Abstract
Coastal tidal marshes are essential ecosystems for both economic and ecological reasons. They necessitate regular monitoring as the effects of climate change begin to be manifested in changes to marsh vegetation healthiness. Small unmanned aerial systems (sUAS) build upon previously established remote sensing techniques to monitor a variety of vegetation health metrics, including biomass, with improved flexibility and affordability of data acquisition. The goal of this study was to establish the use of RGB-based vegetation indices for mapping and monitoring tidal marsh vegetation (i.e., Spartina alterniflora) biomass. Flights over tidal marsh study sites were conducted using a multi-spectral camera on a quadcopter sUAS near vegetation peak growth. A number of RGB indices were extracted to build a non-linear biomass model. A canopy height model was developed using sUAS-derived digital surface models and LiDAR-derived digital terrain models to assess its contribution to the biomass model. Results found that the distance-based RGB indices outperformed the regular radio-based indices in coastal marshes. The best-performing biomass models used the triangular greenness index (TGI; R2 = 0.39) and excess green index (ExG; R2 = 0.376). The estimated biomass revealed high biomass predictions at the fertilized marsh plots in the Long-Term Research in Environmental Biology (LTREB) project at the study site. The sUAS-extracted canopy height was not statistically significant in biomass estimation but showed similar explanatory power to other studies. Due to the lack of biomass samples in the inner estuary, the proposed biomass model in low marsh does not perform as well as the high marsh that is close to shore and accessible for biomass sampling. Further research of low marsh is required to better understand the best conditions for S. alterniflora biomass estimation using sUAS as an on-demand, personal remote sensing tool.
Highlights
The calibrated multi-spectral sensor used in this study provided reliable spectral information to test the concepts of using RGB-indices for S. alterniflora biomass modelling
This study investigated the optimal RGB indices for practical use in estimating biomass measurements in a tidal marsh system. Small unmanned aerial systems (sUAS) are being presented to coastal managers and professionals as a time-saving instrument for coastal wetland vegetation research [11,56]
As we continue into decades of sea-level rise and climate change that are predicted to significantly affect coastal tidal marshes, the development of efficient and effective monitoring practices is sorely needed. sUAS present coastal managers and researchers with cost-effective and on-demand tools for gathering data pertaining to several coastal tidal marsh vegetation health metrics
Summary
Beyond providing nurseries for many important aquatic species and beautiful backdrops for tourists, they are known for carbon sequestration and water runoff filtration [1,2,3]. Despite their utility, tidal marshes face various challenges, including sea-level rise and erosion. Successful marsh health monitoring requires the use of several metrics, including vegetation height, biomass, and density [7,8,9,10]. The complex nature of the tidal marsh environment presents challenges for frequently and efficiently gathering these metrics using in situ methods [11]. Remote sensing techniques have long provided non-intrusive methods for obtaining useful biophysical measurements [12]
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