Simulation and Prediction of LULC Change Detection Using Markov Chain and Geo-spatial Analysis.. by GRD Journals

GRD Journals- Global Research and Development Journal for Engineering | Volume 5 | Issue 12 | November 2020 ISSN- 2455-5703

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China Elhamem. M. Abdalla Department of Information Engineering Ningxia University Du Fang Department of Information Engineering Ningxia University

Hazem T. Abd El-Hamid Department of Marine Environment National Institute of Oceanography and Fisheries (NIOF)

Abstract This paper describes a synthetic research on Land use Land cover changes in Ningxia North China achieved by an intensive change detection using the multi-temporal remotely sensed data Landsat 5 TM images 2005, Landsat 8 OLI images 2013 and 2019). This study is aiming at producing the fundamental land use change data, understanding the driving forces and change mechanism in order to develop a dynamic monitoring system and provide useful references for the local governments in their sustainable land use planning and making decision. Remote sensing and GIS are important tools for studying Land use / Land cover change and integrating the associated driving factors for deriving useful outputs. Geo spatial techniques Such as remote sensing and Geographic Information system are very important for studying the dynamic process of land use. Transition images and matrix ere applied using Arc GIS 10.5. With the Excel Pivot Table function. To determine the driving forces of land use change in the study area. Results showed that, the images of the study area were categorized into (Vegetation, Water, Bare land and Urban). NDVI was applied in the present study showing the vegetation cover was precisely approximately 224.61 Km2 (3.08%), 3051.11 Km2 (41.94%) and 3206.06 Km2 (44.08%) in 2005, 2013 and 2019, respectively, NDWI was applied in the present study typically showing Water bodies represent the largest area of North Ningxia. Water bodies were approximately 3613.67 Km2 (49.67%), 288.99 Km2 (4.91%) and 357.27 Km2 (4.91%) in 2005, 2013 and 2019, respectively, NDBI The reflectance of bare lands has a high frequency in band 5, the bare land have total of area 2065.78 Km2 / 28.39 % in 2005 and increased to 40.37% in 2013 to decreased again in 2019 to 35.20 %. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA-Markov) Chain Model were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 2005 and 2013, were used for calibration and optimization of the Markov algorithm, while 2019 was used for validating the predictions of CA-Markov using the ground based land cover image. After 3 validating the model, plausible future LULC changes for 2025 were predicted using the Cellular Automata (CA-Markov) Chain Model. Remote sensing is a good technique for assessing the actual Sequence in the development of any area that may be caused by human activities. Keywords- LULC, Simulation, Markov, Prediction, Cellular, Remote Sensing, Change Detection

I. INTRODUCTION Remote sensing is an important science in estimating and assessing changes in LULC in different years. Remote sensing has many advantages as safe time and give accurate data about features and phenomena that human cannot explore it easily (Hong and Abd El-Hamid 2020). Modern technology of remote sensing serve many researchers in the world for assessing change in LULC and provide them with accurate temporal and spatial data (Mustafa el at.2019). Land use Land Cover (LULC) is considered as one of the most important components of the terrestrial environment system. Changes in LULC reflects of human activities on global environment (1999). The NDVI algorithm directly correlates with the green biomass, and it is properly applied to positively enhance the vegetation spectral. The climate has been getting dryer and warmer. Since 1999, the Chinese government has inaugurated middle to long term development strategy on the northwestern regions in China. Land use and land cover change research has been applied to landslides, erosion, Land planning and global change. Remote sensing (RS) and geographic information system (GIS) techniques have been utilized as powerful tools in predicting and monitoring urban growth. Environmental and simulation models as Land Change molder (LCM) and Cellular Automata (CA) - Markov model were proposed using IDRISI.The MARKOV module evaluates two images of LULC and outputs a transition probability matrix, a transition regions matrix, and a set of conditional probability images. Then, it forecasts future LULC according to comparative transition potential maps Fung T and Siu W. (2010). Based on the CA-Markov model, this study predicts the spatial patterns of land use in 2019 and 2025 based on the dynamic changes in land use patterns using remote sensing and geographic information All rights reserved by www.grdjournals.com

