Impervious Surface Detection, Urban Area Extraction

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Impervious Surface. Urban Growth. Aerial Image Analysis. General Detection: See also General Urban Area Detection, Change and Growth. See also Urban Heat Islands, Surface Temperature, Remote Sensing.

Thomas, N.[Nancy], Hendrix, C.[Chad], Congalton, R.G.[Russell G.],
A Comparison of Urban Mapping Methods Using High-Resolution Digital Imagery,
PhEngRS(69), No. 9, September 2003, pp. 963-972. An overview of a project in which one-meter digital imagery was used to produce a map of pervious and impervious surfaces to be used by the city of Scottsdale, Arizona for storm-water runoff estimation is provided.
WWW Link. 0309

Yang, L.M.[Li-Min], Xian, G.[George], Klaver, J.M.[Jacqueline M.], Deal, B.[Brian],
Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data,
PhEngRS(69), No. 9, September 2003, pp. 1003-1010. An approach was developed to detect urban land-cover changes by quantifying temporal change of an impervious surface using Landsat and high-resolution imagery. Changes are at sub-pixel level.
WWW Link. 0309

Hodgson, M.E.[Michael E.], Jensen, J.R.[John R.], Tullis, J.A.[Jason A.], Riordan, K.D.[Kevin D.], Archer, C.M.[Clark M.],
Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness,
PhEngRS(69), No. 9, September 2003, pp. 973-980.
WWW Link. 0309
A comparative analysis of pixel-level (maximum-likelihood, ISODATA) and segment-level classifiers is presented for mapping urban parcel imperviousness from color aerial photography fused with lidar-derived cover height. BibRef

McMahon, G.[Gerard],
Consequences of Land-cover Misclassification in Models of Impervious Surface,
PhEngRS(73), No. 12, December 2007, pp. 1343-1354.
WWW Link. 0712
An assessment of the effects of land-cover misclassification on models that use the National Land Cover Data (NLCD) to predict impervious surface by evaluating the consequences on model predictions of adjusting land-cover within a watershed to reflect uncertainty assessment information. BibRef

Wu, C.S.[Chang-Shan], Yuan, F.[Fei],
Seasonal Sensitivity Analysis of Impervious Surface Estimation with Satellite Imagery,
PhEngRS(73), No. 12, December 2007, pp. 1393-1402.
WWW Link. 0712
A seasonal sensitivity analysis conducted for estimating impervious surface distribution using spectral mixture analysis and regression modeling. BibRef

Yuan, F.[Fei], Wu, C.S.[Chang-Shan], Bauer, M.E.[Marvin E.],
Comparison of Spectral Analysis Techniques for Impervious Surface Estimation Using Landsat Imagery,
PhEngRS(74), No. 8, August 2008, pp. 1045-1056.
WWW Link. 0804
Three common spectral analytical techniques (regression modeling, regression tree, and normalized spectral mixture analysis) for estimation of percent impervious surface area explored and compared in terms of model accuracy, factors that influence model performance, and cost of image processing. BibRef

Weng, Q., Hu, X.,
Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN,
GeoRS(46), No. 8, August 2008, pp. 2397-2406.

Zhou, Y.Y.[Yu-Yu], Wang, Y.Q.,
Extraction of Impervious Surface Areas from High Spatial Resolution Imagery by Multiple Agent Segmentation and Classification,
PhEngRS(74), No. 7, July 2008, pp. 857-868.
WWW Link. 0804
An algorithm of multiple agent segmentation and classification (MASC) to extract impervious surface areas from truecolor digital orthophoto and spatial resolution enhanced QuickBird-2 multispectral imagery. BibRef

Luo, L.[Li], Mountrakis, G.[Giorgos],
Converting local spectral and spatial information from a priori classifiers into contextual knowledge for impervious surface classification,
PandRS(66), No. 5, September 2011, pp. 579-587.
Elsevier DOI 1110
Contextual classification; Hybrid classifiers; Impervious surfaces; Landsat ETM+; Partial classification BibRef

Lu, D.S.[Deng-Sheng], Li, G.[Guiying], Moran, E.[Emilio], Batistella, M.[Mateus], Freitas, C.C.[Corina C.],
Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: A case study in an urban-rural landscape in the Brazilian Amazon,
PandRS(66), No. 6, November 2011, pp. 798-808.
Elsevier DOI 1112
Landsat TM; ALOS PALSAR L-band; RADARSAT-2 C-band; Wavelet-merging technique; Spectral mixture analysis; Impervious surface BibRef

Demarchi, L.[Luca], Canters, F.[Frank], Chan, J.C.W.[Jonathan Cheung-Wai], van de Voorde, T.,
Multiple Endmember Unmixing of CHRIS/Proba Imagery for Mapping Impervious Surfaces in Urban and Suburban Environments,
GeoRS(50), No. 9, September 2012, pp. 3409-3424.

