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

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.

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 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 Tunnel Vision

2016 ◽  
Vol 4 (2) ◽  
pp. 192-204 ◽  
Author(s):  
Thomas G. Garrison ◽  
Dustin Richmond ◽  
Perry Naughton ◽  
Eric Lo ◽  
Sabrina Trinh ◽  
...  
Keyword(s):  
3D Models ◽  
Present Form ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Technical Expertise ◽  
Ancient Maya ◽  
Light Detection ◽  
Terrestrial Lidar ◽  
Tunnel Vision ◽  
El Zotz

AbstractArchaeological tunneling is a standard excavation strategy in Mesoamerica. The ancient Maya built new structures atop older ones that were no longer deemed usable, whether for logistical or ideological reasons. This means that as archaeologists excavate horizontal tunnels into ancient Maya structures, they are essentially moving back in time. As earlier constructions are encountered, these tunnels may deviate in many directions in order to document architectural remains. The resultant excavations often become intricate labyrinths, extending dozens of meters. Traditional forms of archaeological documentation, such as photographs, plan views, and profile drawings, are limited in their ability to convey the complexity of tunnel excavations. Terrestrial Lidar (light detection and ranging) instruments are able to generate precise 3D models of tunnel excavations. This article presents the results of a model created with a Faro™ Focus 3D 120 Scanner of tunneling excavations at the site of El Zotz, Guatemala. The lidar data document the excavations inside a large mortuary pyramid, including intricately decorated architecture from an Early Classic (A.D. 300–600) platform buried within the present form of the structure. Increased collaboration between archaeologists and scholars with technical expertise maximizes the effectiveness of 3D models, as does presenting digital results in tandem with traditional forms of documentation.

scholarly journals Mapeamento das áreas de armazenamento de água em depressão, usando dados LIDAR: Estudo de caso avenida Caxangá

2021 ◽  
Vol 14 (1) ◽  
pp. 058
Author(s):  
Camila Gardenea de Almeida Bandim ◽  
Josiclêda Domiciano Galvíncio
Keyword(s):  
Water Storage ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Urban Drainage ◽  
Lidar System ◽  
Light Detection ◽  
Urban Flood ◽  
Digital Elevation ◽  
Elevation Model

O objetivo deste estudo consiste em avaliar as áreas inundáveis em Recife, com especial atenção à avenida Caxangá.  Iniciando uma análise sobre a drenagem convencional utilizando a tecnologia LIDAR (Light Detection And Ranging). Os dados empregados neste trabalho foram captados pelo sistema LIDAR e possuem 50 cm de resolução, sendo um total de 12 quadrículas xyz para a constituição do mosaico Modelo Digital de Elevação (MDE) da avenida Caxangá, com destaque para as quadrículas 81_50-05 e 81_60-05, partindo dessas foram geradas as direções e acúmulos de fluxo. Os resultados obtidos enfatizam a alta resolução através da nítida visualização de elementos naturais e artificiais, e ainda o nivelamento do terreno. Em seguida, observa-se o acúmulo de fluxo que exibe as diferentes direções e acúmulos do escoamento superficial, ainda se percebe a influência na drenagem urbana das construções antrópicas e da vegetação em locais pontuais da avenida Caxangá. Em conclusão os dados do sistema LIDAR responderam positivamente, tanto na captação na modelagem do terreno e topografia artificial, como também para as gerações de direções e acúmulo de fluxos apresentando maiores valores para áreas depressivas naturais e antropizadas. Sendo destaque neste estudo as áreas antropizadas por provocarem problemas de desastres naturais. Conclui-se que as áreas antropizadas exercem um importante papel na drenagem urbana.  Mapping water storage areas in depression, using LIDAR data: Caxangá Avenida case study A B S T R A C TThe objective of this study is to evaluate the floodable areas in Recife, with special attention to Avenida Caxangá. Starting an analysis on conventional drainage using LIDAR (Light Detection And Ranging) technology. The data used in this work were captured by the LIDAR system and have 50 cm of resolution, with a total of 12 xyz squares for the constitution of the Digital Elevation Model (MDE) mosaic on Avenida Caxangá, with emphasis on the squares 81_50-05 and 81_60 -05, from these directions and flow accumulations were generated. The results obtained emphasize the high resolution through the clear visualization of natural and artificial elements, as well as the leveling of the terrain. Then, there is the accumulation of flow that shows the different directions and accumulations of runoff, the influence on the urban drainage of anthropic buildings and vegetation in specific places on Avenida Caxangá is still perceived. In conclusion, the data from the LIDAR system responded positively, both in capturing terrain modeling and artificial topography, as well as for generations of directions and accumulation of flows, presenting higher values for natural and anthropized depressive areas. Being highlighted in this study the areas anthropized because they cause problems of natural disasters. It is concluded that anthropized areas play an important role in urban drainage.Keywords: Geoprocessing. Remote sensing. Urbanization. Urban flood. drainage

