CA-Markov Model Research Articles

Real options analysis for urban flood mitigation under environmental change

As urban flooding becomes more frequent and severe due to climate change and accelerated urbanization, effective urban flood control measures are needed to mitigate urban flooding under environmental change. However, few studies consider both climate change and land use change and formulate urban flood control plans for the uncertainty of climate change. This study proposes a real options method based on trinomial tree model to design urban flood control measures under environmental changes. Future rainfall was predicted based on the SDSM model. Land use changes for 2035 and 2050 were predicted using the CA-Markov model. Considering the uncertainty of future climate change, future urban flood control measures based on the real options method were developed, and to compare and analyze them with fixed investment method. The research results show that rainfall and construction land in the study area will increase in the future. The benefit of real options retrofit scheme is higher than the fixed investment retrofit scheme and climate change uncertainty has a greater impact on flood control planning decisions. The research framework proposed in this paper provides an effective basis for the formulation of urban flood control strategies and the design of flood control measures under environmental change.

Spatial–Temporal Evolution and Prediction of Habitat Quality in Beijing–Tianjin–Hebei Region Based on Land Use Change

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.

Evaluation and prediction of ecological environment of karst world heritage sites based on google earth engine: a case study of Libo–Huanjiang karst

The assessment and prediction of ecological quality can help to quickly and systematically understand the ecological status of World Natural Heritage and assist with developing appropriate strategies to ensure the sustainable and healthy development of that heritage. Using the google earth engine platform, the remote sensing ecological index (RSEI) is rapidly constructed based on principal component analysis to evaluate the spatial and temporal distribution characteristics and the main impact indicators of the ecological environment, and the cellular automata (CA)-Markov model are used to simulate and predict the ecological quality, taking the Libo–Huanjiang karst world natural heritage site as the study area. The results show that: (a) the contribution rate of the four ecological indicators on the first principal component (PC1) are more than 90%. The construction of RSEI based on PC1 are applicable in karst world nature heritage sites and can be used to monitor and evaluate the spatial and temporal variation characteristics of the ecological environment. (b) The mean values of RSEI of the Libo–Huanjiang heritage site during 2000, 2007, 2013 and 2020 were 0.5435, 0.5465, 0.6009 and 0.5101, The overall ecological quality is mainly moderate and good, but in the eastern part of the heritage site the ecological quality is poor. (c) The evolution of the ecological environment quality during 20 years is mainly divided into the development trend of rapidly getting better, slowly getting better, and maintaining stability. (d) In the analysis of the relationship between RSEI and altitude, it is found that the ecological environment quality is mainly inferior, less favorable and moderate in areas with altitudes below 600 m, and there is a positive relationship between ecological quality level and altitude. (e) By analyzing the apparent spatial aggregation between the ecological environment quality, and then simulating the ecological grades in 2027 and 2033 using CA-Markov model, it is predicted that the area of medium and excellent ecological grades will increase in the future, but the ecological environment quality still needs to be improved in the eastern region due to the development of the tourism industry. Overall, the remote sensing ecological index is an effective model for evaluating and monitoring the ecological environment quality of karst heritage sites; the ecological environment quality of the Libo–Huanjiang heritage site is in a steady state of improvement, and the conservation measures of relevant departments are beginning to bear fruit; further coordination between conservation and development is needed to promote the sustainable development of heritage sites and to provide effective solutions for monitoring other karst-like heritage sites.

Simulation of land utilization change and ecosystem service value evolution in Tibetan area of Sichuan Province

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.

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.

The Assessment of the Spatiotemporal Characteristics of the Eco-Environmental Quality in the Chishui River Basin from 2000 to 2020

