Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring

Paramate Horkaew,Supattra Puttinaovarat

doi:10.3390/ijgi6100301

Abstract

Reliable water surface extraction is essential for river delineation and flood monitoring. Obtaining such information from fine resolution satellite imagery has attracted much interest for geographic and remote sensing applications. However, those images are often expensive and difficult to acquire. This study proposes a more cost-effective technique, employing freely available Landsat images. Despite its extensive spectrum and robust discrimination capability, Landsat data are normally of medium spatial resolution and, as such, fail to delineate smaller hydrological features. Based on Multivariate Mutual Information (MMI), the Landsat images were fused with Digital Surface Model (DSM) on the spatial domain. Each coinciding pixel would then contain not only rich indices but also intricate topographic attributes, derived from its respective sources. The proposed data fusion ensures robust, precise, and observer-invariable extraction of water surfaces and their branching, while eliminating spurious details. Its merit was demonstrated by effective discrimination of flooded regions from natural rivers for flood monitoring. The assessments we completed suggest improved extraction compared to traditional methods. Compared with manual digitizing, this method also exhibited promising consistency. Extraction using Dempster–Shafer fusion provided a 91.81% F-measure, 93.09% precision, 90.74% recall, and 98.25% accuracy, while using Majority Voting fusion resulted in an 84.91% F-measure, 75.44% precision, 97.37% recall, and 97.21% accuracy.

Highlights

Floods are one of the most frequently occurring disasters that cause tremendous mortality and devastation to personal property and communities [1], in heavily populated urban areas [2]
By exploiting the benefits of both modalities while minimizing their respective drawbacks, we present a novel method for water extraction by integrating no-cost satellite images with Digital Surface Model (DSM) by using Multivariate Mutual Information (MMI) and data fusion
A large proportion of pixels at the Normalized Difference Water Index 2 (NDWI2) lower end extend over the entire range of Modified Normalized Difference Water Index (MNDWI), whose water discrimination was not definitive, as shown by the horizontal stripe

Summary

Introduction

Floods are one of the most frequently occurring disasters that cause tremendous mortality and devastation to personal property and communities [1], in heavily populated urban areas [2]. Due to the severe disruption caused by floods, flood investigation and monitoring by state officials is often belated and superficial. Those endeavors would require immense financial and human resources. Due to the reflective properties of groundcover, differentiating water passages such as rivers, or permanent water [5] from flooded areas, or temporary water [6] has remained a challenge. This problem is typically addressed by discerning spatio-temporal changes in images acquired in series. Successful flood monitoring is completed by accurately discerning the river from other groundcovers

Objectives

Methods

Results

Conclusion