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

system. CA-Markov integrates the advantages of (Cellular Automata and Markov chain analysis) to predict future land use trends based on studies of land use changes in the past. Based on Landsat 5 TM images from 2005 and Landsat 8 OLI images from 2013 and 2019, this study obtained a land use classification map for each year. Then, the transition probability map from 2005 to 2013 was obtained by IDRISI software. Based on the CA-Markov model, a predicted land use map for 2019 was obtained, and it was validated by the actual land use results of 2019 with a Kappa index of 0.895. Urbanization is one of the greatest marked human induced global alterations worldwide. In the previous 200 years, the world population has increased six times and the urban population has multiplied 100 times causing adverse effect on biodiversity Roy, H.G.; Dennis, M.F.; Emsellem, K.(2014). Finally, the land use patterns and the projected map of 2019 and 2025 in Ningxia North China were determined.

II. MATERIALS AND METHODS A. Study Area Ningxia, extending from longitude 105°45’E to 107°00’E and latitude 38°20’N to 39°30’N, including 50% of the Helan Mountains and 80% of the Yinchuan Plain and surrounded by Inner Mongolia on the east, north and west (Figure 1), is an arid and semi-arid region in North China. The annual precipitation ranges from 78 to 295mm (the maximum, 430 mm, appears in the Helan Mountain's), annual evaporation from 1473 to 2318 mm and annual average temperature from （8.2°C to 9.6°C）in the recent decades (Ningxia Statistical Yearbook, 2005, 2013 and 2019) (16). The analysis on meteorological data in the past half century indicates that the annual temperature has been increasing and precipitation decreasing in Yinchuan region. It is probably a local indicator of the global warming. The climate has been getting dryer and warmer and the natural conditions more and more difficult. Land use, especially, in agriculture, has a long history in this region. As early as 35,000 to 25,000 years B.P., people already begun their activities (Geng et al, 1992). (19). In the dynasty Qin (221 to 207 B.C.), (20). The first irrigation canal came into existence. This signalled the launch of an agricultural era in the Yinchuan Plain. In the successive dynasties Han (206 B.C to 220 A.D.) (17). and Tang (618 to 907 A.D.) (18). the irrigation system was further developed and improved. But due to the frequent wars and large scale of emigrations in history (Geng et al,1992), (19). It is therefore of first importance to undertake a synthetic land use and cover investigation, change rate measurement, driving force analysis and develop a dynamic monitoring system in order to generate fundamental land use data, a management prototype and some useful references for the local governments in their sustainable land use planning and decision making.

Fig. 1: Location map of the study area

B. Data Collection and Image Preprocessing Three satellite images were digitized using topographic maps; 2005, 2013 and 2019. SPOT satellite image data with resolution better than 2.5m.were downloaded using United States Geological Survey (USGS) (http://glovis.usgs.gov). Image preprocessing includes layer stacking and geometric corrections were applied using ERDAS 2014. The study area was strategically located in one scene; (path 129/ raw 33). Landsat Thematic Mapper (TM) and Operational Land Imagery and Thermal Infrared Sensor (OLI/TIRS) images were acquired on 2005, 2013 and 2019, respectively. The classification criteria, accuracy and mathematical

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

foundation were consistent with the interpretation of land use information meeting the research needs. After the images were Geo-referenced, Mosaicked and subset on the basis of Area of Interest (AOI).For each of the predetermined land cover/use type, training samples were selected by delimiting polygons around representative sites. The geographical coordinates are unified by the latitude and longitude coordinate system Xian_1980_GK_CM_105E, and the projection mode is the horizontal axis Mercator projection. Finally, the study area was selected by subset image for further analysis. Markov analysis is a tool in remote sensing software which uses the transition probability matrix based on neighbourhood effects in a spatial influence algorithm (Hyandye & Martz, 2017). ďź&#x2C6;21ďź&#x2030;. C. Classification and Detection of LULC Firstly, unsupervised classification was chiefly applied to classify the classes according to spectral signature. The land use and land cover was classified using supervised classification based on the land cover classification system and field observation as ground truth. Every class was drawing use ERDAS IMAGINE. Every class was identified use ENVI. Four classes were identified with area calculation and percentage use ArcGIS 10.5. The modeling and Predicted Map was created use IDRISI 17.0. D. LULC Transition Transition matrix of LULC reveals the way of every type conversion to another type in the study area. It is a mathematical and statistical method that studies the changes among types of LULC.(Jijun et al., 2003). (1).The land use transfer matrix is used to reflect the analysis trend of land use type change trend This method was applied using Arcgis10.5 and the function of Pivot Table in Excel. Data of LULC were extracted from classification images and change detection during 2005 and 2025. Transition simulates the major changes of every class, helping the decision maker for proper development. (Xiulan, 2010; Jinpeng et al., 2010; Sisi et al., 2012) (3). Transition simulates the major changes of every class, helping the decision maker for proper development.

Where: Pij is the probability of land area in transition from land escape i to j E. Spectral Indices To analyze the land use Change and assess the driving force. SPOT Satellite Images of 2005, 2013 and 2019 were used. Classification of land use was applied Normalized Difference Vegetation Index and Maximum like hood techniques in ENVI 5.1. 1) Normalized Difference Vegetation Index The difference between the red and near infrared and combination divided by the sum of the red and near infra-red band combination. Spectral transformation can be applied by simple contrast stretching method ton increase certain spectral characters itching is one of the most simple and Vital methods. It deals justly with reclassifying of image classes to methods. The produced false composed image (bands 4, 3 and 2) has much better contrast and spectral resolution for geological information. 2) Normalized Difference Water Index The difference between the green and near-infrared and combination divided by the sum of the green and near infra-red band combination. NDWI images accurately reflect the water bodies present in the study area. The value of this index varies from -1 to one, depending on the amount of water, and it resembles high water that value close to one. NDWI was applied using the ERDAS molder to sufficiently reduce any noise pixels. It's difficult to carefully distinguish between built-up areas and bare lands in any area; therefore, we used an index to map bare lands in the selected study area. 3) Normalized Difference Bare land Index This Combination takes advantages of the unique spectral responses of Water, Vegetation, Bare and Urban. The NDBI was designed to identify Bare areas. The bare area can be detected using normalized built up area index (NDBI). All rights reserved by www.grdjournals.com

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

F. LULC Changes Based on the CA–Markov Model Markov chain model analyses and summarizes the change in land use by a numerous number of probability transition areas from one state to another over a period. It is a powerful model which is capable to calculate and forecast the quantity of land use change. The prediction of future land use changes can be calculated based on suitability maps and conditional probability formula.The reliability of land use change modeling methods can be improved by combining two or more simulation methods to incorporate the benefits of each model. It is important to note that CA–Markov model has been used lately in dynamic spatial phenomenon’s simulation and future land use change prediction. The reliability of land use change modeling methods can be improved by combining two or more simulation methods to incorporate the benefits of each model. It is important to note that CA–Markov model has been used lately in dynamic spatial phenomenon’s simulation and future land use change prediction. Furthermore, the transition areas matrix expresses the total area (in cells) expected to change in the next period while transition probabilities matrix shows the likelihood of a pixel from one class changing to another class or within same class in the coming period (Khawaldah, 2016). (21). For validation, which is an essential stage in the development of any land use land cover change prediction model (Khawaldah, 2016).(21). G. Land use Dynamics Dynamic of LULC reflect the development of the study area in the future. A comprehensive index was proposed to study the dynamics of LULC in the study area from 2005 to 2025. This index is mainly depending on the effect of each land use in the environment showing diffident degrees as mentioned in Table 7. (Jiyuan 1992),(1). Land use types were classified into four classes from unused to high level depending on its importance in the environment. Unused level includes Water, Vegetation, Urban and Bare land, where high level include Bare land and urban area which reflect the human interface in the community. Also, the comprehensive index shows the integration among natural variables of environment and anthropogenic activities (Sisi et al. 2012),(3). n

