Publication Date:
2022-09-01
Description:
© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Yang, X., Zhu, Z., Qiu, S., Kroeger, K. D., Zhu, Z., & Covington, S. Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series. Remote Sensing of Environment, 276, (2022): 113047, https://doi.org/10.1016/j.rse.2022.113047.
Description:
Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and natural resource management. Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others – including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km2 of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6 km2 per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.
Description:
This study was supported by USGS North Atlantic Coast Cooperative Ecosystem Studies Unit (CESU) Program for Detection and Characterization of Coastal Tidal Wetland Change (G19AC00354).
Keywords:
Coastal tidal wetland
;
Landsat time series
;
Change detection
;
Classification
;
Condition change
;
Cover change
;
Tide
;
DECODE
Repository Name:
Woods Hole Open Access Server
Type:
Article
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