Factsheet # 27 Canopy Structure From Aerial and Terrestrial LiDAR

Slides:



Advertisements
Similar presentations
Airborne Laser Scanning (ALS) data processing and its usage for forest cover and forest stand parameter estimation Géza Király 1, Gábor Brolly 1 1 University.
Advertisements

Technology Transfer Ideas from the Private Sector John Paul McTague Rayonier, Inc. NCASI – Biometrics Working Group, Chairman SAF National FIA User Group.
Centennial Grove College of Forest Resources University of Washington April 28 th, 2007 This visualization was produced by Dr. L. Monika Moskal at the.
Brian S. Keiling Program Head – Forest Management Dabney S.Lancaster Community College.
FOR 474: Forest Inventory Plot Level Metrics from Lidar Heights Other Plot Measures Sources of Error Readings: See Website.
Active Microwave and LIDAR. Three models for remote sensing 1. Passive-Reflective: Sensors that rely on EM energy emitted by the sun to illuminate the.
Remote sensing is up! Inventory & monitoring Inventory – To describe the current status of forest Landcover / landuse classification Forest structure /
Questions How do different methods of calculating LAI compare? Does varying Leaf mass per area (LMA) with height affect LAI estimates? LAI can be calculated.
Abteilung Biometrie und Informatik SMC Spring Meeting 2007, Vancouver, WA. Mapping Forest Characteristics across the Landscape using Sample Plot and Airborne.
Overview of Biomass Mapping The Woods Hole Research Center Alessandro Baccini, Wayne Walker and Ned Horning November 8 – 12, Samarinda, Indonesia.
Lecture 17 – Forest remote sensing  Reading assignment:  Ch 4.7, 8.23,  Kane et al., Interpretation and topographic correction of conifer forest.
DR. JOHANNES HEINZEL (Dipl.-Geogr.) University of Freiburg, Department of Remote Sensing and Landscape Information Systems, Freiburg, Germany Use.
An overview of Lidar remote sensing of forests C. Véga French Institute of Pondicherry.
Modeling Lateral Line-of-Sight with LiDAR Jayson Murgoitio Idaho State University Boise Center Aerospace Lab.
Introduction OBJECTIVES  To develop proxies for canopy cover and canopy closure based on discrete-return LiDAR data.  To determine whether there is a.
Examination of Tropical Forest Canopy Profiles Using Field Data and Remotely Sensed Imagery Michael Palace 1, Michael Keller 1,2, Bobby Braswell 1, Stephen.
Mapping Forest Canopy Height with MISR We previously demonstrated a capability to obtain physically meaningful canopy structural parameters using data.
Quantitative Estimates of Biomass and Forest Structure in Coastal Temperate Rainforests Derived from Multi-return Airborne Lidar Marc G. Kramer 1 and Michael.
Biomass Mapping The set of field biomass training data and the MODIS observations were used to develop a regression tree model (Random Forest). Biomass.
CSIRO Marine & Atmospheric Research (CMAR) & ENSIS 1 The CSIRO Canopy Lidar Initiative, its ECHIDNA® and an EVI David LB Jupp 1, Darius Culvenor 2, Jenny.
__________. Introduction Importance – Wildlife Habitat – Nutrient Cycling – Long-Term Carbon Storage – Key Indicator for Biodiversity Minimum Stocking.
Citation: Moskal, L. M. and J. Kirsch, Calibrating Estimates of Above- and Below- Ground Forests Biomass Using Remotely Sensed Metrics. Factsheet.
Site Harvard Hemlock Site 305 Site Harvard EMS tower  The LAI estimates are impacted by changes in the occlusion effect at the different scales.  75.
DN Ordinate Length DN Difference Estimating forest structure in tropical forested sites.
Scaling Up Above Ground Live Biomass From Plot Data to Amazon Landscape Sassan S. Saatchi NASA/Jet Propulsion Laboratory California Institute of Technology.
LiDAR Remote Sensing of Forest Vegetation Ryan Anderson, Bruce Cook, and Paul Bolstad University of Minnesota.
THE ISSUE: Physically occupying every location for data collection is not always possible. A blue- tooth enabled laser rangefinder allows the user to collect.
Remote Sensing of Forest Structure Van R. Kane College of Forest Resources.
Arizona Space Grant Consortium Statewide Symposium Arizona Space Grant Consortium Statewide Symposium Light Detection and Ranging (LiDAR) Survey of a Sky.
Citation: Richardson, J. J, L.M. Moskal, S. Kim, Estimating Urban Forest Leaf Area Index (LAI) from aerial LiDAR. Factsheet # 5. Remote Sensing and.
Citation: Moskal., L. M. and D. M. Styers, Land use/land cover (LULC) from high-resolution near infrared aerial imagery: costs and applications.
Citation: Zhang Z.Y.,Kazakova A.N. