Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery

Cheryl Doughty,Kyle Cavanaugh

doi:10.3390/rs11050540

Cheryl Doughty, Kyle Cavanaugh

Open Access

https://doi.org/10.3390/rs11050540

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Journal: Remote Sensing	Publication Date: Mar 6, 2019
Citations: 109	License type: CC BY 4.0

Affiliation: University of California, Los Angeles

Abstract

Salt marsh productivity is an important control of resiliency to sea level rise. However, our understanding of how marsh biomass and productivity vary across fine spatial and temporal scales is limited. Remote sensing provides a means for characterizing spatial and temporal variability in marsh aboveground biomass, but most satellite and airborne sensors have limited spatial and/or temporal resolution. Imagery from unmanned aerial vehicles (UAVs) can be used to address this data gap. We combined seasonal field surveys and multispectral UAV imagery collected using a DJI Matrice 100 and Micasense Rededge sensor from the Carpinteria Salt Marsh Reserve in California, USA to develop a method for high-resolution mapping of aboveground saltmarsh biomass. UAV imagery was used to test a suite of vegetation indices in their ability to predict aboveground biomass (AGB). The normalized difference vegetation index (NDVI) provided the strongest correlation to aboveground biomass for each season and when seasonal data were pooled, though seasonal models (e.g., spring, r2 = 0.67; RMSE = 344 g m−2) were more robust than the annual model (r2 = 0.36; RMSE = 496 g m−2). The NDVI aboveground biomass estimation model (AGB = 2428.2 × NDVI + 120.1) was then used to create maps of biomass for each season. Total site-wide aboveground biomass ranged from 147 Mg to 205 Mg and was highest in the spring, with an average of 1222.9 g m−2. Analysis of spatial patterns in AGB demonstrated that AGB was highest in intermediate elevations that ranged from 1.6–1.8 m NAVD88. This UAV-based approach can be used aid the investigation of biomass dynamics in wetlands across a range of spatial scales.

Highlights

Coastal wetlands, despite being characteristically dynamic ecosystems equipped to deal with a variety of stressors, are threatened by environmental change
Comparison of field and unmanned aerial vehicles (UAVs)-based reflectance measurements pooled for all seasons indicated a strong 1:1 correlation (r2 ≥ 0.94) with root mean square error (RMSE) less than 0.02 for all visible bands (Figure 3)
Reflectance estimated with the UAV imagery and the observed reflectance in the field were well-correlated in the RE and NIR (r2 ≥ 0.93), with RMSE’s less than 0.05

Summary

Introduction

Despite being characteristically dynamic ecosystems equipped to deal with a variety of stressors, are threatened by environmental change. Environmental stressors, such as inundation, salinity, and nutrient availability, influence the overall productivity of coastal wetlands [1]. The ability of coastal wetlands to remain productive and maintain elevation through accretion is a key factor in overall wetland resilience to environmental change [3,4]. Climate change threatens to disrupt the normative patterns and processes by exacerbating environmental stressors, which could have cascading effects on biological response, wetland productivity, and, resilience [5]. Understanding how coastal wetland biomass and productivity change over space and time can indicate vegetation stress and help establish a threshold for resilience to environmental drivers [8,11]

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