A Rapidly Assessed Wetland Stress Index (RAWSI) Using Landsat 8 and Sentinel-1 Radar Data

Matthew Walter,Pinki Mondal

doi:10.3390/rs11212549

Abstract

Wetland ecosystems are important resources, providing great economic benefits for surrounding communities. In this study, we developed a new stress indicator called “Rapidly Assessed Wetlands Stress Index” (RAWSI) by combining several natural and anthropogenic stressors of wetlands in Delaware, in the United States. We compared two machine-learning algorithms, support vector machine (SVM) and random forest (RF), to quantify wetland stress by classifying satellite images from Landsat 8 and Sentinel-1 Synthetic Aperture Radar (SAR). An accuracy assessment showed that the combination of Landsat 8 and Sentinel SAR data had the highest overall accuracy (93.7%) when used with an RF classifier. In addition to the land-cover classification, a trend analysis of the normalized difference vegetation index (NDVI) calculated from Landsat images during 2004–2018 was used to assess changes in healthy vegetation. We also calculated the stream sinuosity to assess human alterations to hydrology. We then used these three metrics to develop RAWSI, and to quantify and map wetland stress due to human alteration of the landscape. Hot-spot analysis using Global Moran’s I and Getis-Ord Gi* identified several statistically significant hot spots (high stress) in forested wetlands and cold spots (low values) in non-forested wetlands. This information can be utilized to identify wetland areas in need of further regulation, with implications in environmental planning and policy decisions.

Highlights

The Environmental Protection Agency (EPA) defines wetlands as “areas in which water covers the soil, or is present at or near the surface of the soil all year or for varying periods of time during a year, including during the growing season” [1], yet, “wetlands” is a broad term and might have varied characteristics in terms of hydrology, vegetation, and soils
The combination of Sentinel-1 Synthetic Aperture Radar (SAR) with Landsat 8 data shows slightly greater accuracy when compared to the use of only Landsat data in the random forest (RF) classification, with overall accuracy increasing from 92.7% to 93.7% and kappa coefficient increasing from 0.912 to 0.924 (Table 3)
This study shows that a combination of optical and radar data when used with a Random Forest classifier might result in accurate land-cover maps, including wetland classes

Summary

Introduction

The Environmental Protection Agency (EPA) defines wetlands as “areas in which water covers the soil, or is present at or near the surface of the soil all year or for varying periods of time during a year, including during the growing season” [1], yet, “wetlands” is a broad term and might have varied characteristics in terms of hydrology, vegetation, and soils. To standardize the classification of wetlands, a classification system was developed by the United States Fish and Wildlife Service (FWS) and adopted by several agencies within the United States government. This system broadly categorizes wetlands into marine, estuarine, riverine, lacustrine, and palustrine wetlands [2]. Despite the value and importance of wetlands, large-scale wetland loss and degradation has been affecting the overall health of wetlands for hundreds of years, leading to the loss of ecosystem services. Common factors that lead to degradation of wetlands include eutrophication from urban and agricultural lands, invasive plants, hydrologic alterations, salinization, and filling, while wetland loss is typically a result of conversion of land use and drainage for agriculture [15]

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