A Novel Index for Impervious Surface Area Mapping: Development and Validation

Abstract

The distribution and dynamic changes in impervious surface areas (ISAs) are crucial to understanding urbanization and its impact on urban heat islands, earth surface energy balance, hydrological cycles, and biodiversity. Remotely sensed data play an essential role in ISA mapping, and numerous methods have been developed and successfully applied for ISA extraction. However, the heterogeneity of ISA spectra and the high similarity of the spectra between ISA and soil have not been effectively addressed. In this study, we selected data from the US Geological Survey (USGS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral libraries as samples and used blue and near-infrared bands as characteristic bands based on spectral analysis to propose a novel index, the perpendicular impervious surface index (PISI). Landsat 8 operational land imager data in four provincial capital cities of China (Wuhan, Shenyang, Guangzhou, and Xining) were selected as test data to examine the performance of the proposed PISI in four different environments. Threshold analysis results show that there is a significant positive correlation between PISI and the proportion of ISA, and threshold can be adjusted according to different needs with different accuracy. Furthermore, comparative analyses, which involved separability analysis and extraction precision analysis, were conducted among PISI, biophysical composition index (BCI), and normalized difference built-up index (NDBI). Results indicate that PISI is more accurate and has better separability for ISA and soil as well as ISA and vegetation in the ISA extraction than the BCI and NDBI under different conditions. The accuracy of PISI in the four cities is 94.13%, 96.50%, 89.51%, and 93.46% respectively, while BCI and NDBI showed accuracy of 77.53%, 93.49%, 78.02%, and 84.03% and 58.25%, 57.53%, 77.77%, and 64.83%, respectively. In general, the proposed PISI is a convenient index to extract ISA with higher accuracy and better separability for ISA and soil as well as ISA and vegetation. Meanwhile, as PISI only uses blue and near-infrared bands, it can be used in a wider variety of remote sensing images.

A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery

“…The spectral curves of several typical urban composition samples are shown in Fig 4. From the blue to NIR bands, the reflectance of the ISA changes gently (slowly risin In contrast, the changes in reflectance of soil and vegetation are relatively significant (c tinuously or sharply rising). Therefore, Tian et al [31] select the blue and NIR as the f ture bands to develop an index (PISI) for ISA mapping. Although PISI can extract I successfully with high accuracy, the water has a significant impact on the ISA extract (red circle in Figure 5a), because some ISA and water have similar spectral characteris in the visible and near-infrared region [44].…”

“…So, when applying the BCI for urban compositions extraction, the MNDWI was used to extract the water first. In this experiment, the proposed threshold [0.0126, 0.1462] of PISI was used for ISA extraction [31], and the value of MNDWI greater than 0 was considered to be water. Since NDBI, BCI, and CBCI do not have the suggested extraction threshold, the extraction thresholds of these indices were determined by iteration.…”

Rubber Identification Based on Blended High Spatio-Temporal Resolution Optical Remote Sensing Data: A Case Study in Xishuangbanna

“…The range of J-M distance is [0,2]. J-M distances were calculated according to the selected sample points; a value of greater than 1.38 indicates good separability, values between 1.0 and 1.38 indicate moderately separable, and values less than 1.0 indicate poor separability [52,53]. Taking the EVI time series features of rubber plantations and natural forest as an example, the J-M distances are as follows:…”

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

“…In general, an index-based ISA extraction method finds the strongest reflection band and the weakest reflection band of the impervious surface in multispectral images, and expands the intensity contrast between the impervious surface and the background through mathematical operations on selected bands [26]. Typical indices include (but are not limited to) Normalized Difference Built-up Index (NDBI) [27], Index-based Built-up Index (IBI) [28], Normalized Difference Impervious Surface Index (NDISI) [26], New Built-up Index (NBI) [29], Biophysical Composition Index (BCI) [23], Normalized Built-up Area Index (NBAI) [30], Modified Built-up Index (MBI) [31], Normalized Difference Impervious Index (NDII) [32], Combinational Build-up Index (CBI) [33], Modified NDISI (MNDISI) [34], and Perpendicular Impervious Surface Index (PISI) [25].…”

“…We selected BCI, NBAI, CBI, and PISI, to extract ISA from images acquired during different seasons in Wuhan city, China. BCI, NBAI, and CBI were selected due to their robustness to extract urban surfaces from a complex relief when dealing with Sentinel-2 images and their capability to distinguish urban areas and backgrounds [25], [42]. Moreover, BCI index was calculated using a Gram-Schmidt orthogonalization method (BCI_GSO) and a principal component-based Procrustes analysis (BCI_PCP), respectively.…”