Modeling and Prediction of Land use/Land Cover Change using Land Change Modeler in Suluh River Basin, Northern Highland of Ethiopia.

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Hailay Hagos Entahabu, Amare Sewnet Minale, Emiru Birhane Hizikias This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1981572/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background: Land use/land cover change has been known globally as an essential driver of environmental change. The study focuses on modeling and prediction of land use/land cover using land change modeler in the Suluh river basin. Landsat images and other ancillary data sources were used to achieve the objectives. The nearest neighbor fuzzy classification was performed in eCognition Developer 9.2 to classify images. Change detection and modeling was performed on IDRISI selva 17.3 software. The data was analyzed qualitatively and quantitatively. Result: The finding confirmed that Bar land by 10.6%, built up land by 29.4% and cultivated land by 65.4% were rapidly expanding in the face of an overall decline of the forest land by 97.2%, grazing land by 89.8%, plantation land by 89.1% shrub-bush land by 1.5% and water body by 84.8% during 1990 to 2002. Conclusion: If the model predictions hold; in the coming 2028 and 2048, bar land, built up land, and cultivated land will be shown an increase on the expense of water body, forest, shrub-bush and plantation land. Rainfall, slope, elevation, distance to rivers, distance to roads, distance from towns and population density were identified as the prominent LULC change drivers in the study area. This will increase the vulnerability of the watershed to soil erosion and soil macro fauna loss of the studied river basin in particular and the Tekeze basin in general. Therefore, suitable and timely management measures must be taken by policy decision makers to enable sustainable development and to protect the river basin in order to reduce the severity of the changes. Nearest neighbor fuzzy classification change detection land change modeler Suluh river basin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Background Land use, land cover (here after LULC) change is global environmental issues (Moghadam & Helbich,2013).On the past two decades, a rapid changes has been occurring since time immemorial exacerbated by a number of anthropogenic factors (Houghton,1994 & Bewket,2002) & as a consequence which alters the interaction between the earth surface (Fu et al. ,2000,Lambin et al. ,2003;Kabat et al. ,2004;Mahmood et al. ,2010 & Prasad et al. ,2010).Lambin et al.( 2003) disclosed that change of forests and grasslands into cultivated land. Amount and direction of LULC change was not equal in all round of the world. For instance, Wood et al. (2004) states that expansion of agriculture was recognized as primary drivers of LULC changes in Africa. LULC change studies in Ethiopia showed that most of LULC change were from the natural forests to cultivated land (Kebrom &Hedlund,2000;Zeleke &Hurni,2001; Dessie & Kleman ,2007; Tegene ,2002;Tsegaye et al. ,2010&Rientjes et al. ,2011)&were caused by anthropogenic factors (Bewket,2002;Gebreslassie,2014;Sewnet,2015,Hailemariam et al. ,2016& Chiemela et al ,2017). Moreover, in different part of Ethiopia researches were conducted on the LULC modeling and predication by Teferi, 2015 ; Han et al. ,2015;Yalew et al. ,2016 & Gashaw et al. ,2017 in which those studies predict that LULC will change from vegetation to cultivated land. Contrastingly, few studies in different part of Ethiopia indicated an improvement of vegetation cover (Bewket 2002 ;Munro et al ,2008 & Bantider et al (2011) due to community afforestation and land rehabilitation activity. These contrasting findings suggest that the assumption that conversion natural vegetative cover in to human managed systems leads to deterioration in soil macro fauna is not always valid. There is a clearly a need for empirical investigation in to the problem at the local catchment level. For better clue on the functioning of the LULC system, the modeling of LULC change has grown rapidly. LULC models are simplifications of reality that gives an important means of predicting future LULC change pressure points (Nourqolipour et al. ,2015).Several studies(eg. (Wang & Maduako,2018, Kumar et al. ,2015, Megahed et al. ,2015, Mas et al. ,2014, Ozturk ,2015, Mishra et al. ,2014, Kamusoko et al. ,2009& Hasan et al. ,2020) confirmed that from the other LULC modeling ways; Land Change Modeler ( here after LCM), based on integrated multilayer perceptron (here after MLP) with Markov chain (hear after MC), is a strong model for the analysis and prediction of LULC change and the validation of results. In Suluh river basin(here after SRB), population growth and lack of alternative livelihood strategies led environmental degradation(Alemayehu et al. ,2009; Aredehey et al. ,2018; Zenebe et al. ,2019 & Hishe et al. ,2020).Because of shortage of land the farmers are taking the following options:(1) expanding in to steep and marginal areas to gain more land to compensate for low yields from their existing holding.2) Conversion of their previous croplands in to Eucalyptus plantations or change of crop types.(3) Recurrent cultivation, overgrazing and using of farm inputs. The analysis and modeling of LULC change in SRB not assessed in depth.The few studies (Alemayehu et al. ,2009; Aredehey et al. ,2018; Zenebe et al. ,2019 & Hishe et al. ,2020) conducted in the Tekeze river basin on LULC change analysis with different spatio-temporal coverage and methodologies. However, modeling of LULC change using land change modeler in SRB was not well investigated.In this paper, we apply LCM for the purpose of modeling & prediction of the LULC on SRB . We expect that the results of this research can help policymakers & land management stakeholder to design land use planning strategies that helps to attain sustainable land management in the study area. 2. Materials And Methods 2.1 The Study Area: Suluh River Basin The study area is found in the highlands of the north-eastern part of Tigray region, northern Ethiopia. The geographic location of the river basin extends from 39°24'59.06 ” E to 39°26'22.73 ” E latitude and 13°38'18.27 ” N to 14°13'53.29 ” N longitude (Fig. 1 ).The total area of the basin is about 930 km 2 .The elevation of the study area varies from 1700 to 3,298 meters above sea level. The study watershed falls in four districts ( Saesie Tsaeda Emba ,Hawuzen, Kilte Awlealo and Degua Tembien ) of Eastern and South East. The climate of SRB is characterized as semi-arid. The warmest months are May & June &coldest months are December and January. Average annual rainfall and temperature from 1988–2018 is 420.4 mm and 17.5°C respectively (Fig. 2 ).The rain fall season is between June to early September which is mono- modal rain fall distribution. Regarding the hydrological condition, drainage pattern of the SRB is dendrite (Zenebe et al., 2019 ).The longitudinal profile of the SRB main stream has a length of 94.77 km starting from volcanic mountains of Mugulat & ending at the embouchure into Genfel. The geology of the basin is trap basalt accounts 2.8%, granite and shale accounts 1.8%, metamorphic rock accounts 28.9%, limestone accounts 13.9%, sandstone accounts 52.7% (Zenebe et al. ,2019 ).The major soil types of SRB area are haplic lixisols covers 41.4%, lithic leptosols covers 22.7%, Eutric leptosols covers 17.8%, Chromic Cambisols covers 15.6% & Vertic Cambisols covers 2.5%. The major soil textures of the study area are sandy clay loam accounts 41.4%, sandy loam accounts 40.5% and clay accounts 18.2% (Zenebe et al. ,2019). Population Census Commission (2015) shows that in 2015 the population density of the river basin was 142 persons per km 2 . The dominant economic activity in the basin is agriculture. The crops grown vary according to altitude. The main crops in the highlands are barley, wheat, maize, teff & pulses. Sorghum is widely grown in the lowlands. Cultivation is done by means of the traditional ox-drawn plough. The main livestock are cattle, sheep, goats, donkeys and chickens. There is a livestock feed crisis, resulting from crop residue and vegetation biomass reduction (Alemayehu et al., 2009 & Aredehey et al., 2018 ). Most of the areas are highly cultivated even on steep valley sides and the remnant original highland forests are being overgrazed & deforested, resulting in shallow soils due to serious soil erosion by water. The major strategies for land management in Tigray highlands are building of stone terraces, micro dams including gully treatment, establishment and development of enclosures and community woodlots, enforcement of used rules, regulations for grazing lands & reduced burning activities(Sembroni et al. ,2017 ; Zenebe et al. ,2019 & Hishe et al. ,2020). 2.2. Sources of Data Sources of data used, data processing image segmentation, nearest neighbor fuzzy classification, accuracy assessment & LULC change modeling were done according to the overall workflow of Fig. 3 . Free satellite images (Landsat-5 TM of 1990, Landsat-7 ETM + of 2002 & Landsat-8 OLI–TIRS of 2018) were used for the LULC change analysis of the study area (Table 1 ).These datasets were acquired from the National Aeronautics and Space Administration (NASA) through their EOS Data Gateway Database. Land sat images that were used for this study area freely available. The Landsat-7 ETM + 2002 & Landsat-8 OLI–TIRS 2018 of a 30 meters pixel were resampled to a 15 meters pixel size. Digital Elevation Model (30 m) based on Aster imagery and ancillary data (topographic maps, field thematic layers (roads and towns), & village and district boundaries) were also utilized during analysis. All data were projected using Universal Transverse Mercator projection system zone 37 0 N and datum of World Geodetic System 84 (WGS84). An intensive pre-processing such as layer-stacking, resolution merge, and sub setting were carried out and to remove disturbances such as haze, noise, steep slope effect & radio metric variation between acquisition dates. Table 1 The characteristics of land sat satellite data Sensor Path / row Acquisition time Spatial Resolution Resolution Sensor Landsat TM 169/50 01/07/1990 30 m TM Landsat ETM+ 169/50 03/08/2002 15 m ETM + Landsat OLI-TIRS 169/50 03/12/2018 15 m OLI-TIRS Aster DEM 30 m Ancillary data Topo map 1:50000 Field data Nov.2017-Jan 2018 Roads and towns District boundary Village boundary We applied nearest neighbor fuzzy (Eq. 1) classification in eCognition Developer 9.2. It is used in eCognition-automatically by generating multidimensional membership functions (Foody,1999; Groenemans et al.,1997;Zhang & Foody,1998;Yan et al., 2006 ;Salman et al. ,2008, Zhou et al. ,2008 and Kalantar et al. ,2017).We designed eight LULC classes (cultivated, bar, built up, grazing, plantation, shrub and bush, water body & forest land) based on the Table 2 . Table 2 Land cover, Land use types and their descriptions LU/LC classes Descriptions CL Areas covered with annual crops followed by harvest and bare soil FL Areas covered with a natural forest community with a closed, deep and complex canopy often consisting of several crown layers GL Landscapes that have a ground story in which grasses are the dominant vegetation forms SBL This category includes low woody plants, generally less than three meters in height, usually with multiple stems, growing vertically BL Areas with little or no vegetation cover consisting of exposed soil and/or bedrock BUL Areas for construction sites and towns PL Areas composed of transplanted seedlings of Cactus, Eucalyptus globules and Cupresus spp. WB Includes rivers, reserves, lakes (artificial dams and natural lakes) and so on Note: Forest land (FL), cultivated land (CL), shrub-bush land (SBL), built up land (BU), grazing land (GL), bare land (BL), plantation land (PL) and water body (WB) A={(X, µA(x));x ϵ X},Where µA →[0,1] (1) Where A = fuzzy set X = a space of objects X = elements belonging to space X µ – membership Function In this study, over all accuracy (Eq. 2) and Kappa coefficient (Eq. 3) were used to assess the accuracy of the classified images. As a result, we found an excellent accuracy in which as to the kappa coefficient results reveals 0.886, 0.883 & 0.852 for the years of 1990, 2000 and 2018, respectively (Table 3 ).Since the values falls above the cut point of the standard overall classification accuracy level of 85% (Fleiss et al. ,2003;Doxani et al. ,2008 and Congalton & Green ,2019) with no class less than 70% (Kumar et al. ,2018). Table 3 Summary of error matrixes for the classified images of 1990 and 2002. LULC classes 1990 2002 User Accuracy Producer Accuracy User Accuracy Producer Accuracy BL 56% 100% 65% 100% BUL 88% 100% 89% 100% CL 100% 55% 100% 56% FL 100% 94% 100% 100% GL 100% 94% 100% 100% PL 86% 100% 88% 100% SBL 78% 100% 79% 100% WB 94% 100% 100% 100% Overall Accuracy 87.12121212 89.79592 Kappa Accuracy 0.852591473 0.883327 $$\text{OA}=\frac{X}{Y}*100 \left(2\right)$$ Where, OA is overall accuracy, x is number of correct values in diagonals of the matrix, and y is total number of values taken as a reference point. K= \(N\sum _{i=1}^{r}xii-\sum _{i=1}^{r}(xi\times x+1)\) / \(N2- \sum _{i=1}^{r}xii-\sum _{i=1}^{r}(xi*x+1) \left(3\right)\) Where: r = is the number of rows in the matrix Xii = is the number of observations in rows i and column I (along the major diagonal) Xi + = the marginal total of row i (right of the matrix) Xi + 1 are the marginal totals of column i (bottom of the matrix) N is the total number of observations. K = kappa coefficient LULC Modeling using LCM LCM model is based on the artificial neural network (ANN), Markov Chain matrices & transition suitability maps, generated by training multilayer perceptron (MLP) or logistic regression (Ramachandra et al. ,2013., Mishra et al. ,2014, Roy et al. ,2014, Gibson et al. ,2018, Dzieszko,2014). Megahed et al. ,2015, Ansari & Golabi,2019). This model predicts the LULC changes from the thematic raster images having the same number of classes in the same sequential order (Mas et al. ,2012). In this study, the LCM is used to predict the future LULC changes SRB for the next 30 years by following four steps, namely: (1) change analysis, (2) transition potential and determination of explanatory variables, (3) change prediction(4) model validation (Megahed et al. ,2015). I.Change Analysis In the change analysis panel, the changes between two different time periods time 1(1990) & time2 (2002) LULC maps were calculated. To calculate the annual rate of change Peng et al. (2008) formula(Eq. 4) was adopted: C= \(\frac{\varDelta f-\varDelta i}{\varDelta i}\times \frac{1}{T}\times 100 \left(4\right)\) Where C = is the annual change rate of a given LULC type, \(\varDelta\) f and \(\varDelta i\) are the final and initial area coverage of LULC type during the specific period and T = year difference between the initial and final period. The gain and loss(Eq. 5) was also calculated adopted from LCM in IDIRISI software. \(\left[\text{P}\text{l}\text{o}\text{s}\text{s}\right(\text{i}),\text{j}\) = (Pj,i- p i,j )/( p i - p i )*100 i#j] \(\left[\text{P}\text{g}\text{a}\text{i}\text{n}\right(\text{i}),\text{j}\) = (Pi,j- p j,i )/(p i - p i )*100 i#j] \(\left(5\right)\) Where \(\text{P}\text{l}\text{o}\text{s}\text{s}\left(\text{i}\right),\text{j}\) Is the percentage taken by j in LUCC in total ‘conversion loss’ of category row i \(\text{P}\text{g}\text{a}\text{i}\text{n}\left(\text{i}\right),\text{j}\) Is the percentage taken by j in LULC change in total ‘conversion gain’ of category row i, p i,j and p j,i II. Transition Potential Modeling and Driving Forces Determination Transition Potential The transition potential determines the area of change (Megahed et al., 2015 ).LULC changes with common driving variables were grouped into sub-models. In addition; evidence likelihood was selected to determine the relative frequency of different LULC types which had occurred within the transitional areas (Megahed et al. ,2015). Selection of Explanatory Variables Explanatory variables responsible for LULC change were selected (Gibbs et al. ,2010 & Li et al., 2015 ).In the current study, biophysical (rainfall, slope and elevation), socio-economic variables (distance to rivers, distance to roads, distance towns) and demographic variables (population density) were identified as variables. For testing, selection and transition of Model variables, Cramer’s V coefficient was used.All the variables were then add into transition sub-mmodel. For transition sub-model structure and running the model MLP neural network were used. In this study, the transition pixels to be occurred in between 1990 to 2002 were assign randomly to one of two groups: the training set & the testing set. The variables were derived via geographical and geo-statistical elaborations of a geographic information system & were formalized as follows (Eq. 6): X = x1 , x2 , …, xn \(\left(6\right)\) Every variable associated with a neuron in the input layer was normalized using (Eq. 7): X i =( X i -min)/(max-min) \(\left(7\right)\) In the hidden layer, the signal that is received by neuron j in the hidden layer for pixel k was calculated as follows(Eq. 8): net j (k)= \(\sum _{i}\text{W}\text{i},\text{j} \text{X}\text{i}\left(\text{k}\right) \left(8\right)\) where netj ( k , t ) is the signal that is received by neuron j , and wi,j is the weight between the input layer I and the hidden layer j . The output layer has 2 neurons that correspond to 2 possible significant states (1 = transition, 2 = permanence of the pixel); the neuron l generates a value that indicates the transition probability. Transition probabilities can be calculated using a sigmoidal function using Eq. 9 (a sigmodial function is used to represent the non-linearity of each node): p(k,1=1)= \(\sum _{i}\text{W}\text{j},\text{i}\frac{1}{1+{x}^{netj(k,t)}}\) \(\left(9\right)\) III. Change Prediction Change prediction is the last step in which the future prediction is executed on the basis of MC, and using the historical rate of change and the transition potential maps (Wang et al. ,2012). MC analysis were run for this study to determine the amount of change using two LULC maps (1990 and 2002) along with the date specified (2018,2028 and 2048). The steps determines how much land was expected to transition from the later date (2002) to the prediction date (2028 and 2048). A MC(Eq. 10) is made of s, a vector of the distribution of LULC classes at time t, and A(ፐ), a matrix of transition probabilities from land use u to land use u’ in a given time interval(ፐ) S t+ፐ= A(ፐ)S t \(\left(10\right)\) Using hard prediction model, a map for 2018 of study area was simulated in order to compare with the ‘actual’ land cover map of 2018. IV. Future Scenario Hard predictions land change modelers was applied in this study. V. Model Validation It is needed to assess the accuracy. In this study validation was done by comparing the simulated and the actual LULC maps of 2018. The qualitative information collect using direct observation, focus group discussion and interview were analyzed and interpreted using qualitative techniques, whereas quantitative data collect were analyzed using descriptive statistics ( like percentage). 3. Results 3.1 LULC Change Analysis From 1990 to 2002 As Table 4 , the trend of LULC change indicated that FL,GL, PL,SBL & WB shown a decrement by 97.2%, 89.8%, 89.1%, 1.5%, 84.8%, from 1990 to 2002 respectively. On the other hand, BL, BUL and CL increased by 10.6%, 29.4% and 65.4%, respectively. Agreeably, elder priest interviewees confirmed that coverage vegetation’s were better in church forests in year of 1990 than 2002.The focus group panelists affirmed the CL in the study area shows with high fragmentation of land due to the last land redistribution made in 1991/92.As an interview made with the district office of agricultural & rural development confirmed that the population density in the river basin have been hampering the LULC change. Table 4 LULC Change trends in from 1990 to 2048 LULC classes LULC area (km 2 ) Trends of Change (%) 1990 2002 2018 2028 2048 1990- 02 2018- 28 2018- 48 BL 82.5 91.2 90.6 92 94.2 11 0.6 2.2 BUL 12.7 16.4 29.9 33 38.7 29 1 5.7 C L 351 580.5 551 586 592 65 0.6 6.1 FL 24.4 0.7 10.3 8 6 -97 -2 -2 GL 186 18.9 95.6 73 65.4 -90 -2 -7.7 PL 46.7 5.1 67.2 71 76 -89 0.6 5 SBL 219 216.1 84.1 66 57 -1.5 -2 -9 WB 7.6 1.2 1.54 1.02 0.68 -85 -3 -0.3 As shown in Fig. 4 (A) the gains and losses major LULC changes include (1) expansion of CL, (2) increase in BL, and (3) increase in BUL whereas decrease in SBL, GL, PL and WB. During 1990–2002, gain and loss in CL was 359.94 km 2 and 33.03 km 2 ,with a net gain of 326.91 km 2 . SBL loss 243.67 km 2 and gained 5.33 km 2 , with a net loss of -238.34 km 2 . PL lost 1.96 km 2 & gained 52 km 2 with a net loss of 50.04 km 2 . BUL, however, increased with a net gain of 9.9 km 2 . 3.2. Simulation Transition Potential Modeling and Determining Driving Variables The total three transitions that have been selected in this study were FL to CL, GL to BL, PL to CL,SBL to CL. Using cramer’s V values as shown in Table 5 , population density(0.345), slope(0.25),elevation(0.18),rainfall(0.22),distance from river(0.31),towns(0.27)& roads(0.17) show significant influence on LULC change of the study area. After the selection of the predictor variables, transitions were modeled in one transition sub-model and generated the transition potential maps through multilayer perceptron with an accuracy of above 70%. Table 5 Cramer’s V values of explanatory variables. Explanatory Variables Cramer’s V Rainfall 0.2162 slope 0.2526 Elevation 0.1787 distance to rivers 0.3107 distance to roads 0.1687 distance towns 0.2665 population density 0.3448 3.3. LULC Transition Analysis The transition probability matrix (Tables 7 – 9 ) shows the probability of a conversion for each LULC class to another class, within the specified time. The change of probabilities between two different time periods reveal the significant increase of CL,BL, BUP at the cost of a decrease in water body, forest, grazing and shrub-bush land in the study area. Table 6 Area statistics of actual and predicted land use land cover map of 2018 LULCT Actual Predicted BL 92 86 BUL 33 38 CL 586 602 FL 8 4.28 GL 73 62 PL 71 75 SBL 66 62 WB 1.02 0.74 930.02 930.02 Table 7 Transition probability matrix of land use /land cover classes for the year 2018 LULCT BL BUL CL FL GL PL SBL WB BL 0.0096 0 0.9485 0.0007 0.004 0.0047 0.0325 0 BUL 0.0034 0.0015 0.9894 0.0001 0 0.0024 0.0031 0.0001 CL 0.0045 0.0003 0.9347 0.0002 0.0011 0.0222 0.0366 0.0004 FL 0 0 0.1788 0.1026 0.0244 0.1212 0.5725 0.0005 GL 0.0002 0 0.8903 0.002 0.0391 0.0047 0.0635 0.0002 PL 0.0001 0.0001 0.5642 0.0048 0.0042 0.0952 0.3301 0.0014 SBL 0.0001 0.0003 0.4061 0.0039 0.0028 0.1113 0.4664 0.0092 WB 0.0014 0 0.2143 0.0014 0 0.0844 0.6878 0.0109 Table 8 Transition probability matrix of land use /land covers classes for the year 202 LULCT BL BUL CL FL GL PL SBL WB BL 0.0106 0.0003 0.9053 0.0005 0.0013 0.0256 0.0557 0.0007 BUL 0.0109 0.0003 0.9177 0.0004 0.0012 0.0235 0.0456 0.0005 CL 0.0105 0.0002 0.8979 0.0005 0.0013 0.0269 0.0618 0.0008 FL 0.0033 0.0002 0.5752 0.0056 0.0034 0.0820 0.3249 0.0054 GL 0.0102 0.0002 0.8893 0.0007 0.0015 0.0283 0.0688 0.0010 PL 0.0071 0.0002 0.7471 0.0020 0.0020 0.0530 0.1856 0.0031 SBL 0.0056 0.0002 0.6802 0.0025 0.0022 0.0646 0.0646 0.0041 WB 0.0037 0.0002 0.5946 0.0032 0.0025 0.0794 0.3111 0.0053 Table 9 Transition probability matrix of land use /land covers classes for the year 202 LULCT BL BUL CL FL GL PL SBL WB BL 0.29 0.68 0 0.02 0 0 0 0 BUL 0.19 0.76 0 0.02 0.001 0.0012 0 0 CL 0.28 0.67 0 0.04 0.006 0.005 0.0001 0 FL 0.07 0.6 0.03 0.12 0.12 0.06 0.007 0 GL 0.15 0.5 0.06 0.1 0.14 0.02 0.001 0 PL 0.06 0.54 0.01 0.27 0.1 0.0098 0.0012 0 SBL 0.08 0.52 0.01 0.2 0.15 0.0199 0.0011 0 WB 0.12 0.55 0.01 0.18 0.11 0.019 0.009 0 3.4 Validations Kappa variations that compared the projected LULC map with the actual LULC map of the year 2018 resulted in a Kappa value = 0.97, Kno = 0.97, Kappa location = 0.99& k standard = 0.96.Both Kappa results confirms that the model is reliable for the SRB and can be used to predict future LULC change under different scenarios (Fig. 5 & Table 6 ) 3.5. Future Scenario/Simulation Based on real LULC maps the model predicted the LULC change and the LULC maps for the years 2028 &2048 (Fig. 6 ).The markov model also provides the transition probability matrix for the years 2028 and 2048 (Table 4 ). From the year of 2018 to 2028, the trend of LULC change in the study area will show a decreasement on FL, GL, SBL and WB 2%, 2%, 0.6%,3% ,respectively(Table 4 and Fig. 7 ) .Whereas BL, BUL, CL and PL areas will be increased by 0.6%, 1%, 0.6%,0.6% respectively. As indicated in Fig. 4 (B) during 2018–2028, gain and loss in CL was 374.31 km 2 and 31.38 km 2 , with a net gain of 335.13 km 2 . SBL loss 289.29 km 2 and gained 5.87 km 2 , with a net loss of -283.22 km 2 . PL lost 80.09 km 2 and gained 3.02 km 2 with a net loss of 77.07 km 2 . Built-up land, however, increased with a net gain of 15.82 km 2 . According to Table 4 ,trend of LULC for bar land, built up land, plantation land, cultivated land will be shown an increase from 2018–2048, 2.2%, 5.7%, 5%,5.7%,respectively. Whereas FL, GL, SBL &WB areas will be increased in between 2018 to 2048 by 2%, 7.7%, 9%,0.3% respectively. As indicated in Fig. 4 (C) during 2018–2048, gain and loss in CL will 378.14 km 2 and 31.7 km 2 , with a net gain of 338.56 km 2 . SBL will show a loss by 249.84 km 2 and gained 5.03 km 2 , with a net loss of -244.6 km 2 . PL lost 85.73.96 km 2 and gained 3.23 km 2 with a net loss of 82.5 km 2 . Built-up land, however, increased with a net gain of 16.2 km 2 . The spatial visualization provided by the LCM reveals that, in the next 30 years, CL, BUL & BL represent the most momentous LULC class and will negatively affect the FL, SBL, PL & WB. 4. Discussion The findings of the study indicated that an increase of BUL, CL &BL that had observed from 1990 to 2002 & will continue up to 2048.The dramatic fall in WB, FL, GL, PL, SBL that had observed from 1990 to 2002 and will continue up to 2048.The findings of the study are similar with other studies conducted in Ethiopia by the authors of (Gebrehiwot et al. ,2014) in Birr and Upper Didesa watersheds of the Blue Nile basin, and as outlined in (Gashaw et al. ,2014) for Dera district of northwestern Ethiopia. The findings of the study show that BUL, CL &BL increase was consistent with other research findings in Africa (Wubie et al. ,2016) and Ethiopia (Gashaw et al. ,2018, Tarasovičová et al. ,2013). The studies in some parts of Europe, for example, (Tarasovičová, et al. ,2013) in Slovakia in Portugal(Wnęk et al. ,2021) in Poland, Slovakia, and Czechia. Contrasting findings were indicated by Bewket ( 2002 ), Munro et al,2008 and Bantider et al. ( 2011 ) and Gebrelibanos & Assen ( 2015 ) in which they confirmed that increasing vegetation cover because government policy of sustainable land management program (Nyssen et al. ,2008 and Nega et al. ,2012). In different corner of Ethiopia different type of driver of LULC change were identified. For instance Hamza &Iyela ( 2012 ) and Bewket & Abebe(2013) identified population pressure, income growth and declining productivity; Zeleke & Hurni ( 2001 ) and Bewket(2002) indicated human drivers ;Tekle and Hedlund ( 2000 ) identified population growth; Pender et al.(2001) showed land degradation, poverty and food insecurity ;Yalew et al.( 2016) identified population, slope, livestock and distances from various infrastructures and Gashaw et al.( 2017) also identified population growth and reduction of land productivity were the drivers of LULC change in different watershed of Ethiopia. Leta et al.,( 2021) identified slope, distance from stream, distance from urban areas, and distance from roads also play an important role in land use change, as each provides convenience to residents to access resources. In our study ,rainfall, slope, elevation, distance to rivers, distance to roads, distance towns and population density were among the drivers of LULC in SRB. Considering drivers of LULC changes at watershed level have paramount importance for sustainably managing of the environment in that watershed. In this study, LULC modeling using LCM in SRB were performed. This model is strong due to its dynamic projection proficiency, suitable calibration, and capability to simulate several types of land cover (Aburas et al. ,2018, Brown et al. ,2004). The LCM embedded in the TGMMS model was successfully used by different researchers in other areas and it confirmed that LCM is a capable model for the assessment and prediction of LULC change and the validation of results (Ahmed and Ahmed,2012; Al-sharif and Pradhan,2014; Ye et al. ,2017; Herold et al. ,2002;McGarigal et al. ,2002).By doing so, the findings would be useful as the inputs for planners and other stakeholders regarding the LULC trends in the study area. 5. Conclusions And Recommendation The current study was carried out to model and predict land use land cover changes from the year 1990 to 2048 using LCM in Suluh river basin, Ethiopia. If the model predictions hold; in the coming three decades, bar land, built up land, and cultivated land shown an increase on the expense of water body, forest, shrub-bush and plantation land. Rainfall, slope, elevation, distance to rivers, distance to roads, distance towns and population density were identified the prominent LULC change drivers in the study area. This will increase vulnerability of the watershed to soil erosion and soil macro fauna loss of the studied river basin in particular and the Tekeze basin in general. Therefore, suitable and timely management measures must be taken by policy decision makers to enable sustainable development and to protect the river basin in order to reduce the severity of the changes. Declarations Ethics approval and consent to participate Not applicable. Consent for publication We, the undersigned, give our consent for the publication of identifiable details, which can include photograph(s) and/or videos and/or case history and/or details within the text (“Material”) to be published in the above Journal and Article. Availability of data and material All Availability of data and material will share as per request. Competing interests The data that support the findings of this study are available from the corresponding author upon reasonable request. Funding Bahir Dar University and Debre Tabor University in Ethiopia were the sponsors of our study. Authors' contributions Hailay Hagos Entahabu was a Ph.D. student in Geography (specialization in Natural resources management and Environment) and participated in proposing the study, managed all data collection, conducted all data analyses performed statistical analyses and interpretation results, and drafted the manuscript. Amare Sewnet Minale as supervisors participated in guiding and reviewing the manuscript. Emiru Birhane Hizikias was supervisor and participated in designing the field study and reviewed the manuscript. All authors read and approved the final manuscript Acknowledgments The authors would like to thank Bahir Dar University and Debre Tabor University in Ethiopia for their financial support. References Aburas, M. M., Abdullah, S. 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University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Amare","middleName":"Sewnet","lastName":"Minale","suffix":""},{"id":141579280,"identity":"ebc205ff-7427-4d75-857a-e2d88655a09f","order_by":2,"name":"Emiru Birhane Hizikias","email":"","orcid":"","institution":"Mekelle University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Emiru","middleName":"Birhane","lastName":"Hizikias","suffix":""}],"badges":[],"createdAt":"2022-08-20 16:29:15","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1981572/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1981572/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27323816,"identity":"fc37d9a2-d626-4b68-80eb-bb8b0ada00e4","added_by":"auto","created_at":"2022-10-04 14:31:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":293420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLocation map of the study area, Suluh river basin in Tigray, northern Ethiopia\u003c/em\u003e\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-1981572/v2/f8955b5f0331501594568438.png"},{"id":27324200,"identity":"9211e484-c911-417c-91a7-3a42669ab1f1","added_by":"auto","created_at":"2022-10-04 14:36:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15324,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual rainfall (RF) and Mean Temperature (T) of the \u003cem\u003eSuluh\u003c/em\u003e River Basin for the period 1988-2018(sources station data)\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-1981572/v2/dac6e6108e818122a6bf04e8.png"},{"id":27323817,"identity":"80c7451f-c180-4dae-b7ca-68d13640e4a1","added_by":"auto","created_at":"2022-10-04 14:31:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13824,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology Flow Chart for Land Change Modeler\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-1981572/v2/d7b7d5a04f536e1dcc6e5ce4.png"},{"id":27323819,"identity":"50a2104a-852a-4c07-98e0-2476bc98b3f7","added_by":"auto","created_at":"2022-10-04 14:31:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20016,"visible":true,"origin":"","legend":"\u003cp\u003eThe LULC of the SRB in 1990(A), 2002(B), and 2018(C)\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-1981572/v2/7a57bd598238988d25a2dc6c.png"},{"id":27324201,"identity":"1350e562-27f0-43ae-9330-5801542088b5","added_by":"auto","created_at":"2022-10-04 14:36:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":160123,"visible":true,"origin":"","legend":"\u003cp\u003eLand use/cover map of study area for 1990 and 2002\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-1981572/v2/8ffbefc46cdf3e83dfdeb448.png"},{"id":27323820,"identity":"e08c43ec-0bdf-4910-bc68-0afead5c345b","added_by":"auto","created_at":"2022-10-04 14:31:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":107846,"visible":true,"origin":"","legend":"\u003cp\u003eThe simulated (a) and the actual (b) land use/land cover of the Suluh river basin in 2018\u003c/p\u003e","description":"","filename":"F6.png","url":"https://assets-eu.researchsquare.com/files/rs-1981572/v2/bda6030e84ede0c1ba093981.png"},{"id":27324721,"identity":"540778c7-7724-4801-8fb7-8fe13e06ae5d","added_by":"auto","created_at":"2022-10-04 14:41:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":107846,"visible":true,"origin":"","legend":"\u003cp\u003eThe 2028 and 2048 land use/land cover of the Suluh river basin\u003c/p\u003e","description":"","filename":"F7.png","url":"https://assets-eu.researchsquare.com/files/rs-1981572/v2/69d4d9dab40aac1db5cca275.png"},{"id":27452934,"identity":"714da3cb-01b7-4900-82a9-e71d30fd5d3f","added_by":"auto","created_at":"2022-10-07 06:14:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1161248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1981572/v2/dc1079f3-a1f4-448e-a7db-22c41029fb11.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eModeling and Prediction of Land use/Land Cover Change using Land Change Modeler in Suluh River Basin, Northern Highland of Ethiopia.\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003eLand use, land cover (here after LULC) change is global environmental issues (Moghadam \u0026amp; Helbich,2013).On the past two decades, a rapid changes has been occurring since time immemorial exacerbated by a number of anthropogenic factors (Houghton,1994 \u0026amp; Bewket,2002) \u0026amp; as a consequence which alters the interaction between the earth surface (Fu \u003cem\u003eet al.\u003c/em\u003e,2000,Lambin \u003cem\u003eet al.\u003c/em\u003e,2003;Kabat \u003cem\u003eet al.\u003c/em\u003e,2004;Mahmood \u003cem\u003eet al.\u003c/em\u003e,2010 \u0026amp; Prasad \u003cem\u003eet al.\u003c/em\u003e,2010).Lambin \u003cem\u003eet al.(\u003c/em\u003e2003) disclosed that change of forests and grasslands into cultivated land. Amount and direction of LULC change was not equal in all round of the world. For instance, Wood \u003cem\u003eet al.\u003c/em\u003e(2004) states that expansion of agriculture was recognized as primary drivers of LULC changes in Africa.\u003c/p\u003e \u003cp\u003eLULC change studies in Ethiopia showed that most of LULC change were from the natural forests to cultivated land (Kebrom \u0026amp;Hedlund,2000;Zeleke \u0026amp;Hurni,2001; Dessie \u0026amp; Kleman ,2007; Tegene ,2002;Tsegaye \u003cem\u003eet al.\u003c/em\u003e,2010\u0026amp;Rientjes \u003cem\u003eet al.\u003c/em\u003e,2011)\u0026amp;were caused by anthropogenic factors (Bewket,2002;Gebreslassie,2014;Sewnet,2015,Hailemariam \u003cem\u003eet al.\u003c/em\u003e,2016\u0026amp; Chiemela \u003cem\u003eet al\u003c/em\u003e,2017). Moreover, in different part of Ethiopia researches were conducted on the LULC modeling and predication by Teferi, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Han \u003cem\u003eet al.\u003c/em\u003e,2015;Yalew \u003cem\u003eet al.\u003c/em\u003e,2016 \u0026amp; Gashaw \u003cem\u003eet al.\u003c/em\u003e,2017 in which those studies predict that LULC will change from vegetation to cultivated land. Contrastingly, few studies in different part of Ethiopia indicated an improvement of vegetation cover (Bewket \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e;Munro \u003cem\u003eet al\u003c/em\u003e,2008 \u0026amp; Bantider \u003cem\u003eet al\u003c/em\u003e(2011) due to community afforestation and land rehabilitation activity. These contrasting findings suggest that the assumption that conversion natural vegetative cover in to human managed systems leads to deterioration in soil macro fauna is not always valid. There is a clearly a need for empirical investigation in to the problem at the local catchment level.\u003c/p\u003e \u003cp\u003eFor better clue on the functioning of the LULC system, the modeling of LULC change has grown rapidly. LULC models are simplifications of reality that gives an important means of predicting future LULC change pressure points (Nourqolipour \u003cem\u003eet al.\u003c/em\u003e,2015).Several studies(eg. (Wang \u0026amp; Maduako,2018, Kumar \u003cem\u003eet al.\u003c/em\u003e,2015, Megahed \u003cem\u003eet al.\u003c/em\u003e,2015, Mas \u003cem\u003eet al.\u003c/em\u003e,2014, Ozturk ,2015, Mishra \u003cem\u003eet al.\u003c/em\u003e,2014, Kamusoko \u003cem\u003eet al.\u003c/em\u003e,2009\u0026amp; Hasan \u003cem\u003eet al.\u003c/em\u003e,2020) confirmed that from the other LULC modeling ways; Land Change Modeler ( here after LCM), based on integrated multilayer perceptron (here after MLP) with Markov chain (hear after MC), is a strong model for the analysis and prediction of LULC change and the validation of results.\u003c/p\u003e \u003cp\u003eIn Suluh river basin(here after SRB), population growth and lack of alternative livelihood strategies led environmental degradation(Alemayehu \u003cem\u003eet al.\u003c/em\u003e,2009; Aredehey \u003cem\u003eet al.\u003c/em\u003e,2018; Zenebe \u003cem\u003eet al.\u003c/em\u003e,2019 \u0026amp; Hishe \u003cem\u003eet al.\u003c/em\u003e,2020).Because of shortage of land the farmers are taking the following options:(1) expanding in to steep and marginal areas to gain more land to compensate for low yields from their existing holding.2) Conversion of their previous croplands in to Eucalyptus plantations or change of crop types.(3) Recurrent cultivation, overgrazing and using of farm inputs. The analysis and modeling of LULC change in SRB not assessed in depth.The few studies (Alemayehu \u003cem\u003eet al.\u003c/em\u003e,2009; Aredehey \u003cem\u003eet al.\u003c/em\u003e,2018; Zenebe \u003cem\u003eet al.\u003c/em\u003e,2019 \u0026amp; Hishe \u003cem\u003eet al.\u003c/em\u003e,2020) conducted in the \u003cem\u003eTekeze\u003c/em\u003e river basin on LULC change analysis with different spatio-temporal coverage and methodologies. However, modeling of LULC change using land change modeler in \u003cem\u003eSRB\u003c/em\u003e was not well investigated.In this paper, we apply LCM for the purpose of modeling \u0026amp; prediction of the LULC on \u003cem\u003eSRB\u003c/em\u003e. We expect that the results of this research can help policymakers \u0026amp; land management stakeholder to design land use planning strategies that helps to attain sustainable land management in the study area.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The Study Area: Suluh River Basin\u003c/h2\u003e \u003cp\u003eThe study area is found in the highlands of the north-eastern part of Tigray region, northern Ethiopia. The geographic location of the river basin extends from 39\u0026deg;24'59.06 \u0026rdquo; E to 39\u0026deg;26'22.73 \u0026rdquo; E latitude and 13\u0026deg;38'18.27 \u0026rdquo; N to 14\u0026deg;13'53.29 \u0026rdquo; N longitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).The total area of the basin is about 930 km\u003csup\u003e2\u003c/sup\u003e.The elevation of the study area varies from 1700 to 3,298 meters above sea level. The study watershed falls in four districts (\u003cem\u003eSaesie Tsaeda Emba ,Hawuzen, Kilte Awlealo and Degua Tembien\u003c/em\u003e ) of Eastern and South East.\u003c/p\u003e \u003cp\u003eThe climate of SRB is characterized as semi-arid. The warmest months are May \u0026amp; June \u0026amp;coldest months are December and January. Average annual rainfall and temperature from 1988\u0026ndash;2018 is 420.4 mm and 17.5\u0026deg;C respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).The rain fall season is between June to early September which is mono- modal rain fall distribution.\u003c/p\u003e \u003cp\u003eRegarding the hydrological condition, drainage pattern of the SRB is dendrite (Zenebe et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).The longitudinal profile of the \u003cem\u003eSRB\u003c/em\u003e main stream has a length of 94.77 km starting from volcanic mountains of \u003cem\u003eMugulat\u003c/em\u003e \u0026amp; ending at the embouchure into \u003cem\u003eGenfel.\u003c/em\u003e The geology of the basin is trap basalt accounts 2.8%, granite and shale accounts 1.8%, metamorphic rock accounts 28.9%, limestone accounts 13.9%, sandstone accounts 52.7% (Zenebe \u003cem\u003eet al.\u003c/em\u003e,2019 ).The major soil types of SRB area are \u003cem\u003ehaplic lixisols\u003c/em\u003e covers 41.4%,\u003cem\u003elithic leptosols\u003c/em\u003e covers 22.7%, \u003cem\u003eEutric leptosols\u003c/em\u003e covers 17.8%,\u003cem\u003eChromic Cambisols\u003c/em\u003e covers 15.6% \u0026amp;\u003cem\u003eVertic Cambisols\u003c/em\u003e covers 2.5%. The major soil textures of the study area are sandy clay loam accounts 41.4%, sandy loam accounts 40.5% and clay accounts 18.2% (Zenebe \u003cem\u003eet al.\u003c/em\u003e,2019).\u003c/p\u003e \u003cp\u003ePopulation Census Commission (2015) shows that in 2015 the population density of the river basin was 142 persons per km\u003csup\u003e2\u003c/sup\u003e. The dominant economic activity in the basin is agriculture. The crops grown vary according to altitude. The main crops in the highlands are barley, wheat, maize, \u003cem\u003eteff\u003c/em\u003e \u0026amp; pulses. Sorghum is widely grown in the lowlands. Cultivation is done by means of the traditional ox-drawn plough. The main livestock are cattle, sheep, goats, donkeys and chickens. There is a livestock feed crisis, resulting from crop residue and vegetation biomass reduction (Alemayehu et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e \u0026amp; Aredehey et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Most of the areas are highly cultivated even on steep valley sides and the remnant original highland forests are being overgrazed \u0026amp; deforested, resulting in shallow soils due to serious soil erosion by water. The major strategies for land management in Tigray highlands are building of stone terraces, micro dams including gully treatment, establishment and development of enclosures and community woodlots, enforcement of used rules, regulations for grazing lands \u0026amp; reduced burning activities(Sembroni \u003cem\u003eet al.\u003c/em\u003e,2017 ; Zenebe \u003cem\u003eet al.\u003c/em\u003e,2019 \u0026amp; Hishe \u003cem\u003eet al.\u003c/em\u003e,2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sources of Data\u003c/h2\u003e \u003cp\u003eSources of data used, data processing image segmentation, nearest neighbor fuzzy classification, accuracy assessment \u0026amp; LULC change modeling were done according to the overall workflow of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Free satellite images (Landsat-5 TM of 1990, Landsat-7 ETM\u003csup\u003e+\u003c/sup\u003e of 2002 \u0026amp; Landsat-8 OLI\u0026ndash;TIRS of 2018) were used for the LULC change analysis of the study area (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).These datasets were acquired from the National Aeronautics and Space Administration (NASA) through their EOS Data Gateway Database. Land sat images that were used for this study area freely available. The Landsat-7 ETM\u003csup\u003e+\u003c/sup\u003e 2002 \u0026amp; Landsat-8 OLI\u0026ndash;TIRS 2018 of a 30 meters pixel were resampled to a 15 meters pixel size. Digital Elevation Model (30 m) based on Aster imagery and ancillary data (topographic maps, field thematic layers (roads and towns), \u0026amp; village and district boundaries) were also utilized during analysis. All data were projected using Universal Transverse Mercator projection system zone 37\u003csup\u003e0\u003c/sup\u003e N and datum of World Geodetic System 84 (WGS84). An intensive pre-processing such as layer-stacking, resolution merge, and sub setting were carried out and to remove disturbances such as haze, noise, steep slope effect \u0026amp; radio metric variation between acquisition dates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe characteristics of land sat satellite data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath / row\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcquisition time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169/50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01/07/1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat ETM+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169/50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e03/08/2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eETM\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat OLI-TIRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169/50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e03/12/2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOLI-TIRS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAster DEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAncillary data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopo map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNov.2017-Jan 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoads and towns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict boundary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVillage boundary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe applied nearest neighbor fuzzy (Eq.\u0026nbsp;1) classification in eCognition Developer 9.2. It is used in eCognition-automatically by generating multidimensional membership functions (Foody,1999; Groenemans et al.,1997;Zhang \u0026amp; Foody,1998;Yan et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2006\u003c/span\u003e;Salman \u003cem\u003eet al.\u003c/em\u003e,2008, Zhou \u003cem\u003eet al.\u003c/em\u003e,2008 and Kalantar \u003cem\u003eet al.\u003c/em\u003e,2017).We designed eight LULC classes (cultivated, bar, built up, grazing, plantation, shrub and bush, water body \u0026amp; forest land) based on the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand cover, Land use types and their descriptions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLU/LC classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescriptions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas covered with annual crops followed by harvest and bare soil\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas covered with a natural forest community with a closed, deep and complex canopy often consisting of several crown layers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandscapes that have a ground story in which grasses are the dominant vegetation forms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis category includes low woody plants, generally less than three\u003c/p\u003e \u003cp\u003emeters in height, usually with multiple stems, growing vertically\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas with little or no vegetation cover consisting of exposed soil\u003c/p\u003e \u003cp\u003eand/or bedrock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas for construction sites and towns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas composed of transplanted seedlings of Cactus, \u003cem\u003eEucalyptus globules\u003c/em\u003e and \u003cem\u003eCupresus spp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncludes rivers, reserves, lakes (artificial dams and natural lakes) and so on\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: Forest land (FL), cultivated land (CL), shrub-bush land (SBL), built up land (BU), grazing land (GL), bare land (BL), plantation land (PL) and water body (WB)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA={(X, \u0026micro;A(x));x ϵ X},Where \u0026micro;A \u0026rarr;[0,1] (1)\u003c/p\u003e \u003cp\u003eWhere A\u0026thinsp;=\u0026thinsp;fuzzy set X\u0026thinsp;=\u0026thinsp;a space of objects X\u0026thinsp;=\u0026thinsp;elements belonging to space X \u0026micro; \u0026ndash; membership\u003c/p\u003e \u003cp\u003eFunction\u003c/p\u003e \u003cp\u003eIn this study, over all accuracy (Eq.\u0026nbsp;2) and Kappa coefficient (Eq.\u0026nbsp;3) were used to assess the accuracy of the classified images. As a result, we found an excellent accuracy in which as to the kappa coefficient results reveals 0.886, 0.883 \u0026amp; 0.852 for the years of 1990, 2000 and 2018, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).Since the values falls above the cut point of the standard overall classification accuracy level of 85% (Fleiss \u003cem\u003eet al.\u003c/em\u003e,2003;Doxani \u003cem\u003eet al.\u003c/em\u003e,2008 and Congalton \u0026amp; Green ,2019) with no class less than 70% (Kumar \u003cem\u003eet al.\u003c/em\u003e,2018).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of error matrixes for the classified images of 1990 and 2002.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLULC classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser Accuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProducer Accuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUser Accuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProducer Accuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e87.12121212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e89.79592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKappa Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.852591473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.883327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{OA}=\\frac{X}{Y}*100 \\left(2\\right)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere, OA is overall accuracy, x is number of correct values in diagonals of the matrix, and y is total number of values taken as a reference point.