scholarly journals Relating Forest Attributes with Area- and Tree-Based Light Detection and Ranging Metrics for Western Oregon

2010 ◽  
Vol 25 (3) ◽  
pp. 105-111 ◽  
Author(s):  
Michael E. Goerndt ◽  
Vincente J. Monleon ◽  
Hailemariam Temesgen
Keyword(s):  
Basal Area ◽  
Light Detection And Ranging ◽  
Stem Volume ◽  
Height Distribution ◽  
Light Detection ◽  
Quadratic Mean ◽  
Crown Width ◽  
Mean Diameter ◽  
Forest Attributes ◽  
Quadratic Mean Diameter

Abstract Three sets of linear models were developed to predict several forest attributes, using stand-level and single-tree remote sensing (STRS) light detection and ranging (LiDAR) metrics as predictor variables. The first used only area-level metrics (ALM) associated with first-return height distribution, percentage of cover, and canopy transparency. The second alternative included metrics of first-return LiDAR intensity. The third alternative used area-level variables derived from STRS LiDAR metrics. The ALM model for Lorey's height did not change with inclusion of intensity and yielded the best results in terms of both model fit (adjusted R2 = 0.93) and cross-validated relative root mean squared error (RRMSE = 8.1%). The ALM model for density (stems per hectare) had the poorest precision initially (RRMSE = 39.3%), but it improved dramatically (RRMSE = 27.2%) when intensity metrics were included. The resulting RRMSE values of the ALM models excluding intensity for basal area, quadratic mean diameter, cubic stem volume, and average crown width were 20.7, 19.9, 30.7, and 17.1%, respectively. The STRS model for Lorey's height showed a 3% improvement in RRMSE over the ALM models. The STRS basal area and density models significantly underperformed compared with the ALM models, with RRMSE values of 31.6 and 47.2%, respectively. The performance of STRS models for crown width, volume, and quadratic mean diameter was comparable to that of the ALM models.

scholarly journals Hyperspectral and LiDAR Data Fusion Classification Using Superpixel Segmentation-Based Local Pixel Neighborhood Preserving Embedding

2019 ◽  
Vol 11 (5) ◽  
pp. 550 ◽  
Author(s):  
Yunsong Li ◽  
Chiru Ge ◽  
Weiwei Sun ◽  
Jiangtao Peng ◽  
Qian Du ◽  
...  
Keyword(s):  
Experimental Data ◽  
Data Fusion ◽  
Experimental Results ◽  
New Method ◽  
Light Detection And Ranging ◽  
Actual Data ◽  
Lidar Data ◽  
Light Detection ◽  
Superpixel Segmentation ◽  
Open Standard

A new method of superpixel segmentation-based local pixel neighborhood preserving embedding (SSLPNPE) is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR) data based on the extinction profiles (EPs), superpixel segmentation and local pixel neighborhood preserving embedding (LPNPE). A new workflow is proposed to calibrate the Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) data, which allows our method to be applied to actual data. Specifically, EP features are extracted from both sources. Then, the derived features of each source are fused by the SSLPNPE. Using the labeled samples, the final label assignment is produced by a classifier. For the open standard experimental data and the actual data, experimental results prove that the proposed method is fast and effective in hyperspectral and LiDAR data fusion.

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