Land Use Cover Change Research Articles

Spatio-temporal characteristics and multi-scenario simulation analysis of ecosystem service value in coastal wetland: A case study of the coastal zone of Hainan Island, China

Coastal wetland ecosystems harbor rich biodiversity and possess significant ecosystem service value (ESV). Therefore, it offers a range of crucial ecosystem services (ES) for human well-being and socio-economic development. Taking the Hainan Island coastal zone (HICZ) as a case study, the spatio-temporal characteristics of land use and land cover change (LULCC), and its associated ESV in wetland landscapes were analyzed over three time points (2000, 2010 and 2020). We explored the spatio-temporal evolution trajectory of ESV on the basis of geo-information tupu. Then, future land use simulation (FLUS) was employed to predict wetland patterns and ESV under three different scenarios: business as usual (BAU), ecological conservation first (ECF), and economic development first (EDF). The results showed that over the past two decades, a significant proportion (exceeding 80%) of the overall wetland region was comprised of offshore and coastal wetlands (OCW) as well as constructed wetlands (CW); these formed the matrix of the landscape. The area of building land (BL) continued to exhibit a consistent upward trend. Expanding by 2.18 times, it represented the most significant increase in the rate of dynamic changes in BL. The main ES in the HICZ corresponded to the regulation services (53.57%) and the support services (27.58%). The ESV of wetland losses accounted for 45.17% (43.08 × 108 yuan) of the total loss. The spatial differentiation of ESV in the HICZ was larger in the southwest and the northeast regions, while it was comparatively lower in the north. The transformation in the area of early and late change types accounted for 236.46 km2 and 356.69 km2, respectively. The scenario ECF was achieved with an optimal development of ESV (1807.72 × 108 yuan), which was coordinated with the high-level of development of regional ES functions and the economy. These findings provide valuable information for the sustainable development as well as the protection of ecology and environment of the coastal zone under the background of the construction of Hainan pilot free trade zone in the future.

Response of habitat quality to land use changes in The Johor River Estuary

ABSTRACT Rich resources and fragile ecosystems of estuaries are increasingly impacted by land use and land cover (LULC). However, previous studies of habitat quality (HQ) typically do not model the impact of LULC in Southeast Asian estuaries. Here, we used the Integrated Valuation of Environmental Services and Trade-offs (InVEST) model and the Patch-generating Land-Use Simulation (PLUS) model to assess and predict HQ and habitat degradation in the Johor River Estuary (JRE) from 1989 to 2047. Habitat quality contribution rate (HQCR) was used to quantify the impact of land-use changes on HQ. We analyzed the correlation and spatio-temporal changes in LULC and HQ, respectively. Our forecasts show (1) Construction land expanding more than 10 times from 1989 to 2047. (2) HQ increases slightly followed by a continuous decrease in the habitat quality index (HQI). (3) Degradation of HQI was from forests, agricultural land, residential land expansion and land reclamation. (4) Population growth, economic development, and national development policies may pose significant future threats to water, land, and marine traffic in the Johor River. Our results show that land-use changes affect HQ and future development of the JRE should integrate the impacts of natural and human factors on LULC changes.

Addressing the impact of land use land cover changes on land surface temperature using machine learning algorithms

Over the past two and a half decades, rapid urbanization has led to significant land use and land cover (LULC) changes in Kabul province, Afghanistan. To assess the impact of LULC changes on land surface temperature (LST), Kabul province was divided into four LULC classes applying the Support Vector Machine (SVM) algorithm using the Landsat satellite images from 1998 to 2022. The LST was assessed using Landsat data from the thermal band. The Cellular Automata-Logistic Regression (CA-LR) model was applied to predict the future patterns of LULC and LST for 2034 and 2046. Results showed significant changes in LULC classes, as the built-up areas increased about 9.37%, while the bare soil and vegetation cover decreased 7.20% and 2.35%, respectively, from 1998 to 2022. The analysis of annual LST revealed that built-up areas showed the highest mean LST, followed by bare soil and vegetation. The future simulation results indicate an expected increase in built-up areas to 17.08% and 23.10% by 2034 and 2046, respectively, compared to 11.23% in 2022. Similarly, the simulation results for LST indicated that the area experiencing the highest LST class (≥ 32 °C) is expected to increase to 27.01% and 43.05% by 2034 and 2046, respectively, compared to 11.21% in 2022. The results indicate that LST increases considerably as built-up areas increase and vegetation cover decreases, revealing a direct link between urbanization and rising temperatures.

Assessing Historical LULC Changes’ Effect on Ecosystem Services Provisioning and Their Values in a Mediterranean Coastal Lagoon Complex

Assessing Historical LULC Changes’ Effect on Ecosystem Services Provisioning and Their Values in a Mediterranean Coastal Lagoon Complex

Dry season assessment of carbon storage and emission from upland and riparian soils in the Ganga River basin

Soil organic carbon (C) and nitrogen (N) are profoundly affected by changes in land use and land cover (LULC), especially by farming in riparian zones. The five LULC classes were selected in contiguous order, i.e., undisturbed forest (CALref), conventional agriculture lands (CAL), reservoir riparian zones (RSRZ), river riparian zones (RRZ), and undisturbed riparian zones (RZref) in the Ganga River basin to study the C storage and emission trends at a landscape scale. The riparian soils showed higher moisture, temperature, and bulk density than the upland soils, strongly determining the spatial variations in the CO2 equivalent (CO2e), C:N ratio, and CO2 efflux. Riparian CO2e was more bulk density-dependent, while upland CO2e showed greater dependence on TOC. The C:N ratio showed a higher mean value in the reference soils (CALref and RZref) than in the other soils (P < 0.05). The total nitrogen (TN), total protein (TP), and NH4-N (ammonium‑nitrogen) showed the following trend: RSRZ > CAL > RRZ > RZref > CALref. The stepwise multiple regression models illustrated that soil moisture was the primary regulator of the C:N ratio and CO2 efflux in the upland soils while the temperature in the riparian soils. These intrinsic soil variables resulted in 1.15 to 2.26 times higher CO2 efflux from the cultivated soils (CAL, RSRZ, and RRZ) than from the reference soils. Hence, the present study revealed how agricultural practices tangibly increase the riparian system's carbon footprint while offering unsustainable livelihood options to farmers.

Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning.

Synergistic approach for land use and land cover dynamics prediction in Uttarakhand using cellular automata and Artificial neural network

Alterations in Land use and Land cover (LULC) stand out as a key catalyst for shifts in global climate patterns, environmental conditions, and ecological dynamics. In order to further enhance our comprehension of the effects of variability and climate shifts on the environment, Remote sensing and GIS analytical approaches have been thoroughly explored and are reflected in an imperative vision. Thus, the objective of this study is to model Uttarakhand’s LULC pattern for 2032 and analyse changes in Uttarakhand's LULC trend between 1992 and 2022. LULC change mapping was conducted utilizing a semi-automated hybrid classification approach for high level of accuracy which integrates both Maximum likelihood and Object based image analysis classification techniques on Landsat datasets. The machine learning techniques of Cellular automata and Artificial neural networks (CA-ANN) within MOLUSCE plugin of QGIS have been applied to model future LULC patterns. The accuracy assessment results showed that the overall accuracy of LULC for the years 1992, 2002, 2012, and 2022 was 96.94 %, 97.77 %, 98.61 % and 98.87 % respectively, and the overall kappa statistics coefficient was 0.92, 0.95, 0.94 and 0.95 respectively. The simulated and projected LULC map for 2022 implies a substantially high level of accuracy, with an overall Kappa coefficient value of 0.77 % and 85.39 % of overall correctness. Then, LULC changes for the year 2032 are predicted using CA-ANN. The observed alterations between 1992 and 2032 are significant, characterized by an augmentation in built-up areas, open land, and water bodies, alongside a decline in snow-covered regions, vegetation cover. Whereas, a slight increase is seen in Forested areas. Planners and policy makers aiming to accomplish a more sustainable future and efficient management of the environment will find the changes in LULC over a prolonged period of time to be a useful asset for optimal land use planning.

