[Ecological environment quality of the Shanxi section of the Yellow River Basin under different development scenarios]. / ä¸åŒå‘å±•æƒ æ™¯ä¸‹é»„æ²³æµåŸŸå±±è¥¿æ®µç”Ÿæ€çŽ¯å¢ƒè´¨é‡.

Shanxi Province holds an important strategic position in the overall ecological pattern of the Yellow River Basin. To investigate the changes of the ecological environment in the Shanxi section of the Yellow River Basin from 2000 to 2020, we selected MODIS remote sensing image data to determine the remote sensing ecological index (RSEI) based on the principal component analysis of greenness, humidity, dryness, and heat. Then, we analyzed the spatial and temporal variations of ecological quality in this region to explore the influencing factors. We further used the CA-Markov model to simulate and predict the ecological environment under different development scenarios in the Shanxi section of the Yellow River Basin in 2030. The results showed that RSEI had good applicability in the Shanxi section of the Yellow River Basin which could be used to monitor and evaluate the spatiotemporal variations in its ecological environment. From 2000 to 2020, the Shanxi section of the Yellow River Basin was dominated by low quality habitat areas, in which the ecological environment quality continued to improve from 2000 to 2010 and decreased from 2010 to 2020. The high quality habitat areas mainly located on the mountainous areas with superior natural conditions and rich biodiversity, while the low ecological quality areas were mainly in the Taiyuan Basin and the northern part of the study area, where the mining industry developed well. Climate factors were negatively correlated with ecological environment quality in the northern and central parts of the study area, and positively correlated with that in the mountainous area. Under all three development scenarios, the area of cultivated land, forest, water and construction land increased in 2030 compared to that in 2020. Compared to the natural development scenario and the cultivated land protection scenario, the ecological constraint scenario with RSEI as the limiting factor had the highest area of new forest and the lowest expansion rate of cultivated land and construction land. The results would provide a reference for land space planning and ecological environment protection in the Shanxi section of the Yellow River Basin.

Land-use simulation for synergistic pollution and carbon reduction: Scenario analysis and policy implications.

Simulations of sustainable land use and management are required to achieve targets to reduce pollution and carbon emissions. Limited research has been conducted on synergistic pollution and carbon reduction (SPCR) in land-use simulations. This study proposed a framework for land-use simulation focused on SPCR. The non-dominated sorting genetic algorithm (NSGA-Ⅱ) and the entropy weight-based technique for order of preference by similarity to an ideal solution (TOPSIS) were used to optimize the land-use structure according to minimum net carbon, nitrogen, and phosphorus emissions. The cellular automata (CA) Markov model was then utilized to simulate the land-use spatial pattern according to the optimal conditions. The proposed framework was applied to the Dongjiang River Basin, South China, and three other scenarios (natural development (ND), carbon minimization (CM), and pollution minimization (PM)) were designed to validate the effectiveness of pollution and carbon emissions reduction under the SPCR scenario. The land-use structure and the pollution and carbon emissions in the scenarios were compared. The results showed the following. (1) The proportions of cultivated land, woodland, grassland, water, and construction land In the SPCR scenario accounted for 14%, 72%, 4%, 3%, and 7% of the total area, respectively. The carbon, nitrogen, and phosphorus emissions were 42.4%, 6.6%, and 7.8% lower, respectively, in the SPCR scenario than in the ND scenario, demonstrating the advantages of simultaneous pollution and carbon reduction. (2) The kappa coefficient of the CA-Markov model was 0.8729, indicating high simulation accuracy. (3) The simulated land-use spatial patterns exhibited low spatial heterogeneity under the CM, PM, and SPCR scenarios. However, there were significant disparities between the ND and SPCR scenarios. The cultivated and construction land areas were significantly smaller in the SPCR scenario than in the ND scenario. In contrast, the woodland and grassland areas were larger, with most differences in the central and southwestern regions of the Dongjiang River Basin. The results of the current study can be used to formulate effective land use policies and strategies in the Dongjiang Basin and similar areas to achieve the Coupling coordination between pollution reduction and carbon reduction. Policy recommendations include increasing the proportion of woodland and grassland, implementing reasonable constraints on expanding cultivated and construction lands, and establishing farmland red lines to promote synergistic pollution and carbon reduction.

[Spatial and Temporal Evolution and Prediction of Carbon Storage in Kunming City Based on InVEST and CA-Markov Model].

Land use/cover change is an important driving factor for carbon stock changes in terrestrial ecosystems and affects the carbon cycle of the whole ecosystem. Taking Kunming City as a case study, based on the modified carbon density coefficient, this study analyzed the spatio-temporal characteristics of carbon storage changes in the terrestrial ecosystem under different land use scenarios from 2000 to 2020 and "three-line" constraints by coupling the carbon storage module of the InVEST model and CA-Markov model. The results showed that:① cultivated land, forest land, and grassland were the main types of land use in Kunming City, and land use transfer also occurred among the three types. ② From 2000 to 2020, the overall carbon storage in Kunming City was low in the south and high in the north, and the carbon storage decreased yearly with a cumulative loss of 5.27×106 t. The degradation of forest land and grassland was the main reason for the decrease in carbon storage. ③ From 2020 to 2030, the carbon storage of the four scenarios should decrease, and the decline in carbon storage in the inertia development scenario was the most obvious, which was mainly caused by the rapid expansion of construction land. The cultivated land protection scenario effectively slowed down the reduction in carbon storage compared with the inertia development scenario. The ecological protection scenario could enhance the carbon sequestration capacity of the study area, with carbon storage reaching 262.49×106 t, but could not effectively control the reduction in cultivated land area. The scenario of preventing urban expansion effectively inhibited the disorderly expansion of construction land and indirectly prevented further reduction in carbon storage. Therefore, the cultivated land protection scenario, ecological protection scenario, and urban expansion prevention scenario can be considered comprehensively in the study area, which could not only increase the carbon sink space of the study area but also ensure food and ecological security.

Potential effects of oasis expansion on ecosystem service value in a typical inland river basin of northwest China.

To satisfy an increasing need for living space and food while preserving ecosystem services remains one of today's biggest challenges. Oases in arid areas have gradually become the main sources for new cultivated land, affecting the supply and transmission of ecosystem services. Yet, little assessment on predicting the effects of oasis expansion on ecosystem service value (ESV) has been available to guide policy makers and ecologists. Here we addressed the connections between oasis expansion and ESV in the middle reaches of the Heihe River Basin in northwest China by linking the Logistic-CA-Markov model and the benefit transfer method. The results showed that the oasis was expected to expand by 419.02 km2 from 2015 to 2029, with the area of farmland and construction land increasing by 18.87% and 39.05%, respectively. With oasis expansion, the total ESV was expected to increase by 104.25 million RMB from 2015 to 2029. However, oasis expansion encroaches on vegetation, resulting in decline of the values of climate regulation, waste treatment, and biodiversity protection. This study will provide a reference for decision-making in trade-offs involved in land management.

Spatial and Temporal Variation and Prediction of Ecosystem Carbon Stocks in Yunnan Province Based on Land Use Change.

The function of ecosystems as carbon sinks has emerged as a key strategy for advancing the concept of "carbon neutrality" and "carbon peaking". Ecosystem carbon stocks are impacted by land use changes that alter ecosystem structure and function. We evaluated the ecosystem carbon stocks of Yunnan Province in different periods with the aid of the carbon stock module of the InVEST model, analyzed the relationship between land use type shift and ecosystem carbon stock changes, and combine them with the CA-Markov model to predict land use types in 2030. The results showed that between 1990 and 2020, changes in land use primarily affected cropland, grassland, and forested areas. The ecosystem's average carbon stock from 1990 to 2020 was 8278.97 × 106 t. The carbon stocks of cropland, grassland, and unused land decreased by 31.36 × 106 t, 32.18 × 106 t, and 4.18 × 106 t during 1990-2020, respectively, while the carbon stocks of forest land, water area, and construction land increased by 24.31 × 106 t, 7.34 × 106 t, and 22.08 × 106 t. The main cause of the increase in carbon stocks in the ecosystem in Yunnan Province throughout the process of land use type shift was the development of forest land area, whereas the main cause of the decline was the shrinkage of cropland and grassland areas.

The effect of water resources on spatial and temporal change of soil salinity in Izdkhast playa, Fars Province, Iran.

