Coastal wetlands contribute greatly to our coasts economically and ecologically. The utility of coastal wetland vegetation, along with the multitude of dynamic forces they encounter, suggests the need of regular monitoring for sustainable management. While traditional in situ survey methods and remote sensing from space and manned platforms have provided means to monitor and study the coastal zone thus far, the recent developments of small unmanned aerial systems (sUAS) fill a small void between traditional in situ survey methods and the high spatial resolution of manned aircraft imagery. As an on-demand personal remote sensing device, an sUAS can be deployed over coastal regions at a low cost and with very fine spatial resolution (i.e. 1–10 cm) imagery and corresponding spatial accuracy. Though an sUAS provides many benefits, recent literature documents several shortcomings and limitations to using them for coastal wetland vegetation research, including changing tides, lighting conditions and legal restrictions on flying. This study reviewed all coastal wetland vegetation-related studies that included an sUAS as a mapping tool to document the current state of the field. Current practices, successes, and limitations are described, and future directions for the field are discussed. Coastal managers and researchers alike will be able use this comprehensive review to determine how to best approach future studies of diverse coastal vegetation.

Deep learning techniques are increasingly being recognized as effective image classifiers. Aside from their successful performance in past studies, the accuracies have varied in complex environments, in comparison with the popularly of applied machine learning classifiers. This study seeks to explore the feasibility of using a U-Net deep learning architecture to classify bi-temporal, high-resolution, county-scale aerial images to determine the spatial extent and changes of land cover classes that directly or indirectly impact tidal marsh. The image set used in the analysis is a collection of a 1-m resolution collection of National Agriculture Imagery Program (NAIP) tiles from 2009 and 2019, covering Beaufort County, South Carolina. The U-Net CNN classification results were compared with two machine learning classifiers, the random trees (RT) and support vector machine (SVM). The results revealed a significant accuracy advantage in using the U-Net classifier (92.4%), as opposed to the SVM (81.6%) and RT (75.7%) classifiers, for overall accuracy. From the perspective of a GIS analyst or coastal manager, the U-Net classifier is now an easily accessible and powerful tool for mapping large areas. Change detection analysis indicated little areal change on marsh extent, though increased land development throughout the county has the potential to negatively impact the health of the marshes. Future work should explore applying the constructed U-Net classifier to coastal environments in large geographic areas, while also implementing other data sources (e.g., LIDAR and multispectral data) to enhance classification accuracy.

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.

Tidal marshes are dynamic environments providing important ecological and economic services in coastal regions. With accelerating climate change and sea level rise (SLR), marsh mortality and wetland conversion have been observed on global coasts. For sustainable coastal management, accurate projection of SLR-induced tidal inundation and flooding requires fine-scale 3D terrain of the intertidal zones. The airborne Lidar systems, although successful in extracting terrestrial topography, suffer from high vertical uncertainties in coastal wetlands due to tidal effects. This study tests the feasibility of drone Lidar leveraging deep learning of point clouds on 3D marsh mapping. In an ocean-front, pristine estuary dominated by Spartina alterniflora, drone Lidar point clouds, and in-field marsh samples were collected. The RandLA-Net deep learning model was applied to classify the Lidar point cloud to ground, low vegetation, and high vegetation with an overall accuracy of around 0.84. With the extracted digital terrain model and digital surface model, the cm-level bare earth surfaces and marsh heights were mapped. The bare earth terrain reached a vertical accuracy (root-mean-square error, or RMSE) of 5.55 cm. At the 65 marsh samples, the drone Lidar-extracted marsh height was lower than the in-field height measurements. However, their strongly significantly linear relationship (Pearson’s r = 0.93) reflects the validity of the drone Lidar for measuring marsh canopy height. The adjusted Lidar-extracted marsh height had an RMSE of 0.12 m. This experiment demonstrates a multi-step operational procedure to deploy drone Lidar for accurate, fine-scale terrain and 3D marsh mapping, which provides essential base layers for projecting wetland inundation in various climate change and SLR scenarios.

As the world’s urban population increases to the predicted 70% of the total population, urban infrastructure and built-up land will continue to grow as well. This growth will continue to have an impact on the urban heat island effect in all of the world’s cities. The urban tree canopy has been found to be one of the few factors that can lessen the effects of the urban heat island effect. This study seeks to accomplish two objectives: first, we examine the use of a commonly used machine learning classifier (e.g., Support Vector Machine) for identifying the urban tree canopy using no-cost high resolution NAIP imagery. Second, we seek to use Land Surface Temperature (LST) maps derived from no-cost Landsat thermal imagery to identify correlations between canopy loss and temperature hot spot increases over a 14-year period in Columbia, SC, USA. We found the SVM imagery classifier was highly accurate in classifying both the 2005 imagery (94.3% OA) and the 2019 imagery (94.25% OA) into canopy and other classes. We found the color infrared image available in the 2019 NAIP imagery better for identifying canopy than the true color images available in 2005 (97.8% vs. 90.2%). Visual analysis based on the canopy maps and LST maps showed temperatures rose near areas where tree canopy was lost, and urban development continued. Future studies will seek to improve classification methods by including other classes, other ancillary data sets (e.g., LiDAR), new classification methods (e.g., deep learning), and analytical methods for change detection analysis.

Defined as “personal remote sensing”, small unmanned aircraft systems (sUAS) have been increasingly utilized for landscape mapping. This study tests a sUAS procedure of 3D tree surveying of a closed-canopy woodland on an earthen dam. Three DJI drones—Mavic Pro, Phantom 4 Pro, and M100/RedEdge-M assembly—were used to collect imagery in six missions in 2019–2020. A canopy height model was built from the sUAS-extracted point cloud and LiDAR bare earth surface. Treetops were delineated in a variable-sized local maxima filter, and tree crowns were outlined via inverted watershed segmentation. The outputs include a tree inventory that contains 238 to 284 trees (location, tree height, crown polygon), varying among missions. The comparative analysis revealed that the M100/RedEdge-M at a higher flight altitude achieved the best performance in tree height measurement (RMSE = 1 m). However, despite lower accuracy, the Phantom 4 Pro is recommended as an optimal drone for operational tree surveying because of its low cost and easy deployment. This study reveals that sUAS have good potential for operational deployment to assess tree overgrowth toward dam remediation solutions. With 3D imaging, sUAS remote sensing can be counted as a reliable, consumer-oriented tool for monitoring our ever-changing environment.