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In the process of societal development, forest land categories often conflict with other land use types, leading to impacts on the ecological environment. Therefore, research on changes in forest land categories has increasingly become a globally focused topic. To anticipate potential forest ecological security issues under urbanization trends, studies on regional land use simulation become more important. This paper, based on land use data from the Ganjiang River basin, analyzes the distribution characteristics and changing trends of land use types from 2000 to 2020. Using the CA-Markov model, it predicts the land use pattern of the basin in 2040 and analyzes the transfer characteristics of forest land categories. The conclusions indicate that, between 2000 and 2020, the most significant trend in land use evolution was the transfer between various subcategories of forest land, especially frequent in the high-altitude mountainous areas in the southern and western parts of the basin. The land use pattern prediction model constructed in this paper has a kappa index of 0.92, indicating high accuracy and reliability of the predictions. In 2040, the most significant land evolution phenomenon would be from forest land to arable land to construction land, particularly pronounced around large cities. Over the next 20 years, the focus of land use evolution may shift from the southern part of the basin to the central and northern parts, with urban expansion possibly becoming the main driving force of land use changes during this period. Forest land restoration work is an effective method to compensate for the loss of forest land area in the Ganjiang River basin, with key areas for such work including Longnan, Yudu, Xingguo, Ningdu, Lianhua, and Yongxin counties. [ABSTRACT FROM AUTHOR]

Since the early 1980s, in southern China, evergreen broad-leaved forests have been replaced by Chinese fir plantations on a large scale. By analyzing the dynamic change characteristics of the landscape pattern of Chinese fir plantations in the case study, the paper explored the current status and development trend of the landscape pattern of Chinese fir plantations after 40 years of manual intervention and natural succession. The paper, based on the three-period survey data on forest resources in 2010, 2015, and 2020, analyzed the dynamic changes of the landscape pattern of Chinese fir plantations from 2010 to 2020 and, by using a transition matrix and landscape index, simulated and predicted the landscape pattern of Chinese fir plantations in Jiangle County in 2025 by constructing a CA–Markov model with Jiangle County, Fujian Province, China, as the study area. The results showed that the landscape of Chinese fir plantations is the main component of the forest landscape in southern China, accounting for 12%. The landscape quality of Chinese fir plantations degraded, mainly shown in the facts that the Chinese fir plantations were juvenile from 2010 to 2020, and that the young and middle-aged forests became the main part of the landscape of Chinese fir plantations, accounting for 54.8%. The landscape area of Chinese fir plantations showed an increasing trend, which mainly came from other coniferous forests, other woodlands, non-woodlands and non-wood forests, and the replaced Chinese fir plantations were mainly eroded by bamboo forests. The evergreen broad-leaved forests, a kind of zonal vegetation, have been effectively protected in the past 10 years. In the future, the total area of Chinese fir plantations will continue to expand, and a small part of them will continue to be eroded by bamboo forests. In order to improve the landscape quality of Chinese fir plantations, it is necessary to adjust the age group structure of Chinese fir plantations, expand the proportion of mature forests, and, meanwhile, continue to protect evergreen broad-leaved forests and curb the expansion of bamboo forests. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many single-land-use simulation models are available to simulate and predict Land Use and Land Cover Change (LUCC). However, few studies have used multiple models to simulate LUCC in the same region. The paper utilizes the CA-Markov model, Land Change Modeler (LCM), and Patch-generating Land Use Simulation model (PLUS) with natural and social driving factors to simulate the LUCC on the Western Sichuan Plateau, using Kappa coefficient, overall accuracy (OA), and Figure of Merit (FoM) to verify the accuracy of the model, and selects a suitable model to predict the LUCC and landscape pattern in the study area from 2020 to 2070. The results are as follows: (1) The LCM has the highest simulation effect, and its Kappa coefficient, OA, and FoM are higher than the other two models. (2) The area of land types other than grassland and wetland will increase from 2020 to 2070. Among them, the grassland area will decrease, but is still most prominent land category in this region. The proportion of wetland areas remains unchanged. The fragmentation degree of forest (F), grassland (GL), shrubland (SL), water bodies (WBs), bare areas (BAs), and permanent ice and snow (PIS) decreases, and the distribution shows a trend of aggregation. The dominance of F and C decreases but still dominates in the landscape. The overall landscape aggregation increased and complexity decreased, and each landscape type's diversity, evenness, and richness increased, presenting as a more reasonable development. Using multiple models to simulate the LUCC in the same region, and choosing the most suitable local land model is of great significance to scientifically manage and effectively allocate the land resources in the field. [ABSTRACT FROM AUTHOR]

Resource depletion, land-use change, and population growth triggered by the industrial revolution represent serious issues shared globally that have altered the structure, processes, and functions of ecosystems and had significant impacts on human well-being and survival security. This paper assesses changes in ecosystem service values (ESVs) in karst areas based on the perspective of land-use change. Guizhou province, which is typical of the South China Karst (SCK) ecologically fragile areas, was taken as a research subject. The past, current, and future spatial-temporal evolution of karst ESVs were assessed, using equivalence factors and CA-Markov modelling methods. The results show that: (1) from 1980 to 2040, arable land, woodland, and grassland occupy the main land types in the study area; at the same time, the water and built-up areas show a steady growth trend, with dramatic land use shifts occurring in the western, middle, and northern parts. (2) The overall ESVs increased by US$ 31.12 million during the study period, showing a temporal change trend of first decreasing and then increasing; forest land, grassland, and arable land area shift are the main factors of ESVs change; soil conservation, biodiversity conservation, and gas regulation functions are enhanced and play a vital role in the single ESVs increase; ESVs show a basin-type spatial distribution pattern. (3) The sensitivity index is <1, the ESV is inelastic to the VC factor adjustment, and the results are true and valid. This paper found that using quantitative methods to predict land use change of the South China Karst can provide accurate assessment of the differences in supply and demand for natural and social ecosystem services (ES) in a region, clarifying the trade-offs and synergies of ecosystem service functions, contributing to the achievement of sustainable development, and providing a practical reference for global land landscape optimization and land resource planners. [ABSTRACT FROM AUTHOR]

Evaluating waterlogging vulnerability and analyzing its characteristics and future trends can provide scientific support for urban disaster prevention and reduction. For this study, taking Fuzhou as an example, an urban waterlogging vulnerability assessment system was constructed from the three dimensions of exposure, sensitivity, and adaptive capacity. The entropy method was used to evaluate urban waterlogging vulnerability in Fuzhou during 2014–2020. The use of CA–Markov to predict waterlogging vulnerability in 2023, 2026, and 2029 in Fuzhou is an important innovation reported in this paper. Study results showed that: (1) Vulnerability to waterlogging in Fuzhou follows a gradually decreasing "center-southeast" distribution pattern, with Level 5 areas mainly located in Cangshan District, Gulou District, and Taijiang District. (2) Changes in waterlogging vulnerability in Fuzhou from 2014 to 2020 can be divided into five change modes, with changing areas, mainly of the late-change type, accounting for 14.13% of the total area. (3) Prediction accuracy verification shows that the CA–Markov model is suitable for predicting waterlogging vulnerability in Fuzhou with high accuracy and a kappa coefficient of 0.9079. (4) From 2020 to 2029, the vulnerability level of the eastern coastal region of Fuzhou is expected to generally increase, and the vulnerability degree will continue to deteriorate. The proportion of Level 5 vulnerable areas will increase by 4.5%, and the growth rate will increase faster and faster with the passage of time. [ABSTRACT FROM AUTHOR]

Based on the land use data changes in Jilin Province in 2011, 2014, 2017, and 2020, this paper analyzes the land use changes during 2011–2020 through the land use transfer matrix, calculates the changes in carbon sinks of recent years, and then uses the CA–Markov model to predict the land use types and carbon sinks in Jilin Province in 2030 and discusses the driving factors. The results show that cultivated land and forest land are the two major land use types in Jilin Province, and the area of cultivated land, water bodies, and artificial ground in the province increased from 2011 to 2020; the increased area of artificial ground was mainly converted from cultivated land, accounting for 70.34% of the total converted area. The area of forest land is mainly converted along with the area of cultivated land, and grassland is mainly converted to arable areas, accounting for 84.96% of the total converted area. Water bodies and wasteland are mainly converted to cropland and artificial ground, and the area of artificial ground undergoing transfer is smaller. The change in carbon sinks mainly comes from woodland carbon sinks and grassland carbon sinks. In 2030, compared with 2020, the area of woodland, grassland, and wasteland and the corresponding carbon sink is predicted to decrease, among which the area and carbon sink of woodland decrease the most. The factors for land use type change include the slope factor, road factor, township center, and socio-economic drivers. [ABSTRACT FROM AUTHOR]

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

Analyzing how rail transit stations affect the land use changes around them in space and time is important for forecasting the future interaction between new stations and land use and for adjusting and optimizing planning schemes. Based on the design concept of station-city integration, this paper uses Zhongshanba Station of Guangzhou Metro as a case study. It examines the land use evolution around the station and predicts the future land use changes around the double-track transfer station of Line 5 and Line 11, using the POI kernel density and the passenger flow heat map of the existing station in the old city. The results show that the POI and passenger flow heat map are innovative driving variables that can increase the simulation accuracy of the CA-Markov model to 91.86%. The prediction results also show that public management-service land and residential land will increase while industrial land will decrease. Although garden land will expand, the surrounding ecological area is still inadequate. Finally, this paper provides some strategies for optimizing subway stations and planning urban land use.

Exploring urban land use change is a classical problem in urban geography. Taking Zhengzhou as an example, this paper analyzes the spatial and temporal characteristics and driving factors of urban land use change, and simulates the spatial pattern of urban land use in the future. The results of the study show that the land use types in Zhengzhou city were mainly farmland and construction land, the area of forestland, grassland, water area, and unused land was smaller, and the main land use change was the transformation of farmland into construction land. The accuracy check of the simulated land use type data in 2020 showed that the kappa coefficient reached 0.9445, which met the accuracy requirement. Then, according to the predicted land use data in 2025, it was found that the area of grassland, construction land, and water area may have decreased, and the area of farmland, forestland and unused land may have increased. Based on the driving force analysis of land use changes, its prediction results can provide an important reference basis for the formulation and planning of policies related to urban construction. [ABSTRACT FROM AUTHOR]

The land use change (LUC) and urbanization caused by human activities have markedly increased the occurrence of landslides, presenting significant challenges in accurately predicting landslide susceptibility despite decades of model advancements. This study, focusing on the Li River Valley (LRV) within the Yongding District, China, employs two common models, namely an analytic hierarchy process–comprehensive index (AHP-CI) model and a logistic regression (LR) model to assess landslide susceptibility (LS). The AHP-CI model is empirically based, with the advantage of being constructible and applicable at various scales without a dataset, though it remains highly subjective. The LR model is a statistical model that requires a training set. The two models represent heuristic and statistical approaches, respectively, to assessing LS. Meanwhile, the basic geological and environmental conditions are considered in the AHP-CI model, while the LR model accounts for the conditions of LUC and urbanization. The results of the multicollinearity diagnostics reflect the rationality of the predisposing factor selection (1.131 < VIF < 4.441). The findings reveal that the AHP-CI model underperforms in LUC and urbanization conditions (AUROC = 0.645, 0.628, and 0.667 for different validation datasets). However, when all the time-varying human activity predisposing factors are considered, the LR model (AUROC = 0.852) performs significantly better under the conditions of solely considering 2010 (AUROC = 0.744) and 2020 (AUROC = 0.810). The CA–Markov model was employed to project the future land use for the short-term (2025), mid-term (2030), and long-term (2040) planning periods. Based on these projections, maps of future LS were created. Importantly, this paper discussed the relationships between landslide management and regional sustainable development under the framework of the UN SDGs, which are relevant to Goal 1, Goal 11, Goal 13, and Goal 15. Finally, this study highlights the importance of integrating strategic land planning, reforestation efforts, and a thorough assessment of human impact predisposing factors with SDG-aligned LS predictions, advocating for a comprehensive, multi-stakeholder strategy to promote sustainable landslide mitigation. [ABSTRACT FROM AUTHOR]

In order to solve the problem of habitat fragmentation in the Beijing–Tianjin–Hebei region and realize the sustainable use of land, based on remote sensing data from 2010 to 2020, this paper uses GIS and the CA–Markov model to predict the land use structure in 2030, and uses the InVEST model to quantitatively analyze the changes in habitat quality in the Beijing–Tianjin–Hebei region from 2010 to 2030. The results indicate that (1) from 2010 to 2020, the area of construction land increased significantly, mainly from cultivated land, woodland and grassland; the proportion of cultivated land decreased by 7.2%. It was predicted that by 2030, the area of construction land will continue to expand, accounting for 19.6%. According to the results of land use dynamic degree, the conversion speed between land types in 2010–2015 was higher than that in 2015–2020. (2) The average habitat quality indexes of the study area in 2010, 2015, 2020 and 2030 were 0.57, 0.56, 0.55 and 0.52, respectively, and the habitat quality level was moderate and continued to decline. The habitat center of gravity moved in the north, from Fengning Manchu Autonomous County to the junction of Fengning Manchu Autonomous County and Longhua County, and then moved to the Mentougou District of Beijing. Therefore, in order to amendthe continuous decline in habitat quality, scientific measures should be taken according to local conditions to promote the improvement of habitat quality and the rational use of land. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is important to understand how changing climate and Land Use Land Cover (LULC) will impact future spatio-temporal water availability across the Munneru river basin as it aids in effective water management and adaptation strategies. The Munneru river basin is one of the important sub-basins of the Krishna River in India. In this paper, the combined impact of LULC and Climate Change (CC) on Munneru water resources using the Soil and Water Assessment Tool (SWAT) is presented. The SWAT model is calibrated and validated for the period 1983–2017 in SWAT-CUP using the SUFI2 algorithm. The correlation coefficient between observed and simulated streamflow is calculated to be 0.92. The top five ranked Regional Climate Models (RCMs) are ensembled at each grid using the Reliable Ensemble Averaging (REA) approach. Predicted LULC maps for the years 2030, 2050 and 2080 using the CA-Markov model revealed increases in built-up and kharif crop areas and decreases in barren lands. The average monthly streamflows are simulated for the baseline period (1983–2005) and for three future periods, namely the near future (2021–2039), mid future (2040–2069) and far future (2070–2099) under Representation Concentration Pathway (RCP) 4.5 and 8.5 climate change scenarios. Streamflows increase in three future periods when only CC and the combined effect of CC and LULC are considered under RCP 4.5 and RCP 8.5 scenarios. When compared to the CC impact in the RCP 4.5 scenario, the percentage increase in average monthly mean streamflow (July–November) with the combined impact of CC and LULC is 33.9% (near future), 35.8% (mid future), and 45.3% (far future). Similarly, RCP 8.5 increases streamflow by 33.8% (near future), 36.5% (mid future), and 38.8% (far future) when compared to the combined impact of CC and LULC with only CC. When the combined impact of CC and LULC is considered, water balance components such as surface runoff and evapotranspiration increase while aquifer recharge decreases in both scenarios over the three future periods. The findings of this study can be used to plan and develop integrated water management strategies for the basin with projected LULC under climate change scenarios. This methodology can be applied to other basins in similar physiographic regions. [ABSTRACT FROM AUTHOR]

In the process of societal development, forest land categories often conflict with other land use types, leading to impacts on the ecological environment. Therefore, research on changes in forest land categories has increasingly become a globally focused topic. To anticipate potential forest ecological security issues under urbanization trends, studies on regional land use simulation become more important. This paper, based on land use data from the Ganjiang River basin, analyzes the distribution characteristics and changing trends of land use types from 2000 to 2020. Using the CA-Markov model, it predicts the land use pattern of the basin in 2040 and analyzes the transfer characteristics of forest land categories. The conclusions indicate that, between 2000 and 2020, the most significant trend in land use evolution was the transfer between various subcategories of forest land, especially frequent in the high-altitude mountainous areas in the southern and western parts of the basin. The land use pattern prediction model constructed in this paper has a kappa index of 0.92, indicating high accuracy and reliability of the predictions. In 2040, the most significant land evolution phenomenon would be from forest land to arable land to construction land, particularly pronounced around large cities. Over the next 20 years, the focus of land use evolution may shift from the southern part of the basin to the central and northern parts, with urban expansion possibly becoming the main driving force of land use changes during this period. Forest land restoration work is an effective method to compensate for the loss of forest land area in the Ganjiang River basin, with key areas for such work including Longnan, Yudu, Xingguo, Ningdu, Lianhua, and Yongxin counties.

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dynamic monitoring and prediction of ecosystem service value (ESV) under land utilization change is of great significance to maintaining regional ecological security. In order to enrich the study of ESV changes in plateau region by using the improved equivalent factor method of unit area value, the western Sichuan Plateau was taken as the research object. In this paper, given the land use data of the western Sichuan Plateau in 2000, 2010 and 2020, efforts have been made to construct the suitability image collection of the study area and to amend the CA-Markov model as to predict the land use pattern in 2030. In addition, the improved ESV evaluation model was constructed to study the spatial and temporal dynamic variation features of ESV. The results showed that: Woodland and grassland are the main types of land utilization in the study area. There are frequent interchanges between various land utilization types. The overall reduction of ESV, decrease of 3.4917 × 109 yuan/a, mainly due to the conversion of woodland to other land types. The high-value areas of ESV in the study area are closely tied to rivers. Two areas with high ESV value were formed in the study area. The findings of the study enriched the research system of land use and ecosystem service value in the plateau region, provided basic data for the construction of regional ecological security pattern, and enhanced regional sustainable development. [ABSTRACT FROM AUTHOR]

In the present paper, land use/land cover (LULC) change was predicted in the Greater Isfahan area (GIA), central Iran. The GIA has been growing rapidly in recent years, and attempts to simulate its spatial expansion would be essential to make appropriate decisions in LULC management plans and achieve sustainable development. Several modeling tools were employed to outline sustainable scenarios for future dynamics of LULCs in the region. Specifically, we explored past LULC changes in the study area from 1996 to 2018 and predicted its future changes for 2030 and 2050. For this purpose, we performed object-oriented and decision tree techniques on Landsat and Sentinel-2 satellite images. The CA-Markov hybrid model was utilized to analyze past trends and predict future LULC changes. LULC changes were quantitatively measured using landscape metrics. According to the results, the majority of changes were related to increasing residential areas and decreasing irrigated lands. The results indicated that residential lands would grow from 27,886.87 ha to 67,093.62 ha over1996–2050 while irrigated lands decrease from 99,799.4 ha to 50,082.16 ha during the same period of time. The confusion matrix of the 2018 LULC map was built using a total of 525 ground truth points and yielded a Kappa coefficient and overall accuracy of 78% and 82%, respectively. Moreover, the confusion matrix constructed base on the Sentinel-2 map, as a reference, to judge the predicted 2018 LULC map with a Kappa coefficient of 88%. The results of this study provide useful insights for sustainable land management. The results of this research also proved the promising capability of remote sensing algorithms, CA-Markov model and landscape metrics future LULC planning in the study area. [ABSTRACT FROM AUTHOR]

Vegetation indexes have been widely used to qualitatively and quantitatively evaluate vegetation cover and its growth vigor. To further extend the study of vegetation indexes, this paper proposes to study the spatial and temporal distribution characteristics and specific driving mechanisms of vegetation indexes based on the example of Yunnan Province, China, and also adds the study of spatial and temporal prediction methods of vegetation indexes. This paper used data on this region’s normalized vegetation index (NDVI), three meteorological factors, and eight social factors from 1998 to 2019. The dynamic change in and driving mechanism of the NDVI were studied using mean value analysis, univariate linear trend regression analysis, and partial correlation analysis. In addition, the Fourier function model and the CA–Markov model were also used to predict the NDVI of Yunnan Province from 2020 to 2030 in time and space. The results show that: (1) The NDVI value in Yunnan Province is high, showing a significant growth trend. The increased vegetation coverage area has increased in the past 22 years without substantial vegetation degradation. (2) The positive promotion of meteorological factors is greater than the negative inhibition. The partial correlation of relative humidity among meteorological factors is the highest, which is the main driving factor. (3) The NDVI value is significantly positively correlated with population and economy and negatively correlated with pasture land and agricultural area. (4) The NDVI values are predicted well in time (R = 0.64) and space (Kappa = 0.8086 and 0.806), satisfying the accuracy requirements. This paper aims to enrich the theoretical and technical system of ecological environment research by studying the dynamic change, driving mechanism, and spatiotemporal prediction of the normalized vegetation index. Its results can provide the necessary theoretical basis for the simulation and prediction of vegetation indexes.

Different landscapes in a river basin make different contributions to non-point source pollution according to the source–sink theory. Changes in landscape pattern have a considerable impact on river water quality. Precise predictions of the future landscape pattern provide strong support for manipulating land use patterns, which is beneficial for sustainable development in the watershed. The Jiulong River Basin in south-eastern China was selected as the study area. The Random Forest classifier, which combined textural characteristics with spectral information, was applied to interpret the landscapes from Landsat images acquired in 1995, 2005 and 2015 in this paper. The overall classification accuracy reached 86%, and the Kappa index was higher than 0.83, which was better than the accuracy of classifiers based on the exclusive use of spectral information. We used a hybrid cellular automaton–Markov (CA–Markov) model to simulate the landscape pattern of the Jiulong River Basin in 2015 and verified the accuracy according to the interpreted landscape classification map in 2015. The pixels that were predicted to the correct landscape account for 88.35% of total pixels, and the Kappa coefficient was 0.88, indicating that the CA–Markov model was credible for predicting the future landscape pattern in the Jiulong River Basin. The CA–Markov model was applied to forecast the landscape pattern of this watershed in 2025, and the transition area matrix from 2015 to 2025 was obtained. The predicted results demonstrated the imbalanced migration of source–sink landscapes in the future, which indicated the deterioration of water quality in the study area. Hence, government regulators need rational manipulation of the landscape pattern for sustainable development in the Jiulong River Basin. [ABSTRACT FROM AUTHOR]

Aiming at the problems of high optimization error and long optimization time in the existing green landscape model optimization methods, this paper proposes a CA-Markov model optimization method for the green landscape model around the environment. Firstly, the scope of green landscape around the environment is defined, and the function mode of green landscape around the environment is determined. Finally, the landscape model of green space around the environment is optimized by CA-Markov model. The experimental results show that the error of the proposed method in the optimization of green landscape mode around the environment is always less than 2, and the optimization time is short, which is feasible. [ABSTRACT FROM AUTHOR]

