The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis

Abstract

Rapid and irregular urbanization is an essential issue in terms of environmental assessment and management. The dynamics of landscape patterns should be observed and analyzed by local authorities for a sustainable environment. The aim of this study is to determine which spectral urban index, originated from old Landsat missions, represents impervious area better when new generation Earth observation satellite Landsat 8 data are used. Two datasets of Landsat 8, acquired on 2 September 2013 and 10 September 2016, were utilized to investigate the consistency of the results. In this study, commonly used urban indices namely normalized difference built-up index (NDBI), index-based built-up index (IBI), urban index (UI), and enhanced built-up and bareness index (EBBI) were utilized to extract impervious areas. The accuracy assessment of urban indices was conducted by comparing the results with pan-sharpened images, which were classified using maximum likelihood classification (MLC) method. The kappa values of MLC, IBI, NDBI, EBBI, and UI for 2013 dataset were 0.89, 0.79, 0.71, 0.59, and 0.49, respectively, and the kappa values of MLC, IBI, NDBI, EBBI, and UI for 2016 dataset were 0.90, 0.78, 0.70, 0.56, and 0.47, respectively. In addition, area information was extracted from indices and classified images, and the obtained outcomes showed that IBI presented better results than the other urban indices, and UI extracted impervious areas worse than the other indices in both selected cases. Consequently, Landsat 8 satellite data can be considered as an important source to extract and monitor impervious surfaces for the sustainable development of cities.

Eco-Environmental Quality Assessment in China’s 35 Major Cities Based On Remote Sensing Ecological Index

“…A number of ecological indicators have been proposed to evaluate the status of ecosystem health. For instances, the Normalized Difference Vegetation Index (NDVI) or leaf area index were used to monitor environmental change [10]- [14]; land surface temperature (LST) was adopted to assess the urban heat island effects [6], [7], [15]- [19]; the normalized difference builtup index (NDBI), an index-based built-up index (IBI) and the normalized difference impervious surface index (NDISI) were applied to delineate the built-up and impervious surface area [20]- [23]; the normalized difference water index (NDWI) and the modified NDWI (MNDWI) were used to extract water bodies [24]- [27]; NDVI and LST were applied to monitor drought or soil moisture [28]- [31]; a bare soil index (BI) [32] and dry bare-soil index (DBSI) [33] was employed to map bare soil areas. It is not sufficient to adopt only one or two ecological indicators to assess the status of the ecosystem due to the complexity and diversity of the influence factors.…”

A comparative study of impervious surface extraction using Sentinel-2 imagery

“…Additionally, the precision and efficiency of impervious surface extraction using different algorithms are different. These algorithms have been widely used in low spatial resolution imagery (e.g., MODIS (Knight & Voth, 2011), AVHRR (Carlson & Arthur, 2000)), moderate resolution imagery (e.g., Landsat series satellite (Sekertekin et al, 2018) and Terra's ASTER images (Weng & Hu, 2008)), and high-resolution satellite imagery such as SPOT (Tan et al, 2009), Quickbird (Lu et al, 2011) and IKONOS (Lu & Weng, 2009). On the one hand, due to the influence of mixed pixels, the ISAs extracted by using low and medium spatial resolution imagery are often too coarse, which also limit the accuracy of the ISA extraction (Y. H. Chen et al, 2018).…”

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

“…Manual thresholding requires expertise when defining a suitable threshold, usually via a trial and error process; an alternative is an automatic thresholding. As the most commonly used automatic thresholding method, Otsu's thresholding [43] has been successfully implemented to extract land covers from satellite index images, including ISA [25], [36], built-up land features [59], water body [60], and shadows [61]. The purpose of Otsu's method is to find a value that minimizes the intra-class variance while maximizes inter-class variance.…”

“…For instance, NDISI, NDII, and MNDISI cannot be used in Sentinel-2 images as the construction of these indices requires a thermal infrared band, which is not covered by the Sentinel-2 MSI sensor. Despite the existing studies that assessed the performances of various indices for ISA mapping on Landsat images [35], [36], only a few studies have evaluated the performance of the impervious surface indices on Sentinel-2 imagery. Deliry et al [18] applied both supervised classification methods and two band-ratioing methods (NDBI and NDII) to extract ISA from Sentinel-2 and Landsat-8 data.…”