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 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.

Predicting forest stand variables from LiDAR data in the Great Lakes – St. Lawrence forest of Ontario

2008 ◽  
Vol 84 (6) ◽  
pp. 827-839 ◽  
Author(s):  
M. Woods ◽  
K. Lim ◽  
P. Treitz
Keyword(s):  
Great Lakes ◽  
Laser Scanning ◽  
Basal Area ◽  
Common Species ◽  
Forest Stand ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Average Height ◽  
Light Detection ◽  
Forest Modelling

Models were developed to predict forest stand variables for common species of the Great Lakes – St. Lawrence forest of central Ontario, Canada from light detection and ranging (LiDAR) data. Stands that had undergone various ranges of partial harvesting or initial spacing treatments from multiple geographic sites were considered. A broad forest stratification was adopted and consisted of: (i) natural hardwoods; (ii) natural conifers; and (iii) plantation conifers. Stand top height (R2 = 0.96, 0.98, and 0.98); average height (R2 = 0.86, 0.76, and 0.98); basal area (R2 = 0.80, 0.80, and 0.85); volume (R2 = 0.89, 0.81, and 0.91); quadratic mean diameter (R2 = 0.80, 0.68, and 0.83); and density (R2 = 0.74, 0.71, and 0.73) were predicted from low density (i.e., 0.5 point m-2) LiDAR data for these 3 strata, respectively. Key words: light detection and ranging, LiDAR, airborne laser scanning, forest modelling, remote sensing, forest stand variables, Great Lakes – St. Lawrence forest

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.

Filtering LiDAR Data Using Binary Quadric Trend Surface and Morphological Approaches

2012 ◽  
Vol 594-597 ◽  
pp. 2361-2366 ◽  
Author(s):  
Feng Li ◽  
Xi Min Cui ◽  
Ling Zhang ◽  
Shu Wei Shan ◽  
Kun Lun Song
Keyword(s):  
Point Clouds ◽  
Experimental Results ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Data Sets ◽  
Type I ◽  
Morphological Filter ◽  
Light Detection ◽  
Trend Surface ◽  
Sample Data

Automatically identifying and removing above-ground laser points from terrain surface is proved to be a challenging task for complicated and discontinuous scenarios. Eight methods have been developed and contrasted with each other for filtering LiDAR (Light Detection and Ranging) data. Only one approach is difficult to acquire high precisions for various landscapes. This paper presents a method filtering point clouds in which firstly a binary quadric trend surface is used to remove most non-terrain points by a defined height threshold and subsequently a progressive morphological filter further is employed to detect ground measurements. The experimental results demonstrate that this method yields less type I and total errors compared with other eight approaches based on ISPRS sample data sets.

scholarly journals A Novel Method for Feature Extraction and Classification for an Aerial Image and LiDAR Data with Genetic Algorithm

2012 ◽  
pp. 264-270
Author(s):  
Manjunath B. E ◽  
D. G. Anand ◽  
Mahant. G. Kattimani
Keyword(s):  
Feature Extraction ◽  
Light Detection And Ranging ◽  
Aerial Image ◽  
Lidar Data ◽  
Light Detection ◽  
Aerial Photos ◽  
The Earth ◽  
Digital Elevation ◽  
Land Surveying ◽  
Novel Method

Airborne Light Detection and Ranging (LiDAR) provides accurate height information for objects on the earth, which makes LiDAR become more and more popular in terrain and land surveying. In particular, LiDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. Aerial photos with LiDAR data were processed with genetic algorithms not only for feature extraction but also for orthographical image. DSM provided by LiDAR reduced the amount of GCPs needed for the regular processing, thus the reason both efficiency and accuracy are highly improved. LiDAR is an acronym for Light Detection and Ranging, which is typically defined as an integration of three technologies into a single system, which is capable of acquiring a data to produce accurate Digital Elevation Models.

scholarly journals AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

2016 ◽  
Vol XLI-B2 ◽  
pp. 35-42 ◽  
Author(s):  
A. W. Lyda ◽  
X. Zhang ◽  
C. L. Glennie ◽  
K. Hudnut ◽  
B. A. Brooks
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Near Field ◽  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Data Sets ◽  
Light Detection ◽  
Icp Algorithm ◽  
Detection Algorithms ◽  
Change Detection Algorithms

Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

scholarly journals Training a terrain traversability classifier for a planetary rover through simulation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773540 ◽  
Author(s):  
Robert A Hewitt ◽  
Alex Ellery ◽  
Anton de Ruiter
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Data Sets ◽  
Light Detection ◽  
Planetary Rover ◽  
Training Methodology ◽  
Sensor Model ◽  
Random Fashion ◽  
Manual Classification

A classifier training methodology is presented for Kapvik, a micro-rover prototype. A simulated light detection and ranging scan is divided into a grid, with each cell having a variety of characteristics (such as number of points, point variance and mean height) which act as inputs to classification algorithms. The training step avoids the need for time-consuming and error-prone manual classification through the use of a simulation that provides training inputs and target outputs. This simulation generates various terrains that could be encountered by a planetary rover, including untraversable ones, in a random fashion. A sensor model for a three-dimensional light detection and ranging is used with ray tracing to generate realistic noisy three-dimensional point clouds where all points that belong to untraversable terrain are labelled explicitly. A neural network classifier and its training algorithm are presented, and the results of its output as well as other popular classifiers show high accuracy on test data sets after training. The network is then tested on outdoor data to confirm it can accurately classify real-world light detection and ranging data. The results show the network is able to identify terrain correctly, falsely classifying just 4.74% of untraversable terrain.

scholarly journals Reconstruction of Axial Tomographic High Resolution Data from Confocal Fluorescence Microscopy: A Method for Improving 3D FISH Images

2000 ◽  
Vol 20 (1) ◽  
pp. 7-15 ◽  
Author(s):  
R. Heintzmann ◽  
G. Kreth ◽  
C. Cremer
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Axial Direction ◽  
Laser Scanning Microscopy ◽  
Three Dimensions ◽  
Confocal Laser ◽  
Data Sets ◽  
Data Set ◽  
High Resolution Data ◽  
3D Data

Fluorescent confocal laser scanning microscopy allows an improved imaging of microscopic objects in three dimensions. However, the resolution along the axial direction is three times worse than the resolution in lateral directions. A method to overcome this axial limitation is tilting the object under the microscope, in a way that the direction of the optical axis points into different directions relative to the sample. A new technique for a simultaneous reconstruction from a number of such axial tomographic confocal data sets was developed and used for high resolution reconstruction of 3D‐data both from experimental and virtual microscopic data sets. The reconstructed images have a highly improved 3D resolution, which is comparable to the lateral resolution of a single deconvolved data set. Axial tomographic imaging in combination with simultaneous data reconstruction also opens the possibility for a more precise quantification of 3D data. The color images of this publication can be accessed from http://www.esacp.org/acp/2000/20‐1/heintzmann.htm. At this web address an interactive 3D viewer is additionally provided for browsing the 3D data. This java applet displays three orthogonal slices of the data set which are dynamically updated by user mouse clicks or keystrokes.

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