Modeling Salt Marsh Vegetation Height Using Unoccupied Aircraft Systems and Structure from Motion

Alexandra E Digiacomo,Virginia G Pan,David W Johnston,Claire Atkins-Davis,Justin T Ridge,Kelly Dobroski,Clara N Bird

doi:10.3390/rs12142333

Alexandra E Digiacomo, Virginia G Pan + Show 5 more

Open Access

https://doi.org/10.3390/rs12142333

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Journal: Remote sensing	Publication Date: Jul 21, 2020
Citations: 22	License type: CC BY 4.0

Affiliation: Marine Conservation Institute, Duke University

Abstract

Salt marshes provide important services to coastal ecosystems in the southeastern United States. In many locations, salt marsh habitats are threatened by coastal development and erosion, necessitating large-scale monitoring. Assessing vegetation height across the extent of a marsh can provide a comprehensive analysis of its health, as vegetation height is associated with Above Ground Biomass (AGB) and can be used to track degradation or growth over time. Traditional methods to do this, however, rely on manual measurements of stem heights that can cause harm to the marsh ecosystem. Moreover, manual measurements are limited in scale and are often time and labor intensive. Unoccupied Aircraft Systems (UAS) can provide an alternative to manual measurements and generate continuous results across a large spatial extent in a short period of time. In this study, a multirotor UAS equipped with optical Red Green Blue (RGB) and multispectral sensors was used to survey five salt marshes in Beaufort, North Carolina. Structure-from-Motion (SfM) photogrammetry of the resultant imagery allowed for continuous modeling of the entire marsh ecosystem in a three-dimensional space. From these models, vegetation height was extracted and compared to ground-based manual measurements. Vegetation heights generated from UAS data consistently under-predicted true vegetation height proportionally and a transformation was developed to predict true vegetation height. Vegetation height may be used as a proxy for Above Ground Biomass (AGB) and contribute to blue carbon estimates, which describe the carbon sequestered in marine ecosystems. Employing this transformation, our results indicate that UAS and SfM are capable of producing accurate assessments of salt marsh health via consistent and accurate vegetation height measurements.

Highlights

Monitoring salt marshes along the southeastern United States coast has become increasingly important in the face of coastal erosion from storms, sea level rise and development [1,2]
The small slopes values of the regression equations demonstrated that all three methods were under-predicting vegetation height proportionally, missing more absolute vegetation at higher stem heights (Figure 4)
All three vegetation height prediction methods produced significant linear relationships (p < 0.0001) between observed and expected data but with low r2 values (r2 = 0.128–0.296) (Figure 4). This finding is consistent with a previous study by Kulawardhana et al using Light Detection and Ranging (LiDAR) returns to quantify the vegetation height of S. alterniflora dominated salt marshes in Texas where the linear regression of the mean vegetation height and field measurements produced an r2 of 0.34 [14]

Summary

Introduction

Monitoring salt marshes along the southeastern United States coast has become increasingly important in the face of coastal erosion from storms, sea level rise and development [1,2]. Salt marshes provide critical ecosystem functions and services such as providing habitat, sediment stabilization, water filtration, and carbon sequestration [1,3,4,5,6]. It is estimated that 50% of salt marshes worldwide have been lost or are degraded [5], and the continued loss of this ecologically valuable habitat necessitates rapid and effective monitoring. This includes assessments of salt marsh extent, vegetation height, and biomass which provide standardized indicators of marsh ecosystem health [7,8,9]

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Conclusion