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.
山西省在黄河流域总体生态格局中具有重要的战略地位。为深入研究2000—2020年黄河流域山西段生态环境的变化,选用MODIS遥感影像数据,基于绿度、湿度、干度和热度的主成分分析确定遥感生态指数(RSEI),对该区域生态环境质量的时空变化进行分析并探讨影响因素;同时,利用CA-Markov模型对2030年黄河流域山西段不同发展情景下生态环境进行模拟和预测。结果表明: RSEI在黄河流域山西段具有较好的适用性,可用于监测和评估其生态环境的时空变化特征。2000—2020年,黄河流域山西段以低生境质量区为主,其中,2000—2010年生态环境质量持续改善,而2010—2020年则有所退化;高生境质量区集中于山区,其自然条件优越、生物多样性丰富,低生态质量区主要分布在城市群集中的太原盆地及研究区北部采矿业发达的地区;在研究区的北部和中部,气候因子与生态环境质量呈负相关关系,而在高山区域二者呈正相关关系。3种发展情景下,2030年研究区的耕地、林地、水体和建设用地面积均较2020年有所增加;相较于自然发展情景和耕地保护情景,在以RSEI为限制因子的生态约束情景中,新增林地面积最多,而耕地和建设用地的扩张速率最低。研究结果可为黄河流域山西段的国土空间规划及生态环境保护提供参考。.

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.

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.

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.

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.

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.

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.

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.
采用Landsat系列多时相影像数据,以像元二分法估算植被覆盖度,运用线性回归分析、重心迁移等方法来探究深圳市2000—2018年植被覆盖的时空变化特征,并结合CA-Markov模型对深圳市未来土地覆盖情况进行预测。结果表明: 2000—2018年,深圳市植被覆盖呈明显的地域分异特征,在区域上表现为东部大于西部、南部大于北部,此分异特征与区域地形效应具有良好的一致性。植被覆盖度重心的空间迁移特征为西北-东南-西北,迁移速率为551.2 m·a-1,此进程与深圳市城市化进程密切相关。2000—2018年间,深圳市植被覆盖度总体呈改善趋势,改善速率为0.005·a-1,其中,植被覆盖度显著改善和退化的面积比例分别为30.8%和12.8%。采用CA-Markov方法分理论、自然两种情景对深圳市2024年土地覆盖/利用类型进行预测,两种预测方法所得土地覆盖/利用类型的面积所占比例之间没有显著差异,其差异阈值在0~1.2%。与2018年之前相比,2024年深圳市乔木林、耕地等转化为建设用地的比例将明显减少,但供需矛盾仍然紧张。.

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.

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.