{"paper_id":"47d93553-6d8f-4941-adb0-2df831e2dc9e","body_text":"Past and future of land use change in the Middle reaches of the Yellow River Basin in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Past and future of land use change in the Middle reaches of the Yellow River Basin in China Xiaofeng Liu, Zenglin Hong, Luhong Zhou, Jiaqi Liu, Xiaodan Guo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2144585/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract When looking at maps of land use land cover (LULC) change, often derived from satellite imagery, China's increasing urban construction and decreasing cultivated land area can be identified. Using the middle reaches of the Yellow River Basin in China as the case study, the present work focuses on (i) identifying historical LULC change in the period 1995–2020; (ii) estimating LULC in the next ten years, combining Geographical Information Systems (GIS) with Markov model. Landsat 5/8 images were combined with field evidence to map LULC in three reference years (1995, 2005, 2015and2020), while the Markov model was applied to forecast LULC in 2025, and 2030.The LULC of the middle reaches of the Yellow River Basin was classified into six classes: forest, grassland, wetland, cultivated land, construction land and unused land. The results show that over the past 25 years, cultivated land and forest land have decreased by 8,600 km 2 and 6,400 km 2 , respectively, while construction land has increased by 7,500 km 2 , mainly due to increased urbanization and industrialization. In fact, during the simulated 2020–2030 period, cultivated land and forest land will continue to decrease, while construction land and grassland will increase. It is worth noting that the reduction of forest land will lead to increased soil erosion, while the reduction of cultivated land will lead to food security problems. The study points out that there is an urgent need for response measures in the region to ensure the sustainability of people and nature. China Yellow River Basin Land use land cover Land-use transition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Land Use and Land Cover Changes (LULC) is the most closely related issue between man and nature, and it is a direct manifestation of the relationship between man and land [ 1 ]. The spatial pattern of the earth's surface features has been changed by humans, and this activity has been objectively recorded by LULC. Changes in LULC are closely related to the following factors: worldwide weather, species decline, the safety of the ecological environment and the continuity of life between humans and nature, etc. [ 2 , 3 ]. Two major international organizations, IGBP (International Geosphere-Biosphere Program) and IHDP (Global Change Human Factors Program) jointly proposed the \"Land Use/Cover Change\" (LULC) scientific research program in 1995, and positioned this research as a global key content of change [ 4 ]. Taking this as an opportunity, in order to emphasize the ensemble and simulation research of human and environmental coupling on land, GLP (Global Land Program) was launched in 2005, further making LULC research a core field and hotspot of global change research and land science research subject [ 5 – 7 ]. Previous LULC studies in many representative areas can help us understand the changing characteristics of LULC, so as to more scientifically explain the relationship between human activities on LULC and the mutual influence between LULC and nature [ 8 ]. The use of long time series is very important for land use change research and forecasting for future land management, and long time series is often used as a study of human impacts on land use change [ 9 , 10 ]. Trend analysis of land-use change is helpful for land-use modeling, as past land-use changes often represent human impacts on land [ 10 , 11 ]. In order to predict the future land use change more accurately and scientifically, the establishment of the model needs to be calibrated and verified. The following techniques are used in this paper: Radiometric Correction, Geometric correction, Image Fusion, Mosaicking, Gram-Schmidt Pan Sharpening(GS), FLUS-Markov,ect. The rational use of land is a prerequisite for ensuring that human beings can carry out normal production and life. The purpose of LULC is to more thoroughly understand the continuous variation of LULC in the region, and its essence is to explain the interaction between people and the environment. In some developing countries and Africa, there are few studies on comprehensive evaluation of the driving force analysis and results of land use change [ 12 , 13 ]. Research in recent years has mainly focused on some underdeveloped areas in Asia and Africa, and the results have shown that land cover has decreased due to population growth, economic development, and globalized human activities [ 14 – 20 ]. Until recently, some scholars have begun to try to link land use change with related functions such as ecological services, in order to provide some policy services [ 21 – 23 ]. Combined with previous scholars' research on land use change and prediction in the Yellow River Basin, the research in this paper can provide some scientific basis for land use change in the middle reaches of the Yellow River Basin. In fact, the LULC intensity and rate in the middle reaches of the Yellow River Basin continue to change dynamically and are closely related to the overexploitation of natural resources, which is influenced by climate, soil characteristics, vegetation, precipitation and natural disasters [ 24 – 27 ]. LULC changes have accelerated considerably over the past few decades due to increased human demand for natural resources. In the middle reaches of the Yellow River Basin, the change of LULC is continuous, and its agricultural development and urban construction are dominant [ 28 , 29 ]. Focusing on the middle reaches of the Yellow River Basin in China, this paper aims to: (i) understand the changes in land use in the region in the past 25 years (1995–2020); (ii) predict the possible land use in the next ten years (2020–2030). The paper mainly uses satellite images and model establishment to study the research area, and at the same time supports the reliability of the research through field investigation and other evidence. The study area and method used will be described in Section 2, and the results are presented in Section 3. In Section 4 , mainly discusses the impact of existing policies on land use, and the direction of possible future land use changes. Section 5 summarizes the main outcomes of the study and its limitations. 2. Materials And Methods 2.1Study Area The middle reaches of the Yellow River Basin are located between Hekou Town in Inner Mongolia Autonomous Region and Huayuankou in Zhengzhou City (Fig. 1). The dimensions are from 32° to 42° north latitude and 104° to 113° east longitude. The total length of the river reaches 1206.4km, and the basin area is 363,000km2 and covers about 46% of the total area of the Yellow River Basin. The terrain of the middle Yellow River Basin is generally high in the northwest and low in the southeast. This area belongs to the semi-arid region of the middle temperate zone and warm temperate zone. The precipitation is mainly concentrated in summer and mainly in the form of heavy rain. The average annual precipitation varies from 320mm in the northwest to 840mm in the southwest [30], of which 6–9 The monthly precipitation accounts for about 70% of the annual precipitation, and the heaviest precipitation from July to August can account for more than 40% of the total annual precipitation. 2.2Datast The data used in the study are free satellite imagery and Digital Elevation Models (DEMs) at 30m resolution.Landsat-5TM(L5,for the years 1995 and 2005)and Landsat-8OLI ( L8, for the years 2015and2020) data were downloaded from the Geospatial Data Cloud Platform(http://www.gscloud.cn). The image selection is concentrated in the months of June to September, when the local vegetation is lush, which is convenient for different types of land use. Since the precipitation is mainly concentrated in these months, the image has been atmospherically corrected. In order to cover whole area, a composite of Landsat images from different paths/rows was created, ensuring that the images refer to the same season (Table 1). Table 1 Details of the Landsat images that were analyzed in the study Satellite Year Acquisition Date Path/Row Spatial Resolution Landsat5 TM 1995 June 29 130/35 30m June 8 127/35 30m June 26 125/35 30m June 1 126/34 30m Landsat5 TM 2005 July 5 127/34 30m June 12 126/35 30m June 8 125/35 30m Landsat 8 OIL 2015 August 13 124/35 30m July 6 130/36 30m July 15 129/36 30m July 24 128/36 30m July 1 127/36 30m Landsat 8 OIL 2020 September 7 128/34 30m September 18 125/34 30m September 18 125/35 30m 2.3Land Use Land Cover Classification of Historical Data To map LULC, satellite images should be classified, assigning predefined LULC classes to some pixels. In this phase could be affected by various factors such as classification methods, algorithms, collecting of training sites, and the correctness of the classification should be assessed via field evidence [31–33]. The study was performed by classifying four reference years (1995, 2005, 2015and 2020) and considering six LULC classes. The selection of these classes was performed based on past studies [34], based on this, the Middle of the Yellow River Basin was classified into six classes, namely forest, grassland, wetland, cultivated land, construction land, unused land (Table 2). Table 2 Land Use Land Cover classes and their description LULC Classes Description forest area to the land where trees, bamboos, and shrubs grow, and the land where mangroves grow along the coast grassland area to the land that grows mainly herbaceous plants wetland area to the land where the surface is too wet or often accumulates water and grows wetland organisms cultivated land area to the land on which crops are grown construction land area to the land on which buildings and structures are constructed unused land area to unused land In the classification process, some plots with similar spectral characteristics are merged, so the accuracy of the results needs to be verified. Select 55 live points (10 forests, 10 grasslands, 10 wetlands, 10 cultivated land, 10 construction land, and 5 unused land) in the L8 image in 2020 for verification with the help of auxiliary tools (Google Earth, etc.), The points are evenly distributed over the study area to ensure their accuracy. To quantitatively assess the accuracy, statistical methods kappa value were applied[35, 36]. P a is the ratio of the number of simulated correct grids to the total number, P b is the ratio of the number of simulated correct grids to the total number in a random state, and 1 is the ratio of the number of simulated correct grids to the total number in an ideal state. Among them, when Kappa > 0.8, it means that the simulation effect is good and the degree of consistency is extremely high; when 0.6 < Kappa ≤ 0.8, it means that the simulation effect is good and the degree of consistency is good; when 0.4 < Kappa ≤ 0.6, it means that the simulation results are better Poor; when Kappa ≤ 0.2, it means the simulation effect is extremely poor[37]. 