A Novel Remote Sensing Index for Extracting Impervious Surface Distribution from Landsat 8 OLI Imagery

[1]  Hanqiu Xu,et al.  Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI) , 2010 .

[2]  M. McKinney,et al.  Urbanization, Biodiversity, and Conservation , 2002 .

[3]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[4]  S. K. McFeeters The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[5]  C. Jordan Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Fenglei Fan,et al.  Enhancing endmember selection in multiple endmember spectral mixture analysis (MESMA) for urban impervious surface area mapping using spectral angle and spectral distance parameters , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[8]  Abbas Mohajerani,et al.  The urban heat island effect, its causes, and mitigation, with reference to the thermal properties of asphalt concrete. , 2017, Journal of environmental management.

[9]  M. Jacobson,et al.  Effects of Urban Surfaces and White Roofs on Global and Regional Climate , 2012 .

[10]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[11]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[12]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[13]  Biswajeet Pradhan,et al.  A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery , 2014 .

[14]  Nilanchal Patel,et al.  Extraction of impervious features from spectral indices using artificial neural network , 2015, Arabian Journal of Geosciences.

[15]  N. Tripathi,et al.  Built-up area extraction using Landsat 8 OLI imagery , 2014 .

[16]  Qihao Weng,et al.  Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends , 2012 .

[17]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .

[18]  M. Ridd Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities , 1995 .

[19]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[20]  D. Helder,et al.  Mapping Urban Land Cover Using QuickBird NDVI and GIS Spatial Modeling for Runoff Coefficient Determination (THIS PAPER WAS THE WINNER OF THE 2006 BAE SYSTEMS AWARD GIVEN AT THE ASPRS 2006 ANNUAL CONFERENCE) , 2007 .

[21]  Gérard Dedieu,et al.  Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas , 2016 .

[22]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[23]  Xiuping Jia,et al.  Combinational Build-Up Index (CBI) for Effective Impervious Surface Mapping in Urban Areas , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[24]  Jianlong Li,et al.  Application of a normalized difference impervious index (NDII) to extract urban impervious surface features based on Landsat TM images , 2015 .

[25]  Hanqiu Xu,et al.  A new index for delineating built‐up land features in satellite imagery , 2008 .

[26]  F. Canters,et al.  A comparison of two spectral mixture modelling approaches for impervious surface mapping in urban areas , 2009 .

[27]  Hanqiu Xu Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .