Predicting the Future Land Use and Land Cover Changes for Bhavani

Predicting the Future Land Use and Land Cover Changes for Bhavani Basin, Tamil Nadu, India Using QGIS MOLUSCE Plugin manikandan kamaraj (  [email protected] ) SRM Institute of Science and Technology Sathyanathan Rangarajan SRM Institute of Science and Technology

Research Article

Keywords: Land use and Land cover, ANN-Multi layer perception, Predicted LULC, Bhavani basin, MOLUSCE, QGIS

Posted Date: July 8th, 2021

DOI: https://doi.org/10.21203/rs.3.rs-616393/v1

License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Predicting the Future Land Use and Land Cover Changes for Bhavani basin, Tamil Nadu, India 2 Using QGIS MOLUSCE Plugin 3 52 The five criteria, such as DEM, gradient, aspect, 4 53 distance from the road and river, and built-up 5 Manikandan Kamaraj1 54 density, are used as spatial variable maps in the 2 6 Sathyanathan Rangarajan 55 processes of learning in ANN-Multi layer 7 56 perception to predict their influences on LULC 57 8 Abstract between 2005 and 2010 and it was found that 58 DEM, distance from the road and river, and 9 Human population growth, movement, and 59 built-up density have significant effects. The 10 demand have a substantial impact on land use 60 projected and accurate LULC maps for 2015 11 and land cover dynamics. Thematic maps of land 61 indicate a good level of accuracy, with an overall 12 use and land cover (LULC) serve as a reference 62 Kappa value of 0.69 and with a percentage of the 63 13 for scrutinizing, source administration, and correctness 76.28 %. ANN-Multi-layer 64 perception model is then used to forecast 14 forecasting, making it easier to establish plans 65 changes in LULC for the years 2025 and 2030 15 that balance preservation, competing uses, and 66 which shows significant rise in cropland and 16 growth compressions. The objective of this study 67 built-up areas, by 20 km2 and 10 km2 17 is to identify the changeover of land-use changes 68 respectively. The findings assist farmers and 18 in the Bhavani basin for the two periods 2005 69 policymakers in developing optimal land use 19 and 2015, as well as to forecast and establish 70 plans and better management techniques for the 20 potential land-use changes in the year 2025 and 71 long-term development of natural resources. 21 2030 by using QGIS 2.18.24 version MOLUSCE 22 plugin (ANN-Multi layer perception) model. 72 Keywords: Land use and Land cover; ANN- 73 Multi layer perception; Predicted LULC; 23 74 Bhavani basin, MOLUSCE, QGIS

24 Manikandan Kamaraj 75 Introduction 25 e-mail: [email protected] 76 26 Biodiversity, distribution of water and radiation 27 77 budgets, greenhouse gas emissions, carbon 28 1. Research Scholar, Department of Civil 78 cycling, and livelihoods are all impacted by the 29 Engineering, College of Engineering 79 land-use changes (LULC) around the world. The 30 and Technology, SRM Institute of 80 visual effect of land use at a given moment is 31 Science and Technology, SRM 81 known as land cover. Land use, on the other 32 Nagar,Kattankulathur, 603 203, 82 hand, refers to the amount of human activity that 33 Kanchipuram, Chennai, TN, India. 83 is directly tied to the land and the utilization of 34 84 its resources (Ebenezer et al., 2018). LULC is 35 2. Associate Professor, Department of 85 gradually increased due to the following 36 Civil Engineering, College of 86 parameters interaction with climate, ecological 37 Engineering and Technology, SRM 87 processes, biogeochemical cycles, biodiversity, 38 Institute of Science and Technology, 88 and human activities (Abdul Rahaman et al., 39 SRM Nagar,Kattankulathur, 603 203, 89 2017). LULC studies are generally adopted to 40 Kanchipuram, Chennai, TN, India. 90 know the change in ecology of the area and 41 e-mail: [email protected] 91 vegetation (El-Tantawi et al., 2019a). The 42 92 change in LULC of a region, especially the 43 44 93 increase in built-up areas, alters hydrological 45 94 processes such as runoff pattern, peak flow 46 95 characteristics, water quality, and so on (Ashaolu 96 et al., 2019a). LULC, being considered one of 47 97 the important factors of streamflow shift as it can 48 98 alter the catchment's hydrological processes 99 (Msovu et al., 2019). Humans have influenced 49 100 and altered ground cover, either directly or 50 101 indirectly (Buğday and Erkan Buğday, 2019). 102 The role of the land, or the complete range of 51 103 direct management activities that impact the

104 land's existence, such as agriculture, forestry, 160 estimates the pixel's current condition based on 105 industry, and other associated activities, 161 its initial situation, adjacent neighbourhood 106 determines the land use. Land cover, on the other 162 eventuality, and changeover laws. Moreover this 107 hand, refers to the current biophysical state of 163 accurately depict nonlinear spatial stochastic 108 the earth's surface and immediate subsurface 164 LULC change processes and produce complex 109 (Srivastava et al., 2020; Wang et al., 165 patterns (Saputra and Lee, 2019). CA models are 110 2021).Deforestation, desertification, soil erosion, 166 also increasingly being used in urban planning 111 and other forms of environmental destruction are 167 studies. They are capable of replicating the 112 all caused by the changes in land use and land 168 spatiotemporal complexity of urban areas as well 113 cover (Bhattacharya et al., 2020). The combined 169 as deforestation caused by natural disasters or 114 remote sensing (RS) and geographical 170 human actions (Saputra and Lee, 2019). 115 information system(GIS) has best tool for 171 MOLUSCE was developed to investigate a range 116 managing the land use change research and 172 of applications, including studying temporal 117 natural resources. Analyzing and tracking 173 LULC shifts and projecting future land use, 118 regional and temporal LULC shifts benefits 174 anticipating prospective shifts in land cover and 119 scientists, environmentalists, agriculturalists, 175 forest cover, and detecting deforestation in 120 legislators, and urban planners. (Guidigan et al., 176 sensitive locations. (Aneesha Satya et al., 2020). 121 2019). Remote sensing techniques assist in the 177 This LULC change model, which is based on a 122 efficient preparation of natural resources, as well 178 multicriteria analysis methodology, was created 123 as land management and long-term change 179 using GIS software. (Mzava et al., 2019). To 124 dynamics tracking (Ebenezer et al., 2018and 180 predict their relative effect on the model, the 125 Bhattacharya et al., 2020). LULC transition 181 parameters depend on the researcher's response 126 models, on the other hand, typically attempt to 182 weight (Singh et al., 2014; Hassan et al., 2016). 127 forecast when and how frequently these changes 183 When dealing with human activities that may 128 will occur. Land prediction models such as 184 alter the region suited for biological populations 129 IDRISI's CA MARKOV, CLUE-S/ Dyna- 185 temporally, the LULC has a significant impact 130 CLUE, DYNAMICS EGO, and Land Change 186 on species distributions, which is one of the most 131 Modeler are being used by the researchers across 187 essential environmental aspects to be considered. 132 the world. The future prediction model proved 188 Using the ANN-Multi-Layer perception 133 extremely helpful in determining how previous 189 approach, this study on Bhavani river basin 134 and future LULC changes may effect soil 190 situated in Tamil Nadu, reveals to classify the 135 erosion, especially on farmland. (Perović et al., 191 changeover of land-use changes for the period 136 2018). Several spatio-temporal prediction 192 2005-2015, as well as to forecast and establish 137 models, such as the Markov chain (MC) model, 193 potential land-use changes in the years 2025 and 138 the Cellular Automata (CA) model, and the 194 2030. 139 conversion of land use and its effects (CLUE) 140 model, have been developed in recent years to 195 Study Area 141 forecast the LULC and their change detections 196 The Bhavani river basin is located between 142 (Alam et al., 2021a). Among this the CA model 197 latitudes 10⁰ 56" 3' N to 11⁰ 46" 14' N and 143 has been frequently used for land-use change 198 longitude 76⁰ 24" 41' E to 77⁰ 41" 11' E in 144 analysis among the several spatio-temporal 199 western Tamil Nadu, bordered on the north by 145 dynamic modeling approaches. MOLUSCE 200 the Upper Cauvery river basin aquifer system, on 146 (Modules of Land Use Change Evaluation), a 201 the south by the Amaravathy river basin aquifer 147 new QGIS plugin that can estimate potential 202 system, on the east by the Lower Cauvery river 148 LULC changes is built with CA model and also 203 aquifer system, and on the west by the Karnataka 149 includes a transition probability matrix, is being 204 state (Wang et al., 2021). The Bhavani river rises 150 used by most researchers (NEXTgis 2017). Four 205 in the Western Ghats' Nilgiri hills, flows through 151 well-known algorithm models are employed in 206 Kerala's Silent Valley National Park and returns 207 to Tamil Nadu. The Bhavani is a 217 km long 152 this plugin: Artificial Neural Networks (ANN), 208 perennial river fed primarily by the southwest 153 Logistic Regression (LR), Multi-criteria 209 monsoon, with the northeast monsoon 154 Evaluation (MCE), and Weights of Evidence 210 supplementing it. Its watershed covers 6,216 155 (WoE). A CA-ANN model in MOLUSCE is a 211 km2; out of which nearly 87 % drains in Tamil 156 reliable tool for predicting future LULC that 212 Nadu, 9 % in Kerala, and 1 % in Karnataka. 90 157 may be utilized in land use planning and 213 % of Bhavani river water is used for irrigation 158 management. This approach is being used for 214 and agriculture (Muthusamy et al., 2013; 159 predicting the spatial LULC shift because it 215 Narayanamurthi, 2020). The river flows

216 primarily through the Coimbatore and Erode 246 from road and river map and the three LULC 217 districts in Tamil Nadu. The average annual 247 thematic maps for the years 2005, 2010 and 218 rainfall of the Bhavani basin is 811.47 mm, 248 2015. The LULC maps were obtained from the 219 ranging from 544.70 mm in Annur, Coimbatore 249 National Remote sensing Centre (NRC), 220 district, to 2251.00 mm in Gudalore, Nilgiri 250 Hyderabad, and the road maps were obtained 221 2 district. The Bhavani basin drains 5537 km in 251 from the open street map website 222 Tamil Nadu and is divided into three sections 252 223 (https://www.openstreetmap.org). The SRTM viz., Erode, Coimbatore, and Nilgiris 253 224 (Muthusamy et al., 2013; Ministry of India water (Shuttle Radar Topography Mission) DEM was 225 resources, 2017). In the previous 50 years, the 254 obtained from bhuvan Indian Geo-platform of 226 population of the basin has increased by nearly 255 ISRO. The detailed information of the source of 227 200 percent, to roughly 2.5 million people. More 256 data sets are given in Table 1. 228 than half of the people in the Nilgiris district 257 229 work in cattle, forestry, fishing, hunting, The following parameters which were 230 plantations, or orchards, and 14 % work as 258 chosen for the study are DEM, slope map, aspect 231 cultivators or harvesters. In the Erode district, 259 map, built-up land, and distance from the road 232 agriculture employs about 55 % of the 260 map and river. In the LULC simulation and 233 population, while non-agricultural activity 261 projection, the parameters were then divided into 234 employs the remaining 45 %. (Muthusamy et al., 262 input data and descriptive data. The three 235 2013). In this basin, most of the farmlands are 263 thematic LULC maps include the initial input 236 irrigated either through canal or groundwater and 264 data, while the descriptive data includes DEM, 237 remaining are rain fed with supplementary 265 gradient, aspect, distance from the road and river 238 irrigation through groundwater (Muthusamy et 266 map, and built up density. 239 al., 2013). Among the crops planted are 240 sugarcane, paddy, peanuts, legumes, fodder 267 241 sorghum, coconut, sesame, turmeric, and banana 242 (Fig. 1). 268 243 Data and Criteria 269 244 The source of data set for the study 245 includes digital elevation model (DEM), distance

270 271 272 Fig. 1 Location of the Bhavani basin

273

274 Table 1 Source of Datasets maps

Data Criteria LULC Year Description Source Data Simulation format

Road Distance from Special variable 2017 The main road map of OpenStreetMap .shp &River the road maps Bhavani basin area map

LULC LULC Input maps 2005 A satellite images from National remote sensing .tif map 2010 Landsat Centre(NRSC) 2015 Welcome to Bhuvan | ISRO's Geoportal | Gateway to Indian Earth Observation (nrsc.gov.in)

275 276 277 Table 2 Eight categories of LULC maps Classification Description 1 Builtup Land Buildings and other artificial structures occupy the land.

2 Crop Land A brief farmed area is followed by harvest and a cycle of bare soil (e.g. single and multiple cropping systems). Perennial woody crops can be categorised as forest or shrubland depending on the environment. Orchards are included in the price. Seasons were not used to separate different forms of agriculture into divisions (e.g., Kharif, rabi, zaid). 3 Current fallow All arable land that is either part of a crop rotation system or is kept in good agricultural and land environmental condition (GAEC), whether farmed or not, but will not be harvested for the duration of a crop year, is considered fallow land.

4 Plantations Plantations, orchards, and tree cash crops for commercial horticulture

5 Forest Deciduous forest, Evergreen forest, Scrubs forest. 6 Grassland Herbaceous covers. Trees and shrubs cover less than 10 % of the area.

7 Wasteland Land that is sparsely vegetated shows signs of erosion and land deformation due to lack of adequate water, soil management and natural causes. These are parcels of land that have been classified as underutilized and could be reclaimed for productive purposes with sufficient effort. Wasteland refers to a degraded forest with signs of deforestation (less than 10 % tree cover).