2.4. Prediction of Future LULC To map future LULC scenarios, the Markov model projection is performed by creating matrixes to estimate the transition probability and the area of each LULC class for future dates[38, 39].Markov model was applied to forecast the future LULC in two scenarios (2025 and 2030), via a few main steps: (i) analysis of historical LULC maps (1995, 2005, 2015and2020) and associated changes, (ii) creation of transition probability matrixes, (iii) model validation, (iv) prediction of future LULC maps, accounting for possible driving forces. In this work, we define the probability transitional matrix as a matrix showing the transfer direction of LULC types from one category to other categories in a given year [40]. To evaluate the capability of Markov model in predicting future LULC, a predicted map of 2015was created based on 1995 and 2005 LULC, and then compared with the actual 2015 map. To evaluate the quality of the 2015 predicted map against the 2015 reference map, the ANN model validation module was used [41], mimicking the approach proposed in similar studies [42]. In ANN model, two tools are available to assess the fit of the model to the sample data. First, the cross-validation tool iteratively removes a sample data point and interpolates a new value for the location. A table is produced to show the difference between the predicted attributes and the known attributes at those locations. Second, a variance image is produced that shows the spatial variation of uncertainty as a result of the fitted model. The variance image provides information to assist in identifying the problem areas where the relationship between the fitted model and the sample data points is poor. 3. Result 3.1. Historical LULC Maps Four reference years (1995, 2005, 2015and 2020) were considered to evaluate historical LULC via a maximum likelihood supervised classification (Fig. 2). As reported in Table 3, forests, grasslands, wetlands, cultivated land, construction land and unused land in the middle reaches of the Yellow River showed the same quantitative distribution, which were cultivated land > grassland > forest land > construction land > wetland > unused land. Exactly, in 1995 most of the study area was covered by agriculture (38.8%), glass (36.9%)and forest (17.8%), with only a very minor part occupied by wetland (1.59%). Similar LULC was observed in 2005, with agriculture (38.4%), glass (34.7%)and forest (19.7%), being the most dominant LULC classes and just a small increase in the area covered by wetland (1.62%). In 2015 and 2020, the class distribution remained similar, with an increase the built-up area from 2.84–4.48%. Table 3 Details of LULC area of the Middle reaches of the Yellow River Basin in the four reference years LULC Type 1995 2005 2015 2020 [km 2 ] [%] [km 2 ] [%] [km 2 ] [%] [km 2 ] [%] forest 64512.25 17.76 71567.32 19.71 71855.36 19.78 71830.07 19.78 grassland 133957.09 36.88 125799.58 34.64 127892.23 35.21 127499.71 35.11 wetland 5774.89 1.59 5881.00 1.62 5041.98 1.39 5310.46 1.46 cultivated land 141136.05 38.86 139425.56 38.39 134666.17 37.08 132487.64 36.48 construction land 8725.67 2.40 10330.94 2.84 14587.39 4.02 16272.98 4.48 unused land 9078.44 2.50 10179.99 2.80 9141.26 2.52 9783.53 2.69 Total 363184.39 100.00 363184.39 100.00 363184.39 100.00 363184.39 100.00 To sum up, in the past, the main LULC in the study area were cultivated land and grassland, followed by forest land. In the past two decade, the construction land in the middle reaches of the Yellow River Basin has continued to increase, nearly doubling the amount, while the cultivated land has continued to decrease, decreasing by nearly 8,600km 2 . The results reported in Fig. 2 are in agreement with Xu et al. [43], who pointed out that the Middle reaches of the Yellow River Basin is characterized by an expansion of built-up LULC, resulting in a decline of natural vegetation and agriculture land. 3.2 Historical LULC Changes and Transition Probability Matrix Comparing the four reference years, it is possible to observe a considerable reduction in the area covered by glass and cultivated during the observation period (Table 4). Specifically, about 258km 2 of grassland and 346 km 2 of agricultural land are converted to other types of LULC each year. The mutual transformation of various types of plots can be seen from Fig. 3: Table 4 Historical LULC changes in the Middle reaches of the Yellow River Basin LULC class 1995–2005 2005–2015 area(km 2 ) area(%) change(km 2 /year) area(km2) area(%) change(km 2 /year) forest 7055.07 0.11 705.51 288.04 0.00 28.80 grassland -8157.51 -0.06 -815.75 2092.65 0.02 209.26 wetland 106.11 0.02 10.61 -839.02 -0.14 -83.90 cultivated land -1710.49 -0.01 -171.05 -4759.39 -0.03 -475.94 construction land 1605.27 0.18 160.53 4256.45 0.41 425.65 unused land 1101.55 0.12 110.16 -1038.73 -0.10 -103.87 LULC class 2015–2020 1995–2020 area(km 2 ) area(%) change(km 2 /year) area(km2) area(%) change(km 2 /year) forest -25.29 -0.00 -5.06 7317.82 0.11 292.71 grassland -392.52 -0.00 -78.50 -6457.38 -0.05 -258.30 wetland 268.48 0.05 53.70 -464.43 -0.08 -18.58 cultivated land -2178.53 -0.02 -435.71 -8648.41 -0.06 -345.94 construction land 1685.59 0.12 337.12 7547.31 0.86 301.89 unused land 642.27 0.07 128.45 705.09 0.08 28.20 During the period from 1995 to 2005, the decrease of grassland mainly turned to forest, cultivated land and unused land. The increase of grassland also came from forest, cultivated land and unused land, but the decrease was greater than the increase; the forest was mainly converted to grassland and cultivated land; Wetlands were mainly converted to cultivated land; during this period, construction land mainly came from cultivated land, and 2100 km² of cultivated land was converted into construction land, but only 800 km² of cultivated land was converted into construction land, and 100km² of unused land was converted into arable land; From 2005 to 2015, the conversion of grassland to forest, wetland, cultivated land and unused land decreased, but the conversion of grassland to construction land increased, from 357 km² to 1140 km²; forest It was mainly converted to grassland and cultivated land, but the conversion rate decreased compared with 1995–2005; the reduction of wetland was mainly converted to construction land and cultivated land; as in 1995–2005, construction land mainly came from cultivated land in 2005–2015. During the period, 3700km² of cultivated land was converted into construction land, but only 1300 km² of construction land and unused land was converted into cultivated land; From 2015 to 2020, the conversion of grassland to cultivated land decreased; the increase in construction land also mainly came from cultivated land. During this period, 2200 km² of cultivated land was converted into construction land, and 1400 km² of construction land and unused land was converted into cultivated land. Compensation is still unbalanced, but compared with 2005–2015, the unbalanced gap has decreased. In conclusion, during the period from 1995 to 2020, the number of forests, grasslands, and construction land showed an upward trend, while the number of wetlands, cultivated land, and unused land showed a downward trend. The increase in the number of forests and grasslands mainly comes from three sources. One is the mutual transformation of the two. The second is the conversion of cultivated land to forest and grassland, and the third is the comprehensive improvement of part of the unused land; the increase of construction land is mainly from cultivated land and grassland; the decrease of wetland is mainly to cultivated land and grassland. 3.3 Model Validation The LULC map of 2015, predicted from the 1995 and 2005 data, has been validated with the classified LULC map of the very same year (Table 5), showing that the Markov model can effectively forecast LULC changes. Table 5 LULC classes in 2015: projected vs classified values LULC Type Projected LULC Classified LULC area[km 2 ] percentage[%] area[km 2 ] percentage[%] forest 71002.55 19.55 71855.36 18.78 grassland 120431.94 33.16 127892.23 35.21 wetland 4285.58 1.18 5041.98 1.39 cultivated land 138409.57 38.11 134666.17 37.08 construction land 19357.73 5.33 14587.39 5.02 unused land 9697.02 2.67 9141.26 2.52 Total 363184.39 100.00 363184.39 100.00 Based on the real 2015 remote sensing interpretation data, the simulated LULC data in 2015 was compared and analyzed (Fig. 4). The results showed that the Kappa coefficient was 0.806, greater than 0.8, and the overall accuracy was 0.865, indicating that the model has high simulation accuracy and can be Conduct multi-scenario simulations of LULC in the middle reaches of the Yellow River in 2025 and 2030. 3.4. Future LULC The multi-scenario simulation of LULC in the Middle reaches of the Yellow River is based on the remote sensing interpretation data in 2015 and 2020, and the Markov model is used to simulate the number of LULC in 2025 and 2035; On this basis, the FLUS model is used to conduct multi-scenario simulations of LULC in the middle reaches of the Yellow River in 2025 and 2035; Combined with the simulation results, the changes in the number of LULCs in the middle reaches of the Yellow River, the mutual transfer trend and spatial distribution of LULCs were analyzed. By analyzing the data of the four reference years, predict the number of LUCCs in 2025 and 2030, and make a graph of changes in the number (Fig. 5); Using the FLUS model, the number of simulated LULCs is allocated under four different scenarios: production space priority, living space priority, ecological space priority, and comprehensive space optimization (Figs. 6 and 7); at the same time, ENVI was used to analyze the land type transfer of the LULC data in the middle reaches of the Yellow River under the comprehensive spatial optimization scenarios in 2020, 2025, and 2030, and the chord diagrams of the LULC transfer in the middle reaches of the Yellow River from 2020 to 2025 and 2025 to 2030 were obtained (Fig. 8). To sum up, the increase of cultivated land and grassland in 2020–2025 is due to the decrease of each other, and the two are transformed into each other. The conversion of grassland is the same as that in 2020–2025. The increase in construction land still comes from the decrease in cultivated land, the difference is that a small amount of construction land is converted into cultivated land. 4. Discussion And Policy Implications As suggested in the review carried out by Wang et al. [ 44 ], most of the studies of LULC in the Yellow River Basin are on a local scale, analyzing the soil salinization and water pollution a series of ecological and environmental problems, such as serious damage to wetland resources protection and threats to wetland resource protection. This is mainly due to the large scope of the