MONITORING HURRICANE EFFECTS ON AQUIFER SALINITY USING ALSM

ABSTRACT During the Atlantic hurricane season of 2004, the Florida Pan Handle, Gulf Coast region, was impacted directly by three hurricanes within approximately a one-month time period. The short temporal span between impacts coupled with the severity of the storms resulted in drastic changes to the littoral zone geomorphology including extensive shoreline erosion and accretion that directly affected the subsurface hydrogeologic environment. The most important direct physical effects of a hurricane are the following: coastal erosion, shoreline inundation owing to higher than normal tide levels plus increased temporary surge levels during storms, and saltwater intrusion primarily into estuaries and groundwater aquifers. Erosion and deposition during the hurricane change the elevation, which causes change in the position of shoreline. The purpose of this study was to investigate the effects of sea level inundation due to the hurricanes on the near shore subsurface freshwater-saltwater interface. By utilizing high-resolution Airborne Laser Swath Mapping (ALSM) altimetry data acquired shortly before and after the three major hurricane landfalls, the change in shoreline topography was estimated to determine both small-scale and large-scale horizontal encroachment and volumetric change in shoreline. This information was used to develop a before and after variable density groundwater flow model to determine the impact of the hurricanes on the subsurface saltwater-freshwater interface. SEAWAT (Langevin 2001

Identification of Brush Species and Herbicide Effect Assessment in Southern Texas Using an Unoccupied Aerial System (UAS)

Cultivation and grazing since the mid-nineteenth century in Texas has caused dramatic changes in grassland vegetation. Among these changes is the encroachment of native and introduced brush species. The distribution and quantity of brush can affect livestock production and water holding capacity of soil. Still, at the same time, brush can improve carbon sequestration and enhance agritourism and real estate value. The accurate identification of brush species and their distribution over large land tracts are important in developing brush management plans which may include herbicide application decisions. Near-real-time imaging and analyses of brush using an Unoccupied Aerial System (UAS) is a powerful tool to achieve such tasks. The use of multispectral imagery collected by a UAS to estimate the efficacy of herbicide treatment on noxious brush has not been evaluated previously. There has been no previous comparison of band combinations and pixel- and object-based methods to determine the best methodology for discrimination and classification of noxious brush species with Random Forest (RF) classification. In this study, two rangelands in southern Texas with encroachment of huisache (Vachellia farnesianna [L.] Wight & Arn.) and honey mesquite (Prosopis glandulosa Torr. var. glandulosa) were studied. Two study sites were flown with an eBee X fixed-wing to collect UAS images with four bands (Green, Red, Red-Edge, and Near-infrared) and ground truth data points pre- and post-herbicide application to study the herbicide effect on brush. Post-herbicide data were collected one year after herbicide application. Pixel-based and object-based RF classifications were used to identify brush in orthomosaic images generated from UAS images. The classification had an overall accuracy in the range 83–96%, and object-based classification had better results than pixel-based classification since object-based classification had the highest overall accuracy in both sites at 96%. The UAS image was useful for assessing herbicide efficacy by calculating canopy change after herbicide treatment. Different effects of herbicides and application rates on brush defoliation were measured by comparing canopy change in herbicide treatment zones. UAS-derived multispectral imagery can be used to identify brush species in rangelands and aid in objectively assessing the herbicide effect on brush encroachment

FloodSim: Flood simulation and visualization framework using position-based fluids

Flood modeling and analysis has been a vital research area to reduce damages caused by flooding and to make urban environments resilient against such occurrences. This work focuses on building a framework to simulate and visualize flooding in 3D using position-based fluids for real-time flood spread visualization and analysis. The framework incorporates geographical information and takes several parameters in the form of friction coefficients and storm drain information, and then uses mechanics such as precipitation and soil absorption for simulation. The preliminary results of the river flooding test case were satisfactory, as the flood extent was reproduced in 220 s with a difference of 7%. Consequently, the framework could be a useful tool for practitioners who have information about the study area and would like to visualize flooding using a particle-based approach for real-time particle tracking and flood path analysis, incorporating precipitation into their models.Flood modeling and analysis has been a vital research area to reduce damages caused by flooding and to make urban environments resilient against such occurrences. This work focuses on building a framework to simulate and visualize flooding in 3D using position-based fluids for real-time flood spread visualization and analysis. The framework incorporates geographical information and takes several parameters in the form of friction coefficients and storm drain information, and then uses mechanics such as precipitation and soil absorption for simulation. The preliminary results of the river flooding test case were satisfactory, as the flood extent was reproduced in 220 s with a difference of 7%. Consequently, the framework could be a useful tool for practitioners who have information about the study area and would like to visualize flooding using a particle-based approach for real-time particle tracking and flood path analysis, incorporating precipitation into their models

Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

Deep learning has already been proved as a powerful state-of-the-art technique for many image understanding tasks in computer vision and other applications including remote sensing (RS) image analysis. Unmanned aircraft systems (UASs) offer a viable and economical alternative to a conventional sensor and platform for acquiring high spatial and high temporal resolution data with high operational flexibility. Coastal wetlands are among some of the most challenging and complex ecosystems for land cover prediction and mapping tasks because land cover targets often show high intra-class and low inter-class variances. In recent years, several deep convolutional neural network (CNN) architectures have been proposed for pixel-wise image labeling, commonly called semantic image segmentation. In this paper, some of the more recent deep CNN architectures proposed for semantic image segmentation are reviewed, and each model’s training efficiency and classification performance are evaluated by training it on a limited labeled image set. Training samples are provided using the hyper-spatial resolution UAS imagery over a wetland area and the required ground truth images are prepared by manual image labeling. Experimental results demonstrate that deep CNNs have a great potential for accurate land cover prediction task using UAS hyper-spatial resolution images. Some simple deep learning architectures perform comparable or even better than complex and very deep architectures with remarkably fewer training epochs. This performance is especially valuable when limited training samples are available, which is a common case in most RS applications.Deep learning has already been proved as a powerful state-of-the-art technique for many image understanding tasks in computer vision and other applications including remote sensing (RS) image analysis. Unmanned aircraft systems (UASs) offer a viable and economical alternative to a conventional sensor and platform for acquiring high spatial and high temporal resolution data with high operational flexibility. Coastal wetlands are among some of the most challenging and complex ecosystems for land cover prediction and mapping tasks because land cover targets often show high intra-class and low inter-class variances. In recent years, several deep convolutional neural network (CNN) architectures have been proposed for pixel-wise image labeling, commonly called semantic image segmentation. In this paper, some of the more recent deep CNN architectures proposed for semantic image segmentation are reviewed, and each model’s training efficiency and classification performance are evaluated by training it on a limited labeled image set. Training samples are provided using the hyper-spatial resolution UAS imagery over a wetland area and the required ground truth images are prepared by manual image labeling. Experimental results demonstrate that deep CNNs have a great potential for accurate land cover prediction task using UAS hyper-spatial resolution images. Some simple deep learning architectures perform comparable or even better than complex and very deep architectures with remarkably fewer training epochs. This performance is especially valuable when limited training samples are available, which is a common case in most RS applications

Partial scene reconstruction for close range photogrammetry using deep learning pipeline for region masking

3D reconstruction is a beneficial technique to generate 3D geometry of scenes or objects for various applications such as computer graphics, industrial construction, and civil engineering. There are several techniques to obtain the 3D geometry of an object. Close-range photogrammetry is an inexpensive, accessible approach to obtaining high-quality object reconstruction. However, state-of the-art software systems need a stationary scene or a controlled environment (often a turntable setup with a black background), which can be a limiting factor for object scanning. This work presents a method that reduces the need for a controlled environment and allows the capture of multiple objects with independent motion. We achieve this by creating a preprocessing pipeline that uses deep learning to transform a complex scene from an uncontrolled environment into multiple stationary scenes with a black background that is then fed into existing software systems for reconstruction. Our pipeline achieves this by using deep learning models to detect and track objects through the scene. The detection and tracking pipeline uses semantic-based detection and tracking and supports using available pretrained or custom networks. We develop a correction mechanism to overcome some detection and tracking shortcomings, namely, object-reidentification and multiple detections of the same object. We show detection and tracking are effective techniques to address scenes with multiple motion systems and that objects can be reconstructed with limited or no knowledge of the camera or the environment.3D reconstruction is a beneficial technique to generate 3D geometry of scenes or objects for various applications such as computer graphics, industrial construction, and civil engineering. There are several techniques to obtain the 3D geometry of an object. Close-range photogrammetry is an inexpensive, accessible approach to obtaining high-quality object reconstruction. However, state-of the-art software systems need a stationary scene or a controlled environment (often a turntable setup with a black background), which can be a limiting factor for object scanning. This work presents a method that reduces the need for a controlled environment and allows the capture of multiple objects with independent motion. We achieve this by creating a preprocessing pipeline that uses deep learning to transform a complex scene from an uncontrolled environment into multiple stationary scenes with a black background that is then fed into existing software systems for reconstruction. Our pipeline achieves this by using deep learning models to detect and track objects through the scene. The detection and tracking pipeline uses semantic-based detection and tracking and supports using available pretrained or custom networks. We develop a correction mechanism to overcome some detection and tracking shortcomings, namely, object-reidentification and multiple detections of the same object. We show detection and tracking are effective techniques to address scenes with multiple motion systems and that objects can be reconstructed with limited or no knowledge of the camera or the environment.This research received no external funding.This research received no external funding

