Multi-Temporal Urban Land Cover Mapping Using Spectral Indices
Multi-temporal urban land cover mapping is of paramount importance for monitoring urban sprawl and managing the ecological environment. For diversified urban activities, it is challenging to map land covers in a complex urban environment. Spectral indices have proved to be effective for mapping urban land covers. To improve multi-temporal urban land cover classification and mapping, we evaluate the performance of three spectral indices, e.g. modified normalized difference bare-land index (MNDBI), tasseled cap water and vegetation index (TCWVI) and shadow index (ShDI). The MNDBI is developed to evaluate its performance of enhancing urban impervious areas by separating bare lands. A tasseled cap index, TCWVI is developed to evaluate its competence to detect vegetation and water simultaneously. The ShDI is developed to maximize the spectral difference between shadows of skyscrapers and water and enhance water detection. First, this paper presents a comparative analysis of three spectral indices using Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM) and Operational Land Imager (OLI) data. Second, optimized thresholds of the spectral indices are imputed to classify land covers, and finally, their performance of enhancing multi-temporal urban land cover mapping is assessed. The results indicate that the spectral indices are competent to enhance multi-temporal urban land cover mapping and achieves an overall classification accuracy of 93-96%.
[1] I. Doustfatemeh and Y. Baleghi, "Comprehensive urban area extraction from multispectral medium spatial resolution remote-sensing imagery based on a novel structural feature," International Journal of Remote Sensing, vol. 37, pp. 4225-4242, 2016.
[2] W. D. Shuster, J. Bonta, H. Thurston, E. Warnemuende, and D. R. Smith, "Impacts of impervious surface on watershed hydrology: A review," Urban Water Journal, vol. 2, pp. 263-275, 2005.
[3] E. Li, P. Du, A. Samat, J. Xia, and M. Che, "An automatic approach for urban land-cover classification from Landsat-8 OLI data," International Journal of Remote Sensing, vol. 36, pp. 5983-6007, 2015.
[4] J. Dujardin, O. Batelaan, F. Canters, S. Boel, C. Anibas, and J. Bronders, "Improving surface–subsurface water budgeting using high resolution satellite imagery applied on a brownfield," Science of the Total Environment, vol. 409, pp. 800-809, 2011.
[5] J. Paneque-Gálvez, J.-F. Mas, G. Moré, J. Cristóbal, M. Orta-Martínez, A. C. Luz, et al., "Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity," International Journal of Applied Earth Observations and Geoinformation, vol. 23, pp. 372-383, 2013.
[6] X. Chen, J. Chen, Y. Shi, and Y. Yamaguchi, "An automated approach for updating land cover maps based on integrated change detection and classification methods," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 71, pp. 86-95, 2012/07/01 2012.
[7] C. He, P. Shi, D. Xie, and Y. Zhao, "Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach," Remote Sensing Letters, vol. 1, pp. 213-221, 2010.
[8] X. Li, X. Liu, and L. Yu, "Aggregative model-based classifier ensemble for improving land-use/cover classification of Landsat TM Images," International Journal of Remote Sensing, vol. 35, pp. 1481-1495, 2014.
[9] G. Li and Y. Wan, "A new combination classification of pixel- and object-based methods," International Journal of Remote Sensing, vol. 36, pp. 5842-5868, 2015.
[10] M. I. Faridatul and B. Wu, "Automatic classification of major urban land covers based on novel spectral indices," ISPRS International Journal of Geo-Information, vol. 7, pp. 1-24, 2018.
[11] A. Hamedianfar, H. Z. M. Shafri, S. Mansor, and N. Ahmad, "Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data," International Journal of Remote Sensing, vol. 35, pp. 1876-1899, 2014.
[12] Y. Zha, J. Gao, and S. Ni, "Use of normalized difference built-up index in automatically mapping urban areas from TM imagery," International Journal of Remote Sensing, vol. 24, pp. 583-594, 2003.
[13] S. K. McFeeters, "The use of the normalized difference water index (NDWI) in the delineation of open water features," International Journal of Remote Sensing, vol. 17, pp. 1425-1432, 1996.
[14] H. Xu, "A study on information extraction of water body with the modified normalized difference water index (MNDWI)," Journal of Remote Sensing, vol. 9, pp. 589-595, 2005.
[15] J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, "Monitoring vegetation systems in the great plains with ERTS," in Third 80 ERTS Symposium, 1973, pp. 309-317.
[16] R. Amine and F. Hadria, "Integration of NDVI indices from the tasseled cap transformation for change detection in satellite images," International Journal of Computer Science Issues (IJCSI), vol. 9, pp. 172-177, 2012.
[17] A. Bhatt, S. K. Ghosh, and A. Kumar, "Spectral indices based object oriented classification for change detection using satellite data," International Journal of System Assurance Engineering and Management, vol. 9, 33-42, 2016.
[18] M. K. Ridd, "Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities†," International Journal of Remote Sensing, vol. 16, pp. 2165-2185, 1995/08/01 1995.
