Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series

© 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.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.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)

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives

Vegetation impact and recovery from oil-induced stress on three ecologically Distinct Wetland Sites in the Gulf of Mexico

April 20, 2010 marked the start of the British Petroleum Deepwater Horizon oil spill, the largest marine oil spill in US history, which contaminated coastal wetland ecosystems across the northern Gulf of Mexico. We used hyperspectral data from 2010 and 2011 to compare the impact of oil contamination and recovery of coastal wetland vegetation across three ecologically diverse sites: Barataria Bay (saltmarsh), East Bird's Foot (intermediate/freshwater marsh), and Chandeleur Islands (mangrove-cordgrass barrier islands). Oil impact was measured by comparing wetland pixels along oiled and oil-free shorelines using various spectral indices. We show that the Chandeleur Islands were the most vulnerable to oiling, Barataria Bay had a small but widespread and significant impact, and East Bird's Foot had negligible impact. A year later, the Chandeleur Islands showed the strongest signs of recovery, Barataria Bay had a moderate recovery, and East Bird's Foot had only a slight increase in vegetation. Our results indicate that the recovery was at least partially related to the magnitude of the impact such that greater recovery occurred at sites that had greater impact

Remote sensing drought impacts on wetland vegetation productivity at the Soetendalsvlei in the Heuningnes Catchment, South Africa

>Magister Scientiae - MScThis work aimed at assessing the response of wetland vegetation productivity to the 2014-2017 climate-induced drought at the Soetendalsvlei wetland system in the Western Cape province of South Africa. To achieve this objective, firstly a literature review on the progress of remotely sensed data applications in assessing and monitoring wetland vegetation productivity was conducted. The review elaborates on the role of remote sensing in monitoring and assessing wetland vegetation productivity, with a detailed discussion of the climate change and variability impacts on wetland vegetation productivity. Accurate assessment results are produced when suitable processing techniques are selected as well as appropriate spatial and spectral resolution for extracting spectral information of wetland vegetation productivity. Secondly, wetland vegetation changes and productivity status was assessed using multi-temporal resolution Landsat series imagery and Normalized Difference Vegetation Index (NDVI) during the wet and dry seasons for the period between 2014 and 2018

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists

A Vegetation Analysis on Horn Island, Mississippi, ca. 1940 Using Characteristic Dimensions Derived from Historical Aerial Photography

Horn Island is part of the MS/AL barrier island chain in the northern Gulf of Mexico located approximately 18kn off the coast of Mississippi. This island’s habitats have undergone many transitions over the last several decades. The goal of this study was to quantify habitat change over a seventy year period using historical black and white photography from 1940. Using present NAIP imagery from the USDA, habitat structure was estimated by using geo-statistics, and second order statistics, from a co-occurrence matrix, to characterize texture for habitat classification. Percent land cover was then calculated to determine overall land cover change over a seventy year period. The geostatistic of the horizontal spectral variation (CV) of image textures was used to estimate habitat structure using a multi scale approach if any characteristics of habitat texture could be delineated from CV histograms. The classification met with a result of an 80% habitat map of Horn Island ca. 1940, at 21x21 window size proving, that CV can be used successfully to classify text of historical black and white imagery. It was, also proven that CV can be used to characterize relative patch size for slash pine woodland habitat types, but not for habitats with smaller horizontal variations (i.e., marsh, and dune herbland)

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl