in Gedaref state over the studied period were at the expense of grassland and forest acreage, whereas the gains in settlements partially targeted cropland. Future LULC predictions showed a slight increase in cropland area from 89.59% to 90.43% and a considerable decrease in forest area (0.47% to 0.41%) between 2018 and 2048. Our findings provide reliable information on LULC patterns in Gedaref region that could be used for designing land use and environmental conservation frameworks for monitoring crop produce and grassland condition. In addition, the result could help in managing other natural resources and mitigating landscape fragmentation and degradation.

Using machine learning and remote sensing to track land use/land cover changes due to armed conflict

Armed conflicts have detrimental impacts on the environment, including land systems. The prevailing understanding of the relation between Land Use/Land Cover (LULC) and armed conflict fails to fully recognize the complexity of their dynamics – a shortcoming that could undermine food security and sustainable land/water resources management in conflict settings. The Syrian portion of the transboundary Orontes River Basin (ORB) has been a site of violent conflict since 2013. Correspondingly, the Lebanese and Turkish portions of the ORB have seen large influxes of refugees. A major challenge in any geoscientific investigation in this region, specifically the Syrian portion, is the unavailability of directly-measured “ground truth” data. To circumvent this problem, we develop a novel methodology that combines remote sensing products, machine learning techniques and quasi-experimental statistical analysis to better understand LULC changes in the ORB between 2004 and 2022. Through analysis of the resulting annual LULC maps, we can draw several quantitative conclusions. Cropland areas decreased by 21–24 % in Syria's conflict hotspot zones after 2013, whereas a 3.4-fold increase was detected in Lebanon. The development of refugee settlements was also tracked in Lebanon and on the Syrian/Turkish borders, revealing different LULC patterns that depend on settlement dynamics. The results highlight the importance of understanding the heterogenous spatio-temporal LULC changes in conflict-affected and refugee-hosting countries. The developed methodology is a flexible, cloud-based approach that can be applied to wide variety of LULC investigations related to conflict, policy and climate.

Implications of future land-use/cover pattern change on landslide susceptibility at a national level: A scenario-based analysis in Romania

Land-use/cover (LUC) change is a global change forcing with important impacts on landslides, which highlights the need to evaluate the potential future evolution of these hazards under future LUC scenarios. Accordingly, the current paper provides a first national-level exploration on the possible effects of LUC pattern change on landslide susceptibility (LS) in Romania, shedding light on the evolution of one of Europe’s landslide hotspot countries as well as one of the territories significantly affected by recent LUC change, especially after the fall of the communist regime. Two countrywide future LS maps were modelled for 2075, by applying a previously developed national scale-adapted LS empirical procedure, integrating therein two existing long-term LUC pattern forecasts and assuming no change for the other landslide controlling factors. The estimated future LS maps were then compared to the current LS zonation using a pixel-based analysis to estimate the contribution of LUC class transitions to the LS variation in time. The quantitative examination of LS changes suggests an overall net slight decrease at the national level under both scenarios (on an average of 7.3% of the LUC change area), with more significant regional variations. The location and extent of the estimated potential LUC transitions reflect themselves into a clear influence on the future LS level manifesting itself, on average, over 7.1% of the country’s area. The analysis demonstrates that the expected increase in the forest-covered area brings the most important contribution to the decrease of the LS level. An average of 44.2% of the total LS decrease area was mainly predicted by the transitions from pastures and natural grasslands/scrubs to forests, and 11.4% by that from heterogeneous agricultural areas to forests. Conversely, transitions to pastures and natural grasslands resulted in the most important contribution to the increase of the LS level (52.2%). Transitions among other agricultural lands resulted in slight susceptibility variations or in no change. Through the analysed LUC scenarios, differing in terms of the implementation of environmental management regulations inside protected areas, the study proves that a more appropriate land management could have an important influence on decreasing LS, a finding of particular relevance for encouraging and extending a conscious application of protective land measures, especially on slopes which are degraded or prone to instability, in a country otherwise characterised by an overall relative implementation of environmental policies. In addition, the analysis reveals that a significant proportion (24.2%) of the projected new built-up areas is expected to spread by 2075 over lands with moderate, high and very high LS, resulting in potentially new landslide exposure hotspots in the future in both rural and urban areas. The obtained results could be used in directing landslide risk reduction actions and decisions primarily towards the management of LUC and towards prioritizing areas for such measures, while, from a theoretical perspective, this approach could be extended to future investigations related to LUC change implications for landslide hazard and risk in Romania, but could also be followed in case of other national territories.

Ecological risk assessment of Kaeng Krachan National Park emphasis on Land Use/Land Cover (LULC)

Land use/land cover (LULC) changes with time, signify disturbances and conservation patterns of landscape types in an environment. Kaeng Krachan National Park (KKNP) is a UNESCO world heritage site with rich biodiversity and provides habitat for indigenous communities who are relying on farming. The study was conducted by calculating landscape ecological risk indices of classified Landsat images based on five landscape classes (Water, Grassland, Built-up-land, Forest, and Cultivated land). Landscape loss indices were calculated based on classified Landsat images. Assigning landscape loss indices in grid cells calculated the ecological risk indices of the area. Ecological risk indices were lowest (ERI ≤ 0.0005), lower (ERI ≤ 0.001), medium (ERI ≤ 0.005), higher (ERI ≤ 0.01), and highest (ERI ≤ 0.015). Ecological risk indices were higher on the east border of KKNP proximate to the semi-urban settlements. The highest risk area demarcates waterbodies and extensively cultivated land. The LULC areas which show the higher variation of changes from 2013 to 2022 are grasslands and forest cover. The changes in the LULC pattern have increased the risk in cultivated land areas and grasslands due to farming obligations. The provision of land permits and land use limitations are addressed issues to conserve KKNP for conservation.