Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics.
References
[1]
Colditz, R.R.; Schmidt, M.; Conrad, C.; Hansen, M.C.; Dech, S. Land cover classification with coarse spatial resolution data to derive continuous and discrete maps for complex regions. Remote Sens. Environ 2011, 115, 3264–3275.
[2]
Sleeter, B.M.; Sohl, T.L.; Loveland, T.R.; Auch, R.F.; Acevedo, W.; Drummond, M.A.; Sayler, K.L.; Stehman, S.V. Land-cover change in the conterminous United States from 1973 to 2000. Glob. Environ. Chang 2013, 23, 733–748.
[3]
Wu, W.; Shibasaki, R.; Yang, P.; Zhou, Q.; Tang, H. Remotely sensed estimation of cropland in China: A comparison of the maps derived from four global land cover datasets. Can. J. Remote Sens 2008, 34, 467–479.
[4]
Bargiel, D.; Herrmann, S. Multi-temporal land-cover classification of agricultural areas in two European regions with high resolution spotlight TerraSAR-X data. Remote Sens 2011, 3, 859–877.
[5]
Wang, Y.; Mitchell, B.R.; Nugranad-Marzilli, J.; Bonynge, G.; Zhou, Y.; Shriver, G. Remote sensing of land-cover change and landscape context of the National Parks: A case study of the Northeast temperate network. Remote Sens. Environ 2009, 113, 1453–1461.
[6]
Jiang, H.; Zhao, D.; Cai, Y.; An, S. A method for application of classification tree models to map aquatic vegetation using remotely sensed images from different sensors and dates. Sensors 2012, 12, 12437–12454.
[7]
Zhou, H.; Aizen, E.; Aizen, V. Deriving long term snow cover extent dataset from AVHRR and MODIS data: Central Asia case study. Remote Sens. Environ 2013, 136, 146–162.
[8]
Wijedasa, L.S.; Sloan, S.; Michelakis, D.G.; Clements, G.R. Overcoming limitations with Landsat imagery for mapping of peat swamp forests in Sundaland. Remote Sens 2012, 4, 2595–2618.
[9]
Gong, P.; Wang, J.; Yu, L.; Zhao, Y.; Zhao, Y.; Liang, L.; Niu, Z.; Huang, X.; Fu, H.; Liu, S.; et al. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens 2013, 34, 2607–2654.
[10]
Hansen, M.C.; DeFries, R.S.; Townshend, J.R.; Sohlberg, R. Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens 2000, 21, 1331–1364.
[11]
Tchuenté, A.T.K.; Roujean, J.L.; De Jong, S.M. Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale. Int. J. Appl. Earth Obs. Geoinf 2011, 13, 207–219.
[12]
Liu, J.; Liu, M.; Deng, X.; Zhuang, D.; Zhang, Z.; Luo, D. The land use and land cover change database and its relative studies in China. J. Geogr. Sci 2002, 12, 275–282.
[13]
Zhou, W.; Troy, A.; Grove, M. Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors 2008, 8, 1613–1636.
[14]
Laliberte, A.S.; Browning, D.M.; Rango, A. A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery. Int. J. Appl. Earth Obs. Geoinf 2012, 15, 70–78.
[15]
Duro, D.C.; Franklin, S.E.; Dubé, M.G. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ 2012, 118, 259–272.
[16]
Batista, M.H.; Haertel, V. On the classification of remote sensing high spatial resolution image data. Int. J. Remote Sens 2010, 31, 5533–5548.
[17]
Clark, M.L.; Aide, T.M.; Grau, H.R.; Riner, G. A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco Ecoregion of South America. Remote Sens. Environ 2010, 114, 2816–2832.
[18]
Mering, C.; Baro, J.; Upegui, E. Retrieving urban areas on Google Earth images: Application to towns of West Africa. Int. J. Remote Sens 2010, 31, 5867–5877.
[19]
Kaimaris, D.; Georgoula, O.; Patias, P.; Stylianidis, E. Comparative analysis on the archaeological content of imagery from Google Earth. J. Cult. Herit 2011, 12, 263–269.
[20]
Yu, L.; Gong, P. Google Earth as a virtual globe tool for Earth science applications at the global scale: Progress and perspectives. Int. J. Remote Sens 2011, 33, 3966–3986.
[21]
Guo, J.; Liang, L.; Gong, P. Removing shadows from Google Earth images. Int. J. Remote Sens 2010, 31, 1379–1389.
[22]
Potere, D. Horizontal positional accuracy of Google Earth’s high-resolution imagery Archive. Sensors 2008, 8, 7973–7981.
[23]
Drǎgut？, L.; Tiede, D.; Levick, S.R. ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci 2010, 24, 859–871.
[24]
Xiao, Y. Spatial-Temporal Land Use Patterns and Master Planning in Wuhan, ChinaM.S. Thesis. Wuhan University, Wuhan, Hubei, China, 2002.
