scholarly journals Use of Light Detection and Ranging (LiDAR) to Obtain High-Resolution Elevation Data for Sussex County, Delaware

Fact Sheet ◽  
2008 ◽  
Author(s):  
Roger A. Barlow ◽  
Mark R. Nardi ◽  
Betzaida Reyes
Keyword(s):  
High Resolution ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Sussex County ◽  
Elevation Data

Generating meshes for tidal wetland modeling using light detection and ranging (LiDAR) data

2014 ◽  
Vol 16 (4) ◽  
pp. 941-951 ◽  
Author(s):  
Ramona Stammermann ◽  
Michael Piasecki
Keyword(s):  
High Resolution ◽  
Light Detection And Ranging ◽  
Tidal Marshes ◽  
Lidar Data ◽  
Tidal Channels ◽  
Light Detection ◽  
Cross Sectional ◽  
Simple Method ◽  
Elevation Data ◽  
Resolution Model

A high resolution model mesh was required to numerically simulate sediment transport in tidal marshes. The timing of flooding is dependent on the tidal marsh ground elevation, which requires accurate topographic elevation data. The tidal prism of the marsh is determined by the volume provided by tidal channels in the system. Hence, their location and bathymetry needed to be represented adequately. Due to the high spatial variability and inaccessibility of marshes, remote sensing techniques such as light detection and ranging (LiDAR) are a significant resource for elevation data. LiDAR measures the highest elevation of elements. To determine the bare ground elevation, filter techniques exist but are often inadequate to eliminate elevation errors that are introduced by the vegetation of marshes. We introduce a simple method to remove remaining vertical elevation errors in high resolution digital terrain models (DTMs) of vegetated marshes and present an approach to determine the bathymetry of tidal channels based on a limited number of cross-sectional measurements. Forcing polygons for mesh generation were extracted from the DTMs to assure an accurate spatial representation of the marsh. DTMs (2 × 2 m/1 × 1 m) derived from LiDAR data from the Blackbird Creek Reserve and Bombay Hook National Wildlife Refuge in Delaware, USA, were used.

scholarly journals LiDAR and other evidence for the southwest continuation and Late Quaternary reactivation of the Norumbega Fault System and a cross-cutting structure near Biddeford, Maine, USA

2019 ◽  
Vol 55 ◽  
pp. 323-359
Author(s):  
Ronald T. Marple ◽  
James D. Hurd
Keyword(s):  
High Resolution ◽  
New Hampshire ◽  
Late Quaternary ◽  
Light Detection And Ranging ◽  
Fault System ◽  
Light Detection ◽  
Drainage Patterns ◽  
Geomorphic Features ◽  
Wide Zone

High-resolution LiDAR (light detection and ranging) images reveal numerous NE-SW-trending geomorphic lineaments that may represent the southwest continuation of the Norumbega fault system (NFS) along a broad, 30- to 50-km-wide zone of brittle faults that continues at least 100 km across southern Maine and southeastern New Hampshire. These lineaments are characterized by linear depressions and valleys, linear drainage patterns, abrupt bends in rivers, and linear scarps. The Nonesuch River, South Portland, and Mackworth faults of the NFS appear to continue up to 100 km southwest of the Saco River along prominent but discontinuous LiDAR lineaments. Southeast-facing scarps that cross drumlins along some of the lineaments in southern Maine suggest that late Quaternary displacements have occurred along these lineaments. Several NW-SE-trending geomorphic features and geophysical lineaments near Biddeford, Maine, may represent a 30-km-long, NW-SE-trending structure that crosses part of the NFS. Brittle NWSE-trending, pre-Triassic faults in the Kittery Formation at Biddeford Pool, Maine, support this hypothesis.

scholarly journals Object-Oriented Classification of Forest Structure from Light Detection and Ranging Data for Stand Mapping

2009 ◽  
Vol 24 (4) ◽  
pp. 198-204 ◽  
Author(s):  
Alicia A. Sullivan ◽  
Robert J. McGaughey ◽  
Hans-Erik Andersen ◽  
Peter Schiess
Keyword(s):  
High Resolution ◽  
Forest Structure ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Object Oriented ◽  
Light Detection And Ranging ◽  
Classification Error ◽  
Lidar Data ◽  
Data Sets ◽  
Light Detection

Abstract Stand delineation is an important step in the process of establishing a forest inventory and provides the spatial framework for many forest management decisions. Many methods for extracting forest structure characteristics for stand delineation and other purposes have been researched in the past, primarily focusing on high-resolution imagery and satellite data. High-resolution airborne laser scanning offers new opportunities for evaluating forests and conducting forest inventory. This study investigates the use of information derived from light detection and ranging (LIDAR) data as a potential tool for delineation of forest structure to create stand maps. Delineation methods are developed and tested using data sets collected over the Blue Ridge study site near Olympia, Washington. The methodology developed delineates forest areas using LIDAR data and object-oriented image segmentation and supervised classification. Error matrices indicate classification accuracies with a kappa hat values of 78 and 84% for 1999 and 2003 data sets, respectively.

scholarly journals Integrating EfficientNet into an HAFNet Structure for Building Mapping in High-Resolution Optical Earth Observation Data