8 Water bodies Surface water, whether impounded in ponds, lakes, or reservoirs, or flowing as streams, rivers, and other bodies of water. Water bodies may be either fresh or salty.

278 Methods 331 279 332 In the Cellular Automation model, the LULC maps 280 transition probabilities from the ANN learning 333 281 process are employed to describe the LULC 2005, 2010 282 changes. The (MOLUSCE) plugin in Quantum 334 283 GIS 2.18.24 software is used for this method (Fig. 284 2). The MOLUSCE plugin features six LULC 335 285 prediction phases (Hakim et al., 2019). 336 Resample the variables 286 1. Inputs 337 Extent, Pixel size, 287 This first step in the model is to include Co-ordinates 288 the LULC maps for the beginning (2005) and 338 289 end year (2010). The spatial variable factors such 339 290 as DEM, slope map, aspect map, distance from 291 road and rivers, and built-up density are fed in 340 292 the model to get a land cover change map from Explanatory maps 293 which the changing pattern for the study area 341 294 between 2005 and 2010 is established (Fig.3). DEM, Slope, Aspect, Distance 342 295 The properties of the explanatory maps are from Road and River, Builtup land 296 extracted in the same raster format for all 343 297 datasets, with the same geographical projected 298 coordinates of UTM 43N and with a resolution 344 299 pixel size of 50 m. 345 300 The plugin calculates the percentage of 346 Land use Change Analysis 301 area change in a given year and generates a 302 transition matrix that shows the proportion of 347 303 pixels shifting from one land use cover to 304 another. The plugin also creates an area change 348 Transition Potential Model 305 map that shows the change in the land between 349 306 2005 and 2010 in all the eight classes’ viz., built- 307 up land, cropland, pasture, fallow land, forest, 350 308 grassland, wasteland, and water bodies. To 351 309 project the change in LULC, the ANN-(Multi Cellular Automation-ANN 310 layer perception) plugin was used (Buğday and 352 311 Erkan Buğday, 2019; Msovu et al., 2019). Also, 353 312 based on the classified raster images of 2005 and 313 2010, LULC transitions are predicted for the 354 Validation 314 years 2025 and 2030. The future LULC maps are Predicted 2015 LULC and Real 2015 LULC 315 predicted assuming that existing LULC pattern 355 316 and dynamics are getting continued. 356 317 2. Evaluation Correlation 357 318 The correlation of geographic variables 358 319 between the two raster images, which are used to Prediction of LULC Maps 2025 and 320 examine the correlation among the spatial 359 2030 321 variables factors, is evaluated using Pearson's 322 correlation, Crammer's coefficient, and Joint 360 323 information uncertainty. (Hakim et al., 2019). 361 Fig. 2 Flowchart of the methodology 324 Then, between the initial year (2005) and the 325 final year (2010), the category of each area and 362 326 the LULC changes are calculated. The transition 327 matrix, which indicates the fraction of pixels 328 changing from one type to the next, is also 329 produced by the algorithm. 330

378 (LR), and Multi-Criteria Evaluation (MCE). For 379 calibrating and modelling land use/cover 380 changes, each methodology takes land use/cover 381 change information and geographic factors as 382 inputs. (Buğday and Erkan Buğday, 2019; El- 383 Tantawi et al., 2019b; Guidigan et al., 2019). To 384 model LULC forecast, the Artificial Neural 385 Network (Multilayer Perception) technique was 386 used for this study to forecast LULC map for the 387 year 2015. The kappa coefficient was measured 388 while validating the real and predicted LULC 389 maps. 390 5. ANN-CA 391 While there are numerous ways for 392 creating a transitional potential map, this module 393 includes computational intelligence aspects such 394 as artificial neural networks (ANN). LULC data is 395 used as an input in every approach for calibrating 396 and modelling LULC change. This strategy is 397 justified in handling problems where the algorithm 398 must deal with enormous amounts of uncertain or 399 difficult-to-implement input data. As a result, a 400 continuous index is created that describes the 401 terrain on a scale of 0 to 1. Since ANN 402 incorporates fuzzy logic requirements, a 403 continuous range, such as 0 and 1, is determined 404 based on terrain usability. The interactions 405 between linked neurons and the alteration of the 406 weight connections between them are the 407 important elements of ANN (Bhattacharya et al., 408 2020). The following parameters were finally 409 arrived while predicting the LULC map for the 410 year 2015 viz., neighbourhood - 1, iterations – 411 1000 nos., hidden layer – 10 nos., momentum 412 value - 0.06, learning rate - 0.001 (El-Tantawi et 413 al., 2019c; Perović et al.,2018; Das and Sarkar, 414 2019) . 415 6. Validation 416 The assessment of LULC was measured widely by 417 kappa coefficient. The validation is carried out 418 363 between the predicted and real LULC maps of 419 2015 by calculating the overall kappa value. The 364 Fig. 3. Explanatory maps: DEM, Slope map, 420 Kappa coefficient is calculated using the 365 Distance from road and river, builtup density and 421 expression given below (Ullah et al., 2019; 366 Aspect map 422 Alawamy et al., 2020; Aneesha Satya et al., 2020)