L  100  AiCI i 1

Lba

R

(2)

n    Lb  La  100   A  Cib     A  Cia  i 1  i 1  n

Lb  a n

 A  C  ia

 100 

(3)

 A  C    A  C  ib

i 1

 A  C  ia

(4) From three equations, L denotes the comprehensive index of land use degree, L: 100-400, the closer the L is to 400, the higher the degree of development and utilization; Ai represents the classification index of land use type. Ci represents the percentage of land use type area; △ Lb-a represents the change in the comprehensive index of land use change; La and Lb represent the comprehensive land use degree index of a and b time phases; Cia and Cib represent the area percentage of the itype land type in the two stages a and b; R represents the rate of change in land use. R>0 is the development stage; R<0 is the Undeveloped stage; R=0 is the stabilization or adjustment stage. According to the grading standards of land use degree index, the land use degree of construction land is the fifth level, including land for residents, mining, industry, transportation, etc.; the fourth level is farmland such as cultivated land; the third level is forest land, grassland and water area, the second is water bodies and the first level is bare land. i 1

i 1

III. RESULTS AND DISCUSSION A. LULC Change Detection Based on supervised classification, four main land use classes were recognized and mapped from both dates of satellite imageries to regulate the changes and transformation (position and rate). These classes are Water, Vegetation, bare land and urban. These classes are shown in Figure 2. The majority area of the study areas are Vegetation areas where the vegetation is dominated by grasses. Annual changes in LULC were shown in Table 1. Table 1: Land use/ land cover change and annual rate of change from 2005 to 2025 2005 2013 2019 2025 Class Area(Km2) % Area(Km2) % Area(Km2) % Area(Km2) % Vegetation 2065.78 28.39 2936.87 40.37 2640.29 36.30 2560.00 35.20 Bare 1370.01 18.83 996.81 13.70 1068.54 14.69 3338.70 45.90 Water 224.61 3.08 3051.11 41.94 3206.06 44.08 351.90 4.83 Urban 3613.67 49.67 288.99 4.91 357.27 4.91 1021.70 14.04 Land use change (Annual rate) Class 2005-2013 2013-2019 2019-2025 2005-2025

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

Vegetation Bare Water Urban

Area(Km2) 871.08 373.20 2826.49 3324.68

% -11.97 5.13 -38.85 45.70

Area (Km2) 296.57 71.73 154.94 -68.28

% 4.07 -0.98 -2.13 -0.93

Area (Km2) 80.29 2270.15 2854.15 664.42

% 1.10 -31.21 39.24 -9.13

Area (Km2) 494.21 1968.69 127.28 2591.97

% -6.79 -27.07 -1.75 2591.97

In the study area, grass land represents 28 and 35.2 % of the study area in 2005 and 2025, respectively. It may be decreased with percentage -6.79 % due to some human activities and climate change from one season to another. Inventory of grassland to prohibit grazing is an effective measure for ecological retrieval in the semi-arid desert steppe. Therefore, grassland reform, creation of artificial grassland, scientific breeding, livestock improvement, prohibition, suspension and rotation of grazing should be taken into consideration to increase the animal husbandry industry and help farmers and herdsmen to become rich. (Woodward et al., 2001) (4). In this way, we can release the vegetation can improve the ecosystem ability for selforganization and modification. The growth in urban areas has been resulted from both the increase in migration to the cities and the fertility of urban populations. The urban poor have less opportunity for education than the urban no poor; nevertheless still they have more chance than rural populations. Urbanization occurs either gradually or scheduled as a result of individual, collective and state action. Living in a city can be socially and economically helpful since it can deliver greater opportunities for access to the labor market, better education, housing and safety conditions, and reduce the time and expense of commuting and transportation. Land desertification that indicated by Zhu (1995) (5). was not found in this study area, however, vegetation degradation (-11.97%) was apparently observed in Yinchuan, Shizuishan. Li and Zhou (2009) (7). stated that the rapid expansion of Urban area in Ningxia is nevertheless undeniable for its very high growth rate (35.20 in 20 years). B. LULC Transition At the beginning of the iterative process, determination of total number of iterations is essential that will be applied to predict future scenario. Generally, after executing each iteration, each LULC category, gain or lose some of its lands to one or more other LULC category. In this respect, the process divides land cover map into two classes: host and claimant class. Each claimant class takes lands from the host class; host classes can be one or more. Claimant class chose to land on the basis of suitability maps. Major transition around the Yellow River in Yinchuan from 2005 to 2025 was shown in Figure 2.