Impact of land use/land cover change on surface water hydrology in Akaki river catchment, Awash basin, Ethiopia

Watershed reactions to environmental changes is critical for water resource use and management planning. The change in land use/land cover (LUCC) alters surface water hydrology and water balance, subsequently it affects the availability of water resources. This study examined Akaki River catchment surface hydrological responses to future land use scenarios. The future LULC simulated using Cellular Automata-Markov Chain (CA-Markov) model in IDRISI software for 2031, 2041, and 2051 under Business as Usual (BAU) and Forest Patch Protection (FPP) scenarios. Water yield (WY), sediment export (SE), and soil loss (SL) were simulated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The findings indicate that under BAU, forest and settlement areas increased 42.64% and 31.47%, whereas water body, grass land, and agriculture dropped 31.99%, 25.72%, and 10.4%, respectively, from 2021 to 2051. The FPP scenario shows a 66.42% increase in forest coverage and a 31.97% decrease in cultivated land between 2021 and 2051. Overall settlement expansion will expand by 31.44%, while water body and grassland will decline by 11.12% and 24.9%. As a result of LULC changes in BAU scenario, the proportions of WY (4.36%), SE (26%), and SL (22.45%) increased. The hydrological components exhibited more gradual variations of 0.55%, 18.8%, and 14.24% for the WY, SE, and SL, respectively, under the FPP scenario. Therefore, the alteration has resulted in a concerning state within the area, necessitating the implementation of comprehensive management techniques to restore the hydrology of the catchment.

Impact of changing urban form and production-living-ecological space on changing ecosystem services of a smart city of Eastern India, Durgapur.

Worldwide land use land cover (LULC) transformation become a serious issue in the last few decades due to its immense importance in environmental and human well-being perspectives. Expansion of urban areas at the expense of natural land covers and changing urban form is mainly responsible for changing environmental conditions. This study focused on identifying the impacts of LULC change on environmental conditions through the assessment of changing ecosystem services (ESs) of the Durgapur Municipal Corporation (DMC) from 1990 to 2020. Changing ESs are assessed based on changing urban forms and production-living-ecological space (PLES) components. Results found that the compactness of urban areas is increasing along with the outward expansion. The core urban area of DMC has risen from 8.11% to 30.11% during 1990-2020. Similarly, living space increased from 15.57% to 42.60%, production space decreased from 53.06% to 25.59%, and ecological space fluctuated from 1990 to 2020. This transformation of PLES components negatively affects DMC's environmental condition, affecting the achievement of Sustainable Development Goals (SDGs). These significant results may be utilized to understand changing environmental conditions and priority issues for DMC's future sustainable urban development.

Spatio-temporal analysis of riverbank changes using remote sensing and geographic information system

Fluvial hazards cause severe damage to human lives and properties. The increased intrusion of anthropogenic activities into natural hydrological cycles has increased hazard severity and confounded river channel dynamics. The present article studies the epochal channel erosion, accretion, and unaltered sites along the 42 km stretch between the Polavaram project and Dowleshwaram Barrage on the Godavari River. Geographic Information System (GIS) and Remote Sensing (RS) techniques were used to identify changes in river morphology. A total of five decades of Landsat images were analyzed, classifying the land use with a supervised learning Support Vector Machine (SVM) algorithm and an ISO cluster unsupervised algorithm to delineate the rivers and study the influence of land use land cover change (LULCC) on river morphology. Accretion and erosion channel changes during the assessment period were measured at all reaches, and the possible driving mechanisms of morphological alterations were studied.The results indicate the susceptibility of human dwellings and agricultural lands to floods due to the shifting of the highly vibrant river shoreline. The study provides the most recent information on Godavari River dynamics. These aspects of the river's evolution will aid in planning and management in the lower portions, which have not been well understood. River channel alterations can be quantitatively and economically analyzed using Remote Sensing (RS) and Geographic Information System (GIS) data.

Analysis of Land Use and Land Cover Changes and Trends in Fantalle Range Land, East Shewa Zone, Oromia Regional State, Ethiopia

This study was conducted in Fantalle Range lands in East Shewa zone of, Oromia Region, Ethiopia, to assess land use land cover changes, trends, drivers and their socioeconomics. Household surveys were conducted through simple random sampling to collect qualitative data. Qualitative data are used to investigate the causes and effects of land use and land cover changes. SPSS software (version 20) was used for data analysis, and descriptive research methods were adopted. Additionally, map processing was done using ERDAS Imagine (version 9.1) and ArcGIS (version 10.1). The land use land cover classification activity was started by obtaining Landsat images of 1972, 1990, 2000 and 2020 at different intervals from the Earth Explorer (USGS) from the Landsat 4, Landsat 5, Landsat 7 and Landsat 8, respectively. Land use land cover change (LULCC) maps are generated based on year classification. Range land, agricultural land, woody vegetation, bare land and settlement are the five main LULCC categories generated from satellite data. The findings show that in the presence of LULCC, agricultural land, settlements and bare land expand significantly, while range land and woodland show a decreasing trend. The classification results of the 1972 image show that rangeland/grazing land accounts for the largest proportion of the land in this area, accounting for 31.6%. In addition, due to various factors, the number of livestock owned in pastoral areas is also decreasing. The main cause of changes in livestock types is drought, which can cause different impacts, such as feed and water shortages and health problems. Therefore, intervention in land use manipulation is needed to maintain ecosystems and natural resources. Furthermore, rangeland policies should be developed to maintain pastoral and pastoral systems.

Dynamics of land use and land cover changes in Amibara and Awash-fentale districts, Ethiopia

The analysis of land-use and land-cover (LULC) changes is crucial for rural development planning, food security monitoring, and natural resource conservation. This study focuses on detecting LULC changes in Amibara and Awash-Fentale districts from 1985 to 2021. We utilized five sets of Landsat data (Landsat 5 TM for 1985, 1995, 2002, and Landsat 8 OLI for 2015 & 2020) and applied supervised maximum likelihood classification. Accuracy assessments revealed overall accuracies ranging from 88.9% to 95.3% for Amibara and 89.5%–93.2% for Awash-Fentale. Both districts exhibited six main LULC classes: agriculture, bareland, built-up, mixed forest, shrubland, and water bodies. In Amibara LULC changes from 1985 to 2021 revealed significant shifts, maintaining its primary bareland characteristic, concentrated agriculture, and expanding Prosopis-dominated shrubland due to livestock-mediated seed dispersal. Conversely, in Awash-Fentale bareland dominance decreased from 92.28% to 67.02%, while agriculture, built-up areas, and shrubland expanded. Water bodies emerged between 2015 and 2021 which is associated with the construction of Kesem Kebena dam for sugar cane farm production. The net gains were observed in shrubland (12.9%), agriculture (5.8%), mixed forest (4.1%), water bodies (1.5%), and built-up areas (0.9%), with bareland experiencing a loss of 25.3%. In conclusion, Amibara and Awash-Fentale underwent both comparable and distinct LULC shifts, featuring prevalent bareland and central agriculture, alongside Prosopis-driven shrubland expansion due to livestock dispersal. While mixed forest exhibited fluctuations, built-up areas and water bodies remained limited. Notably, Awash-Fentale showed higher LULC variability. Understanding these land cover changes helps assess vulnerability to climate impacts like droughts and floods, enhancing climate resilience. Insights from this study can inform sustainable land-use planning, conservation strategies, and policy interventions in the Afar region and similar areas. These observations highlight the need for integrated land management approaches that balance socioeconomic development with environmental sustainability.

Deep learning-based modeling of land use/land cover changes impact on land surface temperature in Greater Amman Municipality, Jordan (1980–2030)

Modeling the impacts of Land Use/Land Cover changes (LULCC) on Land Surface Temperature (LST) is crucial in understanding and managing urban heat islands, climate change, energy consumption, human health, and ecosystem dynamics. This study aimed to model past, present, and future LULCC on Land Surface Temperatures in the Greater Amman Municipality (GAM) in Jordan between 1980 and 2030. A set of maps for land cover, LST, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and topography was integrated into the Cellular Automata-Artificial Neural Network (CA-ANN) and the Long-Short-Term Model (LSTM) models to predict the LULC and LST for 2030. The results showed an expansion of urban areas in GAM from 54.13 km2 (6.6%) in 1980 to 374.1 km2 (45.3%) in 2023. However, agricultural areas decreased from 152.13 km2 (18.5%) in 1980 to 140.38 km2 (17%) in 2023, while barren lands decreased from 54.44 km2 (6.6%) in 1980 to 34.71 km2 (4.22%) in 2023. Forested areas declined from 4.58 km2 (0.56%) in 1980 to 4.35 km2 (0.53%) in 2023. Rangelands/ sparsely vegetated areas declined from 557 km2 (67.7%) in 1980 to 270.71 km2 (32.9%) in 2023. The results of modeling LST showed an increase in average LST for all land cover types, with the most significant increases evident within urban areas and Rangelands/Sparsely vegetated areas. The slightest increase in LST was within forested areas as the average LST increased from 28.42 °C in 1980 to 34.16 °C in 2023. The forecasts for the future showed a continuous increase in LST values in all land cover types. These findings highlight the impact of land surface dynamics and their impact on increasing land surface temperature, which urges the adoption of more sustainable planning policies for more livable and thermally comfortable cities.

Spatio-temporal analysis and prediction of land use landcover (LULC) change in Wular Lake, Jammu and Kashmir, India.

Landsat land use/land cover (LULC) data analysis to establish freshwater lakes' temporal and spatial distribution can provide a solid foundation for future ecological and environmental policy development to manage ecosystems better. Analysis of changes in LULC is a method that can be used to learn more about direct and indirect human interactions with the environment for sustainability. Neural network technology significantly facilitates mapping between asymmetric and high-dimensional data. This paper presents a methodological advancement that integrates the CA-ANN (cellular automata-artificial neural network) technique with the dynamic characteristics of the water body to forecast forthcoming water levels and their spatial distribution in "Wular Lake." We used remote sensing data from 2001 to 2021 with a 10-year interval to predict spatio-temporal change and LULC simulation. The validation of the calibration of predicted and accurate LULC maps for 2021 yielded a maximum kappa value of 0.86. Over the past three decades, the study region has seen an increase in a net change % in the impervious surface of 22.41% and in agricultural land by 52.02%, while water decreased by 14.12%, trees/forests decreased by 40.77%, shrubs decreased by 11.53%, and aquatic vegetation decreased by 4.14%. Multiple environmental challenges have arisen in the environmentally sustainable Wular Lake in the Kashmir Valley due to the vast land transformation, primarily due to human activities, and have been predominantly negative. The research acknowledges the importance of (LULC) analysis, recognizing it as a fundamental cornerstone for developing future ecological and environmental policy frameworks.

Long-Term Landscape Changes in the Ojców National Park (Poland) and Its Surroundings: Implications for the Effectiveness of Buffer Zones

Protected areas (PAs) serve as crucial elements in biodiversity conservation but are in danger of becoming isolated islands in human-dominated landscapes. It is related to landscape changes, especially changes in land use and land cover (LULC). Over the past decades, most research on the effectiveness of nature conservation has focused mainly on PAs, while the areas surrounding PAs are of key importance for maintaining ecological connectivity and biodiversity. Therefore, the main objective of this study was to determine the long-term changes in LULC within the selected national park in Poland and its surroundings and to assess the effectiveness of the BZ based on these changes. We hypothesized that, despite restrictions within the buffer zone, land development has intensified and increased, in the nearest surroundings of the analyzed national park. For the analysis, we selected Ojców National Park (southern Poland), one of the oldest national parks in Poland. We analyzed landscape changes before (since the 1930s) and after establishing the park and its BZ. We conducted a comprehensive quantitative analysis of the landscape structure and LULC. We used historical maps and the contemporary national LULC database. Our results showed that almost 40% of the study area consisted of lands with non-persistent LULC. The main changes include a three-fold increase in built-up areas and an increase in forest cover, mainly on abandoned agricultural land. We also found that land development around the national park is at a level similar to the general rate for the area outside the BZ. It suggests the ineffectiveness of the buffer zone in preventing land development. The identified long-term landscape changes the basis for sustainable development land management from the nature conservation perspective.

LULC changes in the region of the proposed Pwalugu hydropower project using GIS and remote sensing technique

LULC changes in the region of the proposed Pwalugu hydropower project using GIS and remote sensing technique

Impacts of Forest Management‐Induced Productivity Changes on Future Land Use and Land Cover Change

AbstractAnthropogenic land use and land cover change (LULCC) is projected to continue in the future. However, the influence of forest management on forest productivity change and subsequent LULCC projections remains under‐investigated. This study explored the impacts of forest management‐induced change in forest productivity on LULCC throughout the 21st century. Specifically, we developed a framework to softly couple the Global Change Analysis Model and Global Timber Model to consider forest management‐induced forest productivity change and projected future LULCC across the five Shared Socioeconomic Pathways (SSPs). We found future increases in forest management intensity overall drive the increase of forest productivity. The forest management‐induced forest productivity change shows diverse responses across all SSPs, with a global increase from 2015 to 2100 ranging from 3.9% (SSP3) to 8.8% (SSP1). This further leads to an overall decrease in the total area with a change of land use types, with the largest decrease under SSP1 (−7.5%) and the smallest decrease under SSP3 (−0.7%) in 2100. Among land use types, considering forest management‐induced change significantly reduces the expansion of managed forest and also reduces the loss of natural land in 2100 across SSPs. This suggests that ignoring forest management‐induced forest productivity change underestimates the efficiency of wood production, overestimates the managed forest expansion required to meet the future demand, and consequently, potentially introduces uncertainties into relevant analyses, for example, carbon cycle and biodiversity. Thus, we advocate to better account for the impacts of forest management in future LULCC projections.