\u003c/p\u003e \u003cp\u003eK=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(N\\sum _{i=1}^{r}xii-\\sum _{i=1}^{r}(xi\\times x+1)\\)\u003c/span\u003e\u003c/span\u003e/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(N2- \\sum _{i=1}^{r}xii-\\sum _{i=1}^{r}(xi*x+1) \\left(3\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere: r\u0026thinsp;=\u0026thinsp;is the number of rows in the matrix\u003c/p\u003e \u003cp\u003eXii\u0026thinsp;=\u0026thinsp;is the number of observations in rows i and column I (along the major diagonal) Xi\u0026thinsp;+\u0026thinsp;=\u0026thinsp;the marginal total of row i (right of the matrix) Xi\u0026thinsp;+\u0026thinsp;1 are the marginal totals of column i (bottom of the matrix) N is the total number of observations. K\u0026thinsp;=\u0026thinsp;kappa coefficient\u003c/p\u003e \u003cp\u003e \u003cb\u003eLULC Modeling using LCM\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLCM model is based on the artificial neural network (ANN), Markov Chain matrices \u0026amp; transition suitability maps, generated by training multilayer perceptron (MLP) or logistic regression (Ramachandra \u003cem\u003eet al.\u003c/em\u003e,2013., Mishra \u003cem\u003eet al.\u003c/em\u003e,2014, Roy \u003cem\u003eet al.\u003c/em\u003e,2014, Gibson \u003cem\u003eet al.\u003c/em\u003e,2018, Dzieszko,2014). Megahed \u003cem\u003eet al.\u003c/em\u003e,2015, Ansari \u0026amp; Golabi,2019). This model predicts the LULC changes from the thematic raster images having the same number of classes in the same sequential order (Mas \u003cem\u003eet al.\u003c/em\u003e,2012). In this study, the LCM is used to predict the future LULC changes SRB for the next 30 years by following four steps, namely: (1) change analysis, (2) transition potential and determination of explanatory variables, (3) change prediction(4) model validation (Megahed \u003cem\u003eet al.\u003c/em\u003e,2015).\u003c/p\u003e \u003cp\u003eI.Change Analysis\u003c/p\u003e \u003cp\u003eIn the change analysis panel, the changes between two different time periods time 1(1990) \u003cem\u003e\u0026amp;\u003c/em\u003e time2 (2002) LULC maps were calculated. To calculate the annual rate of change Peng \u003cem\u003eet al.\u003c/em\u003e(2008) formula(Eq.\u0026nbsp;4) was adopted:\u003c/p\u003e \u003cp\u003eC=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\varDelta f-\\varDelta i}{\\varDelta i}\\times \\frac{1}{T}\\times 100 \\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere C\u0026thinsp;=\u0026thinsp;is the annual change rate of a given LULC type, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003ef and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta i\\)\u003c/span\u003e\u003c/span\u003e are the final and initial area coverage of LULC type during the specific period and T\u0026thinsp;=\u0026thinsp;year difference between the initial and final period.\u003c/p\u003e \u003cp\u003eThe gain and loss(Eq.\u0026nbsp;5) was also calculated adopted from LCM in IDIRISI software.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\left[\\text{P}\\text{l}\\text{o}\\text{s}\\text{s}\\right(\\text{i}),\\text{j}\\)\u003c/span\u003e \u003c/span\u003e= (Pj,i- p\u003csub\u003ei,j\u003c/sub\u003e)/( p\u003csub\u003ei\u003c/sub\u003e - p\u003csub\u003ei\u003c/sub\u003e)*100 i#j]\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\left[\\text{P}\\text{g}\\text{a}\\text{i}\\text{n}\\right(\\text{i}),\\text{j}\\)\u003c/span\u003e \u003c/span\u003e= (Pi,j- p\u003csub\u003ej,i\u003c/sub\u003e)/(p\u003csub\u003ei\u003c/sub\u003e- p\u003csub\u003ei\u003c/sub\u003e)*100 i#j]\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(5\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{P}\\text{l}\\text{o}\\text{s}\\text{s}\\left(\\text{i}\\right),\\text{j}\\)\u003c/span\u003e\u003c/span\u003e Is the percentage taken by j in LUCC in total \u0026lsquo;conversion loss\u0026rsquo; of category row i\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{P}\\text{g}\\text{a}\\text{i}\\text{n}\\left(\\text{i}\\right),\\text{j}\\)\u003c/span\u003e\u003c/span\u003e Is the percentage taken by j in LULC change in total \u0026lsquo;conversion gain\u0026rsquo; of category row i, p\u003csub\u003ei,j\u003c/sub\u003e and p\u003csub\u003ej,i\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eII. Transition Potential Modeling and Driving Forces Determination\u003c/p\u003e \u003cp\u003eTransition Potential\u003c/p\u003e \u003cp\u003eThe transition potential determines the area of change (Megahed et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).LULC changes with common driving variables were grouped into sub-models. In addition; evidence likelihood was selected to determine the relative frequency of different LULC types which had occurred within the transitional areas (Megahed \u003cem\u003eet al.\u003c/em\u003e,2015).\u003c/p\u003e \u003cp\u003eSelection of Explanatory Variables\u003c/p\u003e \u003cp\u003eExplanatory variables responsible for LULC change were selected (Gibbs \u003cem\u003eet al.\u003c/em\u003e,2010 \u0026amp; Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).In the current study, biophysical (rainfall, slope and elevation), socio-economic variables (distance to rivers, distance to roads, distance towns) and demographic variables (population density) were identified as variables. For testing, selection and transition of Model variables, Cramer\u0026rsquo;s V coefficient was used.All the variables were then add into transition sub-mmodel. For transition sub-model structure and running the model MLP neural network were used. In this study, the transition pixels to be occurred in between 1990 to 2002 were assign randomly to one of two groups: the training set \u0026amp; the testing set. The variables were derived via geographical and geo-statistical elaborations of a geographic information system \u0026amp; were formalized as follows (Eq.\u0026nbsp;6):\u003c/p\u003e \u003cp\u003e \u003cem\u003eX\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ex1\u003c/em\u003e, \u003cem\u003ex2\u003c/em\u003e, \u0026hellip;, \u003cem\u003exn\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(6\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eEvery variable associated with a neuron in the input layer was normalized using (Eq.\u0026nbsp;7):\u003c/p\u003e \u003cp\u003eX\u003csub\u003ei\u003c/sub\u003e=( X\u003csub\u003ei\u003c/sub\u003e-min)/(max-min)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(7\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn the hidden layer, the signal that is received by neuron j in the hidden layer for pixel k was calculated as follows(Eq.\u0026nbsp;8):\u003c/p\u003e \u003cp\u003enet\u003csub\u003ej\u003c/sub\u003e(k)=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i}\\text{W}\\text{i},\\text{j} \\text{X}\\text{i}\\left(\\text{k}\\right) \\left(8\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003enetj\u003c/em\u003e(\u003cem\u003ek\u003c/em\u003e, \u003cem\u003et\u003c/em\u003e) is the signal that is received by neuron \u003cem\u003ej\u003c/em\u003e, and \u003cem\u003ewi,j\u003c/em\u003e is the weight between the input layer \u003cem\u003eI\u003c/em\u003e and the hidden layer \u003cem\u003ej\u003c/em\u003e. The output layer has 2 neurons that correspond to 2 possible significant states (1\u0026thinsp;=\u0026thinsp;transition, 2\u0026thinsp;=\u0026thinsp;permanence of the pixel); the neuron \u003cem\u003el\u003c/em\u003e generates a value that indicates the transition probability. Transition probabilities can be calculated using a sigmoidal function using Eq.\u0026nbsp;9 (a sigmodial function is used to represent the non-linearity of each node):\u003c/p\u003e \u003cp\u003e \u003csub\u003ep(k,1=1)=\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\sum _{i}\\text{W}\\text{j},\\text{i}\\frac{1}{1+{x}^{netj(k,t)}}\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\left(9\\right)\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eIII. Change Prediction\u003c/p\u003e \u003cp\u003eChange prediction is the last step in which the future prediction is executed on the basis of MC, and using the historical rate of change and the transition potential maps (Wang \u003cem\u003eet al.\u003c/em\u003e,2012). MC analysis were run for this study to determine the amount of change using two LULC maps (1990 and 2002) along with the date specified (2018,2028 and 2048). The steps determines how much land was expected to transition from the later date (2002) to the prediction date (2028 and 2048). A MC(Eq.\u0026nbsp;10) is made of s, a vector of the distribution of LULC classes at time t, and A(ፐ), a matrix of transition probabilities from land use u to land use u\u0026rsquo; in a given time interval(ፐ)\u003c/p\u003e \u003cp\u003eS\u003csub\u003et+ፐ=\u003c/sub\u003eA(ፐ)S\u003csub\u003et\u003c/sub\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(10\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eUsing hard prediction model, a map for 2018 of study area was simulated in order to compare with the \u0026lsquo;actual\u0026rsquo; land cover map of 2018.\u003c/p\u003e \u003cp\u003eIV. Future Scenario\u003c/p\u003e \u003cp\u003eHard predictions land change modelers was applied in this study.\u003c/p\u003e \u003cp\u003eV. Model Validation\u003c/p\u003e \u003cp\u003eIt is needed to assess the accuracy. In this study validation was done by comparing the simulated and the actual LULC maps of 2018. The qualitative information collect using direct observation, focus group discussion and interview were analyzed and interpreted using qualitative techniques, whereas quantitative data collect were analyzed using descriptive statistics ( like percentage).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e3.1 LULC Change Analysis From 1990 to 2002\u003c/h2\u003e\n \u003cp\u003eAs Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the trend of LULC change indicated that FL,GL, PL,SBL \u0026amp; WB shown a decrement by 97.2%, 89.8%, 89.1%, 1.5%, 84.8%, from 1990 to 2002 respectively. On the other hand, BL, BUL and CL increased by 10.6%, 29.4% and 65.4%, respectively. Agreeably, elder priest interviewees confirmed that coverage vegetation\u0026rsquo;s were better in church forests in year of 1990 than 2002.The focus group panelists affirmed the CL in the study area shows with high fragmentation of land due to the last land redistribution made in 1991/92.As an interview made with the district office of agricultural \u0026amp; rural development confirmed that the population density in the river basin have been hampering the LULC change.