In the south of Fars Province in Iran, there are several closed basins where the salinity of water and soil resources is one of the main problems. Therefore, to manage the water and soil resources of these basins, identifying the source of salinity and its expansion in playa is necessary. Thus, the Izad-Khast pit in the south of Fars Province of Iran was selected as one of these basins for research. To achieve the goal, 16 soil samples were randomly taken from the basin, and their EC was determined. Landsat 8 image was selected according to sampling day, and ten salinity indices were extracted from it. Then, the best index was determined by the relationship between salinity indices and ground EC using linear regression. Using the determined index and linear equation derived from linear regression and Landsat 7 and 8 images, salinity maps were obtained in three periods 2010, 2015, and 2020. Then, using maps of the three mentioned intervals and the CA-Markov method, soil salinity prediction maps were extracted for 2025 and 2030. Based on the research results, salinity index S2 = (Blue - R)/(Blue + R) provided the best results. Salinity maps derived from this index show that the highest level is related to the area with no salinity or low salinity, and high degrees of salinity are concentrated in some parts of the hills and in some areas of the plain, respectively, which determines the origin of salts. The results also showed that the kappa coefficient of CA-Markov is 0.7282, which shows the high ability of the model to predict soil salinity, in which the distance factor from gypsum and salt minerals is the most critical predictor factor. According to the forecasts, from 2020 to 2025, about 1 km2 and then from 2025 to 2030, about 1.6 km2 will be added to the saline lands.

Simulation of Land Use Pattern Based on Land Ecological Security: A Case Study of Guangzhou, China.

The process of rapid urbanization has intensified the conversion of different land use types, resulting in a substantial loss of ecological land and ecological security being threatened. In the context of China's vigorous advocacy of an ecological civilization, it is important to explore future land use patterns under ecological security constraints to promote sustainable development. The insufficient consideration of land ecological security in existing land use pattern simulation studies makes it difficult to effectively promote improvement in the ecological security level. Therefore, we developed a land use simulation framework that integrates land ecological security. Taking the sustainable development of land ecosystems as the core, the land ecological security index (LESI) and ecological zoning (EZ) were determined by the pressure-state-response (PSR) model and the catastrophe progression method (CPM). Natural development (ND) and ecological protection (EP) scenarios were then constructed taking the LESI and EZ into consideration. The CA-Markov model was used to simulate the land use pattern of Guangzhou for 2030 under the two scenarios. The results showed that (1) the study area was divided into four categories: ecological core zone, ecological buffer zone, ecological optimization zone, and urban development zone, with area shares of 37.53%, 31.14%, 16.96%, and 14.37%, respectively. (2) In both scenarios, the construction land around the towns showed outward expansion; compared with the ND scenario, the construction land in the EP scenario decreased by 369.10 km2, and the woodland, grassland, and farmland areas increased by 337.04, 20.80, and 10.51 km2, respectively, which significantly improved the ecological security level. (3) In the EP scenario, the construction land in the ecological core zone, ecological buffer zone, and ecological optimization zone decreased by 85.49, 114.78, and 178.81 km2, respectively, and no new construction land was added in the ecological core zone, making the land use pattern of the EP scenario more reasonable. The results of the study have confirmed that the land use pattern simulation framework integrating land ecological security can effectively predict land use patterns in different future scenarios. This study can provide suggestions and guidance for managers to use in formulating ecological protection policies and preparing territorial spatial planning.

Surface urban heat island in South Korea's new towns with different urban planning.

A new town is strategically built within a short period compared to naturally developed cities. It is considered an appropriate study area for analyzing the urban climate problems such as surface urban heat islands (SUHIs) that is differently generated according to urban planning and development. In this study, we suggest comprehensive method for determining and comparing changes in surface UHI distribution during 1989-2048 in two new towns with different urban planning. First, a substantial increase in built-up areas was observed from 1989 (< 5%) to 2018 (> 40%) in both new towns. However, SUHI phenomenon-increasing patterns were different of about 12.25% depending on urban planning and urban morphology. Results also showed the importance of vertical and horizontal structures which can have a great influence on SUHI intensity and accordingly, the difference in SUHI distribution between two new towns was confirmed. Moreover, without effective mitigation, the built-up area in both new towns is estimated to increase to approximately 60%, and the SUHI intensity in most areas to increase by 4 °C in 2048. In addition, the spread and intensification of the SUHI phenomenon are predicted to be greater due to the characteristics of the building structure and the active urban expansion. Thus, these results combined with architectural assessment models can improve the understanding of thermal environmental impacts of urbanization and provide directions for sustainable urban development and renovation.

Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing.

With the rapid advancement of urbanization and industrialization, the contradiction between the social economy and resources and the environment has become increasingly prominent. On the basis of limited land resources, the way to promote multi-objective comprehensive development such as economic, social development and ecological and environmental protection through structure and layout regulation, so as to maximize regional comprehensive benefits, is an important task of current land spatial planning. Our aim is to obtain land-use-change data in the study area using remote-sensing data inversion and multiple-model simulation. Based on land suitability evaluation, we predict and optimize the land use structure of the study area in 2030 and evaluate and compare ecosystem services. Based on remote-sensing images and eco-environmental data from 1985 to 2014 in the study area, land use/land cover change (LUCC) and future simulation data were obtained by using supervised classification, landscape metrics and the CA-Markov model. The ecosystem services were evaluated by the InVEST model. The analytic hierarchy process (AHP) method was used to evaluate the land suitability for LUCC. Finally, the LUCC in 2030 under two different scenarios, Scenario_1 (prediction) and Scenario_2 (optimization), were evaluated, and the ecosystem service functions were compared. In the last 30 years, the landscape in the study area has gradually fragmented, and the built-up land has expanded rapidly, increased by one-third, mainly at the cost of cropland, orchards and wasteland. According to the suitability evaluation, giving priority to the land use types with higher environmental requirements will ensure the study area has a higher ecosystem service value. The rapid development of urbanization has a far-reaching impact on regional LUCC. Intensive land resources need reasonable and scientific land use planning, and land use planning should be based on the suitability evaluation of land resources, which can improve the regional ecosystem service function.

An integrated approach of logistic-MCE-CA-Markov to predict the land use structure and their micro-spatial characteristics analysis in Wuhan metropolitan area, Central China.

As human interference with the natural environment accelerates, land use has undergone great changes. However, to realize rational land development in the rural-urban ecotone, the micro-spatial (MS) unit is the best scale for the management and planning of sustainable land use. Taking Wuhan metropolitan area as research area, the integrated logistic-multi-criteria evaluation (MCE)-cellular automata (CA)-Markov model was used to simulate land use pattern for 2025. In addition, the 1 km×1 km, 2 km×2 km, 3 km×3 km, and 4 km×4 km and typical sample belt were built to reveal the spatial microcosmic expression of land use structure. The results showed that the kappa coefficient and figure of merit (FoM) were 88.01% and 26.86%, respectively, indicating the integration model has high prediction accuracy. In 2005-2025, the diversification of land use in the Wuhan metropolitan area will be generally above the medium level, and the types of land combinations will be relatively abundant. As human activities increase, the land use degree will show increases continuously, it will expand outward from Wuhan, and there is a positive correlation between cultivated land-rural residential land and urban land-cultivated land. The spatial distribution of land use structure presents regional scale characteristics, and different regions have micro-spatial scale dependence. The selection of MS scales based on local conditions can be a good way to reflect land use internal structure and provide a better reference for the compilation of regional land use optimization.

Assessment of current and future land use/cover changes in soil erosion in the Rio da Prata basin (Brazil).

