sign in sign up
<<
Home , Kuala Lumpur

Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎

1 Contents lists available at ScienceDirect 2 3 4 Data in Brief 5 6 7 journal homepage: www.elsevier.com/locate/dib 8 9 Data Article 10 11 12 Satellite data for , and Kuala 13 Q2 Lumpur city growth analysis 14 15 Mukesh Singh Boori a,b,d, Komal Choudhary a, 16 a,c a 17 Q1 Alexander Kupriyanov , Viktor Kovelskiy 18 Q3 a Samara State Aerospace University, Samara, Russia 19 b American Sentinel University, CO, USA c 20 Image Processing Systems Institute, Samara, Russia d Hokkaido University, Sapporo, 21 22 23 article info abstract 24 25 Article history: This data article presents satellite data related to city growth of 26 Received 25 2016 Singapore, Manila and cities. The data were col- 27 Received in revised form lected from NASA and USGS websites. A method has been devel- 28 8 April 2016 oped for city built-up density from city center to outward till Accepted 12 April 2016 29 50 km by using satellite data. These data sets consists three decade 30 Landsat images. A detailed description is given to show how to use 31Q4 Keywords: this data to produce urban growth maps. The urban growth maps 32 Urban growth have been used to know the changes and growth pattern in the City density Southeast Cities. 33 Landsat satellite data & 2016 Published by Elsevier Inc. This is an open access article 34 Change detection 35 Remote sensing under the CC BY license 36 GIS (http://creativecommons.org/licenses/by/4.0/). 37 Singapore Manila 38 Kuala Lumpur 39 40 41 42 Specifications Table 43 44 45 Subject area Earth Science and Geo-informatics 46 More specific sub- Remote Sensing and GIS 47 ject area 48 Type of data Satellite image, table, figure, graph 49 50 51 E-mail : [email protected] (M.S. Boori). 52 http://dx.doi.org/10.1016/j.dib.2016.04.028 53 2352-3409/& 2016 Published by Elsevier Inc. This is an open access article under the CC BY license 54 (http://creativecommons.org/licenses/by/4.0/).

Please cite this article as: M.S. Boori, et al., Satellite data for Singapore, Manila and Kuala Lumpur city growth analysis, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.04.028i 2 M.S. Boori et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎

55 How data was Collect from field and download from NASA and USGS website 56 acquired 57 Data format Analyzed 58 Experimental Image processing 59 factors 60 Experimental Image classification, combined satellite data and socio-economic data in GIS 61 features with the help of ArcGIS 10.2 software 62 Data source Scientific Research Laboratory of Automated Systems of Scientific Research 63 location (SRL-35), Samara State Aerospace University, Samara, Russia 64 Data accessibility Data is in this data article 65 66 67 68 69 Value of the data 70 71 72 City growth and land use/cover data are utilize in maximum type of remote sensing data appli- 73 cations such as hydrology, agriculture, forest, urban growth/city planning, vulnerability, natural 74 resources and sustainable development etc. fi 75 Socio-economic or secondary data such a general amenities, facilities and eld data are useful to verify the satellite data and to know the changes of an area. 76 77 Data of urban expansion, land use/cover are very useful to and urban planners for 78 the future plans to sustainable development of the city. 79 80 81 1. Data 82 83 Q5 The following multi-sensor, multi-resolution and multi-temporal satellite data were used: Landsat 84 5 TM (Thematic Mapper) for 1989, Landsat 7 ETMþ (Enhanced Thematic Mapper plus) for 2001 and 85 Landsat 8 OLI (Operational Land Imager) for 2014, an image captured by a different type of sensors 86 with less than 20% cloud cover. In addition, Landsat images are frequently updated and are available 87 free of charge through the Global Land Cover Facility repository [1–3]. All data were downloaded free 88 89 of charge from NASA and USGS website. Fig. 1 presents city growth of Singapore, Manila and Kuala 90 Lumpur in the last three decades. 91 92 93 2. Experimental design, materials and methods 94 95 To convert all row satellite data into meaningful data, first rectified and georeferenced the data in 96 UTM projection (WGS 84 datum). To increase the quality of satellite data, all data processed through 97 band ratio, classification and in last change detection techniques [4–6]. All satellite data were pre- 98 ferred to retain the spatial details such as original pixel value and size. Therefore the satellite data 99 were kept without changing their pixel sizes despite the possible varying accuracy level of classifi- 100 101 cation with the different spatial, spectral and radiometric resolutions. All nine satellite images were fi fi – 102 classi ed through maximum likelihood supervised classi cation in ArcGIS 10.2 software [7 9].To fi 103 create a closer correspondence between the produced output data maps, the classi cation was done 104 by only considering four main classes: urban/built-up area, agriculture land, forest land (tree/park) 105 and water body [10–12]. The produced data maps were presented in Fig. 2. 106 After classification multi-buffer rings were created for every 1 km distance from 1 to 50 km from 107 city center to outward. Than intersect with classified data maps (land cover) for all three dates [13– 108 15]. Later on all land cover classes area were measured from 1 to 50 km distance and derive urban