The Chishui River Basin is located in the bordering area of Yunnan, Guizhou and Sichuan provinces, which serves as an important ecological barrier in the upper reaches of the Yangtze River, and plays a leading role in preserving natural environments, protecting water resources, and maintaining soil functions. However, the eco-environmental quality in the basin has encountered serious challenges in recent years, and the conflict between eco-environmental protection and economic development becomes increasingly prominent. Therefore, it is particularly important to quantitatively assess the extent of the eco-environmental changes in this basin. The present study acquired Landsat series remote sensing images based on the Google Earth Engine (GEE) platform, constructed a remote sensing ecological index (RSEI) as the assessment index that reflects the eco-environmental quality using principal component analysis, studied the changing trend in the eco-environmental quality using the Sen–Mann–Kendall trend test, analyzed the spatial clustering distribution patterns of the eco-environmental quality, based on spatial autocorrelation analysis, and applied the geographical detector model to determine the impacts of natural and anthropogenic factors on the eco-environmental quality. We further applied the CA–Markov model to simulate and predict the eco-environmental quality of the basin in 2025. The results showed the following: (1) between 2000 and 2020, the eco-environmental quality of the Chishui River Basin had been greatly improved. The average RSEI value increased from 0.526 in 2000 to 0.668 in 2020, and the percentage of areas belonging to the good or excellent quality category increased from 42.65% to 68.48%. (2) The main drivers of the eco-environmental quality included population density, mean annual temperature, land use type and elevation. The interactive effect between these drivers was significantly higher than that of individual drivers, and thus possessed stronger explanatory power for quality differences. (3) It is predicted that in 2025, the eco-environmental quality of the basin will continue to improve, and the proportion of land areas with good or excellent quality will continuously increase. The present study can provide reference value for local environmental protection and regional planning.

Spatiotemporal heterogeneity correction in land ecosystem services and its value assessment: a case study of the Loess Plateau of China.

The considerable variation in structures and functions of different ecosystems leads to highly variable ecosystem service values (ESVs). Consequently, the accurate quantification of ESVs can better assess and reflect impacts of land use and cover changes (LUCC) on ecosystem services. In the land use simulations of this study, a CA-Markov model was chosen and nine factors affecting land use change were evaluated, followed by the construction of a multi-criteria evaluation method to simulate land use scenarios between 2025 and 2030 on the Loess Plateau. Six key ecological indicators (economy, water production, net primary plant productivity, habitat quality, accessibility, and soil conservation) were used to correct for spatiotemporal heterogeneity within the terrestrial ESV equivalent weight table for China to obtain an ESV equivalent weight factor table that is applicable to the Loess Plateau. Using the newly corrected table, ESVs for the Loess Plateau region were estimated between 1995 and 2030, and the impacts of LUCC on ESVs were analyzed. The Kappa values for the 2015 land use simulation results were 0.80 and 0.83, which were greater than 0.75, indicating that the CA-Markov model simulations were accurate. Throughout the study period, the largest increases in land use type area were for built-up areas and forest lands, with built-up areas primarily derived from conversions of cultivated lands and grasslands, and forest land increases primarily coming from conversion of grasslands. ESVs increased overall by 933.97 × 108 yuan and 312.86 × 108 yuan from 1995 to 2018 and 2018 to 2030, respectively. The three largest contributors to ecosystem services among land use types were moderate grasses, shrublands, and dense grasslands. In conclusion, ESVs for the Loess Plateau steadily increased year by year from 1995 to 2030, indicating that ecological restoration projects played major roles in improving the stability and sustainability of the region's ecosystems.

A Partitioned and Heterogeneous Land-Use Simulation Model by Integrating CA and Markov Model

Conversion rule is a key element for a cellular automata (CA) model, and it is a significant and challenging issue for both domestic and international experts. Traditional research regarding CA models often constructs a single conversion rule for the entire study area, without differentiating it on the basis of the unique growth features of each location. On the basis of this, a partitioned and heterogeneous land-use simulation model (PHLUS) is constructed by integrating a CA and Markov model: (1) A general conversion rule is constructed for the entire study area. By establishing a land development potential evaluation index system, the conversion rule is refined and differentiated; (2) By coupling a CA model with a Markov model, PHLUS can realize land-use simulation both in micro and macro scales. A simulation study is conducted for the Pearl River Delta region. The results show that: (1) By transforming the CA model rules to further distinguish zones, the accuracy is improved. Compared with the traditional CA-Markov model, the simulation accuracies for 2010 and 2020 are improved by 11.55% and 7.14%, respectively. For built-up land simulation, the PHLUS simulation errors for 2010 and 2020 are only 0.7% and 0.57%, respectively; and (2) Under land-use simulation for 2030, cultivated land and forest land will transfer to built-up land. The built-up land area will reach 10,919 km2. Guangzhou and Shenzhen have the greatest potential for land development, and the built-up land area for the two cities will reach 2727 km2.

Assessing the impacts of current and future changes of the planforms of river Brahmaputra on its land use-land cover

River bankline migration is a frequent phenomenon in the river of the floodplain region. Nowadays, channel dynamics-related changes in land use and land cover (LULC) are becoming a risk to the life and property of people living in the vicinity of rivers. A comprehensive evaluation of the causes and consequences of such changes is essential for better policy and decision-making for disaster risk reduction and management. The present study assesses the changes in the Brahmaputra River planform using the GIS-based Digital Shoreline Analysis System (DSAS) and relates it with the changing LULC of the floodplain evaluated using the CA-Markov model. In this study, the future channel of the Brahmaputra River and its flood plain’s future LULC were forecasted to pinpoint the erosion-vulnerable zone. Forty-eight years (1973–2021) of remotely sensed data were applied to estimate the rate of bankline migration. It was observed that the river’s erosion-accretion rate was higher in early times than in more recent ones. The left and right banks’ average shifting rates between 1973 and 1988 were −55.44 m/y and −56.79 m/y, respectively, while they were −17.25 m/y and −48.49 m/y from 2011 to 2021. The left bank of the river Brahmaputra had more erosion than the right, which indicates that the river is shifting in the leftward direction (Southward). In this river course, zone A (Lower course) and zone B (Middle course) were more adversely affected than zone C (Upper course). According to the predicted result, the left bank is more susceptible to bank erosion than the right bank (where the average rate of erosion and deposition was −72.23 m/y and 79.50 m/y, respectively). The left bank’s average rate of erosion was −111.22 m/y. The research assesses the LULC study in conjunction with river channel dynamics in vulnerable areas where nearby infrastructure and settlements were at risk due to channel migration. The degree of accuracy was verified using the actual bankline and predicted bankline, as well as the actual LULC map and anticipated LULC map. In more than 90% of cases, the bankline’s position and shape generally remain the same as the actual bankline. The overall, and kappa accuracy of all the LULC maps was more than 85%, which was suitable for the forecast. Moreover, chi-square (x2) result values for classified classes denoted the accuracy and acceptability of the CA-Markov model for predicting the LULC map. The results of this work aim to understand better the efficient hazard management strategy for the Brahmaputra River for hazard managers of the region using an automated prediction approach.

Sustainable agricultural lands management by analyzing and predicting urban growth—a case study of Mitidja Plain, Algeria

The suitability of agricultural lands is basic data for both sustainable agricultural planning and optimal orientation of future urbanization projects. Indeed, unplanned urban expansion leads to many social and ecological problems. The prediction of urban expansion will strongly help the decision makers and local authorities to create a suitable future urban planning while preserving the regional ecosystem and especially the agricultural lands. Using multitemporal satellite images acquired in 1997, 2001, and 2013 over the Mitidja plain in Algeria, the aim of this study is the analysis of the land use and land cover change (LULCC) spatio-temporal distributions from 1997 to 2013 to model and then predict the future LULCC in 2025. The cellular automata Markov chain model (CA-Markov) that we developed is used for LULCC prediction with a focus on urban sprawl and agricultural lands degradation over the Mitidja plain. Once calibrated and validated, the CA-Markov model generates land suitability maps that indicate the best locations for urban expansion while safekeeping the agricultural lands. The results are interesting and present valuable tools for urban planners and policymakers to ensure the sustainability management of agricultural areas over the study region.

Scenario-Based LULC Dynamics Projection Using the CA–Markov Model on Upper Awash Basin (UAB), Ethiopia

Understanding the spatiotemporal changes in land use and land cover (LULC) in the watershed is crucial for maintaining the sustainability of land resources. This study intents to understand the historical (1972–2015) and future (2030–2060) spatiotemporal distribution of LULC changes in the Upper Awash Basin (UAB). The supervised Maximum Likelihood Classifier technique (MLC) was implemented for historical LULC classification. The Cellular Automata-Markov (CA–Markov) model was employed to project two scenarios of LULC, ‘business-as-usual’ (BAU) and ‘governance’ (Gov). Results from the historical LULC of the study area show that urban and cropland areas increased from 52.53 km2 (0.45%) to 354.14 km2 (3.01%) and 6040.75 km2 (51.25%) to 8472.45 km2 (71.97%), respectively. Whereas grassland, shrubland, and water bodies shrunk from 2052.08 km2 (17.41%) to 447.63 km2 (3.80%), 2462.99 km2 (20.89%) to 1399.49 km2 (11.89%) and 204.87 km2 (1.74%) to 152.44 km2 (1.29%), respectively, from 1972 to 2015. The historical LULC results indicated that the forest area was highly vulnerable and occupied by urban and cropland areas. The projected LULC under the BAU scenario shows substantial cropland and urban area expansion, increasing from 8472.45 km2 (71.97%) in 2015 to 9159.21 km2 (77.71%) in 2060 and 354.14 km2 (3.1%) in 2015, 1196.78 km2 (10.15%) in 2060, respectively, at the expense of vegetation cover. These results provide insight intothe LULC changes in the area, thus requiring urgent attention by watershed managers, policymakers, and stakeholders to provide sustainable practices for the UAB. Meanwhile, the Gov scenario indicates an increase in vegetable covers and a decrease in cropland, encouraging sustainable development compared to the BAU scenario.

Analysis of Land Use Change Drivers and Simulation of Different Future Scenarios: Taking Shanxi Province of China as an Example.

This study analyzed change and spatial patterns of land use in Shanxi from 2000 to 2020. The drivers of land use and cover change (LUCC) in cultivated lands, forest lands, grasslands, and rural construction areas were explored from four dimensions, including population, natural environment, location traffic, and economic development. The CA-Markov model was used to simulate the scenarios of natural growth (NG), ecological protection (EP), economic development (ED), food security (FS), ecological protection-economic development (EP-ED), and ecological protection-food security (EP-FS) in 2030. The results indicated that: (1) The conversion to built-up areas primarily dominated the LUCC processes, and their expansion was mainly to the detriment of the cultivated lands and grasslands during 2000-2020. (2) From 2000 to 2020, population, economy, and land productivity were the main factors of LUCC; the interaction of drivers for the increase of cultivated lands, forest lands, grasslands, and rural construction areas showed enhancement. (3) Under the NG, ED, and EP-ED scenarios, the rural construction areas would have increased significantly, while under the FS and EP-FS scenarios, the cultivated lands would only just have increased. These future land use scenarios can inform decision-makers to make sound decisions that balance socio-economic, ecological, and food security benefits.

Spatiotemporal Dynamics of Ecological Vulnerability and Its Influencing Factors in Shenyang City of China: Based on SRP Model.

For Shenyang, the central city of Northeast China, its municipal-level Territorial Spatial Planning is of great significance to the whole of Northeast China. Territorial Spatial Planning is an essential carrier of China's ecological civilization construction. The demarcation of "three districts and three lines" defines the scope of ecological protection areas, which is of guiding significance to the future development of ecological civilization construction. The regional ecological vulnerability assessment can provide reference for ecological pattern planning and the demarcation of ecological red lines in "three districts and three lines". In order to explore the spatial distribution pattern of ecological vulnerability in Shenyang, predict the development trend of ecological vulnerability in the future and guide the construction of ecological civilization in Shenyang and provide certain basis for Shenyang's Territorial Spatial Planning and the delineation of "three districts and three lines". This paper based on the "sensitivity-resilience-pressure" model selected 13 indexes, to evaluate the ecological vulnerability of Shenyang from 2010 to 2020. Furthermore, the spatial distribution characteristics and influencing factors of ecological vulnerability in Shenyang are summarized using spatial autocorrelation analysis and geographic detector model, and the future development trend of ecological vulnerability in Shenyang in 2025 is predicted by using CA-Markov model. The results show that: (1) In 2010, 2015 and 2020, the total area of slightly vulnerable areas in Shenyang was large, and the ecological vulnerability showed a gradually vulnerable spatial change trend from south to north and from west to east. (2) The results of geographical detectors show that normalized difference vegetation index, economic density and nighttime light intensity are the main driving factors of ecological vulnerability in Shenyang. (3) The forecast result of CA-Markov model is reliable. In 2025, the ecological vulnerability of Shenyang will be mainly light and extreme vulnerability areas, and the areas of light and extreme vulnerability areas will increase in 2025. The research results can provide some reference for the delineation of "three districts and three lines" and ecological protection in Shenyang's Territorial Spatial Planning, and have certain significance for promoting regional sustainable development and balancing ecological protection and economic development.

Evolution and prediction of land use around metro stations

Metro stations are considered high-quality resources for promoting urban development, which have great influences on the surrounding land use changes. The simulation and prediction of land use change can provide a scientific basis for urban land planning. In this work, the cellular automata (CA)-Markov model was adopted by taking into account point of interest (POI) kernel density and station accessibility as driving factors to predict the land use change of station surrounding areas. Then, the land type compositions of different years, temporal and spatial evolution of landscape patterns, and strategies of different metro stations were explored. The results show that the Kappa coefficients of the Zoo Station and the Lu Xiao Station are 87% and 79%, respectively, indicating that the improved CA-Markov model can predict land use changes more accurately by considering POI kernel density and station accessibility. Finally, different optimized strategies based on systematic predictions of land use landscape patterns according to the spatial and temporal distribution of metro stations were proposed. The work provides important references for predicting the impact of new metro stations on land use in the future and guides the adjustment and optimization of land use policy planning.