middle reaches of the Yellow River Basin and the difficulty of LULC classification, which makes it difficult to conduct LULC research as a whole. So, very few works tried to forecast future LULC changes at the watershed scale, eventually providing new insights that can be useful for developing future basin-wide management strategies. This study was developed to fill this gap, estimating the Middle reaches of the Yellow River Basin LULC for the next decade (2025, and 2030) based on past information (LULC in 1995, 2005, 2015and 2020), to infer trends to be used in multiple ways. Markov model results point out that, in the coming decade, significant changes in LULC should be expected, mostly because of the ever-increasing pressure of humans in need of more land for construction. In fact, due to the rapid economic development and urbanization in China in recent years, a large amount of cultivated land and grassland has been lost in the middle reaches of the Yellow River [ 45 ]. The continuous reduction of cultivated land in the study area pointed out in this study is consistent with the results of previous studies [ 46 – 49 ]. This trend is an inevitable result of China's rapid industrialization and urbanization, such as the 2000 Western Development Strategy, etc. Artificial surfaces occupy a large amount of cultivated land and transform to other LULC types [ 50 – 52 ]. Due to the rapid pace of urbanization, people need more land to build residential sites, which leads to the shift of cultivated land in the study area to the type of construction land. With the continuous reduction of arable land, policy makers realized that in order to ensure food security, the first thing to do is to ensure that the area of arable land cannot be reduced, so they have launched a series of land remediation projects, such as the reclamation of abandoned industrial and mining areas, which can increase the area of arable land. At the same time, urban construction is also developing towards the direction of land intensification, which can effectively alleviate the contradiction between people and land. For the process and causes of grassland degradation in the middle reaches of the Yellow River Basin, scholars at home and abroad have done a lot of research and analysis. Most scholars believe that natural and human factors are the main causes of grassland degradation. Among the natural factors, climatic factors have the greatest impact on grassland, and a large number of studies have shown that temperature, precipitation, and evaporation are important factors affecting grassland degradation in the middle reaches of the Yellow River [ 53 ]. The impact of rodents on the degradation of natural grasslands is mainly reflected in competition with livestock for food, especially when the sand, gravel and soil produced during burrowing and burrowing bury pastures. Erosion [ 54 – 57 ]. With the increase of the population in the study area, the one-sided pursuit of the number of livestock without considering the slaughter rate intensified, resulting in the number of livestock in the area exceeding the carrying capacity of the grassland, the quality of which was deteriorated, and the degradation degree of the degraded and desertified grassland was further aggravated. The middle reaches of the Yellow River Basin have a large scope, spanning many provinces and cities, and the large differences in natural conditions and geographical locations make it have a greater impact on the changes of LULC in national policies. The land types and ecological environment of the Yellow River Basin are constantly being developed and used, resulting in increasingly tight resources and a sharp drop in ecological carrying capacity, which degrades the ecological environment and threatens ecological security. In addition, the provinces and cities in the Yellow River Basin have developed an extensive and solidified development model, which has caused irreversible damage and consumption to the ecology. This industrial development model has caused the ecological environment of the Yellow River Basin to degrade to a certain extent. In response to the deteriorating environment, China implemented the policy of returning farmland to forest in 1999, and promulgated the policy of soil and water conservation in 2012. In the previous paragraph, national leaders put forward the concept of promoting ecological protection and high-quality development in the Yellow River Basin. This series of policies or opinions aims to protect the ecological environment of the Yellow River Basin and enable LULC to achieve sustainable development. 5. Conclusions The present study investigated the historical LULC (years 1995, 2005,2015 and 2019) in the middle reaches of the Yellow River Basin via a combination of satellite imagery and field support data. Based on such analysis, the Markov Model was applied to forecast LULC over the next decade (years 2015and 2030). The 2015 LULC map was used for validating the Markov Model (Fig. 4 ), comparing the forecast situation with the actual one derived from satellite images, indicating that the used Markov model has enough capability to predict future LULC. In the middle reaches of the Yellow River, woodland, grassland, cultivated land, and wetlands have been transformed into each other. Most of the construction land is the transferee, and the occupation and compensation of cultivated land are not balanced. The transfer analysis of LULC in the middle reaches of the Yellow River from 1995 to 2030 found that there was mutual transformation among the four land types, namely forest land, grassland, cultivated land and wetland, and the transformation situation was relatively complex (Figs. 3 and 8 ). Among them, cultivated land and grassland were transformed into forests. The phenomenon is more obvious, and the forest will hardly transform to other LULC in the next ten years; among them, the conversion of cultivated land and grassland to forest is more obvious, and the forest will hardly be converted to other LULC in the next ten years; wetlands may be degraded into grassland and cultivated land in the next ten years; The cultivated land occupied by land use has not been fully supplemented, resulting in the phenomenon of unbalanced cultivated land occupation and compensation. The change trend of forest and grassland in the middle reaches of the Yellow River takes 2005 as the node, and the wetland takes 2015 as the change node (Fig. 5 ). The trend of changes in the number of forests and grasslands takes 2005 as the node. Before 2005, there was a large increase or decrease. After 2005, the changes in the number of forests and grasslands were not significant; the change in the number of wetlands and unused land from 1995 to 2030. Consistent, the change nodes are all in 2015, and since 2015, the change trend of the two has turned to an upward trend. The changes of LULC are jointly affected by social, economic, policy, natural and other factors. Population growth and economic development are inseparable from the expansion of construction land, and related policies such as land use control, ecological protection, and cultivated land red lines will control the occupation of construction land by other types of land, coupled with the constraints of natural factors such as topography, climate, and hydrology, the spatial distribution of various LULC in a region does not change much over time as a whole. Declarations Author Contributions: Methodology, data curation, writing, original draft preparation, X.L., reviewing and editing, supervision, Z.H., supervision, L.Z., writing, editing, investigation, J.L., visualization, investigation, X.G., investigation,S.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the China Geological Survey Project \"Eco-geological survey of the middle reaches of the Yellow River\" (DD20211398) and Shaanxi Provincial Key Laboratory of Land Consolidation Funded Project \"Soil Pollution Research in Typical Mining Areas in Northern Shaanxi\" (2019-ZY01). Institutional Review Board Statement : Approval for the study was not required in accordance with local/national legislation. Informed Consent Statement : Not applicable. Data Availability Statement : The datasets used during the current study available from the corresponding author on reasonable request. Conflicts of Interest : The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Li, X.B.A review of the international researches on Land use/Land cover change. Acta geograohica sinica, 1996,51(6),553-558. Kenea, U., Adeba, D., Regasa, M.S., Nones, M. 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Coupling analysis of land intensive use efficiency and ecological well-being performance of cities in the Yellow River Basin. Journal of Natural Resources, 2021,36(01):114-130. Wang, H., Gong, P., Liu, G. H. The review and prospect on land use and land cover change research in Yellow River Delta. 2004,19(01):110-118. Wang, F., An, L.Z., Dang, A.R., Han, J.Y., Miao, C.H., Wang, J., Zhang, G.H., Zhao, Y. Human-land coupling and sustainable human settlements in the Yellow River Basin. Geographical Research. 2020, 39, 1707–1724. Wu, X., Liu, S.X., Zhao, L.Y., Gao, H.T., Li, J.S. Study on the improvement of farmland use efficiency in the Yellow River Basin from the perspective of high-quality development. Research of Agricultural Modernization, 2022,43(04):702-714. Li, C.S., Yan, J.M. The relationship between cultivated land change and agricultural production, ecological environment in the Yellow River Basin under the background of grain for green program. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-2144585\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":143339413,\"identity\":\"f95bbc7f-0ada-452e-a80c-17d30496263d\",\"order_by\":0,\"name\":\"Xiaofeng Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"School of Land Engineering, Chang’an 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08:44:13\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-2144585/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-2144585/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":27788850,\"identity\":\"c506a649-80f8-4c42-b0c8-3798500d3168\",\"added_by\":\"auto\",\"created_at\":\"2022-10-14 17:37:19\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":181412,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMap of the middle reaches of the Yellow River Basin\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2144585/v1/b177980e609251ce0aa28700.png\"},{\"id\":27788830,\"identity\":\"f7e7a7ec-540a-4986-a8fc-59932418da0b\",\"added_by\":\"auto\",\"created_at\":\"2022-10-14 17:37:16\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":706209,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLULC maps of the middle reaches of the Yellow River Basin in the four reference years\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2144585/v1/8c13c4e3d10004e6feb48bb9.png\"},{\"id\":27788829,\"identity\":\"d811d7b6-e2a7-48ec-9edc-7f75b947c6a0\",\"added_by\":\"auto\",\"created_at\":\"2022-10-14 17:37:16\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":144298,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe chord diagram of the transfer of LULC in the middle reaches of the Yellow River from 1995 to 2020 (land type 1 is the previous year, and land type 2 is the next year)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2144585/v1/d964a52eed104b5b449a532b.png\"},{\"id\":27788826,\"identity\":\"658d70de-85ec-4663-a6e8-d034830838a8\",\"added_by\":\"auto\",\"created_at\":\"2022-10-14 17:37:15\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":656867,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e2015 LULC remote sensing interpretation and prediction map\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2144585/v1/123506e3ee892914dfe5d996.png\"},{\"id\":27788852,\"identity\":\"6d66af58-1300-4018-93e5-96d169d53247\",\"added_by\":\"auto\",\"created_at\":\"2022-10-14 17:37:21\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":45232,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eVariation trend of LULC quantity in the middle reaches of the Yellow River from 1995 to 2030\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2144585/v1/efa7786698e44ba041f30ba2.png\"},{\"id\":27788823,\"identity\":\"b9a9d20c-eac7-4e3a-a7d1-0333a2a8a966\",\"added_by\":\"auto\",\"created_at\":\"2022-10-14 17:37:11\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":667028,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePrediction of multi-scenario spatial distribution of LULC in the middle reaches of the Yellow River in 2025\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2144585/v1/fbad2c1286756ff841baa8c9.png\"},{\"id\":27788828,\"identity\":\"7fc013e9-8482-4ae8-9dd3-a58bbb41af3f\",\"added_by\":\"auto\",\"created_at\":\"2022-10-14 17:37:16\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":764799,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePrediction of multi-scenario spatial distribution of LULC in the middle reaches of the Yellow River in 2030\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2144585/v1/cf2e66876e92b3e8a49116c1.png\"},{\"id\":27788822,\"identity\":\"d52ef3cd-4931-4461-920f-6b5997b76d62\",\"added_by\":\"auto\",\"created_at\":\"2022-10-14 17:37:11\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":171168,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePrediction of LULC transfer chord diagram of the middle reaches of the Yellow River from 2020 to 2030 (land type 1 is the previous year, and land type 2 is the next year)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2144585/v1/bc82e830b581030890c0fa0d.png\"},{\"id\":28022408,\"identity\":\"26f4e488-4ac7-4053-8916-68118ecf0be4\",\"added_by\":\"auto\",\"created_at\":\"2022-10-20 05:14:31\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3853324,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2144585/v1/3d50d31d-b8e6-4318-8117-c1f604fb6271.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Past and future of land use change in the Middle reaches of the Yellow River Basin in China\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eLand Use and Land Cover Changes (LULC) is the most closely related issue between man and nature, and it is a direct manifestation of the relationship between man and land [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. The spatial pattern of the earth's surface features has been changed by humans, and this activity has been objectively recorded by LULC. Changes in LULC are closely related to the following factors: worldwide weather, species decline, the safety of the ecological environment and the continuity of life between humans and nature, etc. [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Two major international organizations, IGBP (International Geosphere-Biosphere Program) and IHDP (Global Change Human Factors Program) jointly proposed the \\\"Land Use/Cover Change\\\" (LULC) scientific research program in 1995, and positioned this research as a global key content of change [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Taking this as an opportunity, in order to emphasize the ensemble and simulation research of human and environmental coupling on land, GLP (Global Land Program) was launched in 2005, further making LULC research a core field and hotspot of global change research and land science research subject [\\u003cspan additionalcitationids=\\\"CR6\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Previous LULC studies in many representative areas can help us understand the changing characteristics of LULC, so as to more scientifically explain the relationship between human activities on LULC and the mutual influence between LULC and nature [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe use of long time series is very important for land use change research and forecasting for future land management, and long time series is often used as a study of human impacts on land use change [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Trend analysis of land-use change is helpful for land-use modeling, as past land-use changes often represent human impacts on land [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. In order to predict the future land use change more accurately and scientifically, the establishment of the model needs to be calibrated and verified. The following techniques are used in this paper: Radiometric Correction, Geometric correction, Image Fusion, Mosaicking, Gram-Schmidt Pan Sharpening(GS), FLUS-Markov,ect.\\u003c/p\\u003e \\u003cp\\u003eThe rational use of land is a prerequisite for ensuring that human beings can carry out normal production and life. The purpose of LULC is to more thoroughly understand the continuous variation of LULC in the region, and its essence is to explain the interaction between people and the environment. In some developing countries and Africa, there are few studies on comprehensive evaluation of the driving force analysis and results of land use change [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Research in recent years has mainly focused on some underdeveloped areas in Asia and Africa, and the results have shown that land cover has decreased due to population growth, economic development, and globalized human activities [\\u003cspan additionalcitationids=\\\"CR15 CR16 CR17 CR18 CR19\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Until recently, some scholars have begun to try to link land use change with related functions such as ecological services, in order to provide some policy services [\\u003cspan additionalcitationids=\\\"CR22\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Combined with previous scholars' research on land use change and prediction in the Yellow River Basin, the research in this paper can provide some scientific basis for land use change in the middle reaches of the Yellow River Basin.\\u003c/p\\u003e \\u003cp\\u003eIn fact, the LULC intensity and rate in the middle reaches of the Yellow River Basin continue to change dynamically and are closely related to the overexploitation of natural resources, which is influenced by climate, soil characteristics, vegetation, precipitation and natural disasters [\\u003cspan additionalcitationids=\\\"CR25 CR26\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. LULC changes have accelerated considerably over the past few decades due to increased human demand for natural resources. In the middle reaches of the Yellow River Basin, the change of LULC is continuous, and its agricultural development and urban construction are dominant [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFocusing on the middle reaches of the Yellow River Basin in China, this paper aims to: (i) understand the changes in land use in the region in the past 25 years (1995\\u0026ndash;2020); (ii) predict the possible land use in the next ten years (2020\\u0026ndash;2030). The paper mainly uses satellite images and model establishment to study the research area, and at the same time supports the reliability of the research through field investigation and other evidence. The study area and method used will be described in Section 2, and the results are presented in Section 3. In Section \\u003cspan refid=\\\"Sec7\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, mainly discusses the impact of existing policies on land use, and the direction of possible future land use changes. Section \\u003cspan refid=\\\"Sec8\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e summarizes the main outcomes of the study and its limitations.\\u003c/p\\u003e \"},{\"header\":\"2. Materials And Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e2.1Study Area\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe middle reaches of the Yellow River Basin are located between Hekou Town in Inner Mongolia Autonomous Region and Huayuankou in Zhengzhou City (Fig.\\u0026nbsp;1). The dimensions are from 32° to 42° north latitude and 104° to 113° east longitude. The total length of the river reaches 1206.4km, and the basin area is 363,000km2 and covers about 46% of the total area of the Yellow River Basin. The terrain of the middle Yellow River Basin is generally high in the northwest and low in the southeast. This area belongs to the semi-arid region of the middle temperate zone and warm temperate zone. The precipitation is mainly concentrated in summer and mainly in the form of heavy rain. The average annual precipitation varies from 320mm in the northwest to 840mm in the southwest [30], of which 6–9 The monthly precipitation accounts for about 70% of the annual precipitation, and the heaviest precipitation from July to August can account for more than 40% of the total annual precipitation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2Datast\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data used in the study are free satellite imagery and Digital Elevation Models (DEMs) at 30m resolution.Landsat-5TM(L5,for the years 1995 and 2005)and Landsat-8OLI ( L8, for the years 2015and2020) data were downloaded from the Geospatial Data Cloud Platform(http://www.gscloud.cn). The image selection is concentrated in the months of June to September, when the local vegetation is lush, which is convenient for different types of land use. Since the precipitation is mainly concentrated in these months, the image has been atmospherically corrected. In order to cover whole area, a composite of Landsat images from different paths/rows was created, ensuring that the images refer to the same season (Table 1).