In recent years, the Cerrado biome in Brazil (Brazilian savannah) has faced severe environmental problems due to abrupt changes in land use/cover (LUC), causing increased soil loss, sediment yield and water turbidity. Thus, this study aimed to evaluate the impacts of soil loss and sediment delivery ratio (SDR) over the last 30 years to simulate future scenarios of soil losses from 2050 to 2100 and to investigate an episode of sediment delivery that occurred in the Rio da Prata Basin (RPB) in 2018. In this study, the following were used: an estimation of soil losses for 1986, 1999, 2007 and 2016 using the Revised Universal Soil Loss Equation (RUSLE), an estimation of SDR, sediment export and sediment deposition using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, an association of RUSLE factor C to LUC data for 2050 and 2100 based on the CA-Markov hybrid model, and an estimation of future soil erosion scenarios for 2050 and 2100. The results show that over the last 30 years (1986-2016), there has been a reduction in the areas of highly intense and severe degrees. Future soil erosion scenarios (2050-2100) showed a 13.84% increase in areas of soil loss >10 Mg ha-1 year-1. The results highlighted the importance of assessing the impacts of LUC changes on soil erosion and the export of sediments to agricultural watersheds in the RPB, one of the best ecotourism destinations in Brazil. In addition, the increase in soil loss in the region intensified sediment yield events and increased water turbidity. Furthermore, riparian vegetation, although preserved, was not able to protect the watercourse, showing that it is essential to adopt the best management practices in the agricultural production areas of the basin, especially where ramps are extensive or the slope is greater than 2%, to reduce the runoff velocity and control the movement of sediments on the surface towards the drainage canals. The results of this study are useful for drawing up a soil and water conservation plan for the sustainable production of agriculture and maintenance of ecosystem services in the region.

Simulation and optimization of land use pattern to embed ecological suitability in an oasis region: A case study of Ganzhou district, Gansu province, China.

High-intensity land use has led to water resource imbalance and land degradation in oasis regions, which pose a great threat to ecological security. Optimization of land use patterns is crucial to ensuring the rational distribution of water and land resources and improving the stability of oasis ecosystems. The purpose of this study is to spatially allocate land use activities to more suitable regions. In this study, we first evaluated the land ecological suitability (LES) in Ganzhou District, a typical oasis region. Then, the LES evaluation results were embedded in an integrated CA-Markov model based on multiple criteria evaluation (MCE) and multi-objective land allocation (MOLA) to simulate and optimize land use patterns for the year 2025 under two scenarios, i.e., Business as Usual (BAU) and Land Ecological Optimization (LEO). The results revealed that the optimized land use pattern generated by LES was more reasonable. The growth rate of construction land was restricted, and a slightly increased area of construction land mainly occupied unused land. Farmland area had a decreasing trend, and was mainly converted to grassland. Moreover, the woodland and water areas had increasing trends. This study can serve as a scientific reference for planners and policy makers in formulating land use planning and land use resource management strategies in oasis regions.

Simulating Land-Use Changes and Predicting Maize Potential Yields in Northeast China for 2050.

Crop potential yields in cropland are the essential reflection of the utilization of cropland resources. The changes of the quantity, quality, and spatial distribution of cropland will directly affect the crop potential yields, so it is very crucial to simulate future cropland distribution and predict crop potential yields to ensure the future food security. In the present study, the Cellular Automata (CA)-Markov model was employed to simulate land-use changes in Northeast China during 2015-2050. Then, the Global Agro-ecological Zones (GAEZ) model was used to predict maize potential yields in Northeast China in 2050, and the spatio-temporal changes of maize potential yields during 2015-2050 were explored. The results were the following. (1) The woodland and grassland decreased by 5.13 million ha and 1.74 million ha respectively in Northeast China from 2015 to 2050, which were mainly converted into unused land. Most of the dryland was converted to paddy field and built-up land. (2) In 2050, the total maize potential production and average potential yield in Northeast China were 218.09 million tonnes and 6880.59 kg/ha. Thirteen prefecture-level cities had maize potential production of more than 7 million tonnes, and 11 cities had maize potential yields of more than 8000 kg/ha. (3) During 2015-2050, the total maize potential production and average yield decreased by around 23 million tonnes and 700 kg/ha in Northeast China, respectively. (4) The maize potential production increased in 15 cities located in the plain areas over the 35 years. The potential yields increased in only nine cities, which were mainly located in the Sanjiang Plain and the southeastern regions. The results highlight the importance of coping with the future land-use changes actively, maintaining the balance of farmland occupation and compensation, improving the cropland quality, and ensuring food security in Northeast China.

[Dynamic change and prediction of vegetation cover in Shenzhen, China from 2000 to 2018]. / 2000­2018年深圳市植被覆盖动态变化与预测.

