Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays

Abstract

The spatial extent and vegetation characteristics of tidal wetlands and their change are among the biggest unknowns and largest sources of uncertainty in modeling ecosystem processes and services at the land-ocean interface. Using a combination of moderate-high spatial resolution (≤30 meters) optical and synthetic aperture radar (SAR) satellite imagery, we evaluated several approaches for mapping and characterization of wetlands of the Chesapeake and Delaware Bays. Sentinel-1A, Phased Array type L-band Synthetic Aperture Radar (PALSAR), PALSAR-2, Sentinel-2A, and Landsat 8 imagery were used to map wetlands, with an emphasis on mapping tidal marshes, inundation extents, and functional vegetation classes (persistent vs. non-persistent). We performed initial characterizations at three target wetlands study sites with distinct geomorphologies, hydrologic characteristics, and vegetation communities. We used findings from these target wetlands study sites to inform the selection of timeseries satellite imagery for a regional scale random forest-based classification of wetlands in the Chesapeake and Delaware Bays. Acquisition of satellite imagery, raster manipulations, and timeseries analyses were performed using Google Earth Engine. Random forest classifications were performed using the R programming language. In our regional scale classification, estuarine emergent wetlands were mapped with a producer’s accuracy greater than 88% and a user’s accuracy greater than 83%. Within target wetland sites, functional classes of vegetation were mapped with over 90% user’s and producer’s accuracy for all classes, and greater than 95% accuracy overall. The use of multitemporal SAR and multitemporal optical imagery discussed here provides a straightforward yet powerful approach for accurately mapping tidal freshwater wetlands through identification of non-persistent vegetation, as well as for mapping estuarine emergent wetlands, with direct applications to the improved management of coastal wetlands.

Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve

“…Additionally, multitemporal data distinguishes species diversity in different periods of plant phenological development [8,9], allowing less frequent or more expensive data to be simulated, which can be used to monitor the condition of plants [10][11][12][13], including the early detection of bark beetle outbreaks in trees (starting from preliminary stages (green phase) up to dry trunks), which is a serious challenge for European managed forests [14]. The results obtained from Landsat and Sentinel-2 images differ from each other based on differences in the purpose of the work (identification of forest types or individual species), the additional data sources available (e.g., digital surface model [15], microwave data), as well as the specificity of the research area, the combination of scenes from different growing seasons [16][17][18], remote sensing vegetation indices [19][20][21], and adopted algorithms [22,23].…”

Observing coastal wetland transitions using national land cover products

“…Thresholding methods have been used successfully with SAR imagery to identify and map surface water (Huang et al, 2017). Similar thresholding can be used to map inundation in wetlands after storm events (Rangoonwala et al, 2016; Yu and Gao, 2023) and during typical tides (Chaouch et al, 2012; Lamb et al, 2019). The upcoming launch of the National Aeronautics and Space Administration and Indian Space Research Organization’ NISAR satellite (the proposed launch is currently in 2024) is predicted to enhance the ability for researchers to detect flooding in vegetated areas (Ottinger and Kuenzer, 2020).…”

The Spatiotemporal Characteristics and Dynamic Changes of Tidal Flats in Florida from 1984 to 2020

“…The US has the eighth longest coastline throughout the world [26] and therefore is a worthwhile region to investigate, but the relative research in this country progressed at a slow pace. Specifically speaking, we noticed the works in Chesapeake and Delaware Bays [27], Nisqually River Delta [28], Waccasassa Bay [29], and Eastern Gulf Coast [30], which glanced through the distributions of tidal flats in some selected places of the US. However, these studies focus on the small areas and therefore none of them could provide an assessment on a large scale to better understand the general change patterns.…”