Please cite this article as: M.S. Boori, et al., Satellite data for Singapore, Manila and Kuala Lumpur city growth analysis, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.04.028i M.S. Boori et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 3

109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 Fig. 1. Singapore, Manila and Kuala Lumpur city growth from 1989, 2001 and 2014. 160 161 162

Please cite this article as: M.S. Boori, et al., Satellite data for Singapore, Manila and Kuala Lumpur city growth analysis, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.04.028i 4 M.S. Boori et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎

163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 Fig. 2. Land cover and multi-buffer ring zones around city center of Singapore, Manila and Kuala Lumpur for the year of 1989, 200 2001 and 2014. 201 202 density according to following formula. 203 Sattlement area=ring 204 Urbandensity ¼ ð1Þ Total ring area 205 206 In all buffer rings only urban/built-up area was calculated in place of whole ring area. After pro- 207 ducing complete land use data maps, the total coverage of different classes were determined. Using 208 this information, we calculated the water, forest, vegetation and built-up area per capita for all the 209 study years (Table 1). 210 In order to evaluate the spatial distribution of urban expansion intensity, we adapted an indicator 211 called annual urban growth rate (AGR) for evaluating the ‘urbanization’ speed of per unit area [16,17]. 212 AGR is defined as follows: 213 UA þ UA 214 AGR ¼ n i i 100% ð2Þ nTA þ 215 n i

216 where TAn þi is the total land area of the target unit to be calculated at the time point of iþn; UAnþ i

Please cite this article as: M.S. Boori, et al., Satellite data for Singapore, Manila and Kuala Lumpur city growth analysis, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.04.028i M.S. Boori et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 5

217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 Area_1989 % Area_2001 % Area_2014 % 261 262 263 264 265 266 267 268 269 Singapore SettlementsAgricultureForestWaterTotalManila Settlements 196.88Agriculture 34.67ForestWaterTotal 60.74 4.89 297.18Kuala Lumpur Settlements 189.61 66.25Agriculture 6.37Forest 11.67WaterTotal 2.19 20.44 0.1 1.65 196.08 100.00 383.23 456.99 96.70 345.46 80.26 3.24 70.50 62.58 9.18 1.11 549.71 23.64 882.13 0.05 100.00 416.87 51.81 69.71 39.16 110.14 14.60 7.99 9.44 11.38 540.81 1.04 100.00 4.36 100.00 4.30 1098.48 464.69 77.08 520.40 80.84 20.37 188.74 89.65 662.05 1824.16 16.54 100.00 1.75 26.87 776.65 60.22 0.81 70.19 28.53 243.89 12.21 10.35 1123.55 13.54 100.00 100.00 98.59 0.91 1663.23 4.42 4.06 69.12 699.21 21.71 209.99 2584.11 100.00 11.68 64.36 8.77 27.06 0.39 100.00 8.13 0.45

270 Table 1 Singapore, Manila and Kuala Lumpur land use/cover classes for 1989, 2001 and 2014.

Please cite this article as: M.S. Boori, et al., Satellite data for Singapore, Manila and Kuala Lumpur city growth analysis, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.04.028i 6 M.S. Boori et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎

271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 Fig. 3. Singapore, Manila and Kuala Lumpur city density from 1 to 50 km distance for the year of 1989, 2001 and 2014. 313

314 and UAi the or built-up area in the target unit at time iþn and i, respectively and n is the 315 interval of the calculated period (in years). 316 317 318 2.1. Urban/built-up area, city density and landscape 319 320 With the help of ArcGIS 10.2 software, urban/built up area data were created. After creation of 321 built-up data, city density data were generated by Eq. (1) from city center to 50 km outward the city 322 for Singapore, Manila and Kuala Lumpur cities (Fig. 3). 323 As Fig. 3 represents three dates data of urban density so this data can be utilize as urban growth 324 rate data of Singapore, Manila and Kuala Lumpur city.

Please cite this article as: M.S. Boori, et al., Satellite data for Singapore, Manila and Kuala Lumpur city growth analysis, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.04.028i M.S. Boori et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 7