Fig. 2: Show the LULC From 2005 to 2019

These transitions reflect the developmental activities in the study area. These maps contain useful information about the land use pattern of past and the present. Appropriated legends are provided highlighting the land use change for Urbanization, Vegetation, Water bodies and Bare land. These classes for the land use land cover distribution for each study year. Class Vegetation Bare

Table 2: Land use/ land cover change area Km2 and parentage % 2005 2013 2019 2025 Area(Km2) % Area(Km2) % Area(Km2) % Area(Km2) % 2065.78 28.39 2936.87 40.37 2640.29 36.30 2560.00 35.20 1370.01 18.83 996.81 13.70 1068.54 14.69 3338.70 45.90

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

Water Urban

224.61 3613.67

3.08 49.67

3051.11 288.99

41.94 4.91

3206.06 357.27

44.08 4.91

351.90 1021.70

4.83 14.04

The result presented in Table 1. Represents the area of each land use land cover category for each study year. Urban area in 2005 occupies 18.83 % of the total land. In 2013, Urban area occupies 13.70 % of the total land as results of increase in population. Bare land in 2005 occupies 28.39 % of the total land. In 2013, Urban area occupies 40.37 % of the total land and decrease in 2019 to 36.30 % due to increase in populations. Water bodies cover decreased to 4.9 % in both year 2013 and 2019 due to use in agriculture equipment and other purpose. Vegetation cover increased to 44.08 % as compared to 3.08 % and 41.9 % in 2005 and 2013 respectfully due to the fact that knowledge of gardening is becoming prominent. Transition matrix of LULC reveals the way of every type conversion to another type in the study area. It is a mathematical and statistical method that studies the changes among types of LULC. The land use transfer matrix is used to reflect the analysis trend of land use type change trend (Jijun et al., 2003) (1). This method was applied using Arcgis10.5 and the function of Pivot Table in Excel. Data of LULC were extracted from classification images and change detection during 2005 and 2019. 1) The Gain Transition Probability Matrix of LULC Among of LULC, Four classes were increased from 2005 to 2025; Water, Vegetation, bare Land and Urban as shown in Table 3. Table 3: The Gain Transition probability matrix of LULC

About 818.06 km2 of bare lands is reserved and not be changed and 254.23 km2 of bare lands converted to Vegetation, small change was observed as a result of no exploitation of bare lands and the Gain of bare lands around 1078.43 km 2. About 1472.55 km2 of Vegetation is remaining constant and not be changed but about 1596.1 km2 of Vegetation was converted to bare lands, About 190.25 km2 of Vegetation was converted to Urban and about 79.6 km2 of Vegetation was converted to water bodies and the Gain of Vegetation almost around 1865.99 km2. About 97.48 km2 of water is remaining constant and not be changed but about 58.47 km2 was converted to Vegetation. These new water bodies are used to reserve the agriculture area especially irrigated lands. For urban and village lands, 131.74 km2 is remaining constant; on the other hand a low remarkable conversion has been occurred during the period of the study. All positive transition from 2005 to 2025 can affect negatively agriculture sector. 2) The Loss Transition Probability Matrix of LULC Among of LULC, four classes were decreased from 2005 to 2019; water bodies, Vegetation, bare Land and Urban as shown in Table 4. Table 4: The Loss Transition probability matrix of LULC

About 1472.55 km2 of Vegetation is reserved and not be changed and 254.23 km2 of Vegetation converted to bare lands. About 131.74 km2 of water bodies is remaining constant and not be changed but about 6.12, 7.13 km 2 of water bodies was converted to bare lands and Urban, respectively.