\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eDetails of the Landsat images that were analyzed in the study\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003eSatellite\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003eYear\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003eAcquisition Date\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003ePath/Row\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003eSpatial Resolution\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"4\\\"\\u003eLandsat5 TM\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" rowspan=\\\"4\\\"\\u003e1995\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJune 29\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e130/35\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJune 8\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e127/35\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJune 26\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e125/35\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJune 1\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e126/34\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"3\\\"\\u003eLandsat5 TM\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" rowspan=\\\"3\\\"\\u003e2005\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJuly 5\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e127/34\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJune 12\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e126/35\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJune 8\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e125/35\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"5\\\"\\u003eLandsat 8 OIL\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" rowspan=\\\"5\\\"\\u003e2015\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eAugust 13\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e124/35\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJuly 6\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e130/36\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJuly 15\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e129/36\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJuly 24\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e128/36\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eJuly 1\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e127/36\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"3\\\"\\u003eLandsat 8 OIL\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" rowspan=\\\"3\\\"\\u003e2020\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eSeptember 7\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e128/34\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eSeptember 18\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e125/34\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eSeptember 18\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e125/35\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e30m\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.3Land Use Land Cover Classification of Historical Data\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo map LULC, satellite images should be classified, assigning predefined LULC classes to some pixels. In this phase could be affected by various factors such as classification methods, algorithms, collecting of training sites, and the correctness of the classification should be assessed via field evidence [31–33].\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was performed by classifying four reference years (1995, 2005, 2015and 2020) and considering six LULC classes. The selection of these classes was performed based on past studies [34], based on this, the Middle of the Yellow River Basin was classified into six classes, namely forest, grassland, wetland, cultivated land, construction land, unused land (Table 2).\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eLand Use Land Cover classes and their description\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003eLULC Classes\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003eDescription\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eforest\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003earea to the land where trees, bamboos, and shrubs grow, and the land where mangroves grow along the coast\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003egrassland\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003earea to the land that grows mainly herbaceous plants\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003ewetland\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003earea to the land where the surface is too wet or often accumulates water and grows wetland organisms\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003ecultivated land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003earea to the land on which crops are grown\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003econstruction land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003earea to the land on which buildings and structures are constructed\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eunused land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003earea to unused land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn the classification process, some plots with similar spectral characteristics are merged, so the accuracy of the results needs to be verified. Select 55 live points (10 forests, 10 grasslands, 10 wetlands, 10 cultivated land, 10 construction land, and 5 unused land) in the L8 image in 2020 for verification with the help of auxiliary tools (Google Earth, etc.), The points are evenly distributed over the study area to ensure their accuracy. To quantitatively assess the accuracy, statistical methods kappa value were applied[35, 36]. P\\u003csub\\u003ea\\u003c/sub\\u003e is the ratio of the number of simulated correct grids to the total number, P\\u003csub\\u003eb\\u003c/sub\\u003e is the ratio of the number of simulated correct grids to the total number in a random state, and 1 is the ratio of the number of simulated correct grids to the total number in an ideal state. Among them, when Kappa \\u0026gt; 0.8, it means that the simulation effect is good and the degree of consistency is extremely high; when 0.6 \\u0026lt; Kappa ≤ 0.8, it means that the simulation effect is good and the degree of consistency is good; when 0.4 \\u0026lt; Kappa ≤ 0.6, it means that the simulation results are better Poor; when Kappa ≤ 0.2, it means the simulation effect is extremely poor[37].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg src=\\\"data:image/png;base64,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\\\"\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ch2\\u003e2.4. Prediction of Future LULC\\u003c/h2\\u003e\\n \\u003cp\\u003eTo map future LULC scenarios, the Markov model projection is performed by creating matrixes to estimate the transition probability and the area of each LULC class for future dates[38, 39].Markov model was applied to forecast the future LULC in two scenarios (2025 and 2030), via a few main steps: (i) analysis of historical LULC maps (1995, 2005, 2015and2020) and associated changes, (ii) creation of transition probability matrixes, (iii) model validation, (iv) prediction of future LULC maps, accounting for possible driving forces. In this work, we define the probability transitional matrix as a matrix showing the transfer direction of LULC types from one category to other categories in a given year [40].\\u003c/p\\u003e\\n \\u003cp\\u003eTo evaluate the capability of Markov model in predicting future LULC, a predicted map of 2015was created based on 1995 and 2005 LULC, and then compared with the actual 2015 map. To evaluate the quality of the 2015 predicted map against the 2015 reference map, the ANN model validation module was used [41], mimicking the approach proposed in similar studies [42]. In ANN model, two tools are available to assess the fit of the model to the sample data. First, the cross-validation tool iteratively removes a sample data point and interpolates a new value for the location. A table is produced to show the difference between the predicted attributes and the known attributes at those locations. Second, a variance image is produced that shows the spatial variation of uncertainty as a result of the fitted model. The variance image provides information to assist in identifying the problem areas where the relationship between the fitted model and the sample data points is poor.\\u003c/p\\u003e\"},{\"header\":\"3. Result\",\"content\":\"\\n\\u003ch2\\u003e3.1. Historical LULC Maps\\u003c/h2\\u003e\\n\\u003cp\\u003eFour reference years (1995, 2005, 2015and 2020) were considered to evaluate historical LULC via a maximum likelihood supervised classification (Fig. 2). As reported in Table 3, forests, grasslands, wetlands, cultivated land, construction land and unused land in the middle reaches of the Yellow River showed the same quantitative distribution, which were cultivated land\\u0026thinsp;\\u0026gt;\\u0026thinsp;grassland\\u0026thinsp;\\u0026gt;\\u0026thinsp;forest land\\u0026thinsp;\\u0026gt;\\u0026thinsp;construction land\\u0026thinsp;\\u0026gt;\\u0026thinsp;wetland\\u0026thinsp;\\u0026gt;\\u0026thinsp;unused land. Exactly, in 1995 most of the study area was covered by agriculture (38.8%), glass (36.9%)and forest (17.8%), with only a very minor part occupied by wetland (1.59%). Similar LULC was observed in 2005, with agriculture (38.4%), glass (34.7%)and forest (19.7%), being the most dominant LULC classes and just a small increase in the area covered by wetland (1.62%). In 2015 and 2020, the class distribution remained similar, with an increase the built-up area from 2.84\\u0026ndash;4.48%.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eDetails of LULC area of the Middle reaches of the Yellow River Basin in the four reference years\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003eLULC Type\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e1995\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e2005\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e2015\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e2020\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e[km\\u003csup\\u003e2\\u003c/sup\\u003e]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e[%]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e[km\\u003csup\\u003e2\\u003c/sup\\u003e]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e[%]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e[km\\u003csup\\u003e2\\u003c/sup\\u003e]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e[%]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e[km\\u003csup\\u003e2\\u003c/sup\\u003e]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e[%]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eforest\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e64512.25\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e17.76\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e71567.32\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e19.71\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e71855.36\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e19.78\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e71830.07\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e19.78\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003egrassland\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e133957.09\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e36.88\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e125799.58\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e34.64\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e127892.23\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e35.21\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e127499.71\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e35.11\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003ewetland\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e5774.89\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e1.59\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e5881.00\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e1.62\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e5041.98\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e1.