VirtuaLot—A Case Study on Combining UAS Imagery and Terrestrial Video with Photogrammetry and Deep Learning to Track Vehicle Movement in Parking Lots

This study investigates the feasibility of applying monoplotting to video data from a security camera and image data from an uncrewed aircraft system (UAS) survey to create a mapping product which overlays traffic flow in a university parking lot onto an aerial orthomosaic. The framework, titled VirtuaLot, employs a previously defined computer-vision pipeline which leverages Darknet for vehicle detection and tests the performance of various object tracking algorithms. Algorithmic object tracking is sensitive to occlusion, and monoplotting is applied in a novel way to efficiently extract occluding features from the video using a digital surface model (DSM) derived from the UAS survey. The security camera is also a low fidelity model not intended for photogrammetry with unstable interior parameters. As monoplotting relies on static camera parameters, this creates a challenging environment for testing its effectiveness. Preliminary results indicate that it is possible to manually monoplot between aerial and perspective views with high degrees of transition tilt, achieving coordinate transformations between viewpoints within one deviation of vehicle short and long axis measurements throughout 70.5% and 99.6% of the study area, respectively. Attempted automation of monoplotting on video was met with limited success, though this study offers insight as to why and directions for future work on the subject

Partial Scene Reconstruction for Close Range Photogrammetry Using Deep Learning Pipeline for Region Masking

3D reconstruction is a beneficial technique to generate 3D geometry of scenes or objects for various applications such as computer graphics, industrial construction, and civil engineering. There are several techniques to obtain the 3D geometry of an object. Close-range photogrammetry is an inexpensive, accessible approach to obtaining high-quality object reconstruction. However, state-of-the-art software systems need a stationary scene or a controlled environment (often a turntable setup with a black background), which can be a limiting factor for object scanning. This work presents a method that reduces the need for a controlled environment and allows the capture of multiple objects with independent motion. We achieve this by creating a preprocessing pipeline that uses deep learning to transform a complex scene from an uncontrolled environment into multiple stationary scenes with a black background that is then fed into existing software systems for reconstruction. Our pipeline achieves this by using deep learning models to detect and track objects through the scene. The detection and tracking pipeline uses semantic-based detection and tracking and supports using available pretrained or custom networks. We develop a correction mechanism to overcome some detection and tracking shortcomings, namely, object-reidentification and multiple detections of the same object. We show detection and tracking are effective techniques to address scenes with multiple motion systems and that objects can be reconstructed with limited or no knowledge of the camera or the environment

VirtuaLot—A case study on combining UAS imagery and terrestrial video with photogrammetry and deep learning to track vehicle movement in parking lots

This study investigates the feasibility of applying monoplotting to video data from a security camera and image data from an uncrewed aircraft system (UAS) survey to create a mapping product which overlays traffic flow in a university parking lot onto an aerial orthomosaic. The framework, titled VirtuaLot, employs a previously defined computer-vision pipeline which leverages Darknet for vehicle detection and tests the performance of various object tracking algorithms. Algorithmic object tracking is sensitive to occlusion, and monoplotting is applied in a novel way to efficiently extract occluding features from the video using a digital surface model (DSM) derived from the UAS survey. The security camera is also a low fidelity model not intended for photogrammetry with unstable interior parameters. As monoplotting relies on static camera parameters, this creates a challenging environment for testing its effectiveness. Preliminary results indicate that it is possible to manually monoplot between aerial and perspective views with high degrees of transition tilt, achieving coordinate transformations between viewpoints within one deviation of vehicle short and long axis measurements throughout 70.5% and 99.6% of the study area, respectively. Attempted automation of monoplotting on video was met with limited success, though this study offers insight as to why and directions for future work on the subject.This study investigates the feasibility of applying monoplotting to video data from a security camera and image data from an uncrewed aircraft system (UAS) survey to create a mapping product which overlays traffic flow in a university parking lot onto an aerial orthomosaic. The framework, titled VirtuaLot, employs a previously defined computer-vision pipeline which leverages Darknet for vehicle detection and tests the performance of various object tracking algorithms. Algorithmic object tracking is sensitive to occlusion, and monoplotting is applied in a novel way to efficiently extract occluding features from the video using a digital surface model (DSM) derived from the UAS survey. The security camera is also a low fidelity model not intended for photogrammetry with unstable interior parameters. As monoplotting relies on static camera parameters, this creates a challenging environment for testing its effectiveness. Preliminary results indicate that it is possible to manually monoplot between aerial and perspective views with high degrees of transition tilt, achieving coordinate transformations between viewpoints within one deviation of vehicle short and long axis measurements throughout 70.5% and 99.6% of the study area, respectively. Attempted automation of monoplotting on video was met with limited success, though this study offers insight as to why and directions for future work on the subject