With rapid economic development and urban expansion, China faces a serious imbalance between production, living, and ecological land use, in which the erosion of water ecological space by urban expansion is especially notable. In order to alleviate or solve this imbalance, this study constructs the water ecological space in the Mulan River Basin based on national land spatial planning using remote sensing statistics and the 2000–2020 statistical yearbooks for the Mulan River Basin. A landscape index is applied to explore this landscape in terms of its production–living–ecological space (PLES) patterns and evolutionary characteristics. Factors affecting the drivers of PLES changes are analyzed through Geo-Detector, and predictions are made using the cellular automata Markov (CA-Markov) model. It was found that (1) PLES distribution patterns in the Mulan River Basin from 2000 to 2020 are dominated by non-watershed ecological spaces, with a significant expansion of living space. Its ecological space is shrinking, and there is significant spatial variation between its near-river and fringe areas. (2) Of the PLES conversions, the most dramatic conversions are those of production space and living space, with 81.14 km2 of production space being transferred into living space. Non-water ecological space and water ecological space are also mainly transferred into production space. (3) As shown by the results of the landscape index calculation, non-water ecological space in the Mulan River Basin is the dominant landscape, the values of the Shannon diversity index (SHDI) and Shannon homogeneity index (SHEI) are small, the overall level of landscape diversity is low, the aggregation index (AI) is high, and the degree of aggregation is obvious. (4) The progressive PLES changes in the Mulan River Basin are influenced by a combination of natural geographic and socioeconomic factors, with the mean population density and mean elevation being the most important factors affecting PLES changes among social and natural factors, respectively. (5) The Kappa coefficient of the CA-Markov model simulation is 0.8187, showing a good simulation accuracy, and it is predicted that the area of water ecological space in the Mulan River Basin will increase by 3.66 km2 by 2030, the area of production space and non-water ecological space will further decrease, and the area of construction land will increase by 260.67 km2. Overall, the aquatic ecological space in the Mulan River Basin has made progress in terms of landscape ecological protection, though it still faces serious erosion. Therefore, attaching importance to the restoration of the water ecological space in the Mulan River Basin, integrating multiple elements of mountains, water, forests, fields, and lakes, optimizing the spatial structure of its PLES dynamics, and formulating a reasonable spatial planning policy are effective means of guaranteeing its ecological and economic sustainable development. This study offers recommendations for and scientific defenses of the logical design of PLES spatial functions in the Mulan River Basin. [ABSTRACT FROM AUTHOR]

The spatio-temporal evolution of land use/land cover (LULC) and habitat quality (HQ) is vital to maintaining ecological balance and realizing regional sustainable development. Using the InVEST and CA-Markov model, with the Kendall coefficient as the sensitivity value, LULC and HQ in Lu'an City from 2000 to 2030 are simulated and evaluated. Then, Spearman is used to analyze the correlation between HQ and driving factors. Finally, the influence of policy factors on HQ is discussed. The results show the following: (1) from 2000 to 2030, the LULC of Lu'an is mainly cropland (about 40%) and forest land (about 30%) which are transferred to construction land; (2) the kappa coefficient is 0.9097 (>0.75), indicating that the prediction results are valid; (3) the Spearman coefficient shows that DEM (0.706), SLOPE (0.600), TRI (0.681), and HFI (−0.687) are strongly correlated with HQ, while FVC (0.356) and GDP (−0.368) are weakly correlated with HQ; (4) the main reasons for the decrease in HQ are the increase in construction land area, the decrease in forest area, the vulnerability of artificial forests to threat factors, and their low biodiversity. This study outlines exploratory research from two perspectives of HQ factors and policy effects to provide policy suggestions for the sustainable development of Lu'an City. [ABSTRACT FROM AUTHOR]

In the face of geopolitical challenges and climate change, economic progress, safe production, and environmental protection have emerged as important directions for chemical industry development. However, the rational optimization of the chemical industry layout under the backdrop of ecological environmental protection necessitates further exploration. This study explores the evolution and future development direction of the chemical industry layout within the coastal region of Jiangsu Province, China, using the CA–Markov model. The findings reveal a trend of spatial agglomeration growth among coastal chemical enterprises, with Moran's Index increasing from 0.109 in 2007 to 0.206 in 2017. The petrochemical industry, in particular, demonstrated the most significant agglomeration effect, with approximately 52.10% being concentrated in 14 coastal industrial parks in 2017. Under the constraints of the ecological environment and policy guidance, the land area allocated for the chemical industry experienced a reduction of over 10%, further strengthening the emphasis on spatial agglomeration. Chemical industries along Jiangsu's coast have become agglomerated and concentrated near industrial parks and ports. Their spatial distribution and connectivity were mainly influenced by factors such as convenient transportation, the ecological environment, local policies, the distance from residential areas, and industrial agglomeration. Under different scenarios—including natural growth, ecological environment constraints, and policy guidance—chemical industries show diverse spatial patterns. Ecological environmental constraints and policy guidance can provide various intervention methods for the government to promote the optimization direction and focus of the chemical industry layout while minimizing its impact on the ecological environment. [ABSTRACT FROM AUTHOR]

