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Frontiers of Earth Science

Front. Earth Sci.    2015, Vol. 9 Issue (2) : 179-191     DOI: 10.1007/s11707-014-0456-5
Spectral data treatments for impervious endmember derivation and fraction mapping from Landsat ETM+ imagery: a comparative analysis
Wei WANG1,Xinfeng YAO2,Minhe JI1,3,*(),Jiao ZHANG1
1. Key Laboratory of GIScience (Ministry of Education of China), East China Normal University, Shanghai 200241, China
2. Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
3. China East-West Cooperation Research Center, East China Normal University, Shanghai 200241, China
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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 (R2) 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 color-infrared aerial photographs of roughly the same time period. The test results indicated that, with the highest R2 (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.

Keywords impervious surface estimation      linear spectral mixture analysis      minimum noise fraction      spectral normalization      image fusion     
Corresponding Authors: Minhe JI   
Online First Date: 31 July 2014    Issue Date: 30 April 2015
 Cite this article:   
Wei WANG,Xinfeng YAO,Minhe JI, et al. Spectral data treatments for impervious endmember derivation and fraction mapping from Landsat ETM+ imagery: a comparative analysis[J]. Front. Earth Sci., 2015, 9(2): 179-191.
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Xinfeng YAO
Minhe JI
Fig.1  Study area: Shanghai CBD and its vicinity (The red box delineates the main study area, and the blue box indicates the coverage of validation data).
Fig.2  Procedure for comparing the effects of different spectral data treatment techniques on impervious surface mapping.
Fig.3  Reflectance (original or transformed) characteristics of endmembers for the four data treatment types: (a) original spectra, (b) fused spectra, (c) normalized spectra, and (d) MNF spectra.
Fig.4  Fraction maps resulting from the four data treatment types (columns from left to right: high albedo imperviousness, low albedo imperviousness, vegetation, and soil.
Fig.5  Urban imperviousness maps resulting from the four data treatment types.
Fig.6  Cross-treatment comparison of imperviousness fraction maps.
Error Original LSMA Fused LSMA Normalized LSMA MNF LSMA
R2 0.7952 0.5185 0.7642 0.8120
RMSE 0.0972 0.1465 0.1417 0.0965
Tab.1  Accuracy measures of impervious surface fraction maps induced from different spectra: original, fused, normalized, and MNF
Fig.7  Scatterplots and regression lines of accuracy validation sample points for each type of data treatment: (a) original, (b) fused, (c) normalized, and (d) MNF.
1 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
doi: 10.1080/01944369608975688
2 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
3 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
4 Foody G M (2002). Status of land cover classification accuracy assessment. Remote Sens Environ, 80(1): 185–201
doi: 10.1016/S0034-4257(01)00295-4
5 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
doi: 10.1080/01431160512331314056
6 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
7 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
8 Hu X, Weng Q (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sens Environ, 113(10): 2089–2102
doi: 10.1016/j.rse.2009.05.014
9 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
doi: 10.1016/j.rse.2011.06.024
10 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
doi: 10.1117/1.JRS.6.061707
11 Ji M, Feng J (2011). Subpixel measurement of mangrove canopy closure via spectral mixture analysis. Front Earth Sci, 5(2): 130–137
doi: 10.1007/s11707-011-0156-3
12 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
doi: 10.1080/10106049908542126
13 Jing L, Cheng Q (2011). An image fusion method for misaligned panchromatic and multispectral data. Int J Remote Sens, 32(4): 1125–1137
doi: 10.1080/01431160903527405
14 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
doi: 10.1016/S1566-2535(01)00037-9
15 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
doi: 10.1080/01431160412331269733
16 Lu D, Hetrick S, Moran E (2011). Impervious surface mapping with Quickbird imagery. Int J Remote Sens, 32(9): 2519–2533
doi: 10.1080/01431161003698393
17 Lu D, Moran E, Batistella M (2003). Linear mixture model applied to Amazonian vegetation classification. Remote Sens Environ, 87(4): 456–469
doi: 10.1016/j.rse.2002.06.001
18 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
doi: 10.14358/PERS.70.9.1053
19 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
doi: 10.14358/PERS.71.11.1275
20 Lu D, Weng Q (2006). Use of impervious surface in urban land-use classification. Remote Sens Environ, 102(1–2): 146–160
doi: 10.1016/j.rse.2006.02.010
21 Mohapatra R P, Wu C (2008). Subpixel imperviousness estimation with IKONOS imagery: an artificial neural network approach. London: Taylor & Francis Group
22 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
doi: 10.1016/j.rse.2006.09.005
23 Rashed T (2008). Remote sensing of within-class change in urban neighborhood structures. Comput Environ Urban Syst, 32(5): 343–354
doi: 10.1016/j.compenvurbsys.2008.06.007
24 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
doi: 10.1016/S0034-4257(98)00037-6
25 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
doi: 10.1029/JB090iS02p0C797
26 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
doi: 10.1080/01431160802665918
27 Wu C (2004). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sens Environ, 93(4): 480–492
doi: 10.1016/j.rse.2004.08.003
28 Wu C (2009). Quantifying high-resolution impervious surfaces using spectral mixture analysis. Int J Remote Sens, 30(11): 2915–2932
doi: 10.1080/01431160802558634
29 Wu C, Murray A T (2003). Estimating impervious surface distribution by spectral mixture analysis. Remote Sens Environ, 84(4): 493–505
doi: 10.1016/S0034-4257(02)00136-0
30 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
doi: 10.1080/01431160600589179
31 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
doi: 10.2747/1548-1603.49.5.687
32 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
doi: 10.1016/j.rse.2006.09.003
33 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
doi: 10.1080/01431160903505286
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