A land use change model: Integrating landscape pattern indexes and Markov-CA

Forest fates: Unraveling the peri-urban social-ecological trajectories in Mexico City's conservation land

Peri-urban areas provide multiple ecosystem services, but face critical challenges, including deforestation, unplanned urban sprawl, and environmental pollution and degradation. To address these issues, environmental public policy instruments have been implemented. This paper aims to investigate the social ecological trajectories of a peri-urban area of Mexico City and the role of environmental public policy instruments in addressing land use change. Focusing on four watersheds of the southern periphery of the city, we analyze land use change drivers through neural networks and Markov chains, and we develop two land use scenarios for the next 20 years: one characterized by business as usual and another with a more restrictive land use regime. Our findings show that infrastructure drivers are the most critical factor overall, when combined with the historical social ecological trajectory of the study area. The impact of environmental public policy instruments on future trajectories demonstrates their potential to decrease deforestation. The results provide insights for the integrated territorial planning of peri-urban areas with similar social ecological dynamics and developing context.

Exploring the predictive ability of the CA-Markov model for urban functional area in Nanjing old city

With advancements in sustainable urban development, research on urban functional areas has garnered significant attention. In recent years, Point-of-Interest, with their large volume of information and ease of acquisition, have been widely applied in research on urban functional domains. However, scholars currently focus on the identification of urban functional areas, usually relying on data from a single period, whereas research on the prediction of functional areas has not yet been well validated. Therefore, in this study, we propose a new method based on several years of POI data to predict urban functional areas. Taking Nanjing City, Jiangsu Province, as an example, we first identified the functional area distribution of the old city of Nanjing over several years using POI data and then designed multiple sets of experiments to explore the CA-Markov model's ability to predict functional areas from various aspects, including model overall accuracy, robustness, and comparison analysis between predictions and actual situations. The results show that (1) for mixed or single functional areas, the model's predictions over several years tend to be stable, and the accuracy of the predictions over many years indicates the robustness of the model in predicting urban functional areas. (2) For mixed functional areas in cities, model predictions largely rely on the distribution of the base years used for prediction, leading to inaccurate results; thus, it is still not applicable for simulating and predicting mixed functional areas. (3) For single functional areas in cities or primary functions within an area, the model's predicted degree of change was close to the actual degree of change, making the results referable.

Analyses of LULC dynamics in a socio-ecological system of the Bale Mountains Eco Region of Southeast Ethiopia

Analysis of land use and land cover (LULC) change and its drivers and impacts in the biodiversity hotspot of Bale Mountain's socio-ecological system is crucial for formulating plausible policies and strategies that can enhance sustainable development. The study aimed to analyze spatio-temporal LULC changes and their trends, extents, drives, and impacts over the last 48 years in the Bale Mountain social-ecological system. Landsat imagery data from the years 1973, 1986, 1996, 2014, and 2021 together with qualitative data were used. LULC classification scheme employed a supervised classification method with the application of the maximum likelihood algorithm technique. In the period between 1973 and 2021, agriculture, bare land, and settlement showed areal increment by 153.13%, 295.57%, and 49.03% with the corresponding increased annual rate of 1.93%, 2.86%, and 0.83%, respectively. On the contrary, forest, wood land, bushland, grass land, and water body decreased by 29.97%, 1.36%, 28.16%, 8.63%, and 84.36% during the study period, respectively. During the period, major LULC change dynamics were also observed; the majority of woodland was converted to agriculture (757.8 km2) and grassland (531.3 km2); and forests were converted to other LULC classes, namely woodland (766.5 km2), agriculture (706.1 km2), grassland (34.6 km2), bushland (31.9 km2), settlement (20.5 km2), and bare land (14.3 km2). LULC changes were caused by the expansion of agriculture, settlement, overgrazing, infrastructure development, and fire that were driven by population growth and climate change, and supplemented by inadequate policy and institutional factors. Social and environmental importance and values of land uses and land covers in the study area necessitate further assessment of potential natural resources' user groups and valuation of ecosystem services in the study area. Hence, we suggest the identification of potential natural resource-based user groups, and assessment of the influence of LULC changes on ecosystem services in Bale Mountains Eco Region (BMER) for the sustainable use and managements of land resources.

Synergizing economic growth and carbon emission reduction in China: A path to coupling the MFLP and PLUS models for optimizing the territorial spatial functional pattern

Carbon emissions caused by economic growth are the main cause of global warming, but controlling economic growth to reduce carbon emissions does not meet China's conditions. Therefore, how to synergize economic growth and carbon emission reduction is not only a sustainable development issue for China, but also significant for mitigating global warming. The territorial spatial functional pattern (TSFP) is the spatial carrier for coordinating economic development and carbon emissions, but how to establish the TSFP of synergizing economic growth and carbon emission reduction remains unresolved. We propose a decision framework for optimizing TSFP coupled with the multi-objective fuzzy linear programming and the patch-generating land use simulation model, to provide a new path to synergize economic growth and carbon emission reduction in China. To confirm the reliability, we took Qionglai City as the demonstration. The results found a significant spatiotemporal coupling between TSFP and the synergistic states between economic growth and carbon emission reduction (q ≥ 0.8220), which resolves the theoretical uncertainty about synergizing economic growth and carbon emission reduction through the path of TSFP optimization. The urban space of Qionglai City in 2025 and 2030 obtained by the decision framework was 6497.57 hm2 and 6628.72 hm2 respectively, distributed in the central and eastern regions; the rural space was 60,132.92 hm2 and 56,084.97 hm2, concentrated in the east, with a few located in the west; and the ecological space was 71,072.52 hm2 and 74,998.31 hm2, mainly located in the western and southeastern areas. Compared with the TSFP in 2020, the carbon emission intensity of the TSFP obtained by the decision framework was reduced by 0.7 and 4.7 tons/million yuan, respectively, and realized the synergy between economic growth and carbon emission reduction (decoupling index was 0.25 and 0.21). Further confirming that TSFP optimization is an effective way to synergize economic growth and carbon emission reduction, which can provide policy implications for coordinating economic growth and carbon emissions for China and even similar developing countries.

Geospatial modeling to assess the past and future land use-land cover changes in the Brahmaputra Valley, NE India, for sustainable land resource management

Satellite remote sensing and geographic information system (GIS) have revolutionalized the mapping, quantifying, and assessing the land surface processes, particularly analyzing the past and future land use-land cover (LULC) change patterns. Worldwide river basins have observed enormous changes in the land system dynamics as a result of anthropogenic factors such as population, urbanization, development, and agriculture. As is the scenario of various other river basins, the Brahmaputra basin, which falls in China, Bhutan, India, and Bangladesh, is also witnessing the same environmental issues. The present study has been conducted on the Brahmaputra Valley in Assam, India (a sub-basin of the larger Brahmaputra basin) and assessed its LULC changes using a maximum likelihood classification algorithm. The study also simulated the changing LULC pattern for the years 2030, 2040, and 2050 using the GIS-based cellular automata Markov model (CA-Markov) to understand the implications of the ongoing trends in the LULC change for future land system dynamics. The current rate of change of the LULC in the region was assessed using the 48 years of earth observation satellite data from 1973 to 2021. It was observed that from 1973 to 2021, the area under vegetation cover and water body decreased by 19.48 and 47.13%, respectively. In contrast, cultivated land, barren land, and built-up area increased by 7.60, 20.28, and 384.99%, respectively. It was found that the area covered by vegetation and water body has largely been transitioned to cultivated land and built-up classes. The research predicted that, by the end of 2050, the area covered by vegetation, cultivated land, and water would remain at 39.75, 32.31, and 4.91%, respectively, while the area covered by built-up areas will increase by up to 18.09%. Using the kappa index (ki) as an accuracy indicator of the simulated future LULCs, the predicted LULC of 2021 was validated against the observed LULC of 2021, and the very high ki observed validated the generated simulation LULC products. The research concludes that significant LULC changes are taking place in the study area with a decrease in vegetation cover and water body and an increase of area under built-up. Such trends will continue in the future and shall have disastrous environmental consequences unless necessary land resource management strategies are not implemented. The main factors responsible for the changing dynamics of LULC in the study area are urbanization, population growth, climate change, river bank erosion and sedimentation, and intensive agriculture. This study is aimed at providing the policy and decision-makers of the region with the necessary what-if scenarios for better decision-making. It shall also be useful in other countries of the Brahmaputra basin for transboundary integrated river basin management of the whole region.

Evolution of the landscape pattern in the Xin'an River Basin and its response to tourism activities

Tourism activities may interfere with the landscape pattern as a type of human activity. Scientific decision-making in the river basin's tourism development requires a precise understanding of the tourism activities' complicated effects on landscape patterns. The research object is to ascertain the direction and magnitude of the influence of tourism activities on the landscape pattern in the Xin'an River Basin. The response index (RI) was calculated by comparing the dynamic characteristics of landscape pattern metrics and Morphological Spatial Pattern Analysis (MSPA) inside and outside the 24 scenic areas. The results show the RI (=-2.56) of the scenic areas is eight times higher than that of the other areas, which indicates the intensity of the tourism activities' negative impact on the landscape pattern. Cultural and low-level scenic areas have more serious landscape fragmentation in general, through the comparative analysis of different types and scales of scenic areas. Combined with the analysis of tourism socio-economic data, scenic area construction is the main factor leading to landscape fragmentation. This study is an effective review of the impact of China's tourism industry on landscape patterns in the past 40 years, and the proposed RI will better help to quantify the effect of tourism activities on landscape patterns.

Dynamics and future prediction of LULC on Pare River basin of Arunachal Pradesh using machine learning techniques

Anthropogenic disturbances caused by increasing population densities are a significant concern as they accelerate climate change. Thus, regular monitoring of land use/land cover (LULC) is essential to mitigate these effects. Pare River basin of Arunachala Pradesh situated in the foothills of Eastern Himalayas was selected for this study. Landsat-5 TM and Landsat-8 OLI data from 2000 (T₁), 2015 (T₂), and 2020 (T₃) were used to prepare the LULC map. A support vector machine (SVM) classifier in the Google Earth Engine (GEE) environment was utilized for classification of LULC, while the TerrSet software environment was used for change analysis and projection using the CA-MC model. The SVM classifier produced overall all classification accuracies of 0.91, 0.85, and 0.91 with kappa values of 0.88, 0.82, and 0.89 for T₁, T₂, and T₃, respectively. The CA-MC model, which combines Markov chain and hybrid cellular automata, was calibrated with various predictor variables, including natural, proximity, and demographic variables along with T₁ and T₂ LULC and validated using T₃ LULC. The MLP was used for calibration, and an accuracy rate of above 0.70 was employed to generate transition potential maps (TPMs). The TPMs were used to project future LULC for 2030, 2040, and 2050. Validation analysis produced satisfactory results, with K_no, K_location, K_quality, and K_standard values of 0.96, 0.95, 0.95, and 0.93, respectively. Receiver operating characteristics (ROC) analysis showed an excellent area under the curve (AUC) value of 0.87. The findings of this study provide important insights to decision-makers and stakeholders in addressing the impacts of LULC changes.

Identification and prediction of mixed-use functional areas supported by POI data in Jinan City of China

The urban development of China is changing from incremental expansion to stock renewal mode. The study of urban functional areas has become one of the important fundamental works in current urban renewal and high-quality urban development. In recent years, big spatiotemporal data has been well applied in the urban function field. However, the study of spatial-temporal evolution characteristics and forecasting optimization for mixed-use urban functional areas has not been examined well. Thus, in this study, we proposed a new approach that applies a revised information entropy method to analyze the degrees of mixing for urban functional areas. We applied our approach in Jinan City, Shandong Province as the study area. We used Point-of-Interest, OpenStreetMap and other datasets to identify the mixed-use urban functional areas in Jinan. Then, the CA-Markov model simulated the urban layout in 2025. The results showed that: (1) the combination of road network and kernel density method has the highest accuracy of identifying urban functional areas. (2)The mixing degree model is constructed by using the improved information entropy, which makes up for the shortcoming of identifying the mixed functional areas simply by the frequency ratio of POI data. (3) The "residence and business" functional area has the highest proportion in the central area of Jinan from 2015 to 2020, and the total area of mixed-use unban functional areas continuously increased during this period. (4) The total area of the central area in Jinan has significantly increased in 2025. The optimization of urban functions should expand mixed-use functional areas and increase the proportion of infrastructure. Also, Jinan should improve the efficiency of space development.

Monitoring of land use land cover dynamics and prediction of urban growth using Land Change Modeler in Delhi and its environs, India

In the recent decades, cities have been expanding at a great pace which changes the landscape rapidly as a result of inflow of people from rural areas and economic progression. Therefore, understanding spatiotemporal dynamics of human induced land use land cover changes has become an important issue to deal with the challenges for making sustainable cities. This study aims to determine the rate of landscape transformations along with its causes and consequences as well as predicting urban growth pattern in Delhi and its environs. Landsat satellite images of 1989, 2000, 2010 and 2020 were used to determine the changes in land use land cover using supervised maximum likelihood classification. Subsequently, Land Change Modeler (LCM) module of TerrSet software was used to generate future urban growth for the year 2030 based on 2010 and 2020 dataset. Validation was carried out by overlaying the actual and simulated 2020 maps. The change detection results showed that urban and open areas increased by 13.44% and 2.40%, respectively, with a substantial decrease in crop land (10.88%) from 1989 to 2020 and forest area increased by 3.48% in 2020 due to restoration programmes. Furthermore, the simulated output of 2030 predicted an increase of 24.30% in urban area and kappa coefficient 0.96. Thus, knowledge of the present and predicted changes will help decision-makers and planners during the process of formulating new sustainable policies, master plans and economic strategies for rapidly growing cities with urban blue-green infrastructures.

Construction and Restoration of Landscape Ecological Network in Urumqi City Based on Landscape Ecological Risk Assessment

The ecological protection and sustainable development of Urumqi have become an important part of the high-quality growth of the urban agglomeration on the northern slope of Tianshan Mountain. Under the impacts of multi-source factors, the ecological landscape pattern of Urumqi has changed due to it being in a fragile eco-environment, so an ecological network is desperately needed to enhance ecological security patterns. Taking Urumqi city as the study area, the ecological risk evaluation model and the minimum cumulative resistance model were integrated to analyze the spatial and temporal features of landscape ecological risk from 2000 to 2020, and the future land use simulation model was used to predict the ecological risk pattern of Urumqi in 2030, construct a landscape ecological network, and propose ecological security protection strategies. Since 2000, land use in Urumqi has undergone drastic changes: the built-up land area has increased significantly, the landscape has diversified, and landscape fragmentation has shown a decreasing trend from the main urban area as the core to the urban fringe. The high-risk landscape ecology shows a decreasing trend from east to west, mainly in the bare land areas with sparse vegetation, whereas the risk is relatively low in woodland, arable land, and built-up areas. The change of risk in the study area is mainly influenced by the typical defective factors of oasis cities such as urban expansion, land desertification, and sparse vegetation. The landscape ecological network is mainly located in the southwest, central, and east of the study area, whereas there is no corridor distribution in the north and southeast, which is mainly caused by the special geographical location and climatic conditions. The ecological network mainly consists of 10 ecological sources and 10 ecological corridors and proposes conservation strategies for the optimization of the landscape pattern and for the construction of the ecological security pattern in Urumqi, providing a guide for the improvement of ecological security.

A Refined Rural Settlements Simulation Considering the Competition Relationship among the Internal Land Use Types: A Case Study of Pinggu District

Simulating the future evolution of the internal land use structure of rural settlements (RSILUS) is vital for rural land management. However, previous simulation studies have mostly regarded rural settlements as a whole, thereby ignoring their internal structural variations. In this paper, as an example, we select Pinggu District, which has experienced the impact of rapid urbanization and has an unstable rural land use structure (LUS); then, we examine the driving factors of the changes in the RSILUS, construct a cellular automata (CA)–Markov simulation model specifying the RSILUS, and simulate its changes in 2025. The results indicate the following. (1) The influencing factors of various land use changes in rural settlements in Pinggu District differ significantly. Basic land, such as living functional land, is greatly influenced by natural resources, whereas production functional land is subject to socioeconomic factors. (2) The simulation results demonstrate that from 2015 to 2025, the production and living functional land areas of rural settlements will decrease as a whole. Accordingly, the distribution of rural public service land (RPSL) will tend to remain stable, and the trends of land use abandonment and functional degradation will continue as rural areas continue to recede. Our study enriches the research on rural land use systems by refining the simulation of rural settlements to focus on their internal structure. The differentiation and complexity of the changes in rural LUS types further suggests that rural planning and renewal should adapt to the changing conditions of the RSILUS, and the LUS should be adjusted to improve the constructed environment in human settlements and equalize urban and rural areas.

Analysis of Land Use Change and the Role of Policy Dimensions in Ecologically Complex Areas: A Case Study in Chongqing

China has adopted policies, such as the Grain for Green program (GFGP) and China’s Western Development Strategy, to maintain ecosystem sustainability and the rational use of land resources based on economic development. Existing studies have revealed the impact of these policies on land use and land cover change (LUCC). However, more research is needed to identify what would happen if the original trajectory of land use change were to continue unaffected by policy. In this research, we employed the future land use (FLUS) model to simulate land use changes in Chongqing under the natural scenario in 2020, assuming the existence of policy and natural contexts. The relative contribution conceptual model (RCCM) estimated the contribution of policies to LUCC, assessed the characteristics of LUCC in both situations using a complex network model, and analyzed the policies affecting LUCC. The findings revealed that cropland was the key land use type in both contexts, and the stability of the land use system in the natural context was greater than in the policy context. This research contributes to new research ideas for analyzing land use change and comprehending the role of policy execution in land use change.

Spatial and Temporal Changes of Landscape Patterns and Their Effects on Ecosystem Services in the Huaihe River Basin, China

Landscape pattern changes caused by human activities are among the most important driving factors affecting ecosystem spatial structure and components, and significantly impact ecosystem services. Understanding the relationship between landscape patterns and ecosystem services is important for improving regional conservation and establishing ecosystem management strategies. Taking the Huaihe River Basin as an example, this study used land-use data, meteorological data, and topographic data to analyze the spatial and temporal changes in landscape patterns via landscape transfer matrix and landscape indices, and measured four ecosystem services (water retention, soil retention, carbon storage, and biodiversity conservation) with the InVEST models. Furthermore, correlation analysis and global spatial autocorrelation coefficient were used to analyze the impact of landscape pattern changes on ecosystem services. The results showed grassland and farmland areas had continuously decreased, while built-up land and affected water had significantly increased. Landscape fragmentation was reduced, the connectivity between patches was weakened, landscape heterogeneity, evenness, and patch irregularity were increased. Changes in landscape composition and configuration have affected the ecosystem services of the Huaihe River Basin. The reduction in grassland areas and the increase in built-up land areas have significantly reduced the capacity for soil retention, carbon storage, and biodiversity conservation. Spatially, regions with low landscape fragmentation and high patch connectivity had a higher water retention capacity and biodiversity conservation, while soil retention and carbon storage were opposite. Temporally, reduction of landscape fragmentation and increase of patch shape irregularity had a negative effect on water retention, carbon storage, and biodiversity conservation, while soil retention was not sensitive to these changes. The findings in this paper promote an understanding of the relationship between landscape patterns and ecosystem services on a large scale and provide theoretical guidance for ecosystem management and protection planning in the Huaihe River Basin, China.

Evolution and Optimization of Territorial-Space Structure Based on Regional Function Orientation

In accordance with the ecological civilization strategy, it is necessary to conduct in-depth analyses and provide a systematic elaboration of the characteristics of territorial-space structure (TSS). In the present paper, we examine Shandong Province and construct a framework for the evolution and optimization of TSS based on regional functions. The evolutionary process, pattern, and driving mechanisms of TSS are clarified using a geo-information atlas, the gravity center shift model, spatial autocorrelation analyses, and a geographic detector model. Furthermore, multi-scenario territorial-space simulations are carried out using the CA–Markov model, based on which an optimal pattern of territorial space is constructed. The results show that the comprehensive dynamic degree of territorial space in Shandong Province was valued at 0.56% from 2000 to 2020. Furthermore, six geo-information Tupu of TSS evolution changed, with a total area of 35,485 km2, distributed mainly in the Yellow River Delta, the central and southern Shandong Mountain area, and the Jiaodong Peninsula. The migration route of the TSS gravity center curved over time. Territorial spaces are characterized by the exchange of ecological and agricultural space, while urban spaces occupy agricultural ones. The level of economic development, policy, and the institutional environment are driving forces in the transformation of ecological into agricultural spaces, as well as in transforming agricultural space into ecological and urban spaces. The trade-off connection of TSSs is made evident after a multi-scenario simulation of territorial space considering the 2020–2025 timeframe. Based on the goal of regional function co-ordination, Shandong Province is divided into three and four types of single and complex TSS, respectively. The obtained results may provide scientific reference for the co-ordination between human–land relationships and the sustainable use of territorial space, and serve to guide territorial spatial planning.

Impacts and Projections of Land Use and Demographic Changes on Ecosystem Services: A Case Study in the Guanzhong Region, China

Land use change and demographic factors directly or indirectly affect ecosystem services value, and the analysis of ecosystem services contributes to optimization of land planning, which is essential for regional sustainable development. In this study, ArcGIS 10.2, IDRISI 17.0 Selva and MATLAB software, value coefficient method, CA-Markov prediction model and population growth model were applied to analyze the spatial and temporal changes of land use trends and ecosystem service values in Guanzhong region, and further predict the impacts of land type changes and population changes on ecosystem services in the context of urbanization. Results showed that the expansion of construction land was the most intense, and the transfer process mainly crowded out arable land; the total ecosystem services value grew spatially in a “low center-high periphery” ring with large differences at the bottom, and forest land was the most important value provider. The total ecosystem services value was estimated to decline in the future, with low-value areas spreading northward and differences in the per capita ecosystem services value increasing. This study provides a reference for optimal simulation of urban expansion and ecological conservation.

Coupling Ecological Security Pattern Establishment and Construction Land Expansion Simulation for Urban Growth Boundary Delineation: Framework and Application

Reasonable delineation of the urban growth boundary (UGB) plays a vital role in guiding orderly urban space growth and ensuring urban environmental health. Existing methodologies for UGB delineation have failed to address the significance of ecological security. Therefore, this study presents a framework that couples ecological security pattern (ESP) establishment and construction land expansion (CLE) simulation to delineate the UGB. The proposed framework is applied to the Nanchang Metropolitan Area (NCMA) in southeastern China. First, we established the regional ESP of the NCMA in 2018 based on an improved minimum cumulative resistance model. The areas of low-, medium-, and high-level ESP were 1050.75, 736.42, and 720.59 km2, respectively. Second, we implemented a multi-scenario simulation of CLE in the NCMA in 2025 based on a cellular automata–Markov model. A natural development scenario was superior to urban growth and ecological protection scenarios for social, economic, and ecological development at the regional scale. Accordingly, we delineated the UGB of the NCMA in 2025 with a scale of 687.87 km2, based on dynamic adjustment using the results of ESP establishment and CLE simulation in the natural development scenario. The rationality and scientificity of the proposed framework were verified by comparing the scale and layout of the delineated UGB with the regional planning of Nanchang City. The framework incorporating dynamic adjustment with ESP establishment and multi-scenario CLE simulation provides a useful tool for the delineation of the UGB in similar urbanized cities. Its application is conducive to achieving a win–win outcome of regional ecological security and urban development.

Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

Assessment and Estimation of the Spatial and Temporal Evolution of Landscape Patterns and Their Impact on Habitat Quality in Nanchang, China

Assessing and predicting the evolution of habitat quality based on land use change under the process of urbanization is important for establishing a comprehensive ecological planning system and addressing the major challenges of global sustainable development. Here, two different prediction models were used to simulate the land use changes in 2025 based on the land use distribution data of Nanchang city in three periods and integrated into the habitat quality assessment model to specifically evaluate the trends and characteristics of future habitat quality changes, explore the impact of landscape pattern evolution on habitat, and analyze the differences and advantages of the two prediction models. The results show that the overall habitat quality in Nanchang declined significantly during the period 1995–2015. Habitat degradation near cities and in various watersheds is relatively significant. During the period 2015–2025, the landscape pattern and habitat quality of Nanchang will continue to maintain the trend of changes observed between 1995 and 2015, i.e., increasing construction land and decreasing habitat quality, with high pressure on ecological restoration. This study also identified that CA-Markov simulates the quantity of land use better, while FLUS simulates the spatial pattern of land use better. Overall, this study provides a reference for exploring the complex dynamic evolution mechanism of habitats.

Analysis of the Future Land Use Land Cover Changes in the Gaborone Dam Catchment Using CA-Markov Model: Implications on Water Resources

Land use/land cover (LULC) changes have been observed in the Gaborone dam catchment since the 1980s. A comprehensive analysis of future LULC changes is therefore necessary for the purposes of future land use and water resource planning and management. Recent advances in geospatial modelling techniques and the availability of remotely sensed data have become central to the monitoring and assessment of both past and future environmental changes. This study employed the cellular automata and Markov chain (CA-Markov) model combinations to simulate future LULC changes in the Gaborone dam catchment. Classified Landsat images from 1984, 1995, 2005 and 2015 were used to simulate the likely LULCs in 2015 and 2035. Model validation compared the simulated and observed LULCs of 2015 and showed a high level of agreement with Kappa variation estimates of Kno (0.82), Kloc (0.82) and Kstandard (0.76). Simulation results indicated a projected increase of 26.09%, 65.65% and 55.78% in cropland, built-up and bare land categories between 2015 and 2035, respectively. Reductions of 16.03%, 28.76% and 21.89% in areal coverage are expected for shrubland, tree savanna and water body categories, respectively. An increase in built-up and cropland areas is anticipated in order to meet the population’s demand for residential, industry and food production, which should be taken into consideration in future plans for the sustainability of the catchment. In addition, this may lead to water quality and quantity (both surface and groundwater) deterioration in the catchment. Moreover, water body reductions may contribute to water shortages and exacerbate droughts in an already water-stressed catchment. The loss of vegetal cover and an increase in built-up areas may result in increased runoff incidents, leading to flash floods. The output of the study provides useful information for land use planners and water resource managers to make better decisions in improving future land use policies and formulating catchment management strategies within the framework of sustainable land use planning and water resource management.

Analyzing the influence of land use/land cover change on landscape pattern and ecosystem services in the Poyang Lake Region, China

The Poyang Lake Region (PLR) is well known for its ecological and economic importance. This paper first analyzed the changes in land use/land cover (LULC), followed by changes in landscape patterns and ecosystem services by landscape metrics and equivalent coefficients table method. Then, the influence of LULC change on landscape pattern and ecosystem services in both historical period (from 1990 to 2015) and future period (2030) was explored. The results showed that the area of construction land was 607.9 km² in 1990 and 972.5 km² in 2015. The increased construction land mainly converted from cultivated land. For the entire PLR, a higher use degree of LULC and a trend of fragmentation existed in recent years. The total ecosystem service values (ESVs) decreased by ¥2.44 × 10⁹ from 1990 to 2015, mainly because of shrinkage of cultivated land and sharp increase in construction land. It was predicted that the areas of construction land and waterbody would increase by 34.6% and 2.2% compared with those in 2015. These changes would lead to more regular in patch shape, longer in patch edge, less connectivity of patches, and an increase of ¥6.2 × 10⁸ ESVs in 2030.

Economic Development Policies and Land Use Changes in Thailand: From the Eastern Seaboard to the Eastern Economic Corridor

The Thai government’s project called “Eastern Economic Corridor (EEC)” was announced in 2016 to stimulate economic development and help the country escape from the middle-income trap. The project provides investment incentives for the private sector and the infrastructure development of land, rail, water, and air transportation. The EEC project encompasses three provinces in the eastern region of Thailand because of their strategic locations near deep seaports and natural resources in the Gulf of Thailand. Clearly, this policy will lead to dramatic changes in land uses and the livelihoods of the people in these three provinces. However, the extent to which land use changes will occur because of this project remains unclear. This study aims to analyze land use changes in the eastern region of Thailand using a Cellular Automata–Markov model. The results show that land uses of the coastal areas have become more urbanized than inland areas, which are primarily agricultural lands. The predicted land uses suggest shrinking agricultural lands of paddy fields, field crops, and horticulture lands but expanding perennial lands. These changes in land uses highlight challenges in urban administration and management as well as threats to Thailand’s agricultural cultures in the future.

Urban Sprawl and Growth Prediction for Lagos Using GlobeLand30 Data and Cellular Automata Model

Urban growth in various cities across the world, especially in developing countries, leads to land use change. Thus, predicting future urban growth in the most rapidly growing region of Nigeria becomes a significant endeavor. This study analyzes land use and land cover (LULC) change and predicts the future urban growth of the Lagos metropolitan region, using Cellular Automata (CA) model. To achieve this, the GlobeLand30 datasets from years 2000 and 2010 were used to obtain LULC maps, which were utilized for modeling and prediction. Change analysis and prediction for LULC scenario for 2030 were performed using LCM and CA Markov chain modeling. The results show a substantial growth of artificial surfaces, which will cause further reductions in cultivated land, grassland, shrubland, wetland, and waterbodies. There was no appreciable impact of change for bare land, as its initial extent of cover later disappeared completely. Additionally, artificial surfaces/urban growth in Lagos expanded to the neighboring towns and localities in Ogun State during the study period, and it is expected that such growth will be higher in 2030. Lastly, the study findings will be beneficial to urban planners and land use managers in making key decisions regarding urban growth and improved land use management in Nigeria.

Land use/land cover (LULC) analysis (2009-2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil

The Amazonian biome is important not only for South America but also for the entire planet, providing essential environmental services. The state of Rondônia ranks third in deforestation rates in the Brazilian Legal Amazon (BLA) political division. This study aims to evaluate the land use/land cover (LULC) changes over the past ten years (2009-2019), as well as, to predict the LULC in the next 10 years, using TerrSet 18.3 software, in the state of Rondônia, Brazil. The machine learning algorithms within the Google Earth Engine cloud-based platform employed a Random Forest classifier in image classifications. The Markov-CA deep learning algorithm predicted future LULC changes by comparing scenarios of one and three transitions. The results showed a reduction in forested areas of about 15.7% between 2009 and 2019 in the Rondônia state. According to the predictive model, by 2030, around 30% of the remaining forests will be logged, most likely converted into occupied areas. The results reinforce the importance of measures and policies integrated with investments in research and satellite monitoring to reduce deforestation in the Brazilian Amazon and ensure the continuity of the Amazonian role in halting climate change.

Analysis of the Current and Future Prediction of Land Use/Land Cover Change Using Remote Sensing and the CA-Markov Model in Majang Forest Biosphere Reserves of Gambella, Southwestern Ethiopia

This study aimed to evaluate land use/land cover changes (1987-2017), prediction (2032-2047), and identify the drivers of Majang Forest Biosphere Reserves. Landsat image (TM, ETM+, and OLI-TIRS) and socioeconomy data were used for the LU/LC analysis and its drivers of change. The supervised classification was also employed to classify LU/LC. The CA-Markov model was used to predict future LU/LC change using IDRISI software. Data were collected from 240 households from eight kebeles in two districts to identify LU/LC change drivers. Five LU/LC classes were identified: forestland, farmland, grassland, settlement, and waterbody. Farmland and settlement increased by 17.4% and 3.4%, respectively; while, forestland and grassland were reduced by 77.8% and 1.4%, respectively, from 1987 to 2017. The predicted results indicated that farmland and settlement increased by 26.3% and 6.4%, respectively, while forestland and grassland decreased by 66.5% and 0.8%, respectively, from 2032 to 2047. Eventually, agricultural expansion, population growth, shifting cultivation, fuel wood extraction, and fire risk were identified as the main drivers of LU/LC change. Generally, substantial LU/LC changes were observed and will continue in the future. Hence, land use plan should be proposed to sustain resource of Majang Forest Biosphere Reserves, and local communities' livelihood improvement strategies are required to halt land conversion.

The Influence of the Calibration Interval on Simulating Non-Stationary Urban Growth Dynamic Using CA-Markov Model

The temporal non-stationarity of land use and cover change (LUCC) processes is one of the main sources of uncertainty that may influence the calibration and the validation of spatial path-dependent LUCC models. In relation to that, this research aims to investigate the influence of the temporal non-stationarity of land change on urban growth modeling accuracy based on an empirical approach that uses past LUCC. Accordingly, the urban development in Rennes Metropolitan (France) was simulated using fifteen past calibration intervals which are set from six training dates. The study used Idrisi’s Cellular Automata-Markov model (CA-Markov) which is an inductive pattern-based LUCC software package. The land demand for the simulation year was estimated using the Markov Chain method. Model validation was carried out by assessing the quantity of change, allocation, and spatial patterns accuracy. The quantity disagreement was analyzed by taking into consideration the temporal non-stationarity of change rate over the calibration and the prediction intervals, the model ability to reproduce the past amount of change in the future, and the time duration of the prediction interval. The results show that the calibration interval significantly influenced the amount and the spatial allocation of the estimated change. In addition to that, the spatial allocation of change using CA-Markov depended highly on the basis land cover image rather than the observed transition during the calibration period. Therefore, this study provides useful insights on the role of the training dates in the simulation of non-stationary LUCC.

Identification before-after Forest Fire and Prediction of Mangrove Forest Based on Markov-Cellular Automata in Part of Sembilang National Park, Banyuasin, South Sumatra, Indonesia

In 1997, the worst forest fire in Indonesia occurred and hit mangrove forest areas including in Sembilang National Park Banyuasin Regency, South Sumatra. Therefore, the Indonesian government keeps in trying to rehabilitate the mangrove forest in Sembilang National Park. This study aimed to identify the mangrove forest changing and to predict on the future year. The situations before and after forest fire were analyzed. This study applied an integrated Markov Chain and Cellular Automata model to identify mangrove forest change in the interval years of 1989–2015 and predict it in 2028. Remote sensing technology is used based on Landsat satellite imagery (1989, 1998, 2002, and 2015). The results showed mangrove forest has decreased around 9.6% from 1989 to 1998 due to forest fire, and has increased by 8.4% between 1998 and 2002, and 2.3% in 2002–2015. Other results show that mangroves area has continued to increase from 2015 to 2028 by 27.4% to 31% (7974.8 ha). It shows that the mangrove ecosystem is periodically changing due to good management by the Indonesian government.

Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data

Mastering the evolution of urban land cover is important for urban management and planning. In this paper, a method for analyzing land cover evolution within urban built-up areas based on nighttime light data and Landsat data is proposed. The method solves the problem of inaccurate descriptions of urban built-up area boundaries from the use of single-source diurnal or nocturnal remote sensing data and was able to achieve an effective analysis of land cover evolution within built-up areas. Four main procedures are involved: (1) The neighborhood extremum method and maximum likelihood method are used to extract nighttime light data and the urban built-up area boundaries from the Landsat data, respectively; (2) multisource urban boundaries are obtained using boundary pixel fusion of the nighttime light data and Landsat urban built-up area boundaries; (3) the maximum likelihood method is used to classify Landsat data within multisource urban boundaries into land cover classes, such as impervious surface, vegetation and water, and to calculate landscape indexes, such as overall landscape trends, degree of fragmentation and degree of aggregation; (4) the changes in the multisource urban boundaries and landscape indexes were obtained using the abovementioned methods, which were supported by multitemporal nighttime light data and Landsat data, to model the urban land cover evolution. Using the cities of Shenyang, Changchun and Harbin in northeastern China as experimental areas, the multitemporal landscape index showed that the integration and aggregation of land cover in the urban areas had an increasing trend, the natural environment of Shenyang and Harbin was improving, while Changchun laid more emphasis on the construction of artificial facilities. At the same time, the method proposed in this paper to extract built-up areas from multi-source city data showed that the user accuracy, production accuracy, overall accuracy and Kappa coefficient are at least 3%, 1%, 1% and 0.04 higher than the single-source data method.

Modeling the artificial lake-surface area change in arid agro-ecosystem: A case study in the newly reclaimed area, Egypt

Land reclamation is critically required to overcome the environmental and anthropogenic challenges in arid lands. The Western Nile Delta region, Egypt, is experiencing rapid reclamation processes for agriculture expansion. West Nubaria (781.92 km²) is one of the newly reclaimed areas in the Western Nile Delta. Due to extensive agricultural practices and poor management, an artificial saline lake formed in this area. Two primary goals of this research; 1) monitoring the annual change in the lake surface area between 2013 and 2017. 2) Predicting the areal extent of the lake surface in 2020, 2030, and 2040 based on two management scenarios. The maximum likelihood classifier (MLC) was applied to distinguish the LULC classes in 2017. Additionally, the annual modified normalized difference water index (MNDWI) calculated between 2013 and 2017. Then, the land change modeler (LCM) was utilized to predict the 2017 free water area based on the resulted MNDWI maps of 2013 and 2016 using two scenarios. With the high agreement between the actual and predicting free water area of 2017 (Kappa index = 0.93), the LCM was applied to predict the future surface water expansion in 2020, 2030, and 2040. Three land use/land cover (LULC) distinguished in 2017; agricultural land, uncultivated land, and free water class based on MLC. The MNDWI results reveal that there was an increase in the surface water area from 593 to 883 ha between 2013 and 2017, respectively. The LCM results indicate that expected increases in the surface water areas of 1068, 1711, and 2267 ha in 2020, 2030 and 2040, respectively (scenario 1) and 1065, 1726, and 2343 ha in the respective dates (scenario 2). These extend will exist over the agricultural and uncultivated lands surrounding the lake causing land degradation. Two solutions were suggested to combat the waterlogging and land degradation in this area by evacuating the artificial saline lake.

Changes in landscape composition and configuration in the Beressa watershed, Blue Nile basin of Ethiopian Highlands: historical and future exploration

Analyzing long-term dynamics of landscape patterns can provide important insights into the changes in landscape functions, that are necessary for optimizing resource management strategies. This study primarily aimed at quantifying landscape structural change. The Land use/land cover (LULC) layers of 1972, 1987, 2002, and 2017 were mapped from Landsat images, and projected to 2032 and 2047. Factor analysis was then employed to select independent core metrics of landscape composition and configuration to characterize the landscape. A post-classification comparison indicated that, between 1972 and 2017, natural vegetation, grassland, barren land and waterbody covers declined by 89.9%, 67.9%, 67.8 and 15.9%, respectively. On the other hand, plantation increased by 692.1% followed by human settlement (138%) and farmland (21.8%). A similar trend is likely to continue in 2032 and 2047 with a slight decline in the plantation category in 2047. Analysis of landscape metrics revealed that between 1972 and 2017, the number of patches increased. Specifically, plantation, barren land, settlement and grassland increased by 171.4%, 69.7%, 65.8% and 28.6%, respectively. In contrast, natural vegetation, farmland and waterbody declined by 53.1%, 46.3% and 33.9%, respectively. Future predictions showed a declining trend of the number of patches for all LULC types. An increasing trend in the largest patch index and patch size for farmland, plantation, and settlement categories was observed across all years, suggesting intensified human activities in the landscape. Consequently, natural habitat category has declined and become fragmented. Landscape pattern has changed considerably and become more fragmented over the last 45 years. Nevertheless, the future projections suggest a decline in fragmentation and potentially increased assemblage of patches forming simple patterns with fewer number of large size class patches. The results of this study could perhaps be applied in designing strategies for landscape management planning and resource conservation decision-making.