Demarchi, L.[Luca], Chan, J.C.W.[Jonathan Cheung-Wai], Ma, J.L.[Jiang-Lin], Canters, F.[Frank],
Mapping impervious surfaces from superresolution enhanced CHRIS/Proba imagery using multiple endmember unmixing,
PandRS(72), No. 1, August 2012, pp. 99-112.
Elsevier DOI 1209
Angular hyperspectral imagery; CHRIS/Proba; Superresolution; Multiple endmember unmixing; Impervious surface mapping; Urban areas BibRef

Yang, F.[Fan], Matsushita, B.[Bunkei], Fukushima, T.[Takehiko], Yang, W.[Wei],
Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan,
PandRS(72), No. 1, August 2012, pp. 90-98.
Elsevier DOI 1209
Impervious surface area; Temporal mixture analysis; MODIS; NDVI time-series BibRef

Parece, T.E.[Tammy E.], Campbell, J.B.[James B.],
Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography,
RS(5), No. 10, 2013, pp. 4942-4960.
DOI Link 1311

Deng, C.B.[Cheng-Bin], Wu, C.S.[Chang-Shan],
The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques,
PandRS(86), No. 1, 2013, pp. 100-110.
Elsevier DOI 1312
Impervious surface BibRef

Liu, X.P.[Xiao-Ping], Hu, G.H.[Guo-Hua], Ai, B.[Bin], Li, X.[Xia], Shi, Q.[Qian],
A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area,
RS(7), No. 12, 2015, pp. 15863.
DOI Link 1601

Kaspersen, P.S.[Per Skougaard], Fensholt, R.[Rasmus], Drews, M.[Martin],
Using Landsat Vegetation Indices to Estimate Impervious Surface Fractions for European Cities,
RS(7), No. 6, 2015, pp. 8224.
DOI Link 1507

Deng, C.B.[Cheng-Bin],
Incorporating Endmember Variability into Linear Unmixing of Coarse Resolution Imagery: Mapping Large-Scale Impervious Surface Abundance Using a Hierarchically Object-Based Spectral Mixture Analysis,
RS(7), No. 7, 2015, pp. 9205.
DOI Link 1506

Guo, W.[Wei], Lu, D.S.[Deng-Sheng], Wu, Y.[Yanlan], Zhang, J.X.[Ji-Xian],
Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data,
RS(7), No. 9, 2015, pp. 12459.
DOI Link 1511

Tsutsumida, N.[Narumasa], Comber, A.J.[Alexis J.], Barrett, K.[Kirsten], Saizen, I.[Izuru], Rustiadi, E.[Ernan],
Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas,
RS(8), No. 2, 2016, pp. 143.
DOI Link 1603

Gallo, K.[Kevin], Xian, G.[George],
Changes in satellite-derived impervious surface area at US historical climatology network stations,
PandRS(120), No. 1, 2016, pp. 77-83.
Elsevier DOI 1610
Impervious surface area BibRef

Zhang, L.[Lei], Weng, Q.[Qihao],
Annual dynamics of impervious surface in the Pearl River Delta, China, from 1988 to 2013, using time series Landsat imagery,
PandRS(113), No. 1, 2016, pp. 86-96.
Elsevier DOI 1602
Landsat BibRef

Li, L.[Longwei], Lu, D.S.[Deng-Sheng], Kuang, W.H.[Wen-Hui],
Examining Urban Impervious Surface Distribution and Its Dynamic Change in Hangzhou Metropolis,
RS(8), No. 3, 2016, pp. 265.
DOI Link 1604

Lefebvre, A.[Antoine], Sannier, C.[Christophe], Corpetti, T.[Thomas],
Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree,
RS(8), No. 7, 2016, pp. 606.
DOI Link 1608

Shao, Z.F.[Zhen-Feng], Fu, H.[Huyan], Fu, P.[Peng], Yin, L.[Li],
Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level,
RS(8), No. 11, 2016, pp. 945.
DOI Link 1612

Guo, W.[Wei], Lu, D.S.[Deng-Sheng], Kuang, W.H.[Wen-Hui],
Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Wang, P.[Panshi], Huang, C.Q.[Cheng-Quan], de Colstoun, E.C.B.[Eric C. Brown],
Mapping 2000-2010 Impervious Surface Change in India Using Global Land Survey Landsat Data,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Silavi, T.[Tolue], Hakimpour, F.[Farshad], Claramunt, C.[Christophe], Nourian, F.[Farshad],
The Legibility and Permeability of Cities: Examining the Role of Spatial Data and Metrics,
IJGI(6), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Liu, S.[Shuai], Gu, G.[Guanghua],
Improving the Impervious Surface Estimation from Hyperspectral Images Using a Spectral-Spatial Feature Sparse Representation and Post-Processing Approach,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706

Tang, F.[Fei], Xu, H.[Hanqiu],
Impervious Surface Information Extraction Based on Hyperspectral Remote Sensing Imagery,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Jia, Y.[Yuqiu], Tang, L.[Lina], Wang, L.[Lin],
Influence of Ecological Factors on Estimation of Impervious Surface Area Using Landsat 8 Imagery,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708

Nez, J.M.[Juan Manuel],
Segmentation of Urban Impervious Surface Using Cellular Neural Networks,
Springer DOI 1511

Zhang, J.[Jun], Li, P.J.[Pei-Jun], Mazher, A.[Abeer], Liu, J.[Jing],
Impervious surface extraction with very high resolution imagery in urban areas: Reducing tree obscuring effect,

Shao, Z.F.[Zhen-Feng], Zhang, Y.[Yuan], Zhang, L.[Lei], Song, Y.[Yang], Peng, M.[Minjun],
Combining Spectral And Texture Features Using Random Forest Algorithm: Extracting Impervious Surface Area In Wuhan,
ISPRS16(B7: 351-358).
DOI Link 1610

Kuang, G.[Guodan], Wang, W.[Weian], Qiao, G.[Gang],
Impervious Surface Information Extraction Using an Improved Object-Oriented Method,

Chapter on Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR continues in
Urban Areas, Change and Growth .

Last update:Sep 18, 2017 at 11:34:11