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Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in processing LIDAR data to effectively and efficiently identify ground measurements for the complex terrains in a large LIDAR data set. The GAPM filter is highly automatic and requires little human input. Therefore, it can significantly reduce the effort of manually processing voluminous LIDAR measurements.

Fingolimod (FTY720), an immunomodulator, is approved as an oral treatment for patients with relapsing forms of multiple sclerosis. Its effects are largely attributed to its mechanism of selectively retaining lymphocytes in the lymph nodes to reduce autoreactive T-cell recruitment in the CNS. In this study, we investigated the therapeutic effect of FTY720 on an animal model of CNS inflammation induced by intracerebral ventricle LPS injection. We found that FTY720 treatment significantly prevented LPS-induced neutrophil recruitment in the CNS by inhibiting leukocyte recruitment in cerebral microvessels. Furthermore, FTY720 also inhibited the expressions of adhesion molecules on the cerebral endothelium, but did not affect the expression levels of pro-inflammatory cytokines (TNF-α and IL-6) and chemokines (CXCL1 and CXCL2) in the CNS parenchyma. The inhibition of endothelial activation was accompanied by reduced phosphorylation of signaling molecules, including serine/threonine-specific protein kinase (Akt), STAT6, and nuclear factor-κB. This FTY720-attenuated inhibition of leukocyte recruitment and endothelial activation was reversed by blocking the functions of sphingosine kinase 2 or sphingosine-1-phosphate receptor 1. Our study demonstrated, for the first time, that FTY720 directly inhibits the phosphorylation of multiple signaling molecules in endothelial cells, thereby effectively blocking leukocyte recruitment in the CNS.

The sensitivity of the mangrove effect on reducing storm surge flooding to hurricane characteristics is investigated by using the numerical model Coastal and Estuarine Storm Tide (CEST). First, the attenuation of storm surge by mangroves is incorporated into the model by updating Manning’s coefficient based on the National Land Cover Dataset (NLCD) 2001. Then CEST is verified by comparing the model results with field observations in South Florida for Hurricane Wilma. Secondly, a set of numerical experiments using synthetic hurricanes with different intensity, forward speed, radius of maximum wind speed and travel direction are conducted for the sensitivity study. Results indicate that storm surge magnitudes and flooding areas are reduced by the mangrove zone more for fast moving hurricanes than slow moving hurricanes in the west coast of South Florida. In addition, increasing hurricane intensity and hurricane size lower the effect of mangroves on attenuating storm surge and reducing the flooding area. The mangrove zone plays a more effective role in reducing flooding areas from hurricanes that travel from east to west than from hurricanes that travel from west to east. The mangrove reduction effect is most sensitive to changes in hurricane forward speed. A 6.7 m/s to 2.2 m/s decrease in forward speed can result in a decrease in flood area reduction by mangroves that is equivalent to the decrease in flood area reduction by mangroves from Category 3 to 5 hurricanes.

The operational forecast demands and constraints of the National Hurricane Center require that a storm surge model in research mode be tested against a benchmark model such as Sea, Lake, and Overland Surges from Hurricanes (SLOSH) for accuracy, computation time, and numerical stability before the model is used for operational forecasts. Additionally, the simulated results must be in a geographic information system format to facilitate the usage of computed storm surge for various applications. This paper presents results from a demonstration project to explore the pathway for the transition of the Coastal and Estuarine Storm Tide (CEST) model to an operational forecast model by testing CEST over SLOSH basins in Florida. The performance and stability of CEST were examined by conducting simulations for Hurricane Andrew (1992) and more than 100 000 synthetic hurricanes for nine SLOSH basins covering the Florida coast and Lake Okeechobee. The results show that CEST produces peak surge heights similar to those from SLOSH. Additionally, CEST has proven to be numerically stable against all synthetic hurricanes and the computation time of CEST is comparable to that of SLOSH. Therefore, CEST has the potential to be used for operational forecasts of storm surge. The potential of producing more detailed real-time surge inundation forecasts was also investigated through the simulations of Andrew's surge on various grids with different cell sizes. The results indicate that CEST can produce 48-h forecasts using a single processor in about 40 min over a grid generated by reducing the cell edge size of the SLOSH grid by 4 times.

