State of Science Assessment of Remote Sensing of Great Lakes Coastal Wetlands: Responding to an Operational Requirement

Lori White,Jon Pasher,Robert A Ryerson,Jason Duffe

doi:10.3390/rs12183024

Lori White, Jon Pasher + Show 2 more

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https://doi.org/10.3390/rs12183024

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Journal: Remote sensing	Publication Date: Sep 16, 2020
Citations: 10	License type: CC BY 4.0

Affiliation: Environment and Climate Change Canada

Abstract

The purpose of this research was to develop a state of science synthesis of remote sensing technologies that could be used to track changes in Great Lakes coastal vegetation for the Great Lakes-St. Lawrence River Adaptive Management (GLAM) Committee. The mapping requirements included a minimum mapping unit (MMU) of either 2 × 2 m or 4 × 4 m, a digital elevation model (DEM) accuracy in x and y of 2 m, a “z” value or vertical accuracy of 1–5 cm, and an accuracy of 90% for the classes of interest. To determine the appropriate remote sensing sensors, we conducted an extensive literature review. The required high degree of accuracy resulted in the elimination of many of the remote sensing sensors used in other wetland mapping applications including synthetic aperture radar (SAR) and optical imagery with a resolution >1 m. Our research showed that remote sensing sensors that could at least partially detect the different types of wetland vegetation in this study were the following types: (1) advanced airborne “coastal” Airborne Light Detection and Ranging (LiDAR) with either a multispectral or a hyperspectral sensor, (2) colour-infrared aerial photography (airplane) with (optimum) 8 cm resolution, (3) colour-infrared unmanned aerial vehicle (UAV) photography with vertical accuracy determination rated at 10 cm, (4) colour-infrared UAV photography with high vertical accuracy determination rated at 3–5 cm, (5) airborne hyperspectral imagery, and (6) very high-resolution optical satellite data with better than 1 m resolution.

Highlights

Sensors and Platforms and Processing ApproachesThe basic GLAM requirements that combined the identification of both marsh meadow and changes in the extent of marsh meadow, several other wetland types, as well as a minimum mapping unit of either 4 square meters or 16 square meters, quickly eliminated most of the remote sensing tools examined
These included making the assumption that the research results could be translated into operational applications, the remote sensing data was available, high quality ground data was collected at the same time as the remote sensing imagery was acquired, and the analysis and interpretation was being performed by wetland experts
We assumed that the results must lead to classification of the six GLAM wetland classes, an accuracy of 90%, a minimum mapping unit of 2 × 2 m or 4 × 4 m, and vertical accuracy or “z” value of 2 m, in test sites in the Great Lakes Basin

Summary

Introduction

Sensors and Platforms and Processing ApproachesThe basic GLAM requirements that combined the identification of both marsh meadow and changes in the extent of marsh meadow, several other wetland types, as well as a minimum mapping unit of either 4 square meters or 16 square meters, quickly eliminated most of the remote sensing tools examined. The sensors and platforms discussed below are those that we deemed to be potentially useful to address the GLAM requirements. Recent research, including some reviewed here, has found that combinations of two or more data types and approaches could lead to a better solution [9,13,14,15]. This multisensor approach has been seen in some recent commercial offerings of direct relevance to this study. Teledyne Optech, for example, combined the value of their Airborne Light Detection and Ranging (LiDAR) sensor with a Compact Airborne Spectrographic Imager CASI hyperspectral sensor

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