367 3. Area Change 423 368 This stage calculates changes in area 푝표 − 푝푒 푘푎푝푝푎 = 369 between the initial year (2005) and final year 424 Where po denotes the 1 proportion− 푝푒 of actual 370 (2010) of the LULC. The changes in land 425 agreements and pe denotes the proportion of 371 use/cover area are represented in km2. (Ashaolu 426 expected agreements. 372 et al., 2019b; Rahman et al., 2017b).

373 4. Transition Potential Modelling 427 푐

374 While there are several methods for 푝표 = ∑ 푝푖푗 375 calculating transition potential maps, this plugin 푖=1 376 includes artificial neural networks (ANN), 428 푐 377 weights of evidence (WoE), logistic regression 푝푒 = ∑ 푝푖푇푝푇푗 푖=1 6

429 Where pij denotes the i-th and j-th cells in the 472 high values, which show no changes because 430 contingency table, piT denotes the sum of all 473 they remain in the same category, the value in 431 cells in the i-th row, pTj denotes the sum of all 474 the table ranges from 0 to 1, with higher values 432 cells in the j-th column, and c denotes the raster 475 signifying bigger changes. 433 category count. The contingency table is a 476 The trend of LULC change from 2005 434 matrix that represents the frequency distribution 477 to 2020 is summarised in Tables 5 and Table 6, 435 of variables and is used in this study to show 478 which show the percentage of area covered 436 how the i-th and j-th cells are related. In a 479 under each LULC classes. (Fig.5) depicts the 437 matrix, the interactions of each cell are tabulated 480 spatial variation of LULC from 2005 to 2020. In 438 and calculated. The result explains the 481 2005, forest is dominated by 47.79 % of the total 439 agreement of every criterion of each cell 482 area, followed by cropland (16.65 %), plantation 440 (Saputra and Lee, 2019). 483 (15.50 %), fallow land (9.27%), wasteland (7.34 441 Several simulations were done to 484 %), water bodies (2.03 %), built upland (1.40 442 predict the LULC change map for 2015 utilising 485 %), and grassland (0.03 %). It was observed that 443 various combination of spatial variables factors, 486 except for water and grassland, changes in the 444 and for the analysis two to three spatial variables 487 trend were detected for all LULC between the 445 were combined to create an ANN-Multi layer 488 years 2005, 2010, 2015, and 2020. When 446 perception (Table 3). 489 compared to 2005, the percentage of area 490 covered in 2020 had decreased for forest land, 447 Table 3. Different combinations of explanatory 491 fallow land, waste land and water bodies by 0.31 448 maps and Kappa coefficients 492 %, 3.28%, 1.67% and 0.02% respectively. An No Spatial variable Percentage Kappa 493 increase in percentage was observed for built-up combinations of kappa coefficients 494 land and crop land by 1.47% and 3.8% correctness 495 respectively. Change in area was not observed 1 DEM, Road, 76.28 0.69 496 for the plantation and grassland category Built-up 497 between the periods. 2 DEM, Road 73.12 0.60 498 The change in land cover classes 3 DEM, Built-up 70.45 0.58 499 between 2015 and 2025 is depicted in Table 7.It 4 DEM, Builtup, 71.01 0.56 500 is observed that the built-up and crop land will Urban, gradient 501 increase by 24.09 km2 and 14.13 km2, 5 DEM, gradient, 68.86 0.54 502 respectively, while fallow land, forest, and waste Aspect 503 land will decline by 10.1 km2, 11.79 km2, and 449 504 16.14 km2, respectively. Land use land cover 450 Table 3 discusses the overall accuracy 505 changes as a percentage of total land area is also 451 and maximum kappa coefficients for the various 506 analysed. A positive value implies that the 452 spatial variable factors combinations. From the 507 categorization has improved, while a negative 453 analysis it was found that the combinations of 508 value denotes the categorization has deteriorated. 454 DEM, built-up density, distance from the road 455 and river, got the maximum Kappa value of 0.69 456 and the maximum percentage of correctness 457 76.28 % (Fig.4). The maximum kappa value of 458 0.63 was considered as a good accuracy by many 459 researchers (Alam et al., 2021b; Aneesha Satya 460 et al., 2020; Perović et al., 2018; Rahman et al., 461 2017b). Hence it can be concluded that these 462 variables have high influence on the predicted 463 LULC map of this basin. The LULC map of 464 2025 and 2030 were predicted by using 2005 and 465 2010 LULC map along with the same spatial 466 variable factors combinations. 467 Results and Discussion 509 468 510 Fig.4 Validation graph between observed 2015 469 Table 4 displays the changeover 511 and predicted 2015 LULC map 470 probability matrix of LULC categories from 471 2005 to 2010. Except for the diagonal cells with 512

513 Table 4 Change over probability matrix LULC from 2005-2010

2010 Classification Built-up Fallow Grass Waste Water Crop Land Plantation Forest Sum Land Land Land Land bodies Builtup Land 0.979113 0.010186 0.006318 0.001418 0.00026 0 0.00065 0.00206 1 Crop Land 0.001698 0.87721 0.10341 0.001471 0.00199 0 0.01345 0.00077 1

Fallow Land 0.001398 0.439624 0.537976 0.001281 0.00155 0 0.01761 0.00056 1 Plantation 0.000511 0.001696 0.00043 0.997213 0 0 9.3E-05 5.8E-05 1 Forest 0.000053 0.000848 0.000128 0 0.99878 0 7.2E-05 0.00012 1 2005 Grass Land 0 0 0 0 0 1 0 0 1 Waste Land 0.088227 0.076846 0.013515 0.001202 0.00113 0 0.81869 0.00039 1 Water bodies 0.000621 0.009588 0.002219 0.000266 0.0008 0 0.00107 0.98544 1 Sum 1.071621 1.415998 0.663996 1.002851 1.00451 1 0.85162 0.98941 8 514

515 Table 5 LULC analysis from 2005-2020

2005 2010 2015 2020 % of % of % of % of Area in Area in Area in Area in km2 area area area area km2 km2 km2 LULC covered covered covered covered Builtup Land 77.56 1.40 114.85 2.07 141.48 2.55 159.68 2.87 Crop Land 924.67 16.65 1074.73 19.35 1125.21 20.25 1136.24 20.45 Fallow Land 515.19 9.27 379.78 6.84 340.48 6.13 332.57 5.99 Plantation 861.04 15.50 861.29 15.50 861.28 15.50 861.28 15.50 Forest 2654.74 47.79 2654.71 47.79 2644.89 47.61 2637.5 47.48 Grass Land 1.61 0.03 1.61 0.03 1.58 0.03 1.58 0.03 Waste Land 407.7 7.34 355.75 6.40 328.48 5.91 314.73 5.67 Water bodies 112.64 2.03 112.69 2.03 112.01 2.02 111.83 2.01 516

517

Fig. 5 LULC maps for the year’s 2005, 2010, 2015 and 2020

518

519

520 Table 6 Land use land cover change analysis for every five years from 2005-2020

YEAR 2005-2010 2010-2015 2015-2020 Changes Changes Area in km2 % Area in km2 Changes % Area in km2 % Builtup Land 37.29 0.671269 26.63 0.47935 18.2 0.327 Crop Land 149.86 2.697677 50.48 0.90866 11.03 0.198 Fallow Land -135.45 -2.438278 -39.3 -0.7074 -7.91 -0.142 Plantation 0.25 0.0045 -0.01 -0.0002 0 0 Forest -0.03 -0.00054 -9.82 -0.1768 -7.39 -0.133 Grass Land 0 0 -0.03 -0.0005 0 0 Waste Land -51.97 -0.935528 -27.27 -0.4909 -13.75 -0.247 Water bodies 0.05 0.0009 -0.68 -0.0122 -0.18 -0.003 521 523522 524 525 Table 7 Distribution of LULC categories between 2015 and 2025 526 2015 2025 Change Year (km2) Year (km2) (km2) 2015% 2025% Δ % Builtup Land 141.48 165.57 24.09 2.55 2.98 0.434 Crop Land 1125.21 1139.34 14.13 20.25 20.51 0.254 Fallow Land 340.48 330.38 -10.1 6.13 5.95 -0.182 Plantation 861.28 861.28 0 15.50 15.50 0.000 Forest 2644.89 2633.1 -11.79 47.61 47.40 -0.212 Grass Land 1.58 1.58 0 0.03 0.03 0.000 Waste Land 328.48 312.34 -16.14 5.91 5.62 -0.291 Water bodies 112.01 111.82 -0.19 2.02 2.01 -0.003 527 528 529 . 530 Table 8 Distribution of LULC categories between 2015 and 2030 2015 2030 Change Year (km2) Year (km2) (km2) 2015% 2030% Δ % Built-up Land 141.48 169.95 28.47 2.55 3.06 0.512 Crop Land 1125.21 1140.42 15.21 20.25 20.53 0.274 Fallow Land 340.48 329.64 -10.84 6.13 5.93 -0.195 Plantation 861.28 861.28 0 15.50 15.50 0.000 Forest 2644.89 2629.86 -15.03 47.61 47.34 -0.271 Grass Land 1.58 1.58 0 0.03 0.03 0.000 Waste Land 328.48 310.86 -17.62 5.91 5.60 -0.317 Water bodies 112.01 111.82 -0.19 2.02 2.01 -0.003 531 532 533

534

535 Table 8 shows how the land cover classifications 536 have changed between 2015 and 2030. By 2030, an 537 increase in area is observed for built-up areas and 538 cropland by 28.47 km2 and 15.21 km2, respectively. 539 For other categories except for plantation and grass 540 land a decrease in area is forecasted. (Fig.6) shows 541 the areal LULC changes of different categories from 542 2005 to 2030 in an incremental span of five years 543 and (Fig.7) shows the expected land-use changes in 544 2025 and 2030.

545 563 3000 564 Fig . 7 Predicted LULC maps for the year’s 2025 and 565 2030 2500 566

2000 567 Conclusion 568 The prediction of LULC plays a vital role in 1500 569 creating plans for balancing conservation, competing 570 users, and developmental pressures. The ANN- 571 1000 Cellular Automation model is utilized to simulate 572 and predict the future LULC maps of the Bhavani 573 basin, Tamil Nadu. The three spatial variable factors 500 574 viz., DEM, distance from the road and river, and 575 built-up density, had a huge effect to predict LULC 0 576 map of this basin. The Kappa value of 0.69 shows a 2005 2010 2015 2020 2025 2030 577 maximum level of accuracy between the observed 578 and predicted 2015 LULC maps. The LULC map of Builtup Land Crop Land Fallow Land 579 2025 and 2030 were predicted by using 2005 and 580 2010 LULC map along with same spatial variable Plantation Forest Grass Land 581 factors combinations. The predicted LULC for the 582 years 2025 and 2030 show significant increase in Waste Land Water bodies 2 2 546 583 cropland and built-up areas by 20 km and 10 km 584 respectively. Meanwhile, relative to the year 2015 547 585 LULC, fallow land, forest land, and wasteland areas 586 2 2 2 2 are expected to decrease by 10 km , 11 km , 16 km 548 Fig. 6 Change of LULC areas in km from 2005 to 587 by 2025, and 10 km2,15 km2 and 17 km2 by 2030. 549 2030 588 The results demonstrate that due to anthropogenic 589 pressures the conversion of forest land and other 550 The percentage difference in LULC 590 land categories would be converted to builtup land 551 categories from 2005 to 2025 and 2005 to 2030 591 and crop land areas. 552 showed an increase in built-up area and crop land by 553 1.58 %, and 3.86 %; 1.66 % and 3.88 % respectively, 592 The forest region in the Nilgiri hills of the 554 whereas for other categories a decrease in percentage 593 Western ghats is a key forest area in Tamil Nadu, 555 (< 3%) was noticed. From the analysis it is learnt that 594 which needs significant preservation and conservation. 556 when the area of one classification increases, it 595 Forest disturbance, particularly land conversion, does, 557 decreases the area of other classes and vice versa. 596 nevertheless, result in forest degradation. Rather than 558 Increase in cropland and built-up areas in the future 597 being retained as forest, human interests in the region 559 imply that these categories have to be given 598 drive the land to be transformed to extra creative uses, 560 importance in designing policy formulations for the 599 such as farmland or industrial types. Conflicts over 561 basin. 600 land interests in general, results in LULC 601 modifications. Residents, for example, prefer crop 562 602 plantings for reforestation, whereas businesses extend 603 their plantations to boost earnings, reducing the 604 region's wooded land. This situation necessitates a 605 LULC approach that emphasizes the change's long-

606 term consequences, notably forest biodiversity loss. 663 approach. Annals of GIS. 607 This study assists us in detecting specific land-use 664 https://doi.org/10.1080/19475683.2020.1870558 608 changes and projecting which land uses will be 665 Alam, N., Saha, S., Gupta, S., Chakraborty, S., 609 affected by future changes. It is also possible to detect 666 2021b. Prediction modeling of riverine landscape 610 biodiversity loss and ecological concerns. The 667 dynamics in the context of sustainable 611 findings assist farmers and policymakers in 668 management of floodplain: a Geospatial 612 developing optimal land use planning and 669 613 approach. Annals of GIS. management methods for the long-term development 670 614 of natural resources. https://doi.org/10.1080/19475683.2020.1870558 671 Alawamy, J.S., Balasundram, S.K., Hanif, A.H.M., 615 Author contribution 672 Sung, C.T.B., 2020. Detecting and analyzing 616 MK carried out all technical details and 673 land use and land cover changes in the Region of 617 prepared the map and performed GIS analysis. SR 674 Al-Jabal Al-Akhdar, Libya using time-series 618 contributed to the verification of the analysis and the 675 Landsat data from 1985 to 2017. Sustainability 619 results. MK has written the manuscript in 676 (Switzerland) 12. 620 consultation with SR. Both authors contributed to 677 https://doi.org/10.3390/su12114490 621 shape the work by discussing the results and 678 622 Aneesha Satya, B., Shashi, M., Deva, P., 2020. contributed to the final manuscript. 679 623 Future land uses land cover scenario simulation 624 Funding: Not applicable 680 using open source GIS for the city of Warangal, 625 681 Telangana, India. Applied Geomatics 12, 281– 626 Availability of data and materials: 682 290. https://doi.org/10.1007/s12518-020-00298-4 627 The Land use and Land cover (LULC) maps used 683 Ashaolu, E.D., Olorunfemi, J.F., Ifabiyi, I.P., 2019a. 628 for the current study are obtained from the National 684 Assessing the Spatio-Temporal Pattern of Land 629 Remote sensing Centre (NRC), Hyderabad, 685 Use and Land Cover Changes in Osun Drainage 630 (https://bhuvan.nrsc.gov.in/bhuvan_links.php) and 686 Basin, Nigeria. Journal of Environmental 631 the road maps are obtained from the open street map 687 Geography 12, 41–50. 632 (https://www.openstreetmap.org). The SRTM (Shuttle 688 https://doi.org/10.2478/jengeo-2019-0005 633 Radar Topography Mission) DEM for this study is 689 Ashaolu, E.D., Olorunfemi, J.F., Ifabiyi, I.P., 2019b. 634 obtained from bhuvan Indian Geo-platform of ISRO 690 Assessing the Spatio-Temporal Pattern of Land 635 (https://bhuvan- 691 Use and Land Cover Changes in Osun Drainage 636 app3.nrsc.gov.in/data/download/index.php). 692 637 Basin, Nigeria. Journal of Environmental 638 Declarations 693 Geography 12, 41–50. 639 694 https://doi.org/10.2478/jengeo-2019-0005 640 Ethical approval: Not applicable 695 Bhattacharya, R.K., das Chatterjee, N., Das, K., 641 696 2020. Land use and Land cover change and its 642 Consent to participate: Not applicable 697 resultant erosion susceptible level: an appraisal 643 698 using RUSLE and Logistic Regression in a 644 Consent to publish: Not applicable 699 tropical plateau basin of West Bengal, India, 645 700 Environment, Development, and Sustainability. 646 Competing interests: The authors declare that they 701 Springer Netherlands. 647 have no competing interests. 702 https://doi.org/10.1007/s10668-020-00628-x 648 703 Buğday, E., Erkan Buğday, S., 2019. Modeling and 649 References 704 650 Abdul Rahaman, S., Aruchamy, S., Balasubramani, simulating land use/cover change using the 705 651 K., Jegankumar, R., 2017. Land use/land cover artificial neural network from remote sensing 706 data. Cerne 25, 246 254. 652 changes in a semi-arid mountain landscape in – 707 https://doi.org/10.1590/01047760201925022634 653 Southern India: A geoinformatics based Markov 708 654 chain approach. International Archives of the Das, S., Sarkar, R., 2019. Predicting the land use and 709 land cover change using Markov model: A 655 Photogrammetry, Remote Sensing and Spatial 710 catchment level analysis of the Bhagirathi-Hugli 656 Information Sciences - ISPRS Archives 42, 231– 711 657 237. https://doi.org/10.5194/isprs-archives-XLII- River. Spatial Information Research 27, 439– 712 658 1-W1-231-2017 452. https://doi.org/10.1007/s41324-019-00251-7 713 Ebenezer, B., Geophery, K.A., Jonathan, A.Q.-B., 659 Alam, N., Saha, S., Gupta, S., Chakraborty, S., 714 Emmanuel, A.D., 2018. Land-use change and 660 2021a. Prediction modeling of riverine landscape 715 661 dynamics in the context of sustainable sediment yield studies in Ghana: Review. Journal 716 of Geography and Regional Planning 11, 122 662 management of floodplain: a Geospatial – 717 133. https://doi.org/10.5897/jgrp2018.0707

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