Two linear methods, including the simple linear addition and linear addition by expansion, and numerical simulations were employed to estimate storm surges and associated flooding caused by Hurricane Andrew for scenarios of sea level rise (SLR) from 0.15 m to 1.05 m with an interval of 0.15 m. The interaction between storm surge and SLR is almost linear at the open Atlantic Ocean outside Biscayne Bay, with slight reduction in peak storm surge heights as sea level rises. The nonlinear interaction between storm surges and SLR is weak in Biscayne Bay, leading to small differences in peak storm surge heights estimated by three methods. Therefore, it is appropriate to estimate elevated storm surges caused by SLR in these areas by adding the SLR magnitude to storm surge heights. However, the magnitude and extent of inundation at the mainland area by Biscayne Bay estimated by numerical simulations are, respectively, 22–24 % and 16–30 % larger on average than those generated by the linear addition by expansion and the simple linear addition methods, indicating a strong nonlinear interaction between storm surge and SLR. The population and property affected by the storm surge inundation estimated by numerical simulations differ up to 50–140 % from that estimated by two linear addition methods. Therefore, it is inappropriate to estimate the exacerbated magnitude and extent of storm surge flooding and affected population and property caused by SLR by using the linear addition methods. The strong nonlinear interaction between surge flooding and SLR at a specific location occurs at the initial stage of SLR when the water depth under an elevated sea level is less than 0.7 m, while the interaction becomes linear as the depth exceeds 0.7 m.

Field observations and numerical simulations indicate that the 6-to-30-km-wide mangrove forest along the Gulf Coast of South Florida effectively attenuated storm surges from a Category 3 hurricane, Wilma, and protected the inland wetland by reducing an inundation area of 1800 km 2 and restricting surge inundation inside the mangrove zone. The surge amplitude decreases at a rate of 40e50 cm/km across the mangrove forest and at a rate of 20 cm/km across the areas with a mixture of mangrove islands with open water. In contrast, the amplitudes of storm surges at the front of the mangrove zone increase by about 10e30% because of the “blockage” of mangroves to surge water, which can cause greater impacts on structures at the front of mangroves than the case without mangroves. The mangrove forest can also protect the wetlands behind the mangrove zone against surge inundation from a Category 5 hurricane with a fast forward speed of 11.2 m/s (25 mph). However, the forest cannot fully attenuate storm surges from a Category 5 hurricane with a slow forward speed of 2.2 m/s (5 mph) and reduced surges can still affect the wetlands behind the mangrove zone. The effects of widths of mangrove zones on reducing surge amplitudes are nonlinear with large reduction rates (15e30%) for initial width increments and small rates (

The potential impacts of sea level rise (SLR) on 95% of the land areas of the Florida Keys were estimated through analysis of a digital elevation model (DEM) derived from airborne light detection and ranging (LiDAR) measurements in a geographic information system. The topographic detail of the LiDAR DEM allowed projections of land, population, and property inundation in 0.15 m increments across a broad range of SLR scenarios for the next century. The results showed that a 0.6 m SLR by 2100 would inundate about 70% of the total land surface, but smaller percentages of the population (17%) and real property (12%). A 1.5 m rise in sea level during the same period would inundate 91% of the land surface, 71% of the population and 68% of property in the study area. Comparison of inundation dynamics indicates that the Lower Florida Keys are more susceptible to SLR than the Upper Florida Keys. The inundation dynamics exhibit non-linear behavior and demonstrate tipping points in inundation processes beyond which the inundation of land, population, and property speeds up. Acceleration of SLR will amplify the non- linear inundation, causing tipping points to be reached sooner.

By analyzing a digital elevation model (DEM) derived from airborne light detection and ranging (LIDAR) data and airborne height finder measurements, this study demonstrates that a 1.5 m sea-level rise by 2100 would cause inundation of large areas of Miami-Dade County, southern Broward County, and Everglades National Park. Inundation processes are non-linear: inundation is gradual before reaching a threshold, and speeds up rapidly afterwards due to the regional topography. Accelerated sea-level rise will cause the threshold to be reached sooner by amplifying the non-linear inundation, and must be considered in policy-making. Comparison of inundated areas extracted from 30 m LIDAR and USGS DEMs indicates that the vertical accuracy of a DEM has a great effect on delineation of inundation areas. For a 1.5 m sea-level rise, the inundated area delineated by USGS DEM for Broward County is 1.65 times greater than that indicated by the LIDAR DEM.

The effectiveness of simulating surge inundation using the Eulerian–Lagrangian circulation (ELCIRC) model over multi-scale unstructured grids was examined in this study. The large domain model grid encompasses the western North Atlantic Ocean, the Gulf of Mexico, and the Caribbean Sea to appropriately account for remote and resonance effects during hurricane events and simplify the specification of the open boundary condition. The U.S. East and Gulf Coasts were divided into 12 overlapping basins with fine-resolution (up to 30 × 30 m) grids to model overland surge flooding. These overlapping basins have different fine-resolution grids near the coastal region, but have an identical coarse-resolution grid in the offshore region within the large model domain. Thus, the storm surge prediction can be conducted without reducing computation efficiency by executing multiple model runs with local fine-resolution grids where potential hurricane landfalls may occur. The capability of the multi-scale approach was examined by simulating storm surge caused by Hurricanes Andrew (1992) and Isabel (2003) along the South Florida coast and in the Chesapeake Bay. Comparisons between simulated and observed results suggest that multi-scale models proficiently simulated storm surges in the Biscayne Bay and the Chesapeake Bay during two hurricanes. A series of sensitivity tests demonstrated that the simulation of surge flooding was improved when LiDAR topographic data and special bottom drag coefficient values for mangrove forests were employed. The tests also showed that appropriate representation of linear hydrologic features is important for computing surge inundation in an urban area.

The distribution of mangrove biomass and forest structure along Shark River estuary in the Florida Coastal Everglades (FCE) has been correlated with elevated total phosphorus concentration in soils thought to be associated with storm events. The passage of Hurricane Wilma across Shark River estuary in 2005 allowed us to quantify sediment deposition and nutrient inputs in FCE mangrove forests associated with this storm event and to evaluate whether these pulsing events are sufficient to regulate nutrient biogeochemistry in mangrove forests of south Florida. We sampled the spatial pattern of sediment deposits and their chemical properties in mangrove forests along FCE sites in December 2005 and October 2006. The thickness (0.5 to 4.5 cm) of hurricane sediment deposits decreased with distance inland at each site. Bulk density, organic matter content, total nitrogen (N) and phosphorus (P) concentrations, and inorganic and organic P pools of hurricane sediment deposits differed from surface (0–10 cm) mangrove soils at each site. Vertical accretion resulting from this hurricane event was eight to 17 times greater than the annual accretion rate (0.30 ± 0.03 cm year−1) averaged over the last 50 years. Total P inputs from storm-derived sediments were equivalent to twice the average surface soil nutrient P density (0.19 mg cm−3). In contrast, total N inputs contributed 0.8 times the average soil nutrient N density (2.8 mg cm−3). Allochthonous mineral inputs from Hurricane Wilma represent a significant source of sediment to soil vertical accretion rates and nutrient resources in mangroves of southwestern Everglades. The gradient in total P deposition to mangrove soils from west to east direction across the FCE associated with this storm event is particularly significant to forest development due to the P-limited condition of this carbonate ecosystem. This source of P may be an important adaptation of mangrove forests in the Caribbean region to projected impacts of sea-level rise.

Sea-level rise presents an imminent threat to freshwater-dependent ecosystems on small oceanic islands, which often harbor rare and endemic taxa. Conservation of these assemblages is complicated by feedbacks between sea level and recurring pulse disturbances (eg hurricanes, fire). Once sea level reaches a critical level, the transition from a landscape characterized by mesophytic upland forests and freshwater wetlands to one dominated by mangroves can occur suddenly, following a single storm-surge event. We document such a trajectory, unfolding today in the Florida Keys. With sea level projected to rise substantially during the next century, ex-situ actions may be needed to conserve individual species of special concern. However, within existing public conservation units, managers have a responsibility to conserve extant biodiversity. We propose a strategy that combines the identification and intensive management of the most defensible core sites within a broader reserve system, in which refugia for biota facing local extirpation may be sought.

Mangrove forests change frequently due to disturbances from tropical storms, frost, lightning, and insects. It has been suggested that the death and regeneration of trees in small gaps due to lightning may play a critical role in mangrove forest turnover; however, the large-scale quantification of spatial pattern and areas of gaps is lacking for investigating this issue. Airborne light detection and ranging (LIDAR) technology provides an effective way for identifying gaps by remotely obtaining direct measurements of ground and canopy elevations. A method based on an alternative sequential filter and black top-hat mathematical morphological transformation was developed to extract gap features. Comparison of identified gap polygons with raw LIDAR measurements and field surveys shows that the proposed method successfully extracted gap features in mangrove forests in Everglades National Park. There are 400–500 lightning gaps per square kilometer in mangrove forests at the study sites. The distribution of gap sizes follows an exponential form and the area of gaps with sizes larger than 100 m2 account for 55–61% of the total area of gaps. The area of gaps in the mangrove forest in Everglades National Park is about 4–5% of the total forest area and the average gap formation rate is about 0.3% of the total forest area per year, indicating that lightning gaps play an important role in mangrove forest dynamics.

The Coastal and Estuarine Storm Tide (CEST) model for the boundary-fitted curvilinear grid has been developed recently to simulate a hurricane-induced storm surge. A new wetting-drying algorithm was based on accumulated volume and was created for simulating overland flooding. To evaluate the capability of the CEST model, it was compared with the well-established storm surge model—Sea, Lake, and Overland Surge from Hurricane (SLOSH)—in model grid representation and surge inundation prediction. Two models were compared: first, by carrying out storm surge simulations for Hurricanes Andrew (1992), Hugo (1989), and Camille (1969) over SLOSH's coarse polar grids with cell sizes of 500–7000 m. Results show that the CEST model agreed better with field observations of storm surge flooding. The CEST model was further evaluated by applying it to a fine-resolution curvilinear grid, which has cell sizes of 100–200 m at the hurricane landfall area, along with a superior representation of coastal topography. Co...

Nutrient loading and eutrophication in coastal waters are the causes of water quality degradation and loss of marine biota, which has led to ecological imbalance. Understanding and modeling the level of eutrophication as a function of environmental parameters can be beneficial to coastal ecosystem management. The limitation of deterministic and empirical models in accurately predicting the level of algal blooms, and the nonlinear relationship between the water quality and environmental parameters and that of the level of chlorophyll a necessitate a new approach using machine learning and data-driven modeling. A multilayer perceptron-back propagation (MLP-BP) algorithm of artificial neural network (ANN) was used to predict the level of eutrophication (chlorophyll a) from water quality parameters monitored at two Florida Bay water quality monitoring stations (FLAB03 and FLAB14). Based on the correlation of monthly nutrients (total phosphate, nitrite, ammonium) and other water data (temperature, tur...

This study used airborne laser data to investigate spatial variations in shoreline migration, beach width, subaerial volume, and subaqueous volume change due to a hurricane event. Five separate airborne laser data sets of Panama City, FL area beaches were collected during a seven-month period before and after landfall of Hurricane Ivan. Contour shorelines were extracted from digital elevation models interpolated from these laser measurements and were used to measure changes in shoreline position and beach width. The shoreline migrated 16 m landward due to Hurricane Ivan and migrated 10 m seaward following Hurricane Ivan. No significant spatial relationship was found between shoreline migration before and after the hurricane. Linear relationships between a time series of beach width and subaerial volume were found at many locations. However, utilization of a single coefficient to represent all relationships is problematic due to the spatial variability in the linear relationship. Differences in two bathymetric data sets for summer and fall show that only a small portion of sediments were transported beyond an active zone and most sediments remain within the active zone despite the occurrence of a hurricane.

Accuracy of predicting storm surge inundation depends on the model grid resolution. The spatial resolutions of many storm surge models are on the order of hundreds of meters to kilometers, which are not sufficient to resolve irregular shorelines and small topographic features. In this paper, storm surge and inundation simulation for Hurricane Andrew of 1992 was carried out with the unstructured tidal, residual, intertidal mudflat (UnTRIM) hydrodynamic model. A high-resolution unstructured grid with cell sizes as small as 20 m was placed over the Key Biscayne Bay area near Miami to better represent irregular shorelines and small morphological features such as barrier islands, rivers, canals, and inlets. A series of experiments was conducted to test model performance with respect to model grid resolution, time step selection, open boundary condition specification, and tidal influence. Results show that the model simulated maximum surge heights and areas of inundation along the Biscayne Bay coast ve...

This paper presents a framework that applies a series of algorithms to automatically extract building footprints from airborne light detection and ranging (LIDAR) measurements. In the proposed framework, the ground and nonground LIDAR measurements are first separated using a progressive morphological filter. Then, building measurements are identified from nonground measurements using a region-growing algorithm based on the plane-fitting technique. Finally, raw footprints for segmented building measurements are derived by connecting boundary points, and the raw footprints are further simplified and adjusted to remove noise caused by irregularly spaced LIDAR measurements. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. A quantitative analysis showed that the total of omission and commission errors for extracted footprints for both institutional and residential areas was about 12%. The results demonstrated that the proposed framework identified building footprints well

We produced a landscape scale map of mean tree height in mangrove forests in Everglades National Park (ENP) using the elevation data from the Shuttle Radar Topography Mission (SRTM). The SRTM data was calibrated using airborne lidar data and a high resolution USGS digital elevation model (DEM). The resulting mangrove height map has a mean tree height error of 2.0 m (RMSE) over a pixel of 30 m. In addition, we used field data to derive a relationship between mean forest stand height and biomass in order to map the spatial distribution of standing biomass of mangroves for the entire National Park. The estimation showed that most of the mangrove standing biomass in the ENP resides in intermediate-height mangrove stands around 8 m. We estimated the total mangrove standing biomass in ENP to be 5.6 � 10 9 kg.

Three-dimensional computer visualization and animation can provide a substitute for coastal residents', lack of personal experience with hurricane-surge flooding. Tremendous progress has been made in 3D animation in the last decade, which movies such as Perfect Storm and The Day After Tomorrow have demonstrated. However, the 3D visualization and animation system for storm-surge flooding differs from those in Hollywood movies in three aspects. First, objects such as buildings, roads, and trees in a synthetic 3D visualization environment not only have to be able to duplicate the real-world feature visually, but also be georeferenced so users can find real locations through addresses or spatial coordinates. The sizes and shapes of buildings and trees have to be accurate so users can sense the severity of flooding by comparing the water level with heights of familiar objects. Second, the magnitude, extent, and process of storm-surge flooding have to be accurate enough to represent the real situation. This information has to be based on hydrodynamics of storm surge. Third, the damage extent of a property caused by storm surge and waves, such as the collapse of a house, must be determined by engineering rules. Recent advances in high-resolution remote-sensing technology and numerical modeling make it possible to provide accurate data for the earth's surface features and storm-surge flooding.