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLULC Change trends in from 1990 to 2048\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eLULC classes\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cem\u003eLULC area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eTrends of Change (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1990\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e2002\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e2018\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e2028\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e2048\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1990- 02\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e2018- 28\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e2018- 48\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e82.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e91.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e90.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e92\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e94.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e2.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBUL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e12.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e16.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e29.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e33\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e38.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e29\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eC L\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e351\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e580.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e551\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e586\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e592\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e65\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e6.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e24.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e10.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-97\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e186\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e18.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e95.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e73\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e65.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-90\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-7.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e46.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e67.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e71\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e76\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-89\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSBL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e219\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e216.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e84.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e66\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e57\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-1.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eWB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e7.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1.54\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1.02\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.68\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-85\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-0.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (A) the gains and losses major LULC changes include (1) expansion of CL, (2) increase in BL, and (3) increase in BUL whereas decrease in SBL, GL, PL and WB. During 1990\u0026ndash;2002, gain and loss in CL was 359.94 km\u003csup\u003e2\u003c/sup\u003e and 33.03 km\u003csup\u003e2\u003c/sup\u003e,with a net gain of 326.91 km\u003csup\u003e2\u003c/sup\u003e. SBL loss 243.67 km\u003csup\u003e2\u003c/sup\u003e and gained 5.33 km\u003csup\u003e2\u003c/sup\u003e, with a net loss of -238.34 km\u003csup\u003e2\u003c/sup\u003e. PL lost 1.96 km\u003csup\u003e2\u003c/sup\u003e \u0026amp; gained 52 km\u003csup\u003e2\u003c/sup\u003e with a net loss of 50.04 km\u003csup\u003e2\u003c/sup\u003e. BUL, however, increased with a net gain of 9.9 km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e3.2. Simulation\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eTransition Potential Modeling and Determining Driving Variables\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe total three transitions that have been selected in this study were FL to CL, GL to BL, PL to CL,SBL to CL. Using cramer\u0026rsquo;s V values as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, population density(0.345), slope(0.25),elevation(0.18),rainfall(0.22),distance from river(0.31),towns(0.27)\u0026amp; roads(0.17) show significant influence on LULC change of the study area. After the selection of the predictor variables, transitions were modeled in one transition sub-model and generated the transition potential maps through multilayer perceptron with an accuracy of above 70%.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCramer\u0026rsquo;s V values of explanatory variables.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExplanatory Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCramer\u0026rsquo;s V\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eslope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edistance to rivers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edistance to roads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edistance towns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e3.3. LULC Transition Analysis\u003c/h2\u003e\n \u003cp\u003eThe transition probability matrix (Tables \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) shows the probability of a conversion for each LULC class to another class, within the specified time. The change of probabilities between two different time periods reveal the significant increase of CL,BL, BUP at the cost of a decrease in water body, forest, grazing and shrub-bush land in the study area.\u003c/p\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea statistics of actual and predicted land use land cover map of 2018\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLULCT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eActual\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredicted\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e930.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e930.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab6\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTransition probability matrix of land use /land cover classes for the year 2018\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLULCT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBUL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSBL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWB\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab7\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTransition probability matrix of land use /land covers classes for the year 202\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLULCT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBUL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSBL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWB\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab8\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTransition probability matrix of land use /land covers classes for the year 202\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLULCT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBUL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSBL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWB\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e3.4 Validations\u003c/h2\u003e\n \u003cp\u003eKappa variations that compared the projected LULC map with the actual LULC map of the year 2018 resulted in a Kappa value\u0026thinsp;=\u0026thinsp;0.97, Kno\u0026thinsp;=\u0026thinsp;0.97, Kappa location\u0026thinsp;=\u0026thinsp;0.99\u0026amp; k standard\u0026thinsp;=\u0026thinsp;0.96.Both Kappa results confirms that the model is reliable for the SRB and can be used to predict future LULC change under different scenarios (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026amp; Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e )\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.5. Future Scenario/Simulation\u003c/h2\u003e\n \u003cp\u003eBased on real LULC maps the model predicted the LULC change and the LULC maps for the years 2028 \u0026amp;2048 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).The markov model also provides the transition probability matrix for the years 2028 and 2048 (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). From the year of 2018 to 2028, the trend of LULC change in the study area will show a decreasement on FL, GL, SBL and WB 2%, 2%, 0.6%,3% ,respectively(Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) .Whereas BL, BUL, CL and PL areas will be increased by 0.6%, 1%, 0.6%,0.6% respectively. As indicated in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e(B) during 2018\u0026ndash;2028, gain and loss in CL was 374.31 km\u003csup\u003e2\u003c/sup\u003e and 31.38 km\u003csup\u003e2\u003c/sup\u003e, with a net gain of 335.13 km\u003csup\u003e2\u003c/sup\u003e. SBL loss 289.29 km\u003csup\u003e2\u003c/sup\u003e and gained 5.87 km\u003csup\u003e2\u003c/sup\u003e, with a net loss of -283.22 km\u003csup\u003e2\u003c/sup\u003e. PL lost 80.09 km\u003csup\u003e2\u003c/sup\u003e and gained 3.02 km\u003csup\u003e2\u003c/sup\u003e with a net loss of 77.07 km\u003csup\u003e2\u003c/sup\u003e. Built-up land, however, increased with a net gain of 15.82 km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eAccording to Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e,trend of LULC for bar land, built up land, plantation land, cultivated land will be shown an increase from 2018\u0026ndash;2048, 2.2%, 5.7%, 5%,5.7%,respectively. Whereas FL, GL, SBL \u0026amp;WB areas will be increased in between 2018 to 2048 by 2%, 7.7%, 9%,0.3% respectively. As indicated in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e(C) during 2018\u0026ndash;2048, gain and loss in CL will 378.14 km\u003csup\u003e2\u003c/sup\u003e and 31.7 km\u003csup\u003e2\u003c/sup\u003e, with a net gain of 338.56 km\u003csup\u003e2\u003c/sup\u003e. SBL will show a loss by 249.84 km\u003csup\u003e2\u003c/sup\u003e and gained 5.03 km\u003csup\u003e2\u003c/sup\u003e, with a net loss of -244.6 km\u003csup\u003e2\u003c/sup\u003e. PL lost 85.73.96 km\u003csup\u003e2\u003c/sup\u003e and gained 3.23 km\u003csup\u003e2\u003c/sup\u003e with a net loss of 82.5 km\u003csup\u003e2\u003c/sup\u003e. Built-up land, however, increased with a net gain of 16.2 km\u003csup\u003e2\u003c/sup\u003e. The spatial visualization provided by the LCM reveals that, in the next 30 years, CL, BUL \u0026amp; BL represent the most momentous LULC class and will negatively affect the FL, SBL, PL \u0026amp; WB.