Simulation of Land Use Change and Ecosystem Service Value Dynamics under Ecological Constraints in Anhui Province, China

Land use change has a significant impact on the structure and function of ecosystems, and the transformation of ecosystems affects the mode and efficiency of land use, which reflects a mutual interaction relationship. The prediction and simulation of future land use change can enhance the foresight of land use planning, which is of great significance to regional sustainable development. In this study, future land use changes are characterized under an ecological optimization scenario based on the grey prediction (1,1) model (GM) and a future land use simulation (FLUS) model. In addition, the ecosystem service value (ESV) of Anhui Province from 1995 to 2030 were estimated based on the revised estimation model. The results indicate the following details: (1) the FLUS model was used to simulate the land use layout of Anhui Province in 2018, where the overall accuracy of the simulation results is high, indicating that the FLUS model is applicable for simulating future land use change; (2) the spatial layout of land use types in Anhui Province is stable and the cultivated land has the highest proportion. The most significant characteristic of future land use change is that the area of cultivated land continues to decrease while the area of built-up land continues to expand; and (3) the ESV of Anhui Province is predicted to increase in the future. The regulating service is the largest ESV contributor, and water area is the land use type with the highest proportion of ESV. These findings provide reference for the formulation of sustainable development policies of the regional ecological environment.

Evaluating water resource sustainability from the perspective of water resource carrying capacity, a case study of the Yongding River watershed in Beijing-Tianjin-Hebei region, China

China is facing great challenges to balance its natural water resource use and eco-environment protection, especially in the north semi-arid region with large water consumption due to the rapid economic growth. This highlights the urgency to use water resource carrying capacity (WRCC) as a measure to maintain the sustainable development of the human and natural water system. Here, we used a coupled model based on the system dynamics and cellular automaton models to assess the WRCC under the critical value of water resource withdrawal ratio (40%) and its sustainability in the Yongding River watershed in Beijing-Tianjin-Hebei region, where the water use highly depends on river flow and nonrenewable groundwater resources. The analytical results showed that the current regional WRCC is severely overloaded due to strong human activities. The predicted results based on four scenarios, i.e., existing development, water saving, industrial restructuring, and integrated development schemes, showed that although the improvement of water saving and water use efficiency has mitigated the regional water shortage, evidenced by the increased WRCC, the water shortage would continue due to the increased water demand. Under the integrated development scenario, it will need at least additional 7.1 × 10⁸ m³ water per year (Beijing: 2.5 × 10⁸ m³, Tianjin: 0.8 × 10⁸ m³, Hebei: 3.8 × 10⁸ m³) via the water transfer project to maintain the sustainability in the next decades. Our research provides recommendations for reasonable water utilization and supplementation under the severe water crisis.

The framework of an agricultural land-use decision support system based on ecological environmental constraints

Agricultural land use is a complicated systems engineering. Modern agriculture faces increasingly more risks. Managers should obtain reliable information to assist in decision-making through certain methods that allow them to achieve an economic, social, and ecological environment that is crucial in coordinating the development of agricultural land-use patterns. This study proposes a framework of an agricultural land-use decision support system (LDSS) based on ecological environmental constraints according to the DSS design philosophy, which provides a scientific basis for managers to allocate land resources. This framework of LDSS consists of a land quality assessment module, an eco-economic coupling module, and a land-use optimisation module. Firstly, it establishes a natural-society-economic land quality evaluation system to simulate the comprehensive benefit relationship of land use. Secondly, it analyses the risks of soil, water, and ecological security in the process of land use; simulates and reveals the mechanism of occurrence; and completes the correlation equation expression between natural-economic-social indictor and land use risk. Based on different scenarios design, the ecological environmental risk factors are used as constraints. Finally, the multi-objective linear programming method is employed to calculate the optimal comprehensive benefits of land use and optimal land-use structure based on the constraints of the ecological environment. Then the study takes Changsha County, a high-intensification, main grain-producing area in Central and South China, as a case area to demonstrate the feasibility of the framework of LDSS, and draws the highest comprehensive benefits and optimal structure of land use under the premise that the rural ecological environment conforms to national standards. Case study shows that the LDSS framework is feasible, easy to operate, and easy to promote. The research results can provide efficient and practical support for managers to allocate land resources and formulate sustainable land-use policies rationally.

Analysis and Projection of Land-Use/Land-Cover Dynamics through Scenario-Based Simulations Using the CA-Markov Model: A Case Study in Guanting Reservoir Basin, China

Understanding the rate and process of land-use/land-cover (LULC) change in a watershed is essential for managing natural resources and achieving sustainable development. Therefore, this study aims to analyze historical LULC change from 1980 to 2010 and project future changes in 2030, 2060, and 2090 in the Guanting Reservoir Basin (GRB), China, a critical water-supplying watershed for China’s capital Beijing, through scenario-based simulations. Two LULC scenarios, ‘business-as-usual’ and ‘governance’ (Gov), were projected using the Cellular Automata-Markov (CA–Markov) model. Historical LULC trend analysis shows that built-up land increased from 2.6% in 1980 to 5.26% in 2010, while cropland, grassland, and water body decreased. LULC conversion analysis indicates that, in general, grassland, cropland, and woodland were converted to built-up area from 1980 to 2010. The BAU scenario projects a dramatic increase in built-up area, rising from 2296.98 km2 (5.26%) in 2010 to 11,757.35 km2 (26.93%) in 2090 at the expense of cropland and grassland areas. Conversely, the Gov scenario predicts an increase in water body, woodland, and grassland, encouraging sustainable development. Overall, these results provide useful inputs to the LULC planners and water resources managers to elaborate on eco-friendly policies and regulations for GRB.

Quantitatively Assessing and Attributing Land Use and Land Cover Changes on China’s Loess Plateau

The global land surface cover is undergoing extensive changes in the context of global change, especially in the Loess Plateau, where ecological restoration policies have been vigorously implemented since 2000. Evaluating the impact of these policies on land cover is of great significance for regional sustainable development. Nonetheless, there are few quantitative assessment studies of the impact of ecological restoration policies on land use and land cover change (LULCC). In this study, a relative contribution conceptual model (RCCM) was used to explore the contribution of the policies to LULCC under the influence of natural background change, which was based on the Markov chain and the future land use simulation (FLUS) model. The results show that LULCC is influenced by ecological restoration policies and the natural environment, of which the policies contribute about 72.37% and natural change contribute about 27.63%. Ecological restoration policies have a profound impact on LULCC, changing the original direction of LULCC greatly. Additionally, these policies regulate the pattern of LULCC by controlling the amount of cropland as a rebalanced leverage. These findings provide useful information for facilitating sustainable ecological development in the Loess Plateau and theoretically supporting environmental decision-making.

The Development Simulation of Urban Green Space System Layout Based on the Land Use Scenario: A Case Study of Xuchang City, China

The development and evolution of an urban green space system is affected by both natural effects and human intervention. The simulation and prediction of an urban green space system can enhance the foresight of urban planning. In this study, several land use change scenarios of the main urban area of Xuchang City were simulated from 2014 to 2030 based on high-resolution land use data. The layout of each scenario was evaluated using landscape indexes. A Cellular Automata–based method (i.e., future land use simulation, FLUS) was applied to develop the urban green space system, which we combined with urban land use evolution. Using recent data, the FLUS model effectively dealt with the uncertainty and complexity of various land use types under natural and human effects and solved the dependence and error transmission of multiperiod data in the traditional land use simulation process. The root mean square error (RMSE) of probability of the suitability occurrence module and the Kappa coefficient of the overall model simulation accuracy verification index both met accuracy requirements. It was feasible to combine the evolution of the urban green space system with urban land development. Moreover, under the Baseline Scenario, the urban land use layout was relatively scattered, and the urban green space system showed a disordered development trend. The Master Plan Scenario had a compact urban land use layout, and the green space system was characterized by networking and systematization, but it did not consider the service capacity of the green space. The Planning Guidance Scenario introduced constraint conditions (i.e., a spatial development strategy, green space accessibility, and ecological sensitivity), which provided a more intensive and efficient urban space and improved the service function of the green space system layout. Managers and planners can evaluate the urban future land use development mode under different constraints. Moreover, they would be able to adjust the urban planning in the implementation process. This work has transformed the technical nature of the planning work from “static results” to a “dynamic process”.

Spatial-Temporal Dynamic Analysis of Land Use and Landscape Pattern in Guangzhou, China: Exploring the Driving Forces from an Urban Sustainability Perspective

Rapid urbanization is one of the most important factors causing land-use change, which mainly results from the orientation of government policies, adjustment of industrial structure, and migration of the rural population. Land use and land cover change (LUCC) is the natural foundation of urban development that is significantly influenced by human activities. By analyzing the LUCC and its inner driving force, as well as landscape pattern change, human activity and urban sustainable development can be better understood. This research adopted a geographic information system (GIS) and remote sensing (RS) technology to comprehensively analyze land use of Guangzhou, respectively, in 1995, 2005, and 2015. Fragmentation Statistics (FRAGSTATS) is the most authoritative software to calculate landscape metrics. Landscape pattern change was analyzed by FRAGSTATS. The results showed that urban land significantly increased from 16.33% in 1995 to 36.05% in 2015. Farmland greatly decreased from 45.16% in 1995 to 27.82% in 2005 and then slightly decreased to 25.10% in 2015. In the first decade, the non-agricultural conversion of rural land and the expansion of urban land was the dominant factor that led to the change. In the second decade, urban land had been supplemented through the redevelopment of low-efficiency land. The fragmentation of landscape patterns significantly increased from 1995 to 2005 and slightly decreased from 2005 to 2015. It indicated that the change in land use in the second decade was different from that in the first. This difference mainly resulted from three aspects: (1) urban development area and ecological conservation area were clearly defined in Guangzhou; (2) many small towns had developed into urban centers, and the scattered urban land gathered into these centers; (3) the establishment of greenway improved the connection of fragmented patches. After that, this study discussed land-use change and its causes and proposed the trend of urban development from the perspective of sustainability.

Variations in ecosystem service value in response to land use/land cover changes in Central Asia from 1995-2035

Acute farmland expansion and rapid urbanization in Central Asia have accelerated land use/land cover changes, which have substantial effects on ecosystem services. However, the spatiotemporal variations in ecosystem service values (ESVs) in Central Asia are not well understood. Here, based on land use products with 300-m resolution for the years 1995, 2005 and 2015 and transfer methodology, we predicted land use and land cover (LULC) for 2025 and 2035 using CA-Markov, assessed changes in ESVs in response to LULC dynamics, and explored the elasticity of the response of ESV to LULC changes. We found significant expansions of cropland (+22.10%) and urban areas (+322.40%) and shrinking of water bodies (-38.43%) and bare land (-9.42%) during 1995-2035. The combined value of ecosystem services of water bodies, cropland, and grassland accounted for over 90% of the total ESVs. Our study showed that cropland ecosystem services value increased by 93.45 billion US$ from 1995 to 2035, which was mainly caused by the expansion of cropland area. However, the area of water bodies decreased sharply during 1995-2035, causing a loss of 64.38 billion US$. Biodiversity, food production and water regulation were major ecosystem service functions, accounting for 80.52% of the total ESVs. Our results demonstrated that effective land-use policies should be made to control farmland expansion and protect water bodies, grassland and forestland for more sustainable ecosystem services.

Measuring and Predicting Urban Expansion in the Angkor Region of Cambodia

Recent increases in urbanization and tourism threaten the viability of UNESCO world heritage sites across the globe. The Angkor world heritage site located in southern Cambodia is now facing such a challenge. Over the past two decades, Angkor has seen over 300,000% growth in international tourist arrivals, which has led to uncontrolled development of the nearby city of Siem Reap. This study uses remote sensing and GIS to comprehend the process of urban expansion during the past 14 years, and has applied the CA-Markov model to predict future urban expansion. This paper analyzes the urban pressure on the Angkor site at different scales. The results reveal that the urban area of Siem Reap city increased from 28.23 km2 in 2004 to 73.56 km2 in 2017, an increase of 160%. Urban growth mainly represented a transit-oriented pattern of expansion, and it was also observed that land surfaces, such as arable land, forests, and grasslands, were transformed into urban residential land. The total constructed land area in the core and buffer zones increased by 12.99 km2 from 2004 to 2017, and 72% of the total increase was in the buffer zone. It is predicted that the built-up area in Siem Reap is expected to cover 135.09 km2 by 2025 and 159.14 km2 by 2030. The number of monuments that are most likely be affected by urban expansion is expected to increase from 9 in 2017 to 14 in 2025 and 17 in 2030. The urban area in Siem Reap has increased dramatically over the past decade and monuments continue to be decimated by urban expansion. This paper urges closer attention and urgent actions to minimize the urban pressure on the Angkor site in the future.

Land use change, urbanization, and change in landscape pattern in a metropolitan area

This paper analyzes land use change, urbanization and their impact on the change in landscape pattern in Tabriz metropolitan area (TMA) during the time period from 1996 to 2016 in order to provide support sustainable regional planning. For this purpose, land use data obtained from satellite images including Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), and Operational Land Imager (OLI) sensors for 1996, 2006, and 2016 with 30 × 30 m spatial resolution. This paper first seeks to analyze the changes in land use and urbanization, followed by changes in landscape patterns by using spatial metrics and Landscape Expansion Index (LEI). Then, using two methods of Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), magnitude and direction of the relationship between land use changes, urbanization, and change in landscape patterns are analyzed. The findings show that most ecological lands such as grasslands have been converted into bare and urban lands over the past two decades. Therefore, in the whole landscape, the expansion of urbanization has led to the prevailing pattern, resulting in increased fragmentation and reduced aggregation. The results also show that changes in landscape patterns have a strong relationship with changes in various land uses. In addition, GWR analysis was used to analyze the impact of urbanization on changes in landscape patterns, indicating that urbanization expansion has different effects with changes in spatial positions, so that in areas adjacent to the built-up lands and the central regions of TMA, with increasing urbanization, we see increasing aggregation in the landscape, but as we move away from the built-up areas, are faced with an increase in fragmentation and heterogeneity, especially in the northeastern, south and southwest areas of TMR.

Dynamic projection of ecological risk in the Manas River basin based on terrain gradients

With large-scale developments, the Manas River Basin (MRB) is in an extreme imbalance especially in land use, thus causing a series of ecological problems. A reliable dynamic ecological risk assessment is expected to provide useful information for the economic development. Through coupling spatial Cellular Automaton-Markov (CA-Markov) model and Landsat satellite images in 2000, 2008 and 2016, we forecasted the land use maps in 2024 and 2032. Based on the ecological risk model, we evaluated the ecological risk at landscape level from 2000 to 2032. More importantly, an improved evaluation of ecological risk was proposed based on terrain gradients and the correlation between terrain niche index (TNI) and future ecological risk was analyzed. The results showed that the artificial oases and urban are expanding, while the natural grassland is shrinking. Corresponding to the rapid development stage and stable consolidation stage, farmland will be followed by a slower increase (2016-2032) after a rapid increase (2000-2016), and water decreases first but then is projected to recover. As the overall spatial diversity increasing, the ecological risk in the whole basin is growing, especially in grassland. Compared with the stable critical state in artificial landscape, the future ecological risks in natural landscape tend to increase due to the cumulative effects of human activities. Also, we found that the great ecological risk mainly happens in "high altitude and complex terrain" or "low altitude and flat terrain" areas. The future ecological risk in medium terrain niche index (TNI) gradient will increase, while it will decrease in the lowest. Above all, the proposed framework can do well in forecasting ecological risk at landscape level, and can help simply infer the changes of ecological risk based on terrain.

Assessment and prediction of spatial patterns of human-elephant conflicts in changing land cover scenarios of a human-dominated landscape in North Bengal

It is of utmost importance to research on the spatial patterns of human-wildlife conflicts to understand the underlying mechanism of such interactions, i.e. major land use changes and prominent ecological drivers. In the north eastern part of India there has been a disparity between nature, economic development and fragmentation of wildlife habitats leading to intense conflicts between humans and Asian elephants (Elephas maximus) in recent times. Both the elephant and human population have increased in the past few decades with large tracts of forests converted to commercial tea plantations, army camps and human settlements. We analyzed data maintained by the wildlife department on human deaths and injuries caused by elephant attacks between 2006–2016 to understand spatial and temporal patterns of human-elephant conflict, frequency and distribution. The average annual number of human deaths and injuries to elephant attacks between 2006 to 2016 was estimated to be 212 (SE 103) with the highest number of such incidents recorded in 2010–2011. Based on a grid based design of 5 km² and 25 km² resolution, the main spatial predictors of human-elephant conflicts identified through Maxent presence only models are annual mean precipitation, altitude, distance from protected area, area under forests, tea plantations and agriculture. Major land use changes were assessed for this region from 2008 to 2018 using satellite imageries in Arc GIS and a predicted imagery of 2028 was prepared using Idrisi Selva. Based on the 2018 imagery it was found that forest area had increased by 446 km² within 10 years (2008–2018) and the annual rate of change was 12%. Area under agriculture had reduced by 128 km² with an annual (-) rate of change of 2.5%. Area under tea plantation declined by 307 km² with an annual (-) rate of change of 12% whereas area under human settlements increased by 61 km² with an annual (-) rate of change of 44%. Hotspots of human-elephant conflicts were identified in an east west direction primarily around protected areas, tea plantations and along major riverine corridors. During informal interactions with farmers, tea estate labors it was revealed that local community members chased and harassed elephants from agriculture fields, human settlements under the influence of alcohol and thus were primary victims of fatal interactions. Our analytical approach can be replicated for other species in sites with similar issues of human-wildlife conflicts. The hotspot maps of conflict risk will help in developing appropriate mitigation strategies such as setting up early warning systems, restoration of wildlife corridors especially along dry river beds, using deterrents and barriers for vulnerable. Awareness about alcohol related incidents and basic biology of elephants should be organized regularly involving non-governmental organizations targeting the marginalized farmers and tea estate workers.

National Green GDP Assessment and Prediction for China Based on a CA-Markov Land Use Simulation Model

Green Gross Domestic Product (GDP) is an important indicator to reflect the trade-off between the ecosystem and economic system. Substantial research has mapped historical green GDP spatially. But few studies have concerned future variations of green GDP. In this study, we have calculated and mapped the spatial distribution of the green GDP by summing the ecosystem service value (ESV) and GDP for China from 1990 to 2015. The pattern of land use change simulated by a CA-Markov model was used in the process of ESV prediction (with an average accuracy of 86%). On the other hand, based on the increasing trend of GDP during the period of 1990 to 2015, a regression model was built up to present time-series increases in GDP at prefecture-level cities, having an average value of R square (R2) of approximately 0.85 and significance level less than 0.05. The results indicated that (1) from 1990 to 2015, green GDP was increased, with a huge growth rate of 78%. Specifically, the ESV value was decreased slightly, while the GDP value was increased substantially. (2) Forecasted green GDP would increase by 194978.29 billion yuan in 2050. Specifically, the future ESV will decline, while the rapidly increased GDP leads to the final increase in future green GDP. (3) According to our results, the spatial differences in green GDP for regions became more significant from 1990 to 2050.

Past and Future Trajectories of Farmland Loss Due to Rapid Urbanization Using Landsat Imagery and the Markov-CA Model: A Case Study of Delhi, India

This study integrated multi-temporal Landsat images, the Markov-Cellular Automation (CA) model, and socioeconomic factors to analyze the historical and future farmland loss in the Delhi metropolitan area, one of the most rapidly urbanized areas in the world. Accordingly, the major objectives of this study were: (1) to classify the land use and land cover (LULC) map using multi-temporal Landsat images from 1994 to 2014; (2) to develop and calibrate the Markov-CA model based on the Markov transition probabilities of LULC classes, the CA diffusion factor, and other ancillary factors; and (3) to analyze and compare the past loss of farmland and predict the future loss of farmland in relation to rapid urban expansion from the year 1995 to 2030. The predicted results indicated the high accuracy of the Markov-CA model, with an overall accuracy of 0.75 and Kappa value of 0.59. The predicted results showed that urban expansion is likely to continue to the year of 2030, though the rate of increase will slow down from the year 2020. The area of farmland has decreased and will continue to decrease at a relatively stable rate. The Markov-CA model provided a better understanding of the past, current, and future trends of LULC change, with farmland loss being a typical change in this region. The predicted result will help planners to develop suitable government policies to guide sustainable urban development in Delhi, India.

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

Impact of anthropogenic climate change and human activities on environment and ecosystem services in arid regions

The implications of anthropogenic climate change, human activities and land use change (LUC) on the environment and ecosystem services in the coastal regions of Saudi Arabia were analyzed. Earth observations data was used to drive land use categories between 1970 and 2014. Next, a Markov-CA model was developed to characterize the dynamic of LUC between 2014 and 2100 and their impacts on regions' climate and environment. Non-parametric change point and trend detection algorithms were applied to temperature, precipitation and greenhouse gases data to investigate the presence of anthropogenic climate change. Lastly, climate models were used to project future climate change between 2014 and 2100. The analysis of LUC revealed that between 1970 and 2014, built up areas experienced the greatest growth during the study period, leading to a significant monotonic trend. Urban areas increased by 2349.61km² between 1970 and 2014, an average increase of >53.4km²/yr. The projected LUC between 2014 and 2100 indicate a continued increase in urban areas and irrigated cropland. Human alteration of land use from natural vegetation and forests to other uses after 1970, resulted in a loss, degradation, and fragmentation, all of which usually have devastating effects on the biodiversity of the region. Resulting in a statistically significant change point in temperature anomaly after 1968 with a warming trend of 0.24°C/decade and a downward trend in precipitation anomaly of 12.2mm/decade. Total greenhouse gas emissions including all anthropogenic sources showed a statistically significant positive trend of 78,090Kt/decade after 1991. This is reflected in the future projection of temperature anomaly between 1900 and 2100 with a future warming trend of 0.19°C/decade. In conclusion, human activities, industrial revelation, deforestation, land use transformation and increase in greenhouse gases had significant implications on the environment and ecosystem services of the study area.

Urban expansion simulation and the spatio-temporal changes of ecosystem services, a case study in Atlanta Metropolitan area, USA

Urban expansion can lead to land use changes and, hence, threatens the ecosystems. Understanding the effects of urbanization on ecosystem services (ESs) can provide scientific guidance for land use planning and the protection of ESs. We established a framework to assess the spatial distributions of ESs based on land use changes in the Atlanta Metropolitan area (AMA) from 1985 to 2012. A new comprehensive ecosystem service (CES) index was developed to reflect the comprehensive level of ESs. Associated with the influential factors, we simulated the business as usual scenario in 2030. Four alternative scenarios, including more compact growth (MCG), riparian vegetation buffer (RVB), soil conservation (SC), and combined development (CD) scenarios were developed to explore the optimal land use strategies which can enhance the ESs. The results showed that forest and wetland had the greatest decreases, while low and high intensity built-up lands had the greatest increases. The values of CES and most of ESs decreased significantly due to the sprawling expansion of built-up land. The scenario analysis revealed that the CD scenario performs best in CES value, while it performs the worst in food supply. Compared with the RVB and SC scenarios, MCG scenario is a more optimal land use strategy to enhance the ESs without at the expense of food supply. To integrate multiple ESs into land use planning and decision making, corresponding land management policies and ecological engineering measures should be implemented to enhance: (1) the water yield and water purification in urban core counties, (2) the carbon storage, habitat quality, and recreational opportunity in counties around the core area, and (3) the soil conservation and food supply in surrounding suburban counties. The land use strategies and ecological engineering measures in this study can provide references for enhancing the ESs in the AMA and other metropolitan areas.