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

Coastal Wetland Vegetation in Response to Global Warming and Climate Change

Under the background of global warming, rising sea level, extreme weather and other global climate changes, vegetation has played a targeted and irreplaceable role. The characteristics of individual plant, community landscape and vegetation succession in response to the major driving factor (mainly includes habitat relative elevation, net loss of coastal habitat, salinity, etc.) were analyzed. An obvious development of vegetation landscape fragmentation has results from the competitive advantages of salt-tolerant species or invasive species, which eventually results in the regressive succession and unreasonable secondary succession of vegetation. Compared with the botanical community statistics method, the method of combined of GIS-mapping and remote sensing data provide a more effective way to extract the individual plant stress information, vegetation community structure and dynamic change of vegetation landscape pattern, which can reflect the spatial differentiation of the vegetation at a macro-scale. In addition, in view of the high-efficiency carbon sequestration capability of coastal wetland vegetation, the spatial distribution, temporal dynamic and extraction method of vegetation and soil sequestration were discussed. Synthesize above analysis result, further studies in vegetation response to global climate change were proposed, which need to be improved or expanded

Long-term monitoring of biophysical characteristics of tidal wetlands in the northern Gulf of Mexico — A methodological approach using MODIS

Accurate and efficient monitoring is critically important for the effective restoration and conservation of threatened tidal wetlands in the Gulf Coast. The high carbon sequestration potential, habitat for important wildlife and fish, and numerous ecosystem services make these tidal wetlands highly valuable both ecologically and economically to Gulf Coast communities. Our study developed a new methodological approach for mapping biophysical health of coastal tidal wetland habitats in terms of green leaf area index (GLAI), canopy level chlorophyll content (CHL), vegetation fraction (VF), and above ground green biomass (GBM). We measured these biophysical characteristics in tidal wetlands of the northern Gulf of Mexico using a combination of ground data collected from field surveys during the growing seasons of 2010 and 2011 and NASA\u27s Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m and 500 m images. Additionally, we compared and evaluated the performances of both in situ proximal and satellite remote sensing measurements in terms of their potential for mapping the wetland biophysical characteristics. MODIS-based models proved superior at the landscape level compared to models developed from in situ proximal sensing, as species level signals seemed to be diluted at coarser spatial scales. We selected Wide Dynamic Range Vegetation Index (WDRVI) for MODIS 250 m and Visible Atmospheric Resistant Index (VARI) for MODIS 500 m to map biophysical characteristics of tidal wetlands. Time-series composites and phenological information derived using the MODIS based models captured the impact of the selected disturbances in the last decade on the ecological and physiological status of the tidal wetland habitats in the Gulf Coast. This is the first study to employ MODIS data to analyze the biophysical characteristics of tidal wetlands in the Gulf Coast, which, in turn, has the potential to improve our ability to predict their productivity and carbon sequestration potential. These techniques could also be used to assess the success of previous and ongoing tidal wetland restoration projects, and evaluate the productivity of marshes under threat from developmental activity, sea level rise, and industrial pollution

Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

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

Multiple stable states and catastrophic shifts in coastal wetlands: Progress, challenges, and opportunities in validating theory using remote sensing and other methods

open5siThe analysis by K.B. Moffett was partially supported by National Science Foundation grant EAR-1013843 to Stanford University. Any opinions, findings, and onclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The analysis by W. Nardin was partially supported by Office of Naval Research Award N00014-14-1-0114 to Boston University. The analysis by C. Wang was partially supported by National Natural Science Funds of China (41376120 and 41401413). The analysis by C. Wang and S. Temmerman was also partially supported by the European Union Programme Erasmus Mundus External Cooperation Window (EMECW)-Lot 14-China. K.B. Moffett thanks B.C. Smith for the analysis for the Wax Lake Delta example of Section 4.2 and S.M. Gorelick for the funding leading to the San Francisco Bay example of Section 4.3. W. Nardin thanks S. Fagherazzi and C. Woodcock for the funding leading to the Mekong River Delta example of Section 4.1. S. Silvestri thanks M. Marani for inspiring ideas and research on coastal wetland processes.Multiple stable states are established in coastal tidal wetlands (marshes, mangroves, deltas, seagrasses) by ecological, hydrological, and geomorphological feedbacks. Catastrophic shifts between states can be induced by gradual environmental change or by disturbance events. These feedbacks and outcomes are key to the sustainability and resilience of vegetated coastlines, especially as modulated by human activity, sea level rise, and climate change. Whereas multiple stable state theory has been invoked to model salt marsh responses to sediment supply and sea level change, there has been comparatively little empirical verification of the theory for salt marshes or other coastal wetlands. Especially lacking is long-term evidence documenting if or how stable states are established and maintained at ecosystem scales. Laboratory and field-plot studies are informative, but of necessarily limited spatial and temporal scope. For the purposes of long-term, coastal-scale monitoring, remote sensing is the best viable option. This review summarizes the above topics and highlights the emerging promise and challenges of using remote sensing-based analyses to validate coastal wetland dynamic state theories. This significant opportunity is further framed by a proposed list of scientific advances needed to more thoroughly develop the field.openMoffett K.B.; Nardin W.; Silvestri S.; Wang C.; Temmerman S.Moffett K.B.; Nardin W.; Silvestri S.; Wang C.; Temmerman S

Human and environmental exposure to hydrocarbon pollution in the Niger Delta:A geospatial approach

This study undertook an integrated geospatial assessment of human and environmental exposure to oil pollution in the Niger Delta using primary and secondary spatial data. This thesis begins by presenting a clear rationale for the study of extensive oil pollution in the Niger Delta, followed by a critical literature review of the potential application of geospatial techniques for monitoring and managing the problem. Three analytical chapters report on the methodological developments and applications of geospatial techniques that contribute to achieving the aim of the study. Firstly, a quantitative assessment of human and environmental exposure to oil pollution in the Niger Delta was performed using a government spill database. This was carried out using Spatial Analysis along Networks (SANET), a geostatistical tool, since oil spills in the region tend to follow the linear patterns of the pipelines. Spatial data on pipelines, oil spills, population and land cover data were analysed in order to quantify the extent of human and environmental exposure to oil pollution. The major causes of spills and spatial factors potentially reinforcing reported causes were analysed. Results show extensive general exposure and sabotage as the leading cause of oil pollution in the Niger Delta. Secondly, a method of delineating the river network in the Niger Delta using Sentinel-1 SAR data was developed, as a basis for modelling potential flow of pollutants in the distributary pathways of the network. The cloud penetration capabilities of SAR sensing are particularly valuable for this application since the Niger Delta is notorious for cloud cover. Vector and raster-based river networks derived from Sentinel-1 were compared to alternative river map products including those from the USGS and ESA. This demonstrated the superiority of the Sentinel-1 derived river network, which was subsequently used in a flow routing analysis to demonstrate the potential for understanding oil spill dispersion. Thirdly, the study applied optical remote sensing for indirect detection and mapping of oil spill impacts on vegetation. Multi-temporal Landsat data was used to delineate the spill impact footprint of a notable 2008 oil spill incident in Ogoniland and population exposure was evaluated. The optical data was effective in impact area delineation, demonstrating extensive and long-lasting population exposure to oil pollution. Overall, this study has successfully assembled and produced relevant spatial and attribute data sets and applied integrated geostatistical analytical techniques to understand the distribution and impacts of oil spills in the Niger Delta. The study has revealed the extensive level of human and environmental exposure to hydrocarbon pollution in the Niger Delta and introduced new methods that will be valuable fo

Discriminating wetland vegetation species in an African savanna using hyperspectral data.

Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.Wetland vegetation is of fundamental ecological importance and is used as one of the vital bio-indicators for early signs of physical or chemical degradation in wetland systems. Wetland vegetation is being threatened by expansion of extensive lowland areas of agriculture, natural resource exploitation, etc. These threats are increasing the demand for detailed information on vegetation status, up-to-date maps as well as accurate information for mitigation and adaptive management to preserve wetland vegetation. All these requirements are difficult to produce at species or community level, due to the fact that some parts of the wetlands are inaccessible. Remote sensing offers nondestructive and real time information for sustainable and effective management of wetland vegetation. The application of remote sensing in wetland mapping has been done extensively, but unfortunately the uses of narrowband hyperspectral data remain unexplored at an advanced level. The aim of this study is to explore the potential of hyperspectral remote sensing for wetland vegetation discrimination at species level. In particular, the study concentrates on enhancing or improving class separability among wetland vegetation species. Therefore, the study relies on the following two factors; a) the use of narrowband hyperspectral remote sensing, and b) the integration of vegetation properties and vegetation indices to improve accuracy. The potential of vegetation indices and red edge position were evaluated for vegetation species discrimination. Oneway ANOVA and Canonical variate analysis were used to statistically test if the species were significantly different and to discriminate among them. The canonical structure matrix revealed that hyperspectral data transforms can discriminate vegetation species with an overall accuracy around 87%. The addition of biomass and water content variables improved the accuracy to 95.5%. Overall, the study demonstrated that hyperspectral data and vegetation properties improve wetland vegetation separability at species level