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

C. Spectral Indices For studying the impact of human activities on the Yinchuan in Ningxia, vegetation cover, water bodies ,Urban and bare lands were applied using remote sensing techniques. Three raw satellite images were naturally acquired during the 3 years. The reflectance pattern of green vegetation in the visible wavelength is due to selective absorption by chlorophyll. The high reflectance of water is due to some organic impurities and suspended matter. The reflectance of bare soil rises through the visible and near infrared wavelength ranges, typically peaking in the middle infrared range; this is due to clay minerals. Total area of Yinchuan in Ningxia , its vegetation cover, water body, Urban and bare lands for the different three years were measured in Km2 and percent according to Table 2. 1) Normalized Difference Vegetation Index These landslides supported us in geology of the area. In landslide susceptibility evaluation, the historic information on landslide rates typically denotes the back bore of the study. As this desirable vision into the occurrence of the phenomena Van Westen et al (11). Stated that landslide inventory maps can be prepared because the accurate data scientifically derived from historical archives data survey, successfully meetings with people and image interpretation are Vital. Image enhancement techniques universally adopted in this study compromised various spectral techniques. For typically assessing Vegetation cover, NDVI maps were typically obtained from OLI satellite images. NDVI value covered the local area of vegetation in any satellite image. The increasing rate positively related to some essential factors as increasing the rate of rainfall, modestly increasing the labor force in modern agriculture and trending to land reclamation as the personal interest of government in agriculture. NDVI was applied in the present study showing the vegetation cover. Vegetation cover was precisely approximately 224.61 Km 2 (3.08%), 3051.11 Km2 (41.94%) and 3206.06 Km2 (44.08%) in 2005, 2013 and 2019, respectively as shown in Figure 3.

(5)

Fig. 3: NDVI of the North Ningxia in difference year

2) Normalized Difference Water Index Was applied in the present study typically showing Water bodies represent the largest area of North Ningxia. Water bodies were approximately 3613.67 Km2 (49.67%), 288.99 Km2 (4.91%) and 357.27 Km2 (4.91%) in 2005, 2013 and 2019, respectively as shown in Figure 4. It was showed that water bodies decrease from 2005 to 2013 and increase from 2013 to 2019. Oppositely way to vegetation covers in the present study. The decrease in the open water surface area in 2013 agreed with Fundamental changes in water bodies from 2013 causing the increase in the open water surface in 2019 are attributed to some human activities in Ningxia North China.

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

Fig. 4: NDWI of the North Ningxia in difference year

3) Normalized Difference Bare land Index The Result of the survey show that the total area of the urban areas in Ningxia North of China is not large. The urban growth has negative impact on the health of urban residents by proportionately increasing average temperature. The intensity of the urban heat effect is influenced by urban geometry and the amount of vegetation cover. NDBI The reflectance of bare lands has a high frequency in band 5 as shown in Figure 5. the bare land have total of area 2065.78 Km2 / 28.39 % in 2005 and increased to 40.37% in 2013 to decreased again in 2019 to 35.20 %.

