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

Remote Sensing of Coastal Wetlands: Long term vegetation stress assessment and data enhancement technique

Apalachicola Bay in the Florida panhandle is home to a rich variety of salt water and freshwater wetlands but unfortunately is also subject to a wide range of hydrologic extreme events. Extreme hydrologic events such as hurricanes and droughts continuously threaten the area. The impact of hurricane and drought on both fresh and salt water wetlands was investigated over the time period from 2000 to 2015 in Apalachicola Bay using spatio-temporal changes in the Landsat based NDVI. Results indicate that salt water wetlands were more resilient than fresh water wetlands. Results also suggest that in response to hurricanes, the coastal wetlands took almost a year to recover while recovery following a drought period was observed after only a month. This analysis was successful and provided excellent insights into coastal wetland health. Such long term study is heavily dependent on optical sensor that is subject to data loss due to cloud coverage. Therefore, a novel method is proposed and demonstrated to recover the information contaminated by cloud. Cloud contamination is a hindrance to long-term environmental assessment using information derived from satellite imagery that retrieve data from visible and infrared spectral ranges. Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Landsat has an ongoing high resolution NDVI record starting from 1984. Unfortunately, the time series NDVI data suffers from the cloud contamination issue. Though simple to complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques are subject to many limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum with the aid of a machine learning tool, namely random forest (RF) trained and tested utilizing multi-parameter hydrologic data. The RF based OCPR model was compared with a simple linear regression (LR) based OCPR model to understand the potential of the model. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance for two specific dates. The RF based OCPR method achieves a root mean squared error of 0.0475 sr?1 between predicted and observed NDVI reflectance values. The LR based OCPR method achieves a root mean squared error of 0.1257 sr?1. Findings suggested that the RF based OCPR method is effective to repair cloudy values and provide continuous and quantitatively reliable imagery for further analysis in environmental applications

Endogenous Risk Perception, Geospatial Characteristics and Temporal Variation in Hurricane Evacuation Behavior

The main focus of this thesis was to gain a better understanding about the dynamics of risk perception and its influence on people’s evacuation behavior. Another major focus was to improve our knowledge regarding geo-spatial and temporal variations of risk perception and hurricane evacuation behavior. A longitudinal dataset of more than eight hundred households were collected following two major hurricane events, Ivan and Katrina. The longitudinal survey data was geocoded and a geo-spatial database was integrated to it. The geospatial database was composed of distance, elevation and hazard parameters with respect to the respondent’s household location. A set of Bivariate Probit (BP) model suggests that geospatial variables have had significant influences in explaining hurricane risk perception and evacuation behavior during both hurricanes. The findings also indicated that people made their evacuation decision in coherence with their risk perception. In addition, people updated their hurricane evacuation decision in a subsequent similar event

Assessing the Resilience of Coastal Wetlands to Extreme Hydrologic Events Using Vegetation Indices: A Review

Coastal wetlands (CWs) offer numerous imperative functions that support a diverse array of life forms that are poorly adapted for other environments and provide an economic base for human communities. Unfortunately, CWs have been experiencing significant threats due to meteorological and climatic fluctuations as well as anthropogenic impacts. The wetlands and marshes in Apalachicola Bay, Florida have endured the impacts of several extreme hydrologic events (EHEs) over the past few decades. These extreme hydrologic events include drought, hurricane, heavy precipitation and fluvial flooding. Remote sensing has been used and continues to demonstrate promise for acquiring spatial and temporal information about CWs thereby making it easier to track and quantify long term changes driven by EHEs. These wetland ecosystems are also adversely impacted by increased human activities such as wetland conversion to agricultural, aquaculture, industrial or residential use; construction of dikes along the shoreline; and sprawl of built areas. In this paper, we review previous works on coastal wetland resilience to EHEs. We synthesize these concepts in the context of remote sensing as the primary assessment tool with focus on derived vegetation indices to monitor CWs at regional and global scales

Fast Eutrophication Assessment For Stormwater Wet Detention Ponds Via Fuzzy Probit Regression Analysis Under Uncertainty

Stormwater wet detention ponds have been a commonly employed best management practice for stormwater management throughout the world for many years. In the past, the trophic state index values have been used to evaluate seasonal changes in water quality and rank lakes within a region or between several regions; yet, to date, there is no similar index for stormwater wet detention ponds. This study aimed to develop a new multivariate trophic state index (MTSI) suitable for conducting a rapid eutrophication assessment of stormwater wet detention ponds under uncertainty with respect to three typical physical and chemical properties. Six stormwater wet detention ponds in Florida were selected for demonstration of the new MTSI with respect to total phosphorus (TP), total nitrogen (TN), and Secchi disk depth (SDD) as cognitive assessment metrics to sense eutrophication potential collectively and inform the environmental impact holistically. Due to the involvement of multiple endogenous variables (i.e., TN, TP, and SDD) for the eutrophication assessment simultaneously under uncertainty, fuzzy synthetic evaluation was applied to first standardize and synchronize the sources of uncertainty in the decision analysis. The ordered probit regression model was then formulated for assessment based on the concept of MTSI with the inputs from the fuzzy synthetic evaluation. It is indicative that the severe eutrophication condition is present during fall, which might be due to frequent heavy summer storm events contributing to high-nutrient inputs in these six ponds

Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects

Resilience Of Coastal Wetlands To Extreme Hydrologic Events In Apalachicola Bay

Extreme hydrologic events such as hurricanes and droughts continuously threaten wetlands which provide key ecosystem services in coastal areas. The recovery time for vegetation after impact from these extreme events can be highly variable depending on the hazard type and intensity. Apalachicola Bay in Florida is home to a rich variety of saltwater and freshwater wetlands and is subject to a wide range of hydrologic hazards. Using spatiotemporal changes in Landsat-based empirical vegetation indices, we investigate the impact of hurricane and drought on both freshwater and saltwater wetlands from year 2000 to 2015 in Apalachicola Bay. Our results indicate that saltwater wetlands are more resilient than freshwater wetlands and suggest that in response to hurricanes, the coastal wetlands took almost a year to recover, while recovery following a drought period was observed after only a month

Assessing the Resilience of Coastal Wetlands to Extreme Hydrologic Events Using Vegetation Indices: A Review

Coastal wetlands (CWs) offer numerous imperative functions that support a diverse array of life forms that are poorly adapted for other environments and provide an economic base for human communities. Unfortunately, CWs have been experiencing significant threats due to meteorological and climatic fluctuations as well as anthropogenic impacts. The wetlands and marshes in Apalachicola Bay, Florida have endured the impacts of several extreme hydrologic events (EHEs) over the past few decades. These extreme hydrologic events include drought, hurricane, heavy precipitation and fluvial flooding. Remote sensing has been used and continues to demonstrate promise for acquiring spatial and temporal information about CWs thereby making it easier to track and quantify long term changes driven by EHEs. These wetland ecosystems are also adversely impacted by increased human activities such as wetland conversion to agricultural, aquaculture, industrial or residential use; construction of dikes along the shoreline; and sprawl of built areas. In this paper, we review previous works on coastal wetland resilience to EHEs. We synthesize these concepts in the context of remote sensing as the primary assessment tool with focus on derived vegetation indices to monitor CWs at regional and global scales

Wetland Dynamics Inferred from Spectral Analyses of Hydro-Meteorological Signals and Landsat Derived Vegetation Indices

The dynamic response of coastal wetlands (CWs) to hydro-meteorological signals is a key indicator for understanding climate driven variations in wetland ecosystems. This study explored the response of CW dynamics to hydro-meteorological signals using time series of Landsat-derived normalized difference vegetation index (NDVI) values at six locations and hydro-meteorological time-series from 1984 to 2015 in Apalachicola Bay, Florida. Spectral analysis revealed more persistence in NDVI values for forested wetlands in the annual frequency domain, compared to scrub and emergent wetlands. This behavior reversed in the decadal frequency domain, where scrub and emergent wetlands had a more persistent NDVI than forested wetlands. The wetland dynamics were found to be driven mostly by the Apalachicola Bay water level and precipitation. Cross-spectral analysis indicated a maximum time-lag of 2.7 months between temperature and NDVI, whereas NDVI lagged water level by a maximum of 2.2 months. The quantification of persistent behavior and subsequent understanding that CW dynamics are mostly driven by water level and precipitation suggests that the severity of droughts, floods, and storm surges will be a driving factor in the future sustainability of CW ecosystems

Assessing The Resilience Of Coastalwetlands To Extreme Hydrologic Events Using Vegetation Indices: A Review

Coastal wetlands (CWs) offer numerous imperative functions that support a diverse array of life forms that are poorly adapted for other environments and provide an economic base for human communities. Unfortunately, CWs have been experiencing significant threats due to meteorological and climatic fluctuations as well as anthropogenic impacts. The wetlands and marshes in Apalachicola Bay, Florida have endured the impacts of several extreme hydrologic events (EHEs) over the past few decades. These extreme hydrologic events include drought, hurricane, heavy precipitation and fluvial flooding. Remote sensing has been used and continues to demonstrate promise for acquiring spatial and temporal information about CWs thereby making it easier to track and quantify long term changes driven by EHEs. These wetland ecosystems are also adversely impacted by increased human activities such as wetland conversion to agricultural, aquaculture, industrial or residential use; construction of dikes along the shoreline; and sprawl of built areas. In this paper, we review previous works on coastal wetland resilience to EHEs. We synthesize these concepts in the context of remote sensing as the primary assessment tool with focus on derived vegetation indices to monitor CWs at regional and global scales