With landsat-series multi-temporal image data, percentage of vegetation cover (PVC) was estimated by pixel dichotomy. The linear regression analysis and center of gravity migration methods were used to explore the characteristics of the spatiotemporal changes of vegetation cover in Shenzhen from 2000 to 2018. The CA-Markov model was combined to predict future land cover in Shenzhen. The results showed that the PVC in Shenzhen demonstrated obvious regional differentiation characteristics from 2000 to 2018. The eastern region occupied larger proportion than the wes-tern part, while the southern region was larger than the north part. This feature exhibited good consistency with regional topographic effect. The spatial migration characteristic of the center of gravity of PVC was from northwest to southeast, and then from southeast to northwest, with a migration rate of 551.2 m·a-1. This process was closely related to urbanization in Shenzhen. The PVC in Shen-zhen tended to be generally improved from 2000-2018, with a improvement rate of 0.005·a-1. The percentage of significantly improved and degraded PVC area was 30.8% and 12.8%, respectively. The CA-Markov method was used to predict the land cover/use pattern of Shenzhen in 2024 under two scenarios, theoretical scenario and natural scenario. There was no significant difference in proportion of the area of the land cover/use patterns obtained by the two kinds of prediction method, with the difference threshold being 0-1.2%. Compared with the data before 2018, the proportion of arbor forests and arable land converted into construction land in Shenzhen would be significantly reduced in 2024, whereas the contradiction between supply and demand would be still tense.

Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: integration of remote sensing, CA-Markov, and landscape metrics.

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.

Future Flood Risk Assessment under the Effects of Land Use and Climate Change in the Tiaoxi Basin.

Global warming and land-use change affects runoff in the regional basin. Affected by different factors, such as abundant rainfall and increased impervious surface, the Taihu basin becomes more vulnerable to floods. As a result, a future flood risk analysis is of great significance. This paper simulated the land-use expansion and analyzed the surface change from 2020 to 2050 using the neural network Cellular Automata Markov (CA-Markov) model. Moreover, the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) dataset was corrected for deviation and used to analyze the climate trend. Second, the verified SWAT model was applied to simulate future runoff and to analyze the future flood risk. The results show that (1) land use is dominated by cultivated land and forests. In the future, the area of cultivated land will decrease and construction land will expand to 1.5 times its present size. (2) The average annual precipitation and temperature will increase by 1.2% and 1.5 degrees from 2020 to 2050, respectively. During the verified period, the NSE and r-square values of the SWAT model are greater than 0.7. (3) Compared with the historical extreme runoff, the extreme runoff in the return period will increase 10%~25% under the eight climate models in 2050. In general, the flood risk will increase further under the climate scenarios.

Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province, China.

Land use and cover change (LUCC) is an important issue affecting the global environment, climate change, and sustainable development. Detecting and predicting LUCC, a dynamic process, and its driving factors will help in formulating effective land use and planning policy suitable for local conditions, thus supporting local socioeconomic development and global environmental protection. In this study, taking Gansu Province as a case study example, we explored the LUCC pattern and its driving mechanism from 1980 to 2018, and predicted land use and cover in 2030 using the integrated LCM (Logistic-Cellular Automata-Markov chain) model and data from satellite remote sensing. The results suggest that the LUCC pattern was more reasonable in the second stage (2005 to 2018) compared with that in the first stage (1980 to 2005). This was because a large area of green lands was protected by ecological engineering in the second stage. From 1980 to 2018, in general, natural factors were the main force influencing changes in land use and cover in Gansu, while the effects of socioeconomic factors were not significant because of the slow development of economy. Landscape indices analysis indicated that predicted land use and cover in 2030 under the ecological protection scenario would be more favorable than under the historical trend scenario. Besides, results from the present study suggested that LUCC in arid and semiarid area could be well detected by the LCM model. This study would hopefully provide theoretical instructions for future land use planning and management, as well as a new methodology reference for LUCC analysis in arid and semiarid regions.

Prediction of hydrological responses to land use change.

