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 Singapore, Manila 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, Japan 21 22 23 article info abstract 24 25 Article history: This data article presents satellite data related to city growth of 26 Received 25 February 2016 Singapore, Manila and Kuala Lumpur 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 Asia 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 address: [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 local government 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 ÀUA 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.