[1] I. Doustfatemeh and Y. Baleghi, "Comprehensive urban area extraction from multispectral medium spatial resolution remote-sensing imagery based on a novel structural feature," International Journal of Remote Sensing, vol. 37, pp. 4225-4242, 2016.
[2] W. D. Shuster, J. Bonta, H. Thurston, E. Warnemuende, and D. R. Smith, "Impacts of impervious surface on watershed hydrology: A review," Urban Water Journal, vol. 2, pp. 263-275, 2005.
[3] E. Li, P. Du, A. Samat, J. Xia, and M. Che, "An automatic approach for urban land-cover classification from Landsat-8 OLI data," International Journal of Remote Sensing, vol. 36, pp. 5983-6007, 2015.
[4] J. Dujardin, O. Batelaan, F. Canters, S. Boel, C. Anibas, and J. Bronders, "Improving surface–subsurface water budgeting using high resolution satellite imagery applied on a brownfield," Science of the Total Environment, vol. 409, pp. 800-809, 2011.
[5] J. Paneque-Gálvez, J.-F. Mas, G. Moré, J. Cristóbal, M. Orta-Martínez, A. C. Luz, et al., "Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity," International Journal of Applied Earth Observations and Geoinformation, vol. 23, pp. 372-383, 2013.
[6] X. Chen, J. Chen, Y. Shi, and Y. Yamaguchi, "An automated approach for updating land cover maps based on integrated change detection and classification methods," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 71, pp. 86-95, 2012/07/01 2012.
[7] C. He, P. Shi, D. Xie, and Y. Zhao, "Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach," Remote Sensing Letters, vol. 1, pp. 213-221, 2010.
[8] X. Li, X. Liu, and L. Yu, "Aggregative model-based classifier ensemble for improving land-use/cover classification of Landsat TM Images," International Journal of Remote Sensing, vol. 35, pp. 1481-1495, 2014.
[9] G. Li and Y. Wan, "A new combination classification of pixel- and object-based methods," International Journal of Remote Sensing, vol. 36, pp. 5842-5868, 2015.
[10] M. I. Faridatul and B. Wu, "Automatic classification of major urban land covers based on novel spectral indices," ISPRS International Journal of Geo-Information, vol. 7, pp. 1-24, 2018.
[11] A. Hamedianfar, H. Z. M. Shafri, S. Mansor, and N. Ahmad, "Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data," International Journal of Remote Sensing, vol. 35, pp. 1876-1899, 2014.
[12] Y. Zha, J. Gao, and S. Ni, "Use of normalized difference built-up index in automatically mapping urban areas from TM imagery," International Journal of Remote Sensing, vol. 24, pp. 583-594, 2003.
[13] S. K. McFeeters, "The use of the normalized difference water index (NDWI) in the delineation of open water features," International Journal of Remote Sensing, vol. 17, pp. 1425-1432, 1996.
[14] H. Xu, "A study on information extraction of water body with the modified normalized difference water index (MNDWI)," Journal of Remote Sensing, vol. 9, pp. 589-595, 2005.
[15] J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, "Monitoring vegetation systems in the great plains with ERTS," in Third 80 ERTS Symposium, 1973, pp. 309-317.
[16] R. Amine and F. Hadria, "Integration of NDVI indices from the tasseled cap transformation for change detection in satellite images," International Journal of Computer Science Issues (IJCSI), vol. 9, pp. 172-177, 2012.
[17] A. Bhatt, S. K. Ghosh, and A. Kumar, "Spectral indices based object oriented classification for change detection using satellite data," International Journal of System Assurance Engineering and Management, vol. 9, 33-42, 2016.
[18] M. K. Ridd, "Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities†," International Journal of Remote Sensing, vol. 16, pp. 2165-2185, 1995/08/01 1995.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:78487", author = "Mst Ilme Faridatul and Bo Wu", title = "Multi-Temporal Urban Land Cover Mapping Using Spectral Indices", abstract = "Multi-temporal urban land cover mapping is of paramount importance for monitoring urban sprawl and managing the ecological environment. For diversified urban activities, it is challenging to map land covers in a complex urban environment. Spectral indices have proved to be effective for mapping urban land covers. To improve multi-temporal urban land cover classification and mapping, we evaluate the performance of three spectral indices, e.g. modified normalized difference bare-land index (MNDBI), tasseled cap water and vegetation index (TCWVI) and shadow index (ShDI). The MNDBI is developed to evaluate its performance of enhancing urban impervious areas by separating bare lands. A tasseled cap index, TCWVI is developed to evaluate its competence to detect vegetation and water simultaneously. The ShDI is developed to maximize the spectral difference between shadows of skyscrapers and water and enhance water detection. First, this paper presents a comparative analysis of three spectral indices using Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM) and Operational Land Imager (OLI) data. Second, optimized thresholds of the spectral indices are imputed to classify land covers, and finally, their performance of enhancing multi-temporal urban land cover mapping is assessed. The results indicate that the spectral indices are competent to enhance multi-temporal urban land cover mapping and achieves an overall classification accuracy of 93-96%.
", keywords = "Land cover, mapping, multi-temporal, spectral indices.", volume = "13", number = "2", pages = "147-7", }