This paper compares three methods for removing non-ground measurements from airborne laser scanning data. These methods, including the elevation threshold with expanding window (ETEW), maximum local slope (MLS), and progressive morphological (PM) filters, analyze data points based on variations of local slope, and elevation. Low and high-relief data sets with various densities of trees, houses, and sand dunes were selected to test the filtering methods. The results show that all three methods can effectively remove most nonground points in both low-relief urban and high-relief forested areas. The PM filter generated the best result in coastal barrier island areas, whereas the other algorithms tended to remove the tops of steep sand dunes. Each method experienced various omission or commission errors, depending on the filtering parameters. Topographic slope is the most sensitive parameter for the three filtering methods.

Quantitative data on beach changes caused by coastal storms is critical to the understanding of coastal morphodynamics and mitigation of coastal erosion hazards. Recent advances in airborne LIDAR technology allow large-scale mapping of beach erosion, dune scarping, and overwash deposition with incredible detail. By comparing 40 km of beaches along the central Florida Atlantic coast surveyed before and after Hurricane Floyd in 1999, we found that most beaches experienced erosion; about −18 to +1 m3/m of sediment per unit shoreline length were removed or deposited. Beach erosion is not spatially uniform, and variations in magnitude occur three dimensionally. The high-density LIDAR data provided accurate information about shore changes both at small and large scales.

This paper examines the feasibility of using LIDAR surveys to update existing historical shoreline data sets by comparing contour shorelines and the high water line (HWL) at eight study locations in North and South Carolina. The analysis was based on airborne LIDAR topography and orthoimagery collected simultaneously during June 2000. The popular method of digitizing the wet-dry line from orthoimagery was used to measure the HWL. Contour shorelines were derived by using the previous high tide (HW), the mean high water datum (MHW), and the mean higher high water datum (MHHW) of nearby tide gauges. A method was developed to quantitatively compare the positions of the HWL and the contour shorelines in a GIS. The mean high water and mean higher high water contour shoreline positions were the best match to the high water line at 7 of 8 locations, and differed by less than 5.4 meters from the digitized high water line positions. This difference is well within the errors associated with past methods for measuring shoreline position. Therefore, it is deemed practical to use LIDAR data to estimate the HWL.

One of the most certain consequences of global warming is an increase of global (eustatic) sea level. The resulting inundation from rising seas will heavily impact low-lying areas; at least 100 million persons live within one meter of mean sea level and are at increased risk in the coming decades. The very existence of some island states and deltaic coasts is threatened by sea level rise. An additional threat affecting some of the most heavily developed and economically valuable real estate will come from an exacerbation of sandy beach erosion. As the beach is lost, fixed structures nearby are increasingly exposed to the direct impact of storm waves, and will ultimately be damaged or destroyed unless expensive protective measures are taken. It has long been speculated that the underlying rate of long-term sandy beach erosion is two orders of magnitude greater than the rate of rise of sea level, so that any significant increase of sea level has dire consequences for coastal inhabitants. We present in this paper an analytical treatment that indicates there is a highly multiplicative association between long-term sandy beach erosion and sea level rise, and use a large and consistent data base of shoreline position field data to show that there is reasonable quantitative agreement with observations of 19th and 20th century sea levels and coastal erosion. This result means that the already-severe coastal erosion problems witnessed in the 20th century will be exacerbated in the 21st century under plausible global warming scenarios.

Recent advances in airborne light detection and ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. This technology is becoming a primary method for generating high-resolution digital terrain models (DTMs) that are essential to numerous applications such as flood modeling and landslide prediction. Airborne LIDAR systems usually return a three-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. In order to generate a DTM, measurements from nonground features such as buildings, vehicles, and vegetation have to be classified and removed. In this paper, a progressive morphological filter was developed to detect nonground LIDAR measurements. By gradually increasing the window size of the filter and using elevation difference thresholds, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Datasets from mountainous and flat urbanized areas were selected to test the progressive morphological filter. The results show that the filter can remove most of the nonground points effectively.

In a few hours or days, scores of meters of beach width can be lost due to a severe storm. However, newly available shoreline data from the U.S. East Coast show that beaches recover after storms to positions consistent with their long‐term (100+ yr) trend. Even the largest storms, such as the Ash Wednesday Storm of 1962, considered to be the most damaging in the twentieth century, appear to have had little effect on the long‐term trend. The gradual recession of beaches along the U.S. East Coast is mainly controlled by other factors such as sea‐level rise and variations of sediment supply. Therefore, it follows that barrier beaches in a coastal plain setting would not experience long‐term erosion in response to storm impact if the sea were to stop rising and sediment supply did not change.

High quality DEM (Digital Elevation Model) is indispensable for a smart city nowadays. A large quantity of useful spatial information can be extracted from the big data acquired by surveying, imaging, and even mobile device with GPS system. This information is critical to many applications such as urban management, emergency event planning, and social location services. The extraction of non-ground features such as buildings, constructions and vegetation can be used to generate accurate boundary shape and three dimensional models. These outcomes can be utilized to provide useful information for the location service for mobile social networks. The DEM can be generated by surveying the area. LIDAR (Light Detection and Ranging) is a widely used remote sensing technology to survey the urban area. High resolution three dimensional data can be acquired from the LIDAR survey. How to separate the terrain surface and non-terrain features from three dimensional spatial data is the most challenging task for generating high quality DEM. In this paper, a multi-pass morphological filter is proposed to generate better DEMs on complex urban areas that have difficulties to use large filtering window sizes. Multi-pass filtering will use relatively small filtering window sizes in multiple rounds to avoid the cut-off problem commonly present when large window sizes are used in filtering. A priority boundary interpolation method is proposed and dedicated to interpolate the intermediate filtering result between each pass. A second pass of the morphological filter will use filtering window sizes in descending order to remove the remaining areas from large non-ground objects that cannot be removed with small window sizes. The filtering results demonstrate that the proposed method is able to effectively use relatively small filtering windows to filter large non-ground objects on complex terrains and avoid the cut-off problem.

It has been speculated that future global warming will change the frequency and severity of tropical and extratropical storms. The U.S. east coast is heavily impacted by such storms, so it is important to determine their natural temporal variability for the last century during which global warming has been relatively small. Storm surge data obtained from hourly tide gauge records provide a unique quantitative measure of storm duration and intensity, unlike qualitative estimates based on eyewitness reports or meteorological hindcasts. To demonstrate the potential of storm surge data for climate analysis, the authors have evaluated 10 very long records of water level anomalies. An analysis of the hourly tide gauge records along the U.S. east coast shows a considerable interdecadal variation but no discernible long-term trend in the number and intensity of moderate and severe coastal storms during this century. However, sea level rise over the last century has exacerbated the damage to fixed structu...

This paper presents a Graphical User Interface (GUI) system to process huge volume geospatial data acquired from LIDAR survey. This GUI system provides friendly user interface to display, edit and process LIDAR data. It also offers useful geospatial tool kits to process area of interest from a big data set. The processing results would be used for high quality digital elevation model (DEM) generation and mapping.

Using plasma assisted pulsed DC magnetron sputtering technology, low loss, high performance optical coatings have been achieved. Coating examples using Ta2O5 and SiO2 materials are demonstrated. This technology is suitable for volume production.

The airborne light detection and ranging (LiDAR) technology provides highly accurate measurements of objects on the Earth’s surface. In contrast to optical remote-sensing technology, a LiDAR remote-sensing system derives voluminous three-dimensional point measurements of the Earth’s surface. The major challenge for the utilization of the airborne LiDARtechnology is to develop appropriate methods to extract the desired features such as topography, vegetation, and buildings from LiDAR measurements. Numerous methods on feature extraction from LiDAR measurements and the applications of LiDAR technology to measuring and monitoring the Earth’s surface processes have been developed in the past 20 years. To appropriately use the LiDAR data, it is necessary to understand the characteristics of airborne LiDAR technology and feature extraction algorithms related to processing LiDAR data. This chapter briey introduces the airborne LiDAR technology, presents the ltering algorithms for separating ground and nonground LiDAR points and discusses the issues related to ltering processes, presents a framework to construct building models from LiDAR measurements, and describes the applications of LiDAR technology on mapping vegetation.

A numerical model for the prediction of storm surges over complex landscapes is assembled using state-of-the-art techniques. The model is based on the full nonlinear depth-averaged long wave equations solved via an explicit finite volume scheme. The interfacial fluxes are computed from slope limited reconstructions of the state variables using Osher’s approximate Riemann solver. The geometric source terms are treated in a simple manner that is well balanced and consistent with the numerical flux. The numerical solution technique has been chosen to enable the accurate simulation of flows over complex topography. Importantly, the scheme conserves both mass and momentum even when the flow involves wetting–drying fronts. This approach makes the model extremely robust in cases that involve bathymetries which include large spatial gradients and even discontinuities. Another important feature of the model is the use of a simple underlying Cartesian mesh with quad-tree-based static and dynamic refinement. This permits the simulation of highly unsteady flows over complex landscapes (including localized features such as canals) by locally increasing (or relaxing) grid resolution in a dynamic fashion. If it is deemed desirable, the numerical and physical boundaries can be aligned. This alignment is achieved by a cut-cell-type approach in combination with quad-tree-based mesh refinement. In this paper, we describe the model and then present a number of tests that have been carefully chosen to verify various aspects of the solution algorithm.

In this chapter, we propose a system which integrates storm surge projection, meteorological, topographical, and road data to simulate storm surge conditions. The motivation behind the system is to serve local governments seeking to overcome difficulties in persuading residents to adhere to evacuation notices. The storm surge simulation system introduces an alternative to traditional evacuation advisories which are often superimposed over two-dimensional maps and distributed through television, newspaper, and other public outreach media. First, topographic data are used to construct virtual replicas of cities in the State of Florida. Storm surge data are then used to construct a virtual ocean positioned over the city models. Ambient details such as wind, vegetation, ocean waves, and traffic are animated based on up-to-date meteorological data. Videos of the storm surge visualizations are recorded and made available to coastal residents through a web-interface. The three-dimensional visualization of geographic and storm surge data provides a more visually compelling representation of the potential effects of storm surge than traditional two-dimensional models and is better able to prepare coastal residents to make potentially life-saving evacuation decisions.

A 1.5 m sea level rise by 2100 will cause calamitous inundation to Miami-Dade and Broward Counties and the Everglades in South Florida. Inundation processes are non-linear: inundation is gradual before reaching a tipping point, and speeds up rapidly afterwards due to the regional topography. Accelerated sea level rise will cause the tipping point to be reached sooner by amplifying non-linear processes, and must be considered in policy-making.

The rise of offshore water caused by the high winds of a low pressure weather system, or storm surge, is a hurricane's greatest threat to human life. As weather forecasters struggle to enable coastal residents to make timely evacuation decisions, the need arises for more visually compelling and interactive storm surge visualization tools. This paper presents an interactive and three-dimensional storm surge visualization system. It integrates road, topographic, and building data to construct accurate three-dimensional models of major cities in the State of Florida. Storm surge data are then used to construct a three-dimensional ocean positioned over the terrain models. Ambient details such as wind, vegetation, ocean waves, and traffic are animated based on up-to-date wind and storm surge data. Videos of the storm surge visualizations are recorded and made available to coastal residents through a web-interface. The three-dimensional visualization of geographic and storm surge data provides a more visually compelling representation of the potential effects of storm surge than traditional two-dimensional models and is more capable to enable coastal residents to make potentially life-saving evacuation decisions.

A framework which includes six major components has been developed to reconstruct 3D building models from airborne LiDAR measurements. The application of the framework to both commercial and adjacent residential areas shows that the proposed methods identified building patches well, and reconstructed 3D building models effectively. The entire process is highly automatic and requires little human aid, which is very useful for processing voluminous LIDAR measurements.

This paper presents a 3D traffic animation system that provides features of vehicle interaction and storm surge response to emulate realistic scenarios in a hurricane affected area. The proposed system constructs road objects based on a series of line segments. Vehicles on the roads are animated by keeping them coherent with the direction of the road segment and adjusting their speeds with respect to one another. The vehicle speed is controlled based upon a collision-circumventing policy and they automatically respond to flood water caused by a storm surge. The system can automatically plant lamp posts along the roads, which also respond to surge flooding. An implementation conducted with the 3D scenes on a couple of south Florida's surge-susceptible areas demonstrates the system's excellence in animating real traffic scenes and impacts of storm surge on coastal regions.

Airborne light detection and ranging (LIDAR) measurements derived before and after Hurricanes Katrina and Wilma (2005) were used to quantify the impact of hurricanes and lightning strikes on the mangrove forest at two sites in Everglades National Park (ENP). Analysis of LIDAR measurements covering 61 and 68 ha areas of mangrove forest at the Shark River and Broad River sites showed that the proportion of high tree canopy detected by the LIDAR after the 2005 hurricane season decreased significantly due to defoliation and breakage of branches and trunks, while the proportion of low canopy and the ground increased drastically. Tall mangrove forests distant from tidal creeks suffered more damage than lower mangrove forests adjacent to the tidal creeks. The hurricanes created numerous canopy gaps, and the number of gaps per square kilometer increased from about 400~500 to 4000 after Katrina and Wilma. The total area of gaps in the forest increased from about 1~2% of the total forest area to 12%. The relative contribution of hurricanes to mangrove forest disturbance in ENP is at least 2 times more than that from lightning strikes. However, hurricanes and lightning strikes disturb the mangrove forest in a related way. Most seedlings in lightning gaps survived the hurricane impact due to the protection of trees surrounding the gaps, and therefore provide an important resource for forest recovery after the hurricane. This research demonstrated that LIDAR is an effective remote sensing tool to quantify the effects of disturbances such as hurricanes and lightning strikes in the mangrove forest.

This paper illustrates branch breaking and flying effect animation for trees in our 3D interactive visualization system for hurricanes and storm surge flooding. The tree branch breaking and flying effect animation extends our current tree animation framework by estimating the destination quadrant, direction (angle) and traveling speed with which a branch breaks and flies across the terrain. These calculations thus assist in simulating the potential threats to property and human lives posed by the flying branches during a hurricane, and add to the realism and appeal of our system. We utilize 3D tree models created with 3DSmax and implement the animation effects via OpenGL and nVidia CG shader language.