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e5310.46\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e1.46\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003ecultivated land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e141136.05\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e38.86\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e139425.56\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e38.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e134666.17\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e37.08\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e132487.64\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e36.48\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003econstruction land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e8725.67\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e2.40\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e10330.94\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e2.84\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e14587.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e4.02\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e16272.98\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e4.48\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eunused land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e9078.44\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e2.50\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e10179.99\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e2.80\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e9141.26\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e2.52\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e9783.53\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e2.69\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eTotal\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e363184.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e100.00\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e363184.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e100.00\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e363184.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e100.00\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e363184.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e100.00\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003c/p\\u003eTo sum up, in the past, the main LULC in the study area were cultivated land and grassland, followed by forest land. In the past two decade, the construction land in the middle reaches of the Yellow River Basin has continued to increase, nearly doubling the amount, while the cultivated land has continued to decrease, decreasing by nearly 8,600km\\u003csup\\u003e2\\u003c/sup\\u003e.\\u003cp\\u003eThe results reported in Fig.\\u0026nbsp;2 are in agreement with Xu et al. [43], who pointed out that the Middle reaches of the Yellow River Basin is characterized by an expansion of built-up LULC, resulting in a decline of natural vegetation and agriculture land.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ch2\\u003e3.2 Historical LULC Changes and Transition Probability Matrix\\u003c/h2\\u003e\\n \\u003cp\\u003eComparing the four reference years, it is possible to observe a considerable reduction in the area covered by glass and cultivated during the observation period (Table\\u0026nbsp;4). Specifically, about 258km\\u003csup\\u003e2\\u003c/sup\\u003e of grassland and 346 km\\u003csup\\u003e2\\u003c/sup\\u003e of agricultural land are converted to other types of LULC each year. The mutual transformation of various types of plots can be seen from Fig. 3:\\u003c/p\\u003e\\n \\u003ctable border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eHistorical LULC changes in the Middle reaches of the Yellow River Basin\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003eLULC class\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e1995\\u0026ndash;2005\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e2005\\u0026ndash;2015\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003earea(km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003earea(%)\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003echange(km\\u003csup\\u003e2\\u003c/sup\\u003e/year)\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003earea(km2)\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003earea(%)\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003echange(km\\u003csup\\u003e2\\u003c/sup\\u003e/year)\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eforest\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e7055.07\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.11\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e705.51\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e288.04\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.00\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e28.80\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003egrassland\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-8157.51\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.06\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-815.75\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e2092.65\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.02\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e209.26\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003ewetland\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e106.11\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.02\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e10.61\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-839.02\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.14\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-83.90\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003ecultivated land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-1710.49\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.01\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-171.05\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-4759.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.03\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-475.94\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003econstruction land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e1605.27\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.18\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e160.53\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e4256.45\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.41\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e425.65\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eunused land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e1101.55\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.12\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e110.16\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-1038.73\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.10\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-103.87\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003eLULC class\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\"\\u003e2015\\u0026ndash;2020\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\"\\u003e1995\\u0026ndash;2020\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003earea(km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003earea(%)\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003echange(km\\u003csup\\u003e2\\u003c/sup\\u003e/year)\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003earea(km2)\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003earea(%)\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003echange(km\\u003csup\\u003e2\\u003c/sup\\u003e/year)\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eforest\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-25.29\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.00\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-5.06\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e7317.82\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.11\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e292.71\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003egrassland\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-392.52\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.00\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-78.50\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-6457.38\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.05\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-258.30\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003ewetland\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e268.48\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.05\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e53.70\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-464.43\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.08\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-18.58\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003ecultivated land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-2178.53\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.02\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-435.71\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-8648.41\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-0.06\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e-345.94\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003econstruction land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e1685.59\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.12\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e337.12\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e7547.31\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.86\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e301.89\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eunused land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e642.27\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.07\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e128.45\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e705.09\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e0.08\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e28.20\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003eDuring