Abstract
Various spectral data preprocessing approaches have been used to improve endmember extraction for urban landscape decomposition, yet little is known of their comparative adequacy for impervious surface mapping. This study tested four commonly used spectral data treatment strategies for endmember derivation, including original spectra, image fusion via principal component analysis, spectral normalization, and the minimum noise fraction (MNF) transformation. Land cover endmembers derived using each strategy were used to build a linear spectral mixture analysis (LSMA) model in order to unmix treated image pixels into fraction maps, and an urban imperviousness map was generated by combining the fraction maps representing imperviousness endmembers. A cross-map comparative analysis was then performed to rank the four data treatment types based on such common evaluation indices as the coefficient of determination (R 2) and root mean square error (RMSE). A Landsat 7 ETM+ multispectral image covering the metropolitan region of Shanghai, China was used as the primary dataset, and the model results were evaluated using high-resolution colorinfrared aerial photographs of roughly the same time period. The test results indicated that, with the highest R 2 (0.812) and the lowest RMSE (0.097) among all four preprocessing treatments, the endmembers in the form of MNF-transformed spectra produced the best model output for characterizing urban impervious surfaces. The outcome of this study may provide useful guidance for future impervious surface mapping using medium-resolution remote sensing data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arnold C L Jr, Gibbons C J (1996). Impervious surface coverage: the emergence of a key environmental indicator. J Am Plann Assoc, 62 (2): 243–258
Chavez P S, Sides S C, Anderson J A (1991). Comparison of three different methods to merge multiresolution and multispectral data-Landsat TM and SPOT panchromatic. Photogramm Eng Remote Sensing, 57(3): 295–303
Chen X, Li L (2008). A comparison of spectral mixture analysis methods for urban landscape using Landsat ETM+ data: Los Angeles, CA. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Beijing, China: 635–640
Foody G M (2002). Status of land cover classification accuracy assessment. Remote Sens Environ, 80(1): 185–201
González-Audícana M, Otazu X, Fors O, Seco A (2005). Comparison between Mallat’s and the’ à trous’ discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. Int J Remote Sens, 26(3): 595–614
González-Audícana M, Saleta J L, Catalán R G, García R (2004). Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing, 42(6): 1291–1299
Green A A, Berman M, Switzer P, Craig M D (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26(1): 65–74
Hu X, Weng Q (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multilayer perceptron neural networks. Remote Sens Environ, 113(10): 2089–2102
Im J, Lu Z, Rhee J, Quackenbush L J (2012). Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data. Remote Sens Environ, 117: 102–113
Ji M, Chen W, Wang W (2012). Improving spectral fidelity of WorldView-2 image fusion via a constrained generalized intensity-hue-saturation model with localized weight structure through land cover classification. J Appl Remote Sens, 6(1): 061707
Ji M, Feng J (2011). Subpixel measurement of mangrove canopy closure via spectral mixture analysis. Front Earth Sci, 5(2): 130–137
Ji M, Jensen J R (1999). Effectiveness of subpixel analysis in detecting and quantifying urban imperviousness from Landsat Thematic Mapper imagery. Geocarto Int, 14(4): 33–41
Jing L, Cheng Q (2011). An image fusion method for misaligned panchromatic and multispectral data. Int J Remote Sens, 32(4): 1125–1137
Li S, Kwok J T, Wang Y (2002). Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Inf Fusion, 3(1): 17–23
Lu D, Batistella M, Moran E, Mausel P (2004). Application of spectral mixture analysis to Amazonian land-use and land-cover classification. Int J Remote Sens, 25(23): 5345–5358
Lu D, Hetrick S, Moran E (2011). Impervious surface mapping with Quickbird imagery. Int J Remote Sens, 32(9): 2519–2533
Lu D, Moran E, Batistella M (2003). Linear mixture model applied to Amazonian vegetation classification. Remote Sens Environ, 87(4): 456–469
Lu D, Weng Q (2004). Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogramm Eng Remote Sensing, 70(9): 1053–1062
Lu D, Weng Q (2005). Urban classification using full spectral information of Landsat ETM+ imagery in Marion County, Indiana. Photogramm Eng Remote Sensing, 71(11): 1275–1284
Lu D, Weng Q (2006). Use of impervious surface in urban land-use classification. Remote Sens Environ, 102(1–2): 146–160
Mohapatra R P, Wu C (2008). Subpixel imperviousness estimation with IKONOS imagery: an artificial neural network approach. London: Taylor & Francis Group
Powell R L, Roberts D A, Dennison P E, Hess L L (2007). Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. Remote Sens Environ, 106(2): 253–267
Rashed T (2008). Remote sensing of within-class change in urban neighborhood structures. Comput Environ Urban Syst, 32(5): 343–354
Roberts D, Gardner M, Church R, Ustin S, Scheer G, Green R (1998). Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sens Environ, 65(3): 267–279
Smith M O, Johnson P E, Adams J B (1985). Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis. J Geophys Res, 90(S02): C797–C804
van de Voorde T, de Roeck T, Canters F (2009). A comparison of two spectral mixture modelling approaches for impervious surface mapping in urban areas. Int J Remote Sens, 30(18): 4785–4806
Wu C (2004). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sens Environ, 93(4): 480–492
Wu C (2009). Quantifying high-resolution impervious surfaces using spectral mixture analysis. Int J Remote Sens, 30(11): 2915–2932
Wu C, Murray A T (2003). Estimating impervious surface distribution by spectral mixture analysis. Remote Sens Environ, 84(4): 493–505
Xu H (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens, 27(14): 3025–3033
Yang B, Kim M, Madden M (2012). Assessing optimal image fusion methods for very high spatial resolution satellite images to support coastal monitoring. GIScience & Remote Sensing, 49(5): 687–710
Yuan F, Bauer M E (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ, 106(3): 375–386
Zurita-Milla R, Clevers J, Van Gijsel J, Schaepman M (2011). Using MERIS fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes. Int J Remote Sens, 32(4): 973–991
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, W., Yao, X., Ji, M. et al. Spectral data treatments for impervious endmember derivation and fraction mapping from Landsat ETM+ imagery: a comparative analysis. Front. Earth Sci. 9, 179–191 (2015). https://doi.org/10.1007/s11707-014-0456-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11707-014-0456-5