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The snake algorithm has been proposed to solve many remote sensing and computer vision problems such as object segmentation, surface reconstruction, and object tracking. This paper introduces a framework for 3-D building model construction from LIDAR data based on the snake algorithm. It consists of nonterrain object identification, building and tree separation, building topology extraction, and adjustment by the snake algorithm. The challenging task in applying the snake algorithm to building topology adjustment is to find the global minima of energy functions derived for 2-D building topology. The traditional snake algorithm uses dynamic programming for computing the global minima of energy functions which is limited to snake problems with 1-D topology (i.e., a contour) and cannot handle problems with 2-D topology. In this paper, we have extended the dynamic programming method to address the snake problems with a 2-D planar topology using a novel graph reduction technique. Given a planar snake, a set of reduction operations is defined and used to simplify the graph of the planar snake into a set of isolated vertices while retaining the minimal energy of the graph. Another challenging task for 3-D building model reconstruction is how to enforce different kinds of geometric constraints during building topology refinement. This framework proposed two energy functions, deviation and direction energy functions, to enforce multiple geometric constraints on 2-D topology refinement naturally and efficiently. To examine the effectiveness of the framework, the framework has been applied on different data sets to construct 3-D building models from airborne LIDAR data. The results demonstrate that the proposed snake algorithm successfully found the global optima in polynomial time for all of the building topologies and generated satisfactory 3-D models for most of the buildings in the study areas. [ABSTRACT FROM PUBLISHER]

Barrier islands, the dominant geomorphic features along the U.S. Atlantic and Gulf Coasts, are a favorite place for living and visiting. Unfortunately, barrier islands are vulnerable to storm-surge flooding and erosion because of low elevations and the movement of sand by waves and tides. In order to estimate the impacts of surge flooding, sea-level rise, and erosion on barrier islands, the lengths and areas of barrier islands and population living there were quantified using high-resolution satellite imagery from Google Earth and 1990-2000 census block data. The total length and area of barrier islands spanning 18 states along the U.S. Atlantic and Gulf Coasts are about 3700 km and 6800 km2, respectively. There are approximately 1.4 million people living on barrier islands, half of which are in Florida according to 2000 census data. The population densities of barrier islands are three times those of coastal states on average, and the population increased 14% from 1990 to 2000. The collision course of population increases and development in the face of accelerated sea-level rise due to global climate change makes barrier islands more vulnerable. [ABSTRACT FROM AUTHOR]

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. Comparison of the model results with field-measured elevations of high water marks and the locations of debris lines indicated that the CEST model, with the use of a fine-resolution grid, greatly reduced the uncertainty in computing storm surge flooding. [ABSTRACT FROM AUTHOR]

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 very well, indicating that the UnTRIM model is useful in simulating storm surge flooding. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article presents information on a three-dimensional visualization system regarding the hurricane storm-surge flooding in the U.S. Storm surge is one of the major threats associated with hurricanes, which can cause damage to the whole infrastructure of the area. Under-and over-evacuation can cause problems in hurricane disasters and their management. This is largely because people tend to misread 2-dimensional natural disaster warning systems and tend to either underestimate or overestimate the potential of a hurricane. 3-dimensional (3-D) computer visualization and animation is important in this context since it can provide a substitute for coastal residents' lack of personal experience with hurricane-surge flooding. A prototype 3-D visualization and animation system for storm-surge flooding is presented in the article that contains three major components. It represents terrain, building roads, vegetation and the sea. It has module for animating surge flooding and a high-resolution storm-surge model to serve as an engine used to drive the animation of flooding. It has two major goals of storm-surge flooding visualization systems used to perform near-real-time animation for forecasted hurricane and offline animation for hypothetical hurricanes.

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. [ABSTRACT FROM AUTHOR]

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 non-ground features such as buildings, vehicles, and vegetation have to be classified and removed. In this paper, a progressive morphological filter was developed to detect non-ground 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 non-ground points effectively. [ABSTRACT FROM AUTHOR]

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 structures from modern storms that would have been relatively minor a century ago. [ABSTRACT FROM AUTHOR]

Since there are no monitoring devices in relay protection equipment of substations, it takes up a lot of manpower and material resources in operation and maintenance (O&M), and the efficiency is very low. In this paper, we solve the giving fault prediction model by applying particle swarm optimization algorithm and support vector machine algorithm for relay protection equipment. The combined model was found to be able to establish the correlation between the variables more accurately and improve the prediction accuracy of the model by comparison. The simulation result shows that the prediction accuracy of the model is at least 91% for three different devices. The combined prediction model can not only provide strong technical support for the maintenance strategy of relay protection equipment but also improve the maintenance efficiency and reduce the failure rate of protection equipment. [ABSTRACT FROM AUTHOR]

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, turbidity, and dissolved oxygen) to the level of chlorophyll a, an input-output data structure was selected. Seven input data scenarios were studied, and model performance was compared using four indices. Monthly data from 1992 to 2004 were partitioned into training and testing subsets. Results show that chlorophyll a was predicted well with the selected inputs, with an average R² and model efficiency (E) of 0.856 and 0.582, respectively. Prediction with antecedent chlorophyll a alone gave a stable result with smaller error and higher performance attributed to easier and more efficient training. It was also found that ANN performed better at FLA03 than at FLA14. It is shown that the MLP-BP technique is applicable to the monitoring and prediction of algal blooms and will be crucial to coastal watershed management. [ABSTRACT FROM AUTHOR]