the period from 1995 to 2005, the decrease of grassland mainly turned to forest, cultivated land and unused land. The increase of grassland also came from forest, cultivated land and unused land, but the decrease was greater than the increase; the forest was mainly converted to grassland and cultivated land; Wetlands were mainly converted to cultivated land; during this period, construction land mainly came from cultivated land, and 2100 km\\u0026sup2; of cultivated land was converted into construction land, but only 800 km\\u0026sup2; of cultivated land was converted into construction land, and 100km\\u0026sup2; of unused land was converted into arable land;\\u003cp\\u003eFrom 2005 to 2015, the conversion of grassland to forest, wetland, cultivated land and unused land decreased, but the conversion of grassland to construction land increased, from 357 km\\u0026sup2; to 1140 km\\u0026sup2;; forest It was mainly converted to grassland and cultivated land, but the conversion rate decreased compared with 1995\\u0026ndash;2005; the reduction of wetland was mainly converted to construction land and cultivated land; as in 1995\\u0026ndash;2005, construction land mainly came from cultivated land in 2005\\u0026ndash;2015. During the period, 3700km\\u0026sup2; of cultivated land was converted into construction land, but only 1300 km\\u0026sup2; of construction land and unused land was converted into cultivated land;\\u003c/p\\u003e\\n \\u003cp\\u003eFrom 2015 to 2020, the conversion of grassland to cultivated land decreased; the increase in construction land also mainly came from cultivated land. During this period, 2200 km\\u0026sup2; of cultivated land was converted into construction land, and 1400 km\\u0026sup2; of construction land and unused land was converted into cultivated land. Compensation is still unbalanced, but compared with 2005\\u0026ndash;2015, the unbalanced gap has decreased.\\u003c/p\\u003e\\n \\u003cp\\u003eIn conclusion, during the period from 1995 to 2020, the number of forests, grasslands, and construction land showed an upward trend, while the number of wetlands, cultivated land, and unused land showed a downward trend. The increase in the number of forests and grasslands mainly comes from three sources. One is the mutual transformation of the two. The second is the conversion of cultivated land to forest and grassland, and the third is the comprehensive improvement of part of the unused land; the increase of construction land is mainly from cultivated land and grassland; the decrease of wetland is mainly to cultivated land and grassland.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv\\u003e\\n \\u003ch2\\u003e3.3 Model Validation\\u003c/h2\\u003e\\n \\u003cp\\u003eThe LULC map of 2015, predicted from the 1995 and 2005 data, has been validated with the classified LULC map of the very same year (Table\\u0026nbsp;5), showing that the Markov model can effectively forecast LULC changes.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 5\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eLULC classes in 2015: projected vs classified values\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003eLULC Type\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003eProjected LULC\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003eClassified LULC\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003earea[km\\u003csup\\u003e2\\u003c/sup\\u003e]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003epercentage[%]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003earea[km\\u003csup\\u003e2\\u003c/sup\\u003e]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003epercentage[%]\\u003cbr\\u003e\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eforest\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e71002.55\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e19.55\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e71855.36\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e18.78\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003egrassland\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e120431.94\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e33.16\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e127892.23\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e35.21\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003ewetland\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e4285.58\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e1.18\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e5041.98\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e1.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003ecultivated land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e138409.57\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e38.11\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e134666.17\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e37.08\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003econstruction land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e19357.73\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e5.33\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e14587.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e5.02\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eunused land\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e9697.02\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e2.67\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e9141.26\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e2.52\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eTotal\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e363184.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e100.00\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e363184.39\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e100.00\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\u003cbr\\u003e\\n \\u003cp\\u003eBased on the real 2015 remote sensing interpretation data, the simulated LULC data in 2015 was compared and analyzed (Fig.\\u0026nbsp;4). The results showed that the Kappa coefficient was 0.806, greater than 0.8, and the overall accuracy was 0.865, indicating that the model has high simulation accuracy and can be Conduct multi-scenario simulations of LULC in the middle reaches of the Yellow River in 2025 and 2030.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv\\u003e\\n \\u003ch2\\u003e3.4. Future LULC\\u003c/h2\\u003e\\n \\u003cp\\u003eThe multi-scenario simulation of LULC in the Middle reaches of the Yellow River is based on the remote sensing interpretation data in 2015 and 2020, and the Markov model is used to simulate the number of LULC in 2025 and 2035; On this basis, the FLUS model is used to conduct multi-scenario simulations of LULC in the middle reaches of the Yellow River in 2025 and 2035; Combined with the simulation results, the changes in the number of LULCs in the middle reaches of the Yellow River, the mutual transfer trend and spatial distribution of LULCs were analyzed.\\u003c/p\\u003e\\n \\u003cp\\u003eBy analyzing the data of the four reference years, predict the number of LUCCs in 2025 and 2030, and make a graph of changes in the number (Fig.\\u0026nbsp;5); Using the FLUS model, the number of simulated LULCs is allocated under four different scenarios: production space priority, living space priority, ecological space priority, and comprehensive space optimization (Figs.\\u0026nbsp;6 and 7); at the same time, ENVI was used to analyze the land type transfer of the LULC data in the middle reaches of the Yellow River under the comprehensive spatial optimization scenarios in 2020, 2025, and 2030, and the chord diagrams of the LULC transfer in the middle reaches of the Yellow River from 2020 to 2025 and 2025 to 2030 were obtained (Fig.\\u0026nbsp;8).\\u003c/p\\u003e\\n \\u003cp\\u003eTo sum up, the increase of cultivated land and grassland in 2020\\u0026ndash;2025 is due to the decrease of each other, and the two are transformed into each other. The conversion of grassland is the same as that in 2020\\u0026ndash;2025. The increase in construction land still comes from the decrease in cultivated land, the difference is that a small amount of construction land is converted into cultivated land.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"4. Discussion And Policy Implications\",\"content\":\"\\u003cp\\u003eAs suggested in the review carried out by Wang et al. [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e], most of the studies of LULC in the Yellow River Basin are on a local scale, analyzing the soil salinization and water pollution a series of ecological and environmental problems, such as serious damage to wetland resources protection and threats to wetland resource protection. This is mainly due to the large scope of the middle reaches of the Yellow River Basin and the difficulty of LULC classification, which makes it difficult to conduct LULC research as a whole. So, very few works tried to forecast future LULC changes at the watershed scale, eventually providing new insights that can be useful for developing future basin-wide management strategies. This study was developed to fill this gap, estimating the Middle reaches of the Yellow River Basin LULC for the next decade (2025, and 2030) based on past information (LULC in 1995, 2005, 2015and 2020), to infer trends to be used in multiple ways. Markov model results point out that, in the coming decade, significant changes in LULC should be expected, mostly because of the ever-increasing pressure of humans in need of more land for construction. In fact, due to the rapid economic development and urbanization in China in recent years, a large amount of cultivated land and grassland has been lost in the middle reaches of the Yellow River [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe continuous reduction of cultivated land in the study area pointed out in this study is consistent with the results of previous studies [\\u003cspan additionalcitationids=\\\"CR47 CR48\\\" citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. This trend is an inevitable result of China's rapid industrialization and urbanization, such as the 2000 Western Development Strategy, etc. Artificial surfaces occupy a large amount of cultivated land and transform to other LULC types [\\u003cspan additionalcitationids=\\\"CR51\\\" citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]. Due to the rapid pace of urbanization, people need more land to build residential sites, which leads to the shift of cultivated land in the study area to the type of construction land. With the continuous reduction of arable land, policy makers realized that in order to ensure food security, the first thing to do is to ensure that the area of arable land cannot be reduced, so they have launched a series of land remediation projects, such as the reclamation of abandoned industrial and mining areas, which can increase the area of arable land. At the same time, urban construction is also developing towards the direction of land intensification, which can effectively alleviate the contradiction between people and land.