A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery

Zhongchang Sun,Cuizhen Wang,Huadong Guo,Ranran Shang

doi:10.3390/rs9090942

Abstract

Impervious surface area (ISA) is a key factor for monitoring urban environment and land development. Automatic mapping of impervious surfaces has attracted growing attention in recent years. Spectral built-up indices are considered promising to map ISA distributions due to their easy, parameter-free implementations. This study explores the potentials of impervious surface indices for ISA mapping from Landsat imagery using a case study area in Boston, USA. A modified normalized difference impervious surface index (MNDISI) is proposed, and a Gaussian-based automatic threshold selection method is used to identify the optimal MNDISI threshold for delineating impervious surfaces from background features. To evaluate its effectiveness, comparison analysis is conducted between MNDISI and the original NDISI using Landsat images from three sensors (TM/ETM+/OLI-TIRS) acquired in four seasons. Our results suggest that built-up indices are sensitive to image seasonality, and summer is the best time phase for ISA mapping. With reduced uncertainties from automatic threshold selection, the MNDISI extracts impervious surfaces from all Landsat images in summer with an overall accuracy higher than 87% and an overall Kappa coefficient higher than 0.74. The proposed method is superior to previous index-based ISA mapping from the enhanced thermal integration and automatic threshold selection. The ISA maps from the TM, ETM+ and OLI-TIRS images are not significantly different. With enlarged data pool when all Landsat sensors are considered and automation of threshold selection proposed in this study, the MNDISI could be an effective built-up index for rapid and automatic ISA mapping at regional and global scales.

Highlights

Impervious surfaces are usually defined as anthropogenic features that do not allow water to penetrate through ground, e.g., building roofs, asphalt/cement roads, parking lots, sidewalks and transportation infrastructures [1]
We propose a modified normalized difference impervious surface index (MNDISI) by integrating the sharpened thermal infrared (TIR) band into the NDISI, and develop an automatic threshold selection method based on Generalized Gaussian Model (GGM) to optimally extract Impervious surface area (ISA) from Landsat imagery
While built-up indices have been considered promising for large-area ISA mapping from optical satellite imagery at local, national, regional and global scales, our results indicate that there still exists limitations and challenges for index-based extraction of impervious surfaces

Summary

Introduction

Impervious surfaces are usually defined as anthropogenic features that do not allow water to penetrate through ground, e.g., building roofs, asphalt/cement roads, parking lots, sidewalks and transportation infrastructures [1]. Impervious surface area (ISA) is an indicator of urbanization process, and a key sensitive factor for monitoring environmental problems in built-up areas such as high surface runoff [2], urban heat island effect [3], transport of water pollutants [4], degradation in water quality [5,6] and air pollution [7]. Due to the relatively low cost, large-area coverage and short revisit cycles, satellite remote sensing has been widely applied to map impervious surfaces. Among these studies, Landsat imagery is the most commonly used because the satellite series provide nearly 45-year data records with wide-swath coverage, free availability and relatively high spatial resolution (e.g., [10,11,12,13]). Various approaches have been employed to extract ISA from Landsat imagery, which can be grouped into five categories: (a) pixel/object-based classification [13,14,15]; (b) spectral mixture analysis (SMA) [16,17];

Objectives

Methods

Results

Discussion

Conclusion