Uncovering the impacts of LUCC on ecological connectivity in suburban open-pit mining concentration areas: A pattern collection of changing relationships between ecological resistance and ecological network elements

The suburban open-pit mining concentration area couples two human activities, urban sprawl and open-pit mining, while destruction of surface vegetation and ecological restoration coexist. This has also resulted in complex land use shifts that significantly affect regional ecological connectivity. However, the impact of such complex Land use/land cover changes (LUCC) on ecological connectivity in this region is not well understood Therefore, this study takes the suburban open-pit mining concentration area of Jinan City as a case study and uses Land use/land cover (LULC) data from the years 2000, 2010, and 2020 to simulate the spatio-temporal evolution of ecological networks (ENs). Subsequently, a pattern collection of changing relationships was proposed that relate changes in ecological resistance to changes in EN elements, addressing the dynamic transmission process from "spatial field" to "spatial structure entity". Based on this approach, GeoDetector is employed to reveal the response extent of EN elements to LUCC. The results indicate: (1) In the past 20 years, the area of construction land has increased by 100 %, while mining land has increased by 69 %. This significant increase in LUCC has led to a notable enhancement in ecological resistance in the study area, driving the generation of 20 % more ecological pinch points and barriers. Additionally, it has resulted in a twofold increase in both the number and length of level 1 ecological corridors. (2) The dominant status and structure of grassland, agriculture land, and woodland remain relatively stable; however, the interflow among them has caused approximately 25 % of the ecological corridors to undergo changes such as disappearance and path alteration. (3) The implementation of ecological protection and restoration has led to a continuous increase in the area of ecological sources, particularly with the mining rehabilitation increasing by 32.7 times compared to 2000, thereby driving gradual improvement in 25 % of the ecological network elements. (4) Although the quantity and length of ecological corridors in the study area remain relatively stable, evaluation results indicate that its spatial structure is gradually deteriorating, with ecological connectivity decreasing by 7.7 %. This study highlights the importance of restoration of ecological processes in sub-urban open-pit mining concentration areas. The study results can provide a scientific basis for ecological restoration and sustainable development in resource-based cities.

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.

Annual time-series 1 km maps of crop area and types in the conterminous US (CropAT-US): cropping diversity changes during 1850–2021

Abstract. Agricultural activities have been recognized as an important driver of land cover and land use change (LCLUC) and have significantly impacted the ecosystem feedback to climate by altering land surface properties. A reliable historical cropland distribution dataset is crucial for understanding and quantifying the legacy effects of agriculture-related LCLUC. While several LCLUC datasets have the potential to depict cropland patterns in the conterminous US, there remains a dearth of a relatively high-resolution datasets with crop type details over a long period. To address this gap, we reconstructed historical cropland density and crop type maps from 1850 to 2021 at a resolution of 1 km × 1 km by integrating county-level crop-specific inventory datasets, census data, and gridded LCLUC products. Different from other databases, we tracked the planting area dynamics of all crops in the US, excluding idle and fallow farm land and cropland pasture. The results showed that the crop acreages for nine major crops derived from our map products are highly consistent with the county-level inventory data, with a residual less than 0.2×103 ha (0.2 kha) in most counties (&gt;75 %) during the entire study period. Temporally, the US total crop acreage has increased by 118×106 ha (118 Mha) from 1850 to 2021, primarily driven by corn (30 Mha) and soybean (35 Mha). Spatially, the hot spots of cropland distribution shifted from the Eastern US to the Midwest and the Great Plains, and the dominant crop types (corn and soybean) expanded northwestward. Moreover, we found that the US cropping diversity experienced a significant increase from the 1850s to the 1960s, followed by a dramatic decline in the recent 6 decades under intensified agriculture. Generally, this newly developed dataset could facilitate spatial data development, with respect to delineating crop-specific management practices, and enable the quantification of cropland change impacts on the environment. Annual cropland density and crop type maps are available at https://doi.org/10.6084/m9.figshare.22822838.v2 (Ye et al., 2023).