2021 ◽  
Vol 13 (21) ◽  
pp. 4361
Author(s):  
Luca Ferrari ◽  
Fabio Dell’Acqua ◽  
Peng Zhang ◽  
Peijun Du
Keyword(s):  
High Resolution ◽  
Earth Observation ◽  
Light Detection And Ranging ◽  
Observation Data ◽  
Building Extraction ◽  
Multiple Sources ◽  
Light Detection ◽  
Single Thread ◽  
Combining Data ◽  
Benchmark Datasets

Automated extraction of buildings from Earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, and policy-making. Combining data from different sensors, such as high-resolution multispectral images (HRI) and light detection and ranging (LiDAR) data, has shown great potential in building extraction. Deep learning (DL) is increasingly used in multi-modal data fusion and urban object extraction. However, DL-based multi-modal fusion networks may under-perform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multi-modal features. Recently, a hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset, including co-located Very-High-Resolution (VHR) optical images and light detection and ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model over-parametrization, which inevitably leads to long training times and heavy computational load. In this paper, the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation.

Progress in operational flood mapping using satellite synthetic aperture radar (SAR) and airborne light detection and ranging (LiDAR) data

2016 ◽  
Vol 40 (2) ◽  
pp. 196-214 ◽  
Author(s):  
Kyle M. Brown ◽  
Crispin H. Hambidge ◽  
Jonathan M. Brownett
Keyword(s):  
Synthetic Aperture Radar ◽  
Light Detection And Ranging ◽  
Surface Elevation ◽  
Synthetic Aperture ◽  
Lidar Data ◽  
Light Detection ◽  
Elevation Data ◽  
Sar Data ◽  
Automated Methods ◽  
Aperture Radar

During flooding, operational tools for mapping flood extent and depth of water in flood-prone areas are required by those planning emergency response, including UK statutory agencies such as the Environment Agency. Satellite data have become a source of information to map and monitor floods, but many of the methods developed to process the data are unsuitable for accurate, near real-time production of flood information products. This paper describes a new semi-automated methodology developed to provide operational mapping of flood extent and flood depth using satellite synthetic aperture radar (SAR) data combined with light detection and ranging (LiDAR) elevation data. In this method, an analyst uses the flood boundary derived from 8 m spatial resolution satellite SAR data to estimate the flood surface elevation at points around a flooded area using a digital terrain model derived from LiDAR data. This method is compared to a simple satellite ‘SAR-only’ method for generating flood extent and alternative, automated methods of generating flood extent and depth that also used SAR and LiDAR. TerraSAR-X and SPOT 5 data were used from an area including the UK Somerset Levels which suffered a major flood event in February 2014. The new semi-automated method produced similar overall accuracy to the SAR-only method ( Po = 95.8% and Po = 95.3%, respectively), but was more accurate at mapping flood extent where large vegetation or other objects appeared in the satellite SAR data. The automated methods were relatively inaccurate (overall accuracy ranged from Po = 83.4% to Po = 88.8%), but were used to identify where further work on improving the semi-automated-elevation method could be carried out. In addition to the flood extent information provided by the semi-automated-elevation method, flood surface elevation data were produced that could be used to estimated flood depths and flood volumes. The accuracy of the flood elevation surface was tested using LiDAR data acquired of the water surface during the flooding (root mean square error = 0.152 m). The paper discusses progress towards operational flood monitoring using SAR and LiDAR remote sensing products.

Application of Light Detection and Ranging Technology to Highway Safety

2003 ◽  
Vol 1836 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Aemal J. Khattak ◽  
Shauna Hallmark ◽  
Reginald Souleyrette
Keyword(s):  
Data Acquisition ◽  
Surface Profile ◽  
Return Time ◽  
Highway Safety ◽  
Data Availability ◽  
Light Detection And Ranging ◽  
Line Of Sight ◽  
Light Detection ◽  
Crash Frequency ◽  
Elevation Data

An application of light detection and ranging (LIDAR) technology to highway intersection safety is presented. LIDAR can be used to collect information about a surface by reflecting thousands of light beams per second off the surface and measuring the return time of the beams. The surface profile is collected as a digital signature that can be used in a variety of applications. Collection of information on the surface profile of the earth in the form of elevation data is one of several LIDAR applications that have been used for mapping and contouring. The focus of the described application is use of LIDAR elevation data to obtain information on intersection geometry that can lead to the discovery of potential obstructions in driver sight lines. After appropriate transformations, LIDAR elevation data were used in line-of-sight analysis to obtain information on sight-line obstructions at six intersections on the IA-1 corridor in Iowa. Intersection crash frequency and data availability were considerations in the selection of the six intersections. Results from the line-of-sight analysis were validated by visits to the intersections in the field and verification of the existence of obstructions detected during the analysis. Sixty-six lines of sight were blocked during the line-of-sight analysis, of which 62 (89.8%) were confirmed during the validation process. Four (5.8%) sight-line obstructions were not confirmed during the validation. At least three (4.4%) potential sight-line obstructions discovered during validation were not detected during the line-of-sight analysis. The intersection with the highest crash frequency was correctly found to have obstructions located within the intersection sight triangles. It can be concluded that LIDAR elevation data can be used successfully for identifying potential sight-distance problems at intersections. Identified potential problems can be verified and rectified in the field. LIDAR is a relatively costly data source, and a single application, such as this one, cannot justify the high cost of LIDAR data acquisition. Other potential highway safety enhancing applications of LIDAR must be investigated to offset the high data-acquisition cost. Suggestions for other highway safety applications are provided.

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