The change of land use and land coverage (LULC) has a direct impact on the underlying surface of the drainage basin, hence alters the rainfall-runoff processes. This study described the procedure to use the CA-Markov model to predict the future distributed land use in the Qinhuai River basin in 2028 based on the historical evolution. The hydrological model HEC-HMS was then utilized to examine the impact of land use change on the hydrological responses of the drainage basin. The major findings were as follows: (1) from 2001 to 2010, major changes detected in land use were substantial conversion of paddy fields, forest land and dryland into urban land (increased by 56.81%), following the trend recognized by CA-Markov model, urban land and dry land were predicted to increase by 17.47% and 14.05% by 2028. (2) the projected land use in 2028 resulted in various degrees of increase in flood peak and volume according to the severity of the floods characterized by flood volumes, the smaller floods were predicted to result in more significant increase with 3.5% increase in flood peak and 2.9% increase in flood volume while these values decreased to 0.4% and 1.1% for a big scale flood; (3) greater increase in urbanization leads to greater change of the flood peak and volume change. For small scale floods, when the proportion of urban land use increases by 30% to 60% hypothetically, the relative increment in flood peaks increase from 4.7% to 8.1% with 4% to 6.6% increase in the flood volume; (4) on the sub-basin scale, the trend of change in urban land use and the flood peak and volume were consistent. The methods and conclusions may shed light on urban land development and management and design of flood control measures in a large river basin.

Dynamic simulation of land use change based on logistic-CA-Markov and WLC-CA-Markov models: a case study in three gorges reservoir area of Chongqing, China.

The construction of The Three Gorges Reservoir has changed land use structure and reconstituted landscape pattern as imparts significant influence upon the land use structure and ecological environment of Three Gorges Reservoir Regions. The ecological safety of reservoir area is extremely dependent on unique location and special geological conditions of Zhongxian County, the center of Three Gorges Reservoir Regions in Chongqing, and therefore, ecological environment of reservoir area will be changed with the transition of land use in Zhongxian County. Based on land use data in 2000, 2005, 2010, this paper chooses influencing factors from aspects of natural topographic and geomorphological conditions, accessibility to economic development and land use expansion, and then establishes Logistic-CA-Markov (Logistic-Cellular Automata-Markov) and WLC-CA-Markov (Weighted Linear Combination- Cellular Automata- Markov) models so as to simulate spatial pattern of land use of Zhongxian County. The results demonstrate that WLC-CA-Markov model established here has better controllability and higher simulation precision (the kappa coefficient is 0.9295). In the future development of Zhongxian County, the area of grassland and plow land will be reduced continuously, the area of construction land will be expanded obviously mostly because of the added area both near the water and in the north of Zhongxian county, the area of woodland will be increased to a little extent, the area of water area and unused land has gentle change. In the sustainable scenario, the area of grassland will be reduced slightly, the area of water area keeps steady, the area of plow land is reduced but higher than red line of plow land, the area of construction land is increased with significantly smaller increase amplitude than that in the natural development scenario, and the woodland is increased. This scenario coordinates ecological environment with economic development of regional society and turns out to be the best development scenario of land use.

Scenario-based modelling for urban sustainability focusing on changes in cropland under rapid urbanization: A case study of Hangzhou from 1990 to 2035.

China is undergoing rapid urbanization, which has brought great pressure on croplands throughout the country, especially in fast developing cities, such as Hangzhou. In this study, an attempt was made to monitor and model the cropland dynamics of Hangzhou from 1990 to 2035. The spatial-temporal changes in the cropland were discussed based on the land cover maps along with urban-rural gradient analysis. After understanding the spatial-temporal patterns of cropland changes, the cellular automata-Markov model was employed using the historical land cover maps and other explanatory data to perform a scenario-based simulation. Accordingly, three scenarios, namely spontaneous scenario (SS), protected area ensuring scenario (PAES), and optimal agriculture developing scenario (OADS), were designed for simulating the cropland distribution in 2035. The monitoring results showed that during 1990-2015, the cropland area decreased 1512.46km2 under rapid urbanization. Areas at a distance of 12km from the city center experienced maximum cropland loss. Among all the spatial metrics, aggregation index of the cropland exhibited the highest correlation with the distance to the city center (r=0.77 in 2015), thereby suggesting an obvious trend in aggregation along the urban-rural gradient. The modelling results reported that under PAES and OADS, the study area could gain 81.76km2 and 255.14km2 more cropland, respectively, than that under SS in 2035. Thus, policies applied in PAES and OADS would be effective for cropland protection.