Predict Land Use Research Articles

Estimation of Potential Impact of Land Use Change on Sediment Yield From a Small Tropical Watershed Using the TREX Erosion Model

ABSTRACTAnalysis of the influence of changes in land use on the sediment yield from watersheds provides crucial inputs that aid the development of appropriate strategies for the sustainable management of consequent land degradation. Event‐based hydrologic models can be used for performing such analysis, as long‐term hydrological datasets are not available for most of the rivers in developing countries like India. In this study, an event‐based Two‐Dimensional Runoff, Erosion, and Export (TREX) model was used to simulate the soil erosion process in Moozhy, an ungauged watershed in the Karamana River basin in Thiruvananthapuram District, Kerala State, India. Rainfall, streamflow, and sediment concentration data corresponding to three isolated storm events were used to calibrate and validate the model. The performance of the model was assessed using four statistical measures, namely, Percent bias (PBIAS), Nash‐Sutcliffe Efficiency Coefficient (NSEC), Coefficient of determination (R2) and RMSE‐observations standard deviation ratio (RSR). The values of PBIAS varied between 46% and 49%, indicating satisfactory performance of the model. Also, from the values of NSEC and RSR, it can be concluded that the TREX model yields acceptable results. For a rainfall event that occurred on the 17 September 2017, the simulated values showed that about 1054 t of suspended sediments are transported from the watershed to the stream channels, and about 1020 t is carried out of the Moozhy watershed. Only a very small amount of sediment is left in the channel at the end of the simulation. About 34 t of sediment is deposited in the channel bed, about 0.04 t remains in suspension, and the balance 0.04 t remains in suspension as a suspended load. About 3% of the total sediment entering the reach of the stream considered in this study is deposited on the riverbed. The major component of the settled sediments is silt; about 12% of the total silt size fraction entering the stream settles on the river bed. Future scenarios of land use in the watershed were modelled using a hybrid Artificial Neural Network (ANN) and Cellular Automata (CA) model. These were used for simulating sediment discharge using the TREX erosion model. The predicted land use scenarios were used to investigate both short‐term (2025 and 2029) and long‐term (2037, 2045, and 2053) variations in sediment discharge. The sediment yield for the storm event that occurred on 17 September 2017 was used to benchmark the variation in sediment yield under the predicted land use scenarios. Results indicate that the peak runoff and the runoff volume are expected to increase by about 29% and 22% respectively in the period from 2005 to 2053. The expected increase in the volume of sediments in this period is about 50%; the peak sediment concentration is likely to increase by about 56%. The study highlights the threats of likely increase in soil erosion and consequent land degradation posed by unscientific changes in land use caused by urbanisation and calls for proper management interventions to address the problem.

Spatio-temporal evolution and prediction of carbon storage at the source of the Fen River and Sanggan River based on a PLUS-InVEST model

Investigating the evolution of land use and its impact on carbon storage is of significant importance for mitigating regional climate change and promoting green low-carbon development. Ningwu County is located in the source region of the Fenhe and Sanggan River, and its ecological status significantly influences the carbon storage (CS) of the watershed ecosystem. In this study, the PLUS-InVEST model was employed to analyze the land use evolution from 1990 to 2020 in Ningwu County, Shanxi Province, as well as their impacts on CS. Additionally, the study simulated and predicted land use changes in Ningwu County by 2040 under four scenarios: natural development (NDS), ecological protection (EPS), cultivated land protection (CLPS), and urban development (UDS), while estimating the corresponding changes in ecosystem CS. Furthermore, the study utilized optimal parameters-based geographical detector to explore the mechanisms underlying the spatial differentiation of CS. The results indicated that the areas of forest land and construction land in the study area consistently increased from 1990 to 2020, whereas the area of cultivated land continuously declined, with grassland, water bodies, and unused land exhibiting a fluctuating increasing trend. The spatial distribution of CS was highest in the northwest, second highest in the southeast, and lowest in the middle region. Over these 3 decades, CS had shown a continuous increase. It is projected that by 2040, the areas of forest and grassland will experience the most significant increase under the EPS; cultivated land only increase under the CLPS; while construction land display the greatest increase under the UDS. Compared to 2020, these four scenarios for 2040 indicate an increase in regional CS, with the EPS showing the largest increment. The primary factors influencing the spatial differentiation of CS in Ningwu County are human activities, followed by topography and climate change; the interactions among these factors exhibit a reinforcing relationship, with the interaction between the distance from construction land and slope having the most substantial impact on the spatial differentiation of CS.

How to Achieve the Ecological Sustainability Goal of Ecologically Fragile Areas on the Qinghai-Tibet Plateau: A Multi-Scenario Simulation of Lanzhou-Xining Urban Agglomerations

The future of the ecologically fragile areas on the Qinghai-Tibet Plateau (QTP) is a matter of concern. With the implementation of the Western Development Strategy, the Lanzhou-Xining Urban Agglomeration (LXUA) has encountered conflicts and compromises between urban expansion, ecological protection, and farmland protection policies in the rapid development of the past 2 decades. These deeply affect the land use layout, making the ecological sustainable development of the ecologically fragile areas of the QTP a complex and urgent issue. Exploring the impact of different policy-led land use patterns on regional ecosystem services is of great significance for the sustainable development of ecologically fragile areas and the formulation of relevant policies. Following the logical main line of “history-present-future”, the Patch-level Land Use Simulation (PLUS) model, which explores potential factors of historical land use, and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model were used to construct three future scenarios for the modernization stage in 2031 dominated by different land use policies in this study. These scenarios include the Business-as-Usual Scenario (BS), the Cropland Protection Scenario (CP), and the Ecological Protection Scenario (EP). The study analyzed and predicted land use changes in the LXUA from 2001 to 2031 and assessed carbon storage, habitat quality at different time points, and water yield in 2021. The results indicated that land use changes from 2001 to 2021 reflect the impacts and conflicts among the Western Development Strategy, ecological protection policies, and cropland preservation policies. In 2031, construction land continues to increase under all three scenarios, expanding northwards around Lanzhou, consistent with the actual “northward expansion” trend of Lanzhou City. Changes in other land uses are in line with the directions guided by land use policy. By 2031, carbon storage and habitat quality decline under all scenarios, with the highest values observed in the EP scenario, the lowest carbon storage in the BS scenario, and the lowest habitat quality in the CP scenario. Regarding water yield, the LXUA primarily relies on alpine snowmelt, with construction land overlapping high evapotranspiration areas. Based on the assessment of ecosystem services, urban expansion, delineation of ecological red lines, and improvement of cropland quality in the LXUA were proposed. These findings and recommendations can provide a scientific basis for policy makers and planning managers in the future.

Predicting land use dynamics, surface temperature and urban thermal field variance index in mild cold climate urban area of Pakistan

Rapid urbanization attributed to population growth is affecting the built environment's thermal and landscape dynamics. Using Landsat satellite datasets, this study investigated the complex interplay between urban Land Cover (LC) modification, fluctuation in Land Surface Temperature (LST) and severity of Urban Heat Island (UHI) from 1990 to 2020 in Peshawar City, Pakistan. Thermal bands were used to calculate LST and severity of UHI using the Urban Thermal Field Variance Index (UTFVI). Furthermore, through Cellular Automata (CA), Logistic Regression (LR), and Artificial Neural Network (ANN), future predictions on thermal characteristics associated with land use changes were made. The results showed that the urban areas expanded by ∼25 % from 1990 to 2020, while a ∼10 % decrease occurred in urban vegetation. The city is projected to expand by ∼45 % and ∼56 % in 2035 and 2050, respectively. Notably, the results also demonstrated that urban hotspots were found the warmest with the strongest UHI severity (∼34 °C), followed by the barren land (∼32 °C), and vegetation. The results further predicted an increase of LST (∼55 % and ∼82 %) and UTFVI (∼62 % and ∼83 %) in 2035 and 2050, respectively. These findings provide useful insights for policymakers and city planners to mitigate heat stress and create a sustainable urban environment through the development of effective urban land use policies and urban greening.

Spatial and Temporal Characteristics of Land Use Changes in the Yellow River Basin from 1990 to 2021 and Future Predictions

Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics, the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model, and a geographic detector to explore the spatial and temporal characteristics of land use changes and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. The most actively changing land use types are cropland, grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution, and the remaining land use types exhibited stable directional distributions. (3) Economic factors had a stronger driving effect on land use changes than topographic and climatic factors. The land use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland, grassland, and forest. However, there will be a significant expansion of impervious surfaces and forest land, with substantial encroachment on cropland and grassland.

Predicting land use and environmental dynamics in Argentina's Pampas region: An agent-based modeling approach across varied price and climatic scenarios.

This study, employing the AGRODEVS Agent-Based Model (ABM), systematically examined land use dynamics in Argentina's Pampas Region. Simulations under diverse scenarios highlighted the significant role of economic determinants, particularly crop price relationships, in influencing maize or wheat/soybean double cropping prevalence. Maize-dominated landscapes consistently achieved carbon sequestration goals, while wheat/soybean landscapes faced challenges, notably in ecotoxicity. Scenarios encompassed varying climatic conditions and soybean/maize price ratios, providing insights into the interplay shaping agricultural land use decisions among individual agents. The AGRODEVS model's robust performance underscored its effectiveness in integrating economic and environmental factors, contributing to a practical understanding of sustainable land use planning complexities.

Urban land use simulation and carbon-related driving factors analysis based on RF-CA in Shanghai, China

As global climate change intensifies, climate protection is important for the sustainable development of human society. In the process of urbanization and industrialization, carbon dioxide emissions are an important factor contributing to global warming. Therefore, modelling projections of future urban land use under low-carbon scenarios are essential for sustainability policy development. Few studies have focused on the impact of carbon emissions on urban land use change in Shanghai, as current research has primarily concentrated on developing methods for urban land use modelling. This paper utilizes a cellular automata (CA) simulation model based on the Random Forest (RF) algorithm to select various spatial variables of carbon emissions as the driving factors that affect urban land use changes. These variables include traffic location factors, economic development factors, electricity consumption, and population density. In this study, remote sensing imagery of urban nighttime lighting is also used to construct a simulation model of land use types in Shanghai. The model is then used to analyze the contribution of carbon emission constraints to urban land use changes. Actual historical land use data from 2013 and 2019 are used for validation, and the prediction model is used to predict land use outcomes under different low-carbon scenarios in 2025. The model is validated by simulating multiple intra-city land use maps for 2019 (kappa = 0.88, OA=92.71 %). The method of out-of-bag error from the random forest is used to evaluate the significance of carbon emission constraints. Using the validated model, the constraints in the CA model are changed to predict the land use simulation results of Shanghai in 2025 under different low-carbon scenarios. In terms of significance, factors such as distance to power plants, distance to major roads, real GDP, and population density can all have a significant impact on changes in urban land use. By selecting the low-carbon scenario with the most appropriate thresholds for each driver, it is possible to obtain the land use simulation results of Shanghai in 2025 under the optimal low-carbon scenario, while ensuring the high accuracy of the RF-CA model and simultaneously reducing the impact of factors on the city’s overall carbon emissions. This paper provides a scientific base for urban planners and scholars to thoughtfully design urban land use while cutting down on carbon emissions. Furthermore, it can aid government agencies in establishing associated planning approaches.

Evaluating ecological conservation effectiveness of security patterns under multiple scenarios: A case study of Hubei Province

With the development of the social economy, ecological environment damage has become increasingly serious, and how to better protect ecological security has gradually attracted people’s attention. This paper takes Hubei Province in China as the research object, using the PLUS model version 1.4 to design four development scenarios to predict land use and land cover changes (LUCC) in 2035: the natural development scenario (S1), the cultivated land protection scenario (S2), the ecological protection area restriction scenario (S3), and the ecological security pattern (ESP) restriction scenario (S4). The study evaluates the ecological effects under these four different scenarios using Conefor Sensinode 2.6 software, Fragstats v4.2.1 software, and the InVEST model. The conclusions are as follows: (1) ESP has better ecological landscape connectivity. (2) Comparing the ecological security indices of the four scenarios, they are 0.5378, 0.5288, 0.5318, and 0.5405, respectively, with the S4 scenario showing the best protection effect. (3) Comparing the habitat quality of the four scenarios, high-grade habitats degrade under S1 and S2 scenarios; homogenization occurs under S3 and S4 scenarios, but the retention rate of high-grade habitat areas is the highest under the S4 scenario. In conclusion, compared to natural progression and prioritizing cultivated land protection, implementing ecological protection policies yields better ecological effects, and a planned ESP provides more targeted policy recommendations.

Prediction of Land Use and Habitat Quality in Harbin City Based on the PLUS- InVEST Model

Analyzing the spatiotemporal evolution of urban land use and habitat quality can reveal the correlation between land use and habitat quality, aiding in rational urban land policies and high-quality ecological environment development. This study was based on land use transition matrices and an Intensity-Migration （IM） model to analyze the changes in land use in Harbin from 2000 to 2020. It combined the PLUS model to explore the driving factors of land use expansion for various land types and predicted land use scenarios for 2030 under natural development, ecological protection, farmland protection, and urban development scenarios. Finally, the InVEST model was used to complete the habitat quality analysis. The results indicated： ① The dominant land use types in Harbin were cropland, forest land, and grassland. From 2000 to 2020, there was a significant tendency of conversion into grassland, cropland, forest land, and artificial surfaces, with noticeable changes in land use intensity. Water bodies, bare land, and wetland types showed less conversion and tended to be restrictive. ② Elevation was the primary factor influencing the expansion of grassland, cropland, forest land, bare land, wetland, and water bodies. Socioeconomic factors were the main factor affecting the expansion of artificial surfaces. ③ Simulation of land use types in Harbin for 2030 under the four scenarios showed an increase in forest land area and a decrease in grassland area, with insignificant changes in wetland, water bodies, and bare land areas. Except for in the ecological protection scenario, the area of artificial surfaces increased, whereas cropland decreased. ④ Overall, habitat quality in Harbin improved from 2000 to 2020. ⑤ In 2030, the spatial pattern of habitat quality in Harbin remained consistent across all scenarios, showing an overall improvement in habitat quality. Under the ecological protection scenario, areas with low and medium habitat quality decreased, whereas areas with higher habitat quality increased, indicating a relatively significant improvement in habitat quality. The research results provide a scientific basis and insights for the development of ecological civilization and urban planning and construction in Harbin.

Changes in Wuhan’s Carbon Stocks and Their Spatial Distributions in 2050 under Multiple Projection Scenarios

Urbanization in the 21st century has reshaped carbon stock distributions through the expansion of cities. By using the PLUS and InVEST models, this study predicts land use and carbon stocks in Wuhan in 2050 using three future scenarios. Employing local Moran’s I, we analyze carbon stock clustering under these scenarios, and the Getis–Ord Gi* statistic identifies regions with significantly higher and lower carbon-stock changes between 2020 and 2050. The results reveal a 2.5 Tg decline in Wuhan’s carbon stock from 2000 to 2020, concentrated from the central to the outer city areas along the Yangtze River. By 2050, the ecological conservation scenario produced the highest carbon stock prediction, 77.48 Tg, while the economic development scenario produced the lowest, 76.4 Tg. High-carbon stock-change areas cluster in the north and south, contrasting with low-change area concentrations in the center. This research provides practical insights that support Wuhan’s sustainable development and carbon neutrality goals.

Predicting Land Use and Land Cover Changes in the Chindwin River Watershed of Myanmar Using Multilayer Perceptron-Artificial Neural Networks

Predicting Land Use and Land Cover Changes in the Chindwin River Watershed of Myanmar Using Multilayer Perceptron-Artificial Neural Networks

Predicting land use and land cover change dynamics in the eThekwini Municipality: a machine learning approach with Landsat imagery

ABSTRACT Monitoring and providing accurate land use and land cover (LULC) change information is vital for sustainable environmental planning. This study used Landsat imagery from 2002 to 2022 to create updated LULC change maps for the eThekwini Municipality. Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were used to conduct these LULC classifications, with XGBoost achieving the highest accuracy (80.57%). The generated maps revealed a significant decrease in cropland and an increase in impervious surfaces. As such, this research established a framework for continuous LULC mapping and highlighted Landsat 9’s potential in LULC classifications.

Predicting Land Use Changes under Shared Socioeconomic Pathway–Representative Concentration Pathway Scenarios to Support Sustainable Planning in High-Density Urban Areas: A Case Study of Hangzhou, Southeastern China

Amidst the challenges posed by global climate change and accelerated urbanization, the structure and distribution of land use are shifting dramatically, exacerbating ecological and land-use conflicts, particularly in China. Effective land resource management requires accurate forecasts of land use and cover change (LUCC). However, the future trajectory of LUCC, influenced by climate change and urbanization, remains uncertain. This study developed an integrated multi-scenario framework by combining system dynamics and patch-generating land use simulation models to predict future LUCC in high-density urban regions under various Shared Socioeconomic Pathway (SSP)–Representative Concentration Pathway (RCP) scenarios. The results showed the following: (1) From 2020 to 2050, cultivated land, unused land, and water are projected to decrease, while construction land is expected to increase. (2) Future land use patterns exhibit significant spatial heterogeneity across three scenarios. Construction land will expand in all districts of Hangzhou, particularly in the main urban areas. Under the SSP585 scenario, the expansion of construction land is most significant, while it is the least under the SSP126 scenario. (3) Distinct factors drive the expansion of different land use types. The digital elevation model is the predominant factor for the expansion of forest and grassland, contributing 19.25% and 30.76%, respectively. Night light contributes the most to cultivated land and construction land, at 13.94% and 20.35%, respectively. (4) The average land use intensity (LUI) in central urban districts markedly surpasses that in the surrounding suburban areas, with Xiacheng having the highest LUI and Chun’an the lowest. Under the SSP126 scenario, the area with increased LUI is significantly smaller than under the SSP245 and SSP585 scenarios. These findings offer valuable guidance for sustainable planning and built environment management in Hangzhou and similarly situated urban centers worldwide.

Spatio-temporal Evolution and Prediction of Carbon Storage in Huaibei City Based on InVEST-PLUS Model

This study aimed to investigate the impact of spatiotemporal changes in land use on ecosystem carbon storage. The study analyzed the spatiotemporal changes in carbon storage in the study area based on land use data from five periods (1985, 1995, 2005, 2015, and 2020) using the InVEST model. The PLUS model was used to predict land use changes in the study area under four different scenarios (natural development, farmland protection, ecological protection, and double protection of farmland and ecology) in 2035, and the ecosystem carbon storage under different scenarios was estimated. The results of the study indicated that the farmland in the area under investigation had been decreasing consistently from 1985 to 2020, with a more rapid rate of change observed between 2015 and 2020. During this period, the overall dynamic attitude towards land use reached 34.62 %. Additionally, the carbon storage in the area showed a decreasing trend over the years, with a decrease of 1.55×105 t from 1985 to 2020. Between 2005 and 2015, the carbon storage showed a decrease of 1.22×105 t, with an average annual decrease of 1.22×104 t. The areas with higher carbon storage were located in the eastern part of the study area, whereas areas with lower carbon storage were found in the central and northwestern parts. Although the proportion of carbon storage in farmland decreased from 66.89 % to 57.73 %, farmland remained the most important carbon pool in the study area. The conversion of other land use types to grassland and forestland was advantageous for increasing ecosystem carbon storage. Finally, the study projected that by 2035, the carbon storage in the natural development scenario, the farmland protection scenario, the ecological protection scenario, and the dual protection scenario would be 81.77×105, 82.45×105, 82.82×105, and 82.51×105 t, respectively.

Land use predicition accuracy of different supervised classifiers over agriculture and livestock economy-based municipality in Brazil

The dynamics of land use and land cover (LULC) are of great importance for the management of natural resources, sustainable development and urban planning over geographic space, and this condition is sometimes supported by geoprocessing and remote sensing techniques. In addition, machine learning methods automate the classification and modeling of spatialized prediction processes on orbital images, and with the high precision and adherence of these data, important results and conclusions are the result of these methods. The decision for which classification typology presents the best results is related to the application and considering the LULC prediction as input to a cellular automata (CA) network, the performances of Classification and Regression Tree (CART), Random Forest (RF) and Minimum Distance (MID) for predicting land use and occupation in Sinop, Brazil were assessed. Using the median of the reference years 2013 and 2015 to create a transition potential modelling (TPM) neural network, and then predict a scenario in 2017, the performance was verified with Kappa and global accuracy (OA) statistics. With the highest performance, the RF typology reached the best performance in an area of mostly agricultural occupation, separated into four classes (native forest, urban area, water and bare soil/agricultural activity). The errors inherent to each classifier were decisive for a greater prediction error, where the other classifiers (CART and MID) mistakenly classified the urban area class, but which statistically were not gross errors. Considering the ground truth and the best statistical performance, the prediction of land use and occupation for a scenario as seen in Sinop potentially achieves better results with the Random Forest classifier.

Predicting Land Use and Land Cover Changes in Pathumthani, Thailand: A Comprehensive Analysis from 2013 to 2023 Using Landsat Satellite Imagery and CA-ANN Algorithm, with Projections for 2028

Predicting Land Use and Land Cover Changes in Pathumthani, Thailand: A Comprehensive Analysis from 2013 to 2023 Using Landsat Satellite Imagery and CA-ANN Algorithm, with Projections for 2028

An adaptive transition probability matrix with quality seeds for cellular automata models

ABSTRACT The cellular automata (CA) model is the predominant method for predicting land use and land cover (LULC) changes. The accuracy of this model critically depends on well-defined transition rules, which encapsulate the local dynamics of complex systems and facilitate the manifestation of organized global patterns. While current studies largely concentrate on land use transition matrices as core elements of these rules, exclusive reliance on these matrices is insufficient for capturing the full spectrum of land use change potential. Addressing this gap, our research introduces the adaptive transition probability matrix with quality seeds (ATPMS) model, which incorporates both the Markov model and the genetic algorithm (GA) into the traditional CA framework. Furthermore, an artificial neural network (ANN) is utilized to determine land suitability. Implemented in Beijing, Wuhan, and the Pearl River Delta (PRD), our results indicate that the ATPMS-ANN-CA model surpasses the standard Markov-ANN-CA model in various validation metrics, displaying improvements in overall accuracy (OA) by 0.03% to 0.74% and figure of merit (FoM) by 3.67% to 63.14%. Additionally, the ATPMS-ANN-CA model excels in providing detailed landscape analysis.

Assessing the efficiency of bird habitat conservation strategies in farmland ecosystems

Land use changes negatively impact agroecosystems, where the loss of wildlife habitats is increasingly threatening global biodiversity. Ecological conservation strategies that seek to reduce habitat destruction are important and strongly rely on understanding the spatial and temporal dynamics of land use change. In recent years, an expert-based approach using the Analytic Network Process (ANP) has been applied commonly to wildlife habitat assessment, and the Conversion of Land Use and its Effects at Small regional extent (CLUE-s) model is a well-known simulation tool used to predict land use dynamics. Although their effectiveness has been proven widely, existing studies have yet to integrate the two approaches into ecological modeling for conserving bird habitats under the influence of land use changes. Thus, there is a notable research gap regarding the potential synergies of the ANP and CLUE-s models to evaluate the spatial nonstationarity between bird habitats and land use patterns. In this study, we integrated use of the ANP to identify suitable habitats for birds on farmland with the CLUE-s model to predict future changes in habitat suitability by 2035. Our study aims to evaluate the efficiency of various conservation strategies for bird habitat in farmland ecosystems within the Zhuoshui River basin in Taiwan. We formulated conservation strategies to protect areas of farmland based on their relative suitability as bird habitats. The research findings reveal a significant reduction of farmland habitats coinciding with expanding built-up areas and forests, while designating farmland areas for conservation purposes effectively ensures the maintenance of bird habitats amidst these transformations. Conserving highly suitable farmland habitats (those whose suitability values ranked at or above the 75th percentile) presents the most effective strategy to mitigate the impacts of land use change on bird habitats. Under this strategy, changes in farmland habitats lead to a notable decrease of 16.4 % in low and very low suitability, whereas farmland habitats with moderate and high suitability significantly expand by 879.8 % and 225.9 %, respectively. Farmland habitats characterized by high suitability are concentrated in the western region of the Zhuoshui River basin, accounting for 2289 hectares. These findings underscore the magnitude of habitat loss resulting from land use changes outside conserved areas. Our research emphasizes the importance of implementing conservation strategies for bird habitats, providing policymakers with a framework to manage farmland ecosystems effectively.

Vulnerability assessment of hydrological ecosystem services under future climate and land use change dynamics

Hydrological Ecosystem Services (HESs), as an important part of ecosystem services, are those services that depend on hydrological processes. HESs vulnerability assessment at the watershed scale is used to explore highly vulnerable areas for management and protection measures. This study aimed at developing a method for HESs vulnerability assessment and achieve a quantitative integrated index under future climate and land use change scenarios. We focused on three regulating HESs, including water flow regulation, erosion regulation, and local climate regulation (LC). An integrated model of Markov chain and automatic cells was used to predict land use changes. Future climate data were estimated using the Climate Change Toolkit (CCT). The Soil and Water Assessment Tool (SWAT) model was developed for HESs vulnerability assessment in the Taleghan Watershed in Iran. The model was calibrated by river discharge using the Sequential Uncertainty Fitting (SUFI-2) and the Parallel Processing option. The results show that sub-watersheds with higher precipitation (PCP) and lower evapotranspiration (ET) are more efficient in water flow and local climate regulation. However, sub-watersheds with lower PCP and higher ET are more vulnerable to erosion. Changes in water flow regulation services and local climate regulation were more consistent with climate changes, and conversely, changes in erosion and sediment regulation services had a very high level of compliance with land use changes. The method applied in this study can easily be adapted to other regions to identify areas with more vulnerability and less resilience in improving the water and soil structure and function in the watershed.

Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin

The external environment in the transitional zone of the ecological barrier is fragile, and economic growth has resulted in a series of land degradation issues, significantly impacting regional economic development and the ecological environment. Therefore, monitoring, assessing, and predicting land use changes are crucial for ecological security and sustainable development. This study developed an integrated model comprising convolutional neural network, cellular automata, and Markov chain to forecast the land use status of western Jilin, located in the transitional zone of the ecological barrier, by the year 2030. Additionally, the study evaluated the role of land use policies in the context of land use changes in western Jilin. The findings demonstrate that the coupled modeling approach exhibits excellent predictive performance for land use prediction in western Jilin, yielding a Kappa coefficient of 93.26%. Policy drivers play a significant role in shaping land use patterns in western Jilin, as evidenced by the declining farmland accompanied by improved land utilization, the sustained high levels of forest aligning with sustainable development strategies, the ongoing restoration of waters and grassland, which are expected to show positive growth by 2030, and the steady growth in built-up areas. This study contributes to understanding the dynamics of land use in the transitional zone of the ecological barrier, thereby promoting sustainable development and ecological resilience in the region.