(7)

Fig. 5: NDBI of the North Ningxia in difference year

D. Simulation and Prediction using Markov Chain Analysis (CA_Markov) Model The simulation results also indicate a significant change of land use land cover in Yinchuan Yellow River Basin through the narrowing of vegetation cover to 2640.29 km2 in 2019 was decrease compared to the area it will cover 2025. The area covered by water is expected to increase slightly by 14.04 percent in the year 2025 as shown in Table 1. However, the result indicates an expected increase in Urban areas from the 357.27 km2 in 2019 to 1021.70 km2 (as shown in Table 1), this could be a result of

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

natural population growth or due to tourism, which has significant implications for the Yinchuan Yellow River Basin. Moreover, the projected land use land cover image of Yinchuan Yellow River Basin as Figure 8. indicate the total area covered by vegetation, water, Urban and Bare will 2560.00 km2, 351.90 km2, 1021.70 km2 and 3338.70 km2 which is equivalent to 36.30 %, 44.08 %, 4.91 % and 14.69 % respectively Table (5). Table 5: Per Category Kappa Statistics (CA_Markov) CA _MARKOV for 2019 Per Category Values Classes Kappa K loc Vegetation 0.586 0.986 Bare Land 0.586 0.986 Water 0.547 0.94 Urban 0.2 0.43 Over All Kappa 0.895 0.925

K histo 1 1 0.93 0.43 0.916

Provided the effect of climate change and other climatic events such as floods, excessive rain, government transitions and political interventions has not been considered in the current study, these factors have likely to affect the predicted situations. All above operations have been done in IDRISI. Markov model applies contiguity rule for a pixel near to a vegetation area is most likely to be transformed into vegetation area. A 5×5 mean contiguity filter has been applied to this study. This contiguity filtering has been used for suitability images of each LULC class. The pixels of one land cover category exist in the neighborhood, increases the suitability value for that particular land cover type. In another way, the pixel value remains the same P. Cabral and A. Zamyatin, (23). The Figure 6. was shown the projected map of LULC 2025.

Fig. 6: Transition Probability matrix around the Yellow River from 2005 to 2025 E. LULC Changes Based on the CA–Markov Model A comparison of the actual LULC map for the year 2013 with the CA–Markov-simulated map, based on the Kappa statistic as well as a comparison of each area of the simulated LULC types with the actual area (Table 1), were used for model validation. The Kappa statistics values (0.91) and an overall accuracy exceeding 91% (Table 5) show that there is good agreement between the predicted result and the actual value of the LULC types for the base year. The change in magnitude between the two maps of the year 2019 reveals that all LULC classes have relative errors of less than 5%. kappa statistics divide into two statistics, kappa histo deals with quantitative similarity and kappa location represent location similarity among LULC map and projected map To predict future LULC two models have been employed was shown in Figure (7). One is the most suitable model for this study All rights reserved by www.grdjournals.com

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

which is selected based on kappa values. The validity of the Cellular Automata Markovian (CA-Markov) simulation was evaluated using multiple of base resolution statistical algorithms to measure the agreement and disagreement between two land use land cover images (Aburas et al., 2015). (20).

Fig. 7: CA_MARKOV Projected Map of LULC 2025 The predicted maps in Yinchuan district were produced based on LULC maps of 2005, 2013 and 2019. and relevant factors was compared to the actual LULC 2019. The Validation of Predicted LULC and Classified LULC shows a moderate to high agreement between the predictions LULC 2019 with actual LULC 2019 date of the base year (2019) was shown in Figure (8). This reveal that CA-Markov is an appropriate model for prediction future LULC change. The analysis of LULC change shows natural Bare land has been decreasing and the predictions show this cover would continue reducing in the future the projected results of LULC year 2025 Bare land would dramatically decrease, The decrease may primarily be caused by increasing population and expanding residential area, leading need of land for agriculture, Furthermore , replacing a large area of Bare land by vegetation and urbanization would be also the result of Bare land decrease Projected Land Use for 2025.

Fig. 8: Prediction Map of 2025

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

F. Land use Dynamics Table 6. show that the comprehensive index of land use in 2005, 2013, and 2019 was 300.94, 292.88 and 279.52, respectively; all data in the range of 100â&#x20AC;&#x201C;400, representing that land use has been in a reasonable development stage; the comprehensive land use index from 2005 to 2019 has continued to decrease with an decrease of -21.41. The land use dynamic of urban areas has been decreased from 2005 to 2019. Table 6: Predicted Areas of Land Use Base on the CA-Markov Model for 2025 in the Study Area LULC Class 2025 Water Vegetation Urban Bare land Area Km2 1021.703 351.904 3338.701 2560.003 % 14.04 4.83 45.9 35.2

The degree of transformation in land use is larger than zero. R values were -0.02, -0.04 and -0.07 for 2005, 2013 and 2019, respectively as shown in Figure (9).

Fig. 9: L = Total = Sum of Area for Difference Year

The urbanization or nature development are mainly responsible for transformations of agricultural land not agriculture sector. The changes in agricultural production are driven by universal improvements on the market for agricultural products, ecological guidelines and scientific innovations. The change towards Multi-functional agricultural land use varies per area, and is driven by regulations and subsidies for nature and landscape management, and by the attractiveness of various chances for farmers to raise their incomes. (Abd El-Hamid et al., 2020) (10). was shown in Figure (10). y = 0.0127x3 - 76.011x2 + 152193x - 1E + 08 R2 =1

Fig. 10: Change of urban areas around North Ningxia %

The Change of Vegetation around Ningxia has been a clear from field visits and focus group discussions with local people that the vegetation cover is decreasing due to bad management, cropland expansion, and extensive human activities. Conversion of vegetation to other classes leads to negative impact on the economy (Abd El-Hamid et al.2020).(10). was shown in Figure (11). In the fact, large deforestation has occurred due to some development in agriculture projects. Conversion of vegetation into urban areas also occurred around the two branches due to adequate climatic conditions. The reduction of vegetation related to habitats of people in the selected area who use remains of these materials for their activities. y = 0.0158x3 - 35.09x2 + 71119x - 5E + 07 (9) R2 =1

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

Fig. 11: Change of Vegetation around North Ningxia%

The Change in water and vegetation can reflect the dynamic of LULC in the study area. Change in water can serve the stakeholders and government to take aware of low land area and preventing the flood in any climatic change. Also, vegetation index give accurate data about degradation of lands in the study area. The urbanization process has led to chaotic growth in city, deteriorated the living conditions and has worsened the environmental scenario having detrimental impacts on human health. Therefore, it is required to determine the rate and trend of land cover/use conversion for devising a rational land use policy.was shown in Figure (12). y = 0.0341x3 + 205.64x2 - 413107x + 3E+08 (10) R2 = 1

Fig. 12: Change of water surface North Ningxia

IV. CONCLUSION The study results show that there was a significant change in land use. Monitoring of land use dynamic via LULC is very important for stockholders, policy maker and researchers to take aware of areas which sensitive to change and degradation. The CA-Markov chain analysis indicate the difference in one land use to another land use and further uses this to predict the future. Modelling of land use land cover change play an essential role in understanding the concepts of land use changes. The results further demonstrate that the Remote Sensing and GIS innovation is a compelling methodology in the analysis of land use change modelling using the CA- Markov. Finally CA- Markov analysis of land use land cover changes projected for the year 2025. Statistical analysis of vegetation showed that by the end of 2019, the disappearance of the every green areas of trees and desert plants around region may be predicted in the future due to human activities and climatic global change. The main causes of urbanization problem are the rapid population growth and economic growth.

ACKNOWLEDGEMENT First and foremost, I would like to express my sincere publish to my supervisor Prof. DU FANG whose unreserved support and guidance at every stage of doing this research mentored me to reach the goal. I would also like to offer my indebtedness to my committee members Dr.Hazem Taha Abd_El hamed for their guidance throughout this research. I would like to offer my appreciation to my parents and all my family Member and friends who are with me to support any time. The paper is funded by Ningxia Natural Science Foundation Research on regional disease-drug design pathway based on medical Big data (No. 2018AAC03025). All rights reserved by www.grdjournals.com

Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China (GRDJE/ Volume 5 / Issue 12 / 004)

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