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe findings of the study indicated that an increase of BUL, CL \u0026amp;BL that had observed from 1990 to 2002 \u0026amp; will continue up to 2048.The dramatic fall in WB, FL, GL, PL, SBL that had observed from 1990 to 2002 and will continue up to 2048.The findings of the study are similar with other studies conducted in Ethiopia by the authors of (Gebrehiwot \u003cem\u003eet al.\u003c/em\u003e,2014) in Birr and Upper Didesa watersheds of the Blue Nile basin, and as outlined in (Gashaw \u003cem\u003eet al.\u003c/em\u003e,2014) for Dera district of northwestern Ethiopia. The findings of the study show that BUL, CL \u0026amp;BL increase was consistent with other research findings in Africa (Wubie \u003cem\u003eet al.\u003c/em\u003e,2016) and Ethiopia (Gashaw \u003cem\u003eet al.\u003c/em\u003e,2018, Tarasovičov\u0026aacute; \u003cem\u003eet al.\u003c/em\u003e,2013). The studies in some parts of Europe, for example, (Tarasovičov\u0026aacute;, \u003cem\u003eet al.\u003c/em\u003e,2013) in Slovakia in Portugal(Wnęk \u003cem\u003eet al.\u003c/em\u003e,2021) in Poland, Slovakia, and Czechia. Contrasting findings were indicated by Bewket (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), Munro et al,2008 and Bantider et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Gebrelibanos \u0026amp; Assen (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in which they confirmed that increasing vegetation cover because government policy of sustainable land management program (Nyssen \u003cem\u003eet al.\u003c/em\u003e,2008 and Nega \u003cem\u003eet al.\u003c/em\u003e,2012).\u003c/p\u003e \u003cp\u003eIn different corner of Ethiopia different type of driver of LULC change were identified. For instance Hamza \u0026amp;Iyela (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Bewket \u0026amp; Abebe(2013) identified population pressure, income growth and declining productivity; Zeleke \u0026amp; Hurni (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and Bewket(2002) indicated human drivers ;Tekle and Hedlund (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) identified population growth; Pender et al.(2001) showed land degradation, poverty and food insecurity ;Yalew \u003cem\u003eet al.(\u003c/em\u003e2016) identified population, slope, livestock and distances from various infrastructures and Gashaw \u003cem\u003eet al.(\u003c/em\u003e2017) also identified population growth and reduction of land productivity were the drivers of LULC change in different watershed of Ethiopia. Leta \u003cem\u003eet al.,(\u003c/em\u003e2021) identified slope, distance from stream, distance from urban areas, and distance from roads also play an important role in land use change, as each provides convenience to residents to access resources. In our study ,rainfall, slope, elevation, distance to rivers, distance to roads, distance towns and population density were among the drivers of LULC in SRB. Considering drivers of LULC changes at watershed level have paramount importance for sustainably managing of the environment in that watershed.\u003c/p\u003e \u003cp\u003eIn this study, LULC modeling using LCM in SRB were performed. This model is strong due to its dynamic projection proficiency, suitable calibration, and capability to simulate several types of land cover (Aburas \u003cem\u003eet al.\u003c/em\u003e,2018, Brown \u003cem\u003eet al.\u003c/em\u003e,2004). The LCM embedded in the TGMMS model was successfully used by different researchers in other areas and it confirmed that LCM is a capable model for the assessment and prediction of LULC change and the validation of results (Ahmed and Ahmed,2012; Al-sharif and Pradhan,2014; Ye \u003cem\u003eet al.\u003c/em\u003e,2017; Herold \u003cem\u003eet al.\u003c/em\u003e,2002;McGarigal \u003cem\u003eet al.\u003c/em\u003e,2002).By doing so, the findings would be useful as the inputs for planners and other stakeholders regarding the LULC trends in the study area.\u003c/p\u003e"},{"header":"5. Conclusions And Recommendation","content":"\u003cp\u003eThe current study was carried out to model and predict land use land cover changes from the year 1990 to 2048 using LCM in \u003cem\u003eSuluh\u003c/em\u003e river basin, Ethiopia. If the model predictions hold; in the coming three decades, bar land, built up land, and cultivated land shown an increase on the expense of water body, forest, shrub-bush and plantation land. Rainfall, slope, elevation, distance to rivers, distance to roads, distance towns and population density were identified the prominent LULC change drivers in the study area. This will increase vulnerability of the watershed to soil erosion and soil macro fauna loss of the studied river basin in particular and the \u003cem\u003eTekeze\u003c/em\u003e basin in general. Therefore, suitable and timely management measures must be taken by policy decision makers to enable sustainable development and to protect the river basin in order to reduce the severity of the changes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe, the undersigned, give our consent for the publication of identifiable details, which can include photograph(s) and/or videos and/or case history and/or details within the text (\u0026ldquo;Material\u0026rdquo;) to be published in the above Journal and Article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll Availability of data and material will share as per request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBahir Dar University and Debre Tabor University in Ethiopia were the sponsors of our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHailay Hagos Entahabu was a Ph.D. student in Geography (specialization in Natural resources management and Environment) and participated in proposing the study, managed all data collection, conducted all data analyses performed statistical analyses and interpretation results, and drafted the manuscript. Amare Sewnet Minale as supervisors participated in guiding and reviewing the manuscript. Emiru Birhane Hizikias was supervisor and participated in designing the field study and reviewed the manuscript. All authors read and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Bahir Dar University and Debre Tabor University in Ethiopia for their financial support.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAburas, M. M., Abdullah, S. H., Ramli, M. F., Ash'aari, Z. H., \u0026amp; Ahamad, M. S. S. (2018). Simulating and monitoring future land-use trends using CA-Markov and LCM models. In \u003cem\u003eIOP conference series: Earth and environmental science\u003c/em\u003e (Vol. 169, No. 1, p. 012050). IOP Publishing.\u003c/li\u003e\n\u003cli\u003eAhmed, B., \u0026amp; Ahmed, R. (2012). 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Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. \u003cem\u003eSensors\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(3), 1613-1636.\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":"[email protected]","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":"Nearest neighbor fuzzy classification, change detection, land change modeler, Suluh river basin ","lastPublishedDoi":"10.21203/rs.3.rs-1981572/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1981572/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Land use/land cover change has been known globally as an essential driver of environmental change. The study focuses on modeling and prediction of land use/land cover using land change modeler in the Suluh river basin. Landsat images and other ancillary data sources were used to achieve the objectives. The nearest neighbor fuzzy classification was performed in eCognition Developer 9.2 to classify images. Change detection and modeling was performed on IDRISI selva 17.3 software. The data was analyzed qualitatively and quantitatively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e The finding confirmed that Bar land by 10.6%, built up land by 29.4% and cultivated land by 65.4% were rapidly expanding in the face of an overall decline of the forest land by 97.2%, grazing land by 89.8%, plantation land by 89.1% shrub-bush land by 1.5% and water body by 84.8% during 1990 to 2002.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e If the model predictions hold; in the coming 2028 and 2048, bar land, built up land, and cultivated land will be shown an increase on the expense of water body, forest, shrub-bush and plantation land. Rainfall, slope, elevation, distance to rivers, distance to roads, distance from towns and population density were identified as the prominent LULC change drivers in the study area. This will increase the vulnerability of the watershed to soil erosion and soil macro fauna loss of the studied river basin in particular and the Tekeze basin in general. Therefore, suitable and timely management measures must be taken by policy decision makers to enable sustainable development and to protect the river basin in order to reduce the severity of the changes.\u003c/p\u003e","manuscriptTitle":"Modeling and Prediction of Land use/Land Cover Change using Land Change Modeler in Suluh River Basin, Northern Highland of Ethiopia.","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-10-04 14:31:09","doi":"10.21203/rs.3.rs-1981572/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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}},{"code":1,"date":"2022-09-02 17:42:37","doi":"10.21203/rs.3.rs-1981572/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"f8ae9743-a574-47d4-9043-1e8103937327","owner":[],"postedDate":"October 4th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-07T06:14:21+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-04 14:31:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-1981572","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1981572","identity":"rs-1981572","version":["v2"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}