Energy-minimizing active contour models (snakes) have been proposed for solving many computer vision problems such as object segmentation, surface reconstruction, and object tracking. Dynamic programming which allows natural enforcement of constraints is an effective method for computing the global minima of energy functions. However, this method is only limited to snake problems with one dimensional (ID) topology (i.e., a contour) and cannot handle problems with two-dimensional (2D) topology. In this paper, we have extended the dynamic programming method to address the snake problems with 2D topology using a novel graph reduction algorithm. Given a 2D snake with first order energy terms, a set of reduction operations are defined and used to simplify the graph of the 2D snake into one single vertex while retaining the minimal energy of the snake. The proposed algorithm has a polynomial-time complexity bound and the optimality of the solution for a reducible 2D snake is guaranteed. However, not all types of 2D snakes can be reduced into one single vertex using the proposed algorithm. The reduction of general planar snakes is an NP-complete problem. The proposed method has been applied to optimize 2D building topology extracted from airborne LIDAR data to examine the effectiveness of the algorithm. The results demonstrate that the proposed approach successfully found the global optima for over 98% of building topology in a polynomial time.

This study develops a method to identify the depth of closure using airborne laser bathymetric data, and compares the measured depth of closure with estimated depth of closures. Airborne laser data sets were collected before and after the 2004 hurricane season in southeast Florida. Estimated depth of closures in terms of wave data is compatible to the measured depth of closures. However, the estimated depth of closure is on average larger than was measured, especially in the southern end of the study area, which could be due to geologic influence. Further research is needed to determine if geology plays a critical role in the depth of closure at this study's location.

A polarization insensitive, low wavefront distortion 50% beamsplitter is presented. 0.2% split of s and p polarization from 1540nm to 1575nm and less than 1/30th waves peak to valley surface error at 632.8nm were achieved.

Remote sensing technologies have provided valuable data for landscape modeling, vegetation mapping and comprehensive studies of ecosystems. Airborne laser mapping or LIDAR (Light Detection and Ranging) can directly measure the three dimensional structure of plant canopies, as well as, provide accurate digital terrain models (DTM). While temperate and boreal pine forests have been studied using these methods, very limited work has been done in subtropical pine forests. In this study, airborne LIDAR was used to characterize the three dimensional structure of the forest on Long Pine Key at Everglades National Park. Analysis of the vertical distribution of airborne LIDAR data points has shown that distinctive patterns can be described which are characteristic of the vegetation communities and transition zones for pine forests , hammocks and marshes. This information is a valuable resource for forest managers by providing landscape structural data over large areas.

In this paper we describe a methodology to map mangrove forests in 3D in the Caribbean region. We used shuttle radar topography mission (SRTM) elevation, lidar and field data to estimate mangrove mean tree height at the landscape scale. This paper emphasizes two regions which are undergoing ecosystem restoration activities: The Everglades National Park, USA and Cienaga Grande de Santa Marta, Colombia. In these regions we used, respectively, airborne and spaceborne (ICEsat (ice, cloud,and land elevation satellite )) lidar data to calibrate SRTM data and estimate mean tree height. Our results show the method is accurate if mangrove forest canopy vertical structure is well characterized.

In this paper we present a method using airborne laser mapping data to describe the vertical structure of a coastal mangrove forest in southeastern Florida. Mangroves are critical to coastal processes and the maintenance of vital habitats; however they are also highly threatened due to coastal development and rising sea levels. Access to mangroves for detailed ground surveys is extremely difficult due to dense vegetation and lack of stable ground in the tidal zone. Airborne laser mapping can provide accurate measurements of vegetation structure over large areas without the logistical difficulties of extensive ground surveys. In our method we derive frequency height histograms from the laser mapping data followed by the creation of image objects from areas sharing similar vertical height profiles. These image objects are classified based on their vertical height profiles and related to heights of mangrove forest communities obtained from field surveys. We show that airborne laser mapping technology can be used to classify mangrove forests and to study the spatial relationships among the different forest types.

We report the most recent advances of optical beamsplitters for laser communications developed at Barr Associates. The challenges for meeting the two key requirements, polarization control and wavefront control, of the lasercom beamsplitters are discussed. It is demonstrated that in order to meet the two key requirements, the repeatability of the refractive indices and the thickness of the coatings need to be controlled within 0.3% and 0.1% respectively from run to run, and the coating stress need to be controlled to less than 25 MPa. The latest result of various lasercom beamsplitters will be presented. For a typical 50% reflection and 50% transmission beamsplitter at 22.5 degree angle of incidence, the s and p polarization split is less than 0.2% from 1540nm to 1575nm and the surface flatness of the beamsplitter after coating is less than 1/40 th waves peak to valley at 632.8nm.

This paper describes an implementation of vertex weighting for real-time animation of 3D trees as a result of wind force and direction. This method improves over similar techniques because it incorporates individual branch animation on 3D models, whereas previous works perform animations on the model as a whole or use planar billboards to visualize the tree. Our tree animation is used in an interactive system that visualizes the effects caused by a hurricane's impact on a virtual city. The system uses models created with 3D Studio Max and the animations are implemented in OpenGL and the nVidia Cg shader language