A Comparative Analysis of Index-Based Methods for Impervious Surface Mapping Using Multiseasonal Sentinel-2 Satellite Data

Abstract

Studies have shown that Sentinel-2 images have advantages over Landsat images in impervious surface area (ISA) extraction. The performance of index-based methods can be affected by different binary methods and subject to seasonal variation. This study marks the first attempt to assess the performance of different spectral indices for ISA extraction using multi-seasonal Sentinel-2 images. Specifically, five indices (i.e., the Biophysical Composition Index calculated using the Gram-Schmidt orthogonalization method (BCI_GSO), Biophysical Composition Index calculated using a principal component-based Procrustes analysis (BCI_PCP), Normalized Built-up Area Index (NBAI), Combinational Build-up Index (CBI), and Perpendicular Impervious Surface Index (PISI)) and three impervious surface binary methods (i.e., Otsu's method, manual method, and ISODATA (Iterative Self-Organizing Data Analysis Technique Algorithm) classification method) were tested on multi-seasonal Sentinel-2 images in the main urban area of Wuhan, China. Results showed that PISI combined with the ISODATA classification method achieved the highest accuracy with 92.64% OA, and 0.8410 Kappa coefficient, and NBAI combined with Otsu's method achieved the lowest accuracy with 35.37% OA, and 0.013 Kappa coefficient. Regarding the seasonal sensitivity, PISI is relatively more stable than the other methods. The superior performance of PISI is largely due to its capability in separating ISA from soil and vegetation. In addition, summer is the best season to map ISA from Sentinel-2 images when the impervious surface is generally less confused with bare soil. This study serves as a reference for the selection of spectral indices for

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“…As reviewed in Section 2.3, TCT coefficients have not been published for atmospherically corrected S2 imagery, and Landsat's surface reflectance coefficients [96] do not cover all S2 bands. Whereas certain authors have used at-sensor-derived coefficients on surface reflectance S2 imagery [87][88][89][90][91], in a multi-temporal change application, the ever-changing effects of atmosphere result in inconsistent indices of brightness, greenness, and wetness that are directly a result of fluctuating atmospheric conditions. Although the exact effects onto index values such as dDI are not fully understood, Crist et al [42] suggests that changing atmospheric conditions will alter subsequent results derived from TCT indices.…”

“…Recently, some authors [85,86] have used coefficients developed for Landsat at-sensor reflectance products on S2 level-2A imagery. Others [87][88][89][90][91] have applied TCT coefficients to S2 level-2A data that were developed for at-sensor S2 (level-1C) imagery [43,92]. The spectral range and similarities of bands from Landsat and S2 bands are well documented [93][94][95], and the coefficients as developed by Crist [96] have been used in various studies using surface reflectance Landsat imagery [51,[97][98][99].…”

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“…NBAI is calculated by the equation 17 NBAI=ρSWIR 2−ρSWIR 1ρGreenρSWIR 2+ρSWIR 1ρGreen=Band 12−Band 11Band 3Band 12+Band 11Band 3.…”

“…Many studies have focused on comparing the performance of LULC data derived from Sentinel-2 and Landsat imagery. [12][13][14][15][16][17] In the current and past research, 18,19 we have been using Sentinel-2 imagery, as this data is fully relevant to the research objectives. Generating various-resolution LULC maps require massive amounts of data as huge storage capacities, high processing power, and the flexibility to apply diverse approaches are all required.…”

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“…Corresponding author: Z. Shao (email: shaozhenfeng@whu.edu.cn). the urban environment [2], climate [3], [4], and hydrology [5]- [7]. Therefore, the evaluation of ISA distribution should focus on not only its spatial expansion but also its environmental consequences.…”