325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 Fig. 4. Singapore, Manila and Kuala Lumpur land cover class density from 1 to 50 km distance for the year of 1989, 2001 349 and 2014. 350 With the help of supervised classification, previous knowledge and experience, landscape data 351 352 were created for all three cities for the year of 1989, 2001 and 2014. Here a trimble hand-held GPS 353 with an accuracy of 10 m was used to map and collect the coordinates of important landscape fea- 354 tures during pre- and post-classification field visits in order to prepare land-use and land-cover 355 datasets. Fig. 4 shows landscape data with urban growth and urban density datasets for Singapore, 356 Manila and Kuala Lumpur cities from 1989 to 2014. 357 358 359 360 Acknowledgments 361 fi fi 362 This data work is nancially supported by the Russian Scienti c Foundation (RSF), Grant no. 14-31- 363 00014 “Establishment of a Laboratory of Advanced Technology for Earth Remote Sensing”. 364 365 366 367 References 368 369 [1] Kai-ya Wu, Z. Hao, Land use dynamics, built-up land expansion patterns, and driving forces analysis of the fast-growing – – 370 Hangzhou , eastern (1978 2008), Appl. Geogr. 34 (2012) 137 145. Q6 [2] M.S. Boori, R.R. Ferraro, Global land cover classification based on microwave polarization and gradient ratio (MPGR), in: 371 Geo-informatics for Intelligent Transportation, Springer International Publishing, Switzerland, vol. 71, 2015, pp. 17–37, 372 http://dx.doi.org/10.1007/978-3-319-11463-7-2. 373 [3] A.M. Dewan, Y. Yamaguchi, M.Z. Rahman, Dynamics of land use/cover changes and the analysis of landscape fragmentation in metropolitan, , GeoJ. 77 (3) (2012) 315–330. 374 [4] M.S. Boori, V. Vozenilek, K. Choudhary, Land use/cover disturbances due to in Jeseniky Mountain, Czech Republic: a 375 remote sensing and GIS based approach, . J. Remote Sens. Space Sci. 17 (3) (2015) 01–10. http://dx.doi.org/10.1016/j. 376 ejrs.2014.12.002. [5] T.M. Lillesand, R.W. Kiefer, Remote Sensing and Image Interpretation, John Wiley and Sons, New York, 1999. 377 [6] J.R. Anderson, E. Hardey, J. Roach, R.E. Witmer, A land use and land cover classification system for use with remote sensor 378 data, US geological survey professional paper, Washington, DC, vol. 964, 1976, p. 28.

Please cite this article as: M.S. Boori, et al., Satellite data for Singapore, Manila and Kuala Lumpur city growth analysis, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.04.028i 8 M.S. Boori et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎

379 [7] M.S. Boori, V. Vozenilek, K. Choudhary, Exposer intensity, vulnerability index and landscape change assessment in Olo- 380 mouc, Czech Republic, ISPRS: Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XL-7/W3 (2015) 771–776. http://dx.doi. org/10.5194/isprsannals-II-8-77-2014. 381 [8] J.R. Jensen, J. Im, Remote sensing change detection in urban environments, in: R.R. Jensen, J.D. Gatrell, D. McLean (Eds.), 382 Geo-Spatial Technologies in Urban Environments: Policy, Practice and Pixels, 2nd ed.,Springer-Verlag, Heidelberg, 2007, 383 pp. 7–30. [9] M.S. Boori, V. Vozenilek, Land-cover disturbances due to tourism in Jeseniky mountain region: a remote sensing and GIS 384 based approach, SPIE: Remote Sensing, vol. 9245, 92450T, 2014, pp. 01–11, http://dx.doi.org/10.1117/12.2065112. 385 [10] United Nations, World urbanization prospects, The 2014 Revision, New York, 2014. 386 [11] M.S. Boori, K. Choudhary, A. Kupriyanoy, V. Kovelskiy, Four decades urban growth and land use change in Samara Russia – 387 through remote sensing and GIS techniques, SPIE: Remote Sensing and Image Formation, vol. 9817, 2015, pp. 01 07, http:// dx.doi.org/10.1117/12.2227992. 388 [12] D. Haiyong, W. Shi, Land-use/land-cover change and its influence on surface temperature: a case study in City, Int. 389 J. Remote. Sens. 34 (15) (2013) 5503–5517. 390 [13] M.S. Boori, A.V. Kuznetsov, K. Choudhary, A. Kupriyanoy, Satellite image analysis to evaluate the urban growth and land use changes in the city of Samara from 1975 to 2015, Comput. Opt. 39 (5) (2015) 818–822. http://dx.doi.org/10.18287/ 391 0134-2452-2015-39-5-818-822. 392 [14] B. Feizizadeh, H. Helali, Comparison pixel-based, object based image analysis and effective parameters in classification 393 land cover e land use of west province , J. Geogr. Res. 47 (2010) 73–84. [15] M.S. Boori, M. Netzband, V. Vozenilek, K. Choudhary, Urban growth in last three decades in Kuala Lumpur, IEEE: 394 Urban Remote Sensing Event (JURSE), 2015 Joint, 2015e, pp. 01–04, http://dx.doi.org/10.1117/12.222799210.1109/JURSE. 395 2015.7120536. 396 [16] K.O. Were, Ø.B. Dick, Remotely sensing the spatial and temporal landcover changes in Eastern Mau forest reserve and Lake Nakuru drainage basin, Appl. Geogr. 41 (2013) 75–86. 397 [17] M.S. Boori, K. Choudhary, A. Kupriyanoy, V. Kovelskiy, Urbanization data of Samara City, Russia, Data Brief 6 (2016) 398 885–889. http://dx.doi.org/10.1007/978-3-319-11463-7-2.

Please cite this article as: M.S. Boori, et al., Satellite data for Singapore, Manila and Kuala Lumpur city growth analysis, Data in Brief (2016), http://dx.doi.org/10.1016/j.dib.2016.04.028i