scholarly journals A comparison of synthetic flowpaths derived from light detection and ranging topobathymetric data and National Hydrography Dataset High Resolution Flowlines

2018 ◽  
Author(s):  
Cynthia Miller-Corbett
Keyword(s):  
High Resolution ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
National Hydrography Dataset

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.

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 Using Airborne Light Detection and Ranging (LIDAR) to Characterize Forest Stand Condition on the Kenai Peninsula of Alaska

2009 ◽  
Vol 24 (2) ◽  
pp. 95-102 ◽  
Author(s):  
Hans-Erik Andersen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Grid Cell ◽  
Forest Stand ◽  
Light Detection And Ranging ◽  
Inventory Analysis ◽  
Light Detection ◽  
Individual Tree ◽  
Kenai Peninsula

Abstract Airborne laser scanning (also known as light detection and ranging or LIDAR) data were used to estimate three fundamental forest stand condition classes (forest stand size, land cover type, and canopy closure) at 32 Forest Inventory Analysis (FIA) plots distributed over the Kenai Peninsula of Alaska. Individual tree crown segment attributes (height, area, and species type) were derived from the three-dimensional LIDAR point cloud, LIDAR-based canopy height models, and LIDAR return intensity information. The LIDAR-based crown segment and canopy cover information was then used to estimate condition classes at each 10-m grid cell on a 300 × 300-m area surrounding each FIA plot. A quantitative comparison of the LIDAR- and field-based condition classifications at the subplot centers indicates that LIDAR has potential as a useful sampling tool in an operational forest inventory program.

Precision and shortcomings of yaw error estimation using spinner-based light detection and ranging

Wind Energy ◽  
2012 ◽  
Vol 16 (3) ◽  
pp. 353-366 ◽  
Author(s):  
Knud A. Kragh ◽  
Morten H. Hansen ◽  
Torben Mikkelsen
Keyword(s):  
Error Estimation ◽  
Light Detection And Ranging ◽  
Light Detection
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