Evaluation and Prediction of Landscape Ecological Security Based on a CA-Markov Model in Overlapped Area of Crop and Coal Production

Overlapped areas of crop and coal production are responsible for national food security and mineral resource supply. However, long-term coal mining and urban and rural construction have continuously impacted the structure and functions of the original agricultural landscape ecosystem in the area and brought serious ecological security problems. On the basis of the results of remote sensing image classification of the Zhaogu mining area, this study explores the spatio-temporal variation characteristics of the landscape pattern and the evolution of ecological security and predicts the landscape ecological security status in 2029. Research results show that the evolution of the landscape pattern in the study area manifests obvious stage characteristics: from 2004 to 2014, the landscape pattern developed in the direction of fragmentation, irregularity, heterogeneity, and low connectivity; after 2014, the landscape pattern showed continuity, regularization, and high connectivity trends. By comparing the landscape ecological security of the study area from 2004 to 2019, a temporal change characteristic of first deteriorating and then gradually improving can be observed. By analyzing the changes in the distribution of the security status in the study area on a spatial scale, it can be found that the proportion of unsafe areas is significantly reduced in 2019 due to the influence of land remediation and reclamation. By constructing a CA-Markov prediction model with both spatial and quantitative advantages, the prediction results show that the degree of landscape fragmentation in the study area will be reduced, and the connectivity will be enhanced between 2019 and 2029. The shape of landscape patches tends to be regular, and the landscape heterogeneity will be enhanced. Overall, the landscape ecological security situation will continue to improve. The results could provide reference for ecological protection and related land reclamation planning of the mine area.

Land Use and Land Cover Change Monitoring and Prediction of a UNESCO World Heritage Site: Kaziranga Eco-Sensitive Zone Using Cellular Automata-Markov Model

The Kaziranga Eco-Sensitive Zone is located on the edge of the Eastern Himalayan biodiversity hotspot region. In 1985, the Kaziranga National Park (KNP) was declared a World Heritage Site by UNESCO. Nowadays, anthropogenic interference has created a significant negative impact on this national park. As a result, the area under natural habitat is gradually decreasing. The current study attempted to analyze the land use land cover (LULC) change in the Kaziranga Eco-Sensitive Zone using remote sensing data with CA-Markov models. Satellite remote sensing and the geographic information system (GIS) are widely used for monitoring, mapping, and change detection of LULC change dynamics. The changing rate was assessed using thirty years (1990–2020) of Landsat data. The study analyses the significant change in LULC, with the decrease in the waterbody, grassland and agricultural land, and the increase of sand or dry river beds, forest, and built-up areas. Between 1990 and 2020, waterbody, grassland, and agricultural land decreased by 18.4, 9.96, and 64.88%, respectively, while sand or dry river beds, forest, and built-up areas increased by 103.72, 6.96, and 89.03%, respectively. The result shows that the area covered with waterbodies, grassland, and agricultural land is mostly converted into built-up areas and sand or dry river bed areas. According to this study, by 2050, waterbodies, sand or dry river beds, and forests will decrease by 3.67, 3.91, and 7.11%, respectively; while grassland and agriculture will increase by up to 16.67% and 0.37%, respectively. The built-up areas are expected to slightly decrease during this period (up to 2.4%). The outcome of this study is expected to be useful for the long-term management of the Kaziranga Eco-Sensitive Zone.

内蒙古自治区沙漠化敏感性时空演变格局和趋势分析

PDF HTML阅读 XML下载 导出引用 引用提醒 内蒙古自治区沙漠化敏感性时空演变格局和趋势分析 DOI: 10.5846/stxb202112183592 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重大专项（04-H30G01-9001-20/22-03）；国家自然科学基金项目（211035210511）；中央高校基本科研业务费专项资金项目；陕西省科技计划项目重点研发项目（211435220242） Analysis of the temporal and spatial evolution pattern and trend of desertification sensitivity in the Inner Mongolia Autonomous Region Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以内蒙古自治区为研究区，基于压力-状态-响应模型（PSR）分析框架，耦合空间距离指数模型（SDIM）构建土地沙漠化敏感性评价体系（LDSES），结合沙漠化敏感性时空演变图谱，定量探讨内蒙古自治区土地沙漠化敏感性在2000年、2005年、2010年、2015年、2020年期间的时空演变，并引入元胞自动机-马尔科夫（CA-Markov）模型模拟沙漠化敏感性未来演变机制，进而采用地理探测器揭示了前20 a演变进程中主要驱动因子对其演变的影响特征及解释力。结果表明：（1）内蒙古自治区沙漠化敏感性整体上呈现由东北向西南递增的趋势，低敏感区位于大兴安岭和阴山山脉周边林草地覆盖区，高敏感区则位于西南部的巴丹吉林沙漠、腾格里沙漠、乌兰布和沙漠、库布其沙漠、毛乌素沙漠等沙地和戈壁地区；（2）从沙漠化敏感性时空演变上来看，研究期间沙漠化敏感性改善的区域，占总面积的30.65%，沙漠化敏感性恶化的区域，占总面积的4.48%，沙漠化敏感性类型维持稳定不变的区域占总面积的52.72%，整体来看土地沙漠化潜力逐渐降低，土地生态质量逐渐好转；（3）2030年沙漠化敏感性模拟结果显示未来10年沙漠化敏感性以低度敏感为主要类型（26.43%），沙漠化潜力将大幅下降，生态工程措施将取得一个较好的结果；（4）驱动力分析表明，沙漠化敏感性对气候因素的响应尤为敏感，其中降雨量对沙漠化敏感性分异的解释力最大；在社会经济发展因素中，经济发展水平起主要影响，说明经济水平的提高在一定程度上将影响土地沙漠化敏感性，各驱动因子交互对沙漠化敏感性呈现增强作用，表明沙漠化敏感性的分异是由多种因素共同作用的结果。研究可为半干旱区沙漠化防治和策略的制定提供借鉴。 Abstract:Taking the Inner Mongolia Autonomous Region as the study area, based on the Pressure-State-Response (PSR) analysis framework and coupled with the spatial distance index model (SDIM), the Land Desertification Sensitivity Evaluation System (LDSES) is constructed. Then combined with the spatio-temporal evolution map of desertification sensitivity, this paper quantitatively discusses the temporal and spatial evolution of land desertification sensitivity in the Inner Mongolia Autonomous Region in 2000, 2005, 2010, 2015 and 2020. At the same time, CA-Markov model is introduced to simulate the future evolution mechanism of desertification sensitivity, and then the geographic detector is used to reveal the characteristics and explanatory power of the main driving factors in the evolution of the first 20 years. The results showed that:(1) the desertification sensitivity of the Inner Mongolia Autonomous Region showed an increasing trend from the northeast to the southwest. Insensitive areas were located in the forest and grassland areas around Great Khingan and Yin Mountains. Highly sensitive areas were located in sandy land and the Gobi Desert such as the Badain Jaran Desert, Tengger Desert, Urumqi Desert, Rambuh Desert, Kubuqi Desert, and Mu Us Desert in the southwest. (2) From the perspective of the temporal and spatial evolution of desertification sensitivity, the areas with improved desertification sensitivity during the study period accounted for 30.65% of the total area. Areas with deteriorating desertification sensitivity accounted for 4.48% of the total area, and areas where the type of desertification sensitivity remained stable accounted for 52.72% of the total area. On the whole, the potential for land desertification is gradually decreasing and the ecological quality of the land was gradually improving. (3) The simulation results of desertification sensitivity in 2030 showed that in the next 10 years, low sensitivity will be the main type (26.43%), the desertification potential will be greatly reduced, and ecological engineering measures will achieve a good result. (4) The driving force analysis showed that the sensitivity of desertification was particularly sensitive to climatic factors, among them, rainfall had the greatest explanatory power for the differentiation of desertification sensitivity. Among the factors of social and economic development, the level of economic development played a major role, indicating that the improvement of economic level would affect the sensitivity of land desertification to a certain extent. And the interaction of various driving factors enhanced the sensitivity of desertification, indicating that the differentiation of desertification sensitivity was the result of a combination of multiple factors. The research can provide reference for the formulation of desertification control and strategies in semi-arid regions. 参考文献 相似文献 引证文献

Spatiotemporal evolution and prediction of habitat quality in Hohhot City of China based on the InVEST and CA-Markov models

Spatiotemporal evolution and prediction of habitat quality in Hohhot City of China based on the InVEST and CA-Markov models

Driving forces and prediction of urban land use change based on the geodetector and CA-Markov model: a case study of Zhengzhou, China

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