[25]
Cheng, J.; Masser, I. Urban growth pattern modeling: A case study of Wuhan city, PR China. Landsc. Urban. Plan 2003, 62, 199–217.
[26]
Jensen, J.R. Thematic Map Accuracy Assessment. In Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2005; pp. 476–482.
[27]
Shao, Y.; Lunetta, R.S. Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS J. Photogramm. Remote Sens 2012, 70, 78–87.
[28]
Aitkenhead, M.J.; Aalders, I.H. Automating land cover mapping of Scotland using expert system and knowledge integration methods. Remote Sens. Environ 2011, 115, 1285–1295.
[29]
Yu, Q.; Gong, P.; Clinton, N.; Biging, G.; Kelly, M.; Schirokauer, D. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogramm. Eng. Remote Sens 2006, 72, 799–811.
[30]
Lisita, A.; Sano, E.E.; Durieux, L. Identifying potential areas of Cannabis sativa plantations using object-based image analysis of SPOT-5 satellite data. Int. J. Remote Sens 2013, 34, 5409–5428.
[31]
Lu, D.; Weng, Q. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens 2007, 28, 823–870.
[32]
Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens 2010, 65, 2–16.
[33]
Dribault, Y.; Chokmani, K.; Bernier, M. Monitoring seasonal hydrological dynamics of minerotrophic peatlands using multi-date GeoEye-1 very high resolution imagery and object-based classification. Remote Sens 2012, 4, 1887–1912.
[34]
Mathieu, R.; Aryal, J.; Chong, A. Object-based classification of Ikonos imagery for mapping large-scale vegetation communities in urban areas. Sensors 2007, 7, 2860–2880.
[35]
Myint, S.W.; Gober, P.; Brazel, A.; Grossman-Clarke, S.; Weng, Q. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ 2011, 115, 1145–1161.
[36]
Manandhar, R.; Odeh, I.; Ancev, T. Improving the accuracy of land use and land cover classification of landsat data using post-classification enhancement. Remote Sens 2009, 1, 330–344.
[37]
Zhou, W.; Troy, A. An object-oriented approach for analysing and characterizing urban landscape at the parcel level. Int. J. Remote Sens 2008, 29, 3119–3135.
[38]
Duro, D.C.; Franklin, S.E.; Dubé, M.G. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ 2012, 118, 259–272.
[39]
Benz, U.C.; Hofmann, P.; Willhauck, G.; Lingenfelder, I.; Heynen, M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens 2004, 58, 239–258.
[40]
Hu, Q.; Zhang, J.; Xu, B.; Li, Z. A comparison of Google Earth imagery and the homologous Quick Bird imagery being used in land-use classification. J. Huazhong Norm. Univ 2013, 52, 287–291.
[41]
Gao, Y.; Mas, J.F.; Navarrete, A. The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data. Int. J. Digit. Earth 2009, 2, 219–236.
[42]
Zhou, W.; Huang, G.; Troy, A.; Cadenasso, M.L. Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study. Remote Sens. Environ 2009, 113, 1769–1777.
[43]
Ghosh, A.; Joshi, P.K. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. Int. J. Appl. Earth Obs. Geoinf 2014, 26, 298–311.
[44]
Foody, G.M. Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy. Photogramm. Eng. Remote Sens 2004, 5, 627–633.
[45]
Huang, X.; Zhang, L.; Li, P. Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery. IEEE Geosci. Remote Sens.Lett 2007, 4, 260–264.
[46]
Smith, A. Image segmentation scale parameter optimization and land cover classification using the Random Forest algorithm. J. Spat. Sci 2010, 55, 69–79.
[47]
Castillejo-González, I.L.; López-Granados, F.; García-Ferrer, A.; Pe？a-Barragán, J.M.; Jurado-Expósito, M.; de la Orden, M.S.; González-Audicana, M. Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Comput. Electron. Agric 2009, 68, 207–215.
[48]
Petropoulos, G.P.; Kalaitzidis, C.; Prasad Vadrevu, K. Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Comput. Geosci 2012, 41, 99–107.
[49]
Elatawneh, A.; Kalaitzidis, C.; Petropoulos, G.P.; Schneider, T. Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data. Int. J. Digit. Earth 2012, doi:10.1080/17538947.2012.671378..
[50]
Anders, N.S.; Seijmonsbergen, A.C.; Bouten, W. Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sens. Environ 2011, 115, 2976–2985.
[51]
Salehi, B.; Zhang, Y.; Zhong, M.; Dey, V. Object-based classification of urban areas using VHR imagery and height points ancillary data. Remote Sens 2012, 4, 2256–2276.
[52]
Haala, N.; Brenner, C. Extraction of buildings and trees in urban environments. ISPRS J. Photogramm. Remote Sens 1999, 54, 130–137.
[53]
Yu, Q.; Wu, W.; Yang, P.; Li, Z.; Xiong, W.; Tang, H. Proposing an interdisciplinary and cross-scale framework for global change and food security researches. Agric. Ecosyst. Environ 2012, 156, 57–71.