Desertification is a phenomenon that has more destructive effects in arid, semi-arid, and semi-humid regions than in other regions. This paper tries to provide a map of the future of desertification in Tehran Province, for futurism in the face of land degradation and desertification. The IMDPA model was used to evaluate land degradation and desertification. To use this model and evaluate desertification, three criteria of groundwater including groundwater depletion, electrical conductivity, and sodium adsorption ratio indices, climate criterion including precipitation, aridity, and drought indices, and land use criteria were selected as key criteria effected on desertification according to regional conditions. Land use index map with IGBP standard and zoning map of other indicators were prepared by IDW method for 2011 and 2016. The maps of land use index and other indices were predicted using the CA-Markov model in TerrSet software, and using the RBF method in artificial neural network toolbox, respectively. Scoring based on the IMDPA model, the maps of indices and criteria maps were prepared for 2011, 2016, and 2021. Finally, the desertification intensity map was calculated by geometric averaging for all three criteria for all three time periods. The results showed that 59.78% and 40.22% of the area of Tehran Province were in the low and medium classes, respectively. However, in 2016, the area of the medium class has increased to a 44.8%, and it is predicted that this increase will continue until 2021 so that 47.65% of the area of Tehran Province will be in the medium class. In addition, in this year, about 1% of the area of Tehran Province will be allocated to the high class in the western regions, which did not exist in the previous two periods. In general, due to human activities, the intensity of desertification in the western and southern parts of the province is higher than in the eastern and northern regions. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The establishment of refugee settlements has caused profound segmented impacts on the coverage of land use/cover types. Few studies have computed the spatial drivers of land use/cover changes in Refugee prone areas. This study explored how refugee settlements induced geo-changes in land use/cover in Bidi-Bidi refugee settlement in Uganda. Sentinel-2B images (2015, 2017 and 2020) were used to assess and predict (2030 and 2040) the spatial areal extent of land use/cover changes. The images were classified using Supervised algorithm (Maximum-likelihood) and used CA-Markov model to generate transitions into the future. A Binary Logistic regression was used to compute the spatial drivers of geo-changes. Our results reveal that the settlements triggered more geo-changes in built-up (0.6%), refugee settlements (4.1%), and subsistence farmlands (7.0%) at the expense of woodlands (−0.3%), wetlands (−2.9%), and grasslands (−8.3%). The same trajectory will also be expected between 2030 and 2040. The most critical spatial drivers of these changes are as follows: population growth, increased temperatures, precipitation variation, distance to water sources, distance to roads, distance to police posts, and distance to trading centres. To reduce the acceleration of changes in land use/cover types, this study agitates for equal spatial establishment of woodlots, promotion of alternative sources of energy and livelihoods, and settlement construction materials. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The global ecosystem is facing a severe situation, with increasing ecological risks threatening ecosystem protection. The research is conducted based on land use data and socio-economic data of Wuhan during the three periods of 2000, 2010, and 2020. Various models including the ecosystem service value model, ecosystem risk model, bivariate spatial autocorrelation model, and CA-Markov model are employed to calculate the spatial and temporal evolution as well as the correlation of ecosystem service values and ecological risks in Wuhan City over the past 20 years. Moreover, the study aims to predict the ecosystem service values and ecological risks for the year 2030 and establish an ecological zoning framework accordingly. The results show that: (1) The value of Wuhan's ecosystem services show a trend of first decreasing and then increasing in the past 20 years. (2) The ecological risks of Wuhan show an overall upward trend, and the ecological risks in the study area are generally characterized by "high in the middle and low around". (3) There is a significant positive spatial correlation between the value of ecosystem services and ecological risks in Wuhan. The overall ecological risk in the study area in 2030 is relatively alleviated compared with the previous two periods, and the ecological zones with a larger proportion are "low-low" and "low-high" zones. This study can provide critical information for building resilient cities. [ABSTRACT FROM AUTHOR]

Amidst rapid urbanization, the conflict between urban population and land is intensifying due to ecological degradation and imbalanced supply and demand of land resources in and around cities. Demarcating the urban development boundary is a specific measure to regulate the scale and form of urban expansion while considering internal urban demand as well as ecological security. This study took Haikou City, China, as the study area, exploring a new way to take into account the external constraints and endogenous mechanisms of urban expansion, constructing a comprehensive ecological security pattern (ESP) using the MCR model, demarcating recent rigid development boundaries, and demarcating future elastic development boundaries using the CA–Markov model. The results were the following: (1) By identifying the current urban boundary in 2020, the urban land area of Haikou City was found to be 261.64 km2. (2) Using the MCR model to construct comprehensive ESP and demarcate a rigid development boundary revealed that the total area within the boundary was 398.37 km2, with an additional growth potential of up to 136.73 km2. (3) Demarcating elastic boundaries for Haikou City in 2030, 2040 and 2050 using the CA–Markov model while considering natural and socio-economic driving factors and constraints showed the internal areas within these boundaries to be calculated at 451.80, 489.46 and 523.37 km2, respectively, which were higher than that in 2020 by 190.16, 227.82 and 261.73 km2. (4) Some suggestions, such as establishing a comprehensive technical system, ensuring robust policy support and legal protection, and improving the responsibility management system, were proposed in the implementation of urban development boundaries. Scientifically and reasonably demarcating the recent rigid urban development boundary and future elastic urban development boundaries can ensure sustainable urban development while preserving the ecological environment and satisfying urban development demand. [ABSTRACT FROM AUTHOR]

Mangroves are endemic forest communities in tropical and subtropical coastal zones. China's Hainan Island is very rich in mangrove plant species. In recent years, due to climate change and human disturbance, the living environment of many mangrove distribution areas has been seriously threatened. This study used land satellite series remote sensing images from 1990 to 2020 to monitor the coverage and area changes of mangroves on Hainan Island. The spatial distribution pattern and change trend of mangroves were explored using the standard deviation ellipse method, and the CA–Markov model was used to predict the possible changes of mangroves in the study area in 2025 and 2030. The development trend of mangroves in the future and the driving factors affecting the evolution of mangroves were also analyzed. Over the past 30 years, the area of mangroves has witnessed fluctuations. It decreased from 4578 hectares in 1990 to its lowest at 3870 hectares in 2005 and then rebounded to 4474 hectares by 2020, indicating an initial decline followed by a gradual increase. Although conservation and restoration efforts have yielded success, specific areas have witnessed a decline in mangrove coverage. From 1990 to 2020, mangrove areas in Huiwen decreased from 1055 hectares to 904 hectares, areas in Guannan decreased from 227 hectares to 167 hectares, areas in Xinyinggang decreased from 328 hectares to 298 hectares, areas in Yangpugang decreased from 747 hectares to 682 hectares, areas in Huachangwan decreased from 355 hectares to 327 hectares, and areas in Puqian decreased from 170 hectares to 141 hectares. In particular, the growth in the Eastern and Mayao port areas is especially significant. Additionally, data analysis has revealed the spatial distribution characteristics of mangroves in different regions, such as the mangrove area in Dongzhaigang, which remained relatively stable from 1990 to 2020, while in other areas like Huiwen and Guannan, the mangrove area decreased during these 20 years. By calculating the standard deviation ellipse, we observed that the overall change of mangroves on Hainan Island from 1990 to 2020 was relatively slow, mainly distributed along the northern coastal area of Hainan Island. Furthermore, the standard deviation ellipse and SDE center point of each mangrove growth area have visualized the growth trends of the mangroves. The Markov chain simulation results show that future changes in mangroves will mainly be concentrated in the marginal areas of the mangroves. These areas may be affected by rising sea levels, climate change, soil salinity, and human activities. In the future, mangrove areas are expected to display a dynamic equilibrium, experiencing periods of expansion and reduction, ultimately moving towards a more consistent state. To protect and restore mangroves, it is necessary to strengthen the monitoring and management of their ecological environment and socio-economic factors and improve their stability and diversity. [ABSTRACT FROM AUTHOR]

The unreasonable development of land use and use of landscape patterns are the primary causes of the unsustainable growth of tourism cities. Dawa District, a well-known tourist destination in China, was chosen as the research object in order to scientifically evaluate and simulate the dynamic changes in land use. Landsat remote sensing images of the Dawa District from 2011, 2016, and 2021 were processed, using the development of regional tourism in 2016 as the dividing point. Using Arcgis10.3, ENVI5.3, and Fragstats tools, GIS spatial analysis and landscape index analysis were used to explore the spatiotemporal dynamic characteristics of land use and landscape patterns. The CA-Markov model was used to simulate and predict the land use landscape pattern in 2026. The results show that the rapid expansion of built-up areas in 2011–2021 and the increasing marginal complexity and fragmentation of landscape patterns are the main evolution trends. In 2016, emphasis was placed on sustainable land resources, and the area of forested water gradually recovered. However, the patch density (PD) and number of patches (NP) indices reflecting fragmentation still showed a 1.5–2 fold increase. It is expected that in 2026, the dual pressure of urban development and ecological sustainable construction will enhance connectivity and aggregation, and the ecological environment will gradually recover. At the same time, the competition for various types of land will become increasingly prominent, and agricultural activities will be affected. In summary, optimization strategies are proposed from the perspectives of government, land, landscape, and industry, aiming to promote the sustainable development of land and landscape resources in tourist cities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To reveal the influence process of land use changes on runoff variation trends, this paper takes the Luojiang River of China as the study area, and the Soil and Water Assessment Tool (SWAT) model was constructed to quantitatively analyze the impact of different land uses on runoff formation in the watershed, and used the Cellular Automata-Markov (CA-Markov) model to predict future land use scenarios and runoff change trends. The results show that: (1) the SWAT model can simulate the runoff in the Luojiang River basin; (2) the runoff in the Luojiang River basin has a decreasing trend in recent 10 years, caused by the decrease of rainfall and runoff due to changes in land use; (3) the forecast shows that the land-use changes in the basin will lead to an increase in runoff coefficient in 2025. The increase of the runoff coefficient will bring some adverse effects, and relevant measures should be taken to increase the water storage capacity of urban areas. This study can help plan future management strategies for the study area land coverage and put forward a preventive plan for the possible adverse situation of runoff variation. [ABSTRACT FROM AUTHOR]

Dynamic monitoring and prediction of ecosystem service value (ESV) under land utilization change is of great significance to maintaining regional ecological security. In order to enrich the study of ESV changes in plateau region by using the improved equivalent factor method of unit area value, the western Sichuan Plateau was taken as the research object. In this paper, given the land use data of the western Sichuan Plateau in 2000, 2010 and 2020, efforts have been made to construct the suitability image collection of the study area and to amend the CA-Markov model as to predict the land use pattern in 2030. In addition, the improved ESV evaluation model was constructed to study the spatial and temporal dynamic variation features of ESV. The results showed that: Woodland and grassland are the main types of land utilization in the study area. There are frequent interchanges between various land utilization types. The overall reduction of ESV, decrease of 3.4917 × 109 yuan/a, mainly due to the conversion of woodland to other land types. The high-value areas of ESV in the study area are closely tied to rivers. Two areas with high ESV value were formed in the study area. The findings of the study enriched the research system of land use and ecosystem service value in the plateau region, provided basic data for the construction of regional ecological security pattern, and enhanced regional sustainable development.

The development of a reasonable landscape ecological risk (LER) assessment system is the key means to establish an eco‐safety early warning mechanism, promote ecological protection, and achieve sustainable management of regional ecosystems. Based on the Cellular Automaton‐Markov (CA‐Markov) model, we predicted the land use/cover changes (LUCC) in 2026 and 2034 in the typical arid and semiarid mountainous area of the Qilian Mountains. We constructed an LER model to analyze the spatial–temporal changes in LER, quantified the driving mechanism, and synthesized the constraining effect of terrain features. The results showed that the study area was dominated by ecologically stable areas in 1980–2000 and 2010–2018. Ecologically improved and deteriorated areas in the central and northwest expanded in 2000–2010 and 2018–2036, which was attributed to ecological restoration policies (returning farmland to forest and grassland, closing mountains to forest and grassland, etc.) and economic development (livestock and mineral development, etc.). Due to the frequent interference of human activities in flat and complex terrain areas, the degree of landscape fragmentation, separation, and vulnerability is enhanced, and the ecological conditions of bare land and snow cover are relatively fragile, resulting in high ecological risk. Therefore, we could focus on zoning management and rational land use planning. This study provides a new framework for controlling LER in the Qilian Mountains, and will provide a scientific reference for the formulation of risk mitigation strategies in other arid and semiarid mountains worldwide. [ABSTRACT FROM AUTHOR]

Simulating the change in ecosystem service values (ESVs) caused by land use changes in metropolitan areas under multiple scenarios is of great significance to ensure regional ecological security and sustainable urban development. This study assessed the variations in land use and ESV in the main urban area of Hangzhou, China, from 2000 to 2020. A total of four future land use scenarios for 2030 were created using the cellular automata–Markov prediction model and ESVs were calculated for four future scenarios. The results are as follows: (1) Arable land and construction land were the most drastic types of land use changes in the main urban area of Hangzhou during the study period. From 2000 to 2020, construction land in the main urban area of Hangzhou expanded rapidly, with an increase of 46,916.82 hm2, while the cultivated land area decreased significantly by 38,396.43 hm2. (2) The ESV in the study area continuously declined from 2000 to 2020, with high-value ecosystem service areas predominantly found in forest areas and water areas. (3) The values of ecosystem services under the simulated future scenarios of natural development, rapid development, planned development, and ecological development were CNY 15.053, 14.525, 16.690, and 16.799 billion, respectively. The planned development and ecological development scenarios effectively ensure a high ESV. The results indicated that balancing various types of land use is essential to guarantee ecological security in urban development. Moreover, urban development and construction should be undertaken in areas with a low ESV. This forecasting study can serve as a key reference for policy makers regarding the urban landscape sustainability of Hangzhou City. The integrated simulation method of land use and ESV proposed in this study can shed light on the urban spatial layout and spatial regulation in urban land use planning. [ABSTRACT FROM AUTHOR]

Terrestrial vegetation, a critical component of the Earth's land surface, directly impacts the planet's material and energy balance. This study investigated the dynamics of terrestrial vegetation in China from 2000 to 2019 using three remote sensing products (NDVI, EVI, and SIF) and explored the driving mechanisms behind these changes. We considered three meteorological factors, nine land use types, and two socio-economic factors while employing mathematical models to analyze the data. Additionally, we used the CA–Markov model to predict the spatial distribution of vegetation remote sensing products for 2020–2025. Our findings indicate the following: (1) Throughout the study period, the vegetation indices, NDVI, EVI, and SIF, all exhibited increasing trends. The SIF showed a more direct response to vegetation cover changes and was less influenced by other driving factors. The SIF outperforms the NDVI and EVI in detecting vegetation trend changes, particularly regarding sensitivity. (2) Vegetation cover changes are driven by multiple meteorological factors, such as temperature, precipitation, and relative humidity. These factors exhibit a strong spatial correlation with the distribution of vegetation remote sensing products. Among these factors, the SIF shows a higher sensitivity to temperature compared to the NDVI and EVI, while the NDVI and EVI display greater sensitivity to precipitation and relative humidity. (3) Within the study area, land use types reveal a gradient from northwest to southeast, which is consistent with the spatial distribution of the vegetation remote sensing products. For green vegetation types, the three remote sensing products exhibit varying sensitivity levels, with the SIF demonstrating the highest sensitivity to green vegetation types. (4) Overall, the future vegetation outlook in China is promising, especially in the southeastern regions where significant vegetation improvement trends are evident. However, the vegetation conditions in some northwestern areas remain less favorable, necessitating the reinforcement of ecological construction and improvement measures. Additionally, a significant positive correlation exists between population size, GDP, and vegetation remote sensing products. This study highlights the variability in the dynamics and driving mechanisms of terrestrial vegetation remote sensing products in China and employs the CA–Markov model for predicting future vegetation patterns. Our research contributes to the theoretical and technical understanding of remote sensing for terrestrial vegetation in the Chinese context. [ABSTRACT FROM AUTHOR]

As the impacts of climate change worsen, the global community prioritizes addressing it and fostering low-carbon societies. Urban planning focuses on creating compact, smart-growth cities that prioritize low-carbon, green development, with resource and environmental capacities as hard constraints. Balancing urban development, environmental protection, and accurate urban boundary delineation is vital for stable growth. In this study, the ecosystem services of Weiyuan County, Gansu Province, were assessed using the InVEST model's habitat quality and carbon storage modules. Key ecological protection areas with high biodiversity and carbon storage were identified. The CA-Markov model simulated urban expansion, dynamically coordinating ecological and urban development. Weiyuan County's habitat quality was mainly intermediate. In the county's central area, construction land coverage was 0.29 km2 in the priority protection zone and 0.49 km2 in the controlled development zone. Urban development boundaries in Weiyuan County were delineated based on ecosystem function rating and CA-Markov delineation. This method enhances urban management in ecologically fragile areas, promoting sustainable development and providing a reference for eco-economic sustainability in other fragile Chinese cities. [ABSTRACT FROM AUTHOR]

The desert-oasis ecotone, as a crucial natural barrier, maintains the stability of oasis agricultural production and protects oasis habitat security. This paper investigates the dynamic evolution of the desert-oasis ecotone in the Tarim River Basin and predicts the near-future land-use change in the desert-oasis ecotone using the cellular automata–Markov (CA-Markov) model. Results indicate that the overall area of the desert-oasis ecotone shows a shrinking trend (from 67,642 km2 in 1990 to 46,613 km2 in 2015) and the land-use change within the desert-oasis ecotone is mainly manifested by the conversion of a large amount of forest and grass area into arable land. The increasing demand for arable land for groundwater has led to a decline in the groundwater level, which is an important reason for the habitat deterioration in the desert-oasis ecotone. The rising temperature and drought have further exacerbated this trend. Assuming the current trend in development without intervention, the CA-Markov model predicts that by 2030, there will be an additional 1566 km2 of arable land and a reduction of 1151 km2 in forested area and grassland within the desert-oasis ecotone, which will inevitably further weaken the ecological barrier role of the desert-oasis ecotone and trigger a growing ecological crisis. [ABSTRACT FROM AUTHOR]