\\u003c/p\\u003e \\u003cp\\u003eFor the process and causes of grassland degradation in the middle reaches of the Yellow River Basin, scholars at home and abroad have done a lot of research and analysis. Most scholars believe that natural and human factors are the main causes of grassland degradation. Among the natural factors, climatic factors have the greatest impact on grassland, and a large number of studies have shown that temperature, precipitation, and evaporation are important factors affecting grassland degradation in the middle reaches of the Yellow River [\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. The impact of rodents on the degradation of natural grasslands is mainly reflected in competition with livestock for food, especially when the sand, gravel and soil produced during burrowing and burrowing bury pastures. Erosion [\\u003cspan additionalcitationids=\\\"CR55 CR56\\\" citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]. With the increase of the population in the study area, the one-sided pursuit of the number of livestock without considering the slaughter rate intensified, resulting in the number of livestock in the area exceeding the carrying capacity of the grassland, the quality of which was deteriorated, and the degradation degree of the degraded and desertified grassland was further aggravated.\\u003c/p\\u003e \\u003cp\\u003eThe middle reaches of the Yellow River Basin have a large scope, spanning many provinces and cities, and the large differences in natural conditions and geographical locations make it have a greater impact on the changes of LULC in national policies. The land types and ecological environment of the Yellow River Basin are constantly being developed and used, resulting in increasingly tight resources and a sharp drop in ecological carrying capacity, which degrades the ecological environment and threatens ecological security. In addition, the provinces and cities in the Yellow River Basin have developed an extensive and solidified development model, which has caused irreversible damage and consumption to the ecology. This industrial development model has caused the ecological environment of the Yellow River Basin to degrade to a certain extent. In response to the deteriorating environment, China implemented the policy of returning farmland to forest in 1999, and promulgated the policy of soil and water conservation in 2012. In the previous paragraph, national leaders put forward the concept of promoting ecological protection and high-quality development in the Yellow River Basin. This series of policies or opinions aims to protect the ecological environment of the Yellow River Basin and enable LULC to achieve sustainable development.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eThe present study investigated the historical LULC (years 1995, 2005,2015 and 2019) in the middle reaches of the Yellow River Basin via a combination of satellite imagery and field support data. Based on such analysis, the Markov Model was applied to forecast LULC over the next decade (years 2015and 2030). The 2015 LULC map was used for validating the Markov Model (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), comparing the forecast situation with the actual one derived from satellite images, indicating that the used Markov model has enough capability to predict future LULC.\\u003c/p\\u003e \\u003cp\\u003eIn the middle reaches of the Yellow River, woodland, grassland, cultivated land, and wetlands have been transformed into each other. Most of the construction land is the transferee, and the occupation and compensation of cultivated land are not balanced. The transfer analysis of LULC in the middle reaches of the Yellow River from 1995 to 2030 found that there was mutual transformation among the four land types, namely forest land, grassland, cultivated land and wetland, and the transformation situation was relatively complex (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e). Among them, cultivated land and grassland were transformed into forests. The phenomenon is more obvious, and the forest will hardly transform to other LULC in the next ten years; among them, the conversion of cultivated land and grassland to forest is more obvious, and the forest will hardly be converted to other LULC in the next ten years; wetlands may be degraded into grassland and cultivated land in the next ten years; The cultivated land occupied by land use has not been fully supplemented, resulting in the phenomenon of unbalanced cultivated land occupation and compensation.\\u003c/p\\u003e \\u003cp\\u003eThe change trend of forest and grassland in the middle reaches of the Yellow River takes 2005 as the node, and the wetland takes 2015 as the change node (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). The trend of changes in the number of forests and grasslands takes 2005 as the node. Before 2005, there was a large increase or decrease. After 2005, the changes in the number of forests and grasslands were not significant; the change in the number of wetlands and unused land from 1995 to 2030. Consistent, the change nodes are all in 2015, and since 2015, the change trend of the two has turned to an upward trend.\\u003c/p\\u003e \\u003cp\\u003eThe changes of LULC are jointly affected by social, economic, policy, natural and other factors. Population growth and economic development are inseparable from the expansion of construction land, and related policies such as land use control, ecological protection, and cultivated land red lines will control the occupation of construction land by other types of land, coupled with the constraints of natural factors such as topography, climate, and hydrology, the spatial distribution of various LULC in a region does not change much over time as a whole.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions:\\u0026nbsp;\\u003c/strong\\u003eMethodology, data curation, writing, original draft preparation, X.L., reviewing and editing, supervision, Z.H., supervision, L.Z., writing, editing, investigation, J.L., visualization, investigation, X.G., investigation,S.L. All authors have read and agreed to the published version of the manuscript.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u0026nbsp;\\u003c/strong\\u003eThis research was funded by the China Geological Survey Project \\u0026quot;Eco-geological survey of the middle reaches of the Yellow River\\u0026quot; (DD20211398) and Shaanxi Provincial Key Laboratory of Land Consolidation Funded Project \\u0026quot;Soil Pollution Research in Typical Mining Areas in Northern Shaanxi\\u0026quot; (2019-ZY01).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInstitutional Review Board Statement\\u003c/strong\\u003e: Approval for the study was not required in accordance with local/national legislation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInformed Consent Statement\\u003c/strong\\u003e: Not applicable.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability Statement\\u003c/strong\\u003e: The datasets used during the current study available from the corresponding author on reasonable request.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflicts of Interest\\u003c/strong\\u003e: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eLi, X.B.A review of the international researches on Land use/Land cover change. 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Investigation and research On rodents damage of effect and control in the Sanjiangyuan. Qinghai Prataculture,2020,29(2),35-40.\\u003c/li\\u003e\\n\\u003cli\\u003eNing, J. Problems and counter measures of grassland rodent control in Qinghai province. Animal Husbandry and Veterinary Today,2020,36(08),20.\\u003c/li\\u003e\\n\\u003cli\\u003eDu, J.Z., Wang, G. X., Li,Y.S. Rate and causes of degradation of alpine grassland in the source regions of the Yangtze and Yellow Rivers during the last45years.Acta Prataculturae Sinica,2015,24(6),5-15.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu, Y. H., Wei,W. D., Yang, Y. W., Zhang Y. Redundancy analysis on relationships between grassland vegetation and soil factors on degraded alpine meadow. Acta Agriculturae Boreali-occidentalis Sinica, 2018,27(4):480-490.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"China, Yellow River Basin, Land use land cover, Land-use transition\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-2144585/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-2144585/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eWhen looking at maps of land use land cover (LULC) change, often derived from satellite imagery, China's increasing urban construction and decreasing cultivated land area can be identified. Using the middle reaches of the Yellow River Basin in China as the case study, the present work focuses on (i) identifying historical LULC change in the period 1995\\u0026ndash;2020; (ii) estimating LULC in the next ten years, combining Geographical Information Systems (GIS) with Markov model. Landsat 5/8 images were combined with field evidence to map LULC in three reference years (1995, 2005, 2015and2020), while the Markov model was applied to forecast LULC in 2025, and 2030.The LULC of the middle reaches of the Yellow River Basin was classified into six classes: forest, grassland, wetland, cultivated land, construction land and unused land. The results show that over the past 25 years, cultivated land and forest land have decreased by 8,600 km\\u003csup\\u003e2\\u003c/sup\\u003e and 6,400 km\\u003csup\\u003e2\\u003c/sup\\u003e, respectively, while construction land has increased by 7,500 km\\u003csup\\u003e2\\u003c/sup\\u003e, mainly due to increased urbanization and industrialization. In fact, during the simulated 2020\\u0026ndash;2030 period, cultivated land and forest land will continue to decrease, while construction land and grassland will increase. It is worth noting that the reduction of forest land will lead to increased soil erosion, while the reduction of cultivated land will lead to food security problems. The study points out that there is an urgent need for response measures in the region to ensure the sustainability of people and nature.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Past and future of land use change in the Middle reaches of the Yellow River Basin in China\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2022-10-14 17:36:26\",\"doi\":\"10.21203/rs.3.rs-2144585/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"6ecac3ce-d14b-460c-bce4-51cc7e84ec80\",\"owner\":[],\"postedDate\":\"October 14th, 2022\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2022-10-20T05:14:24+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2022-10-14 17:36:26\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-2144585\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-2144585\",\"identity\":\"rs-2144585\",\"version\":[\"v1\"]},\"buildId\":\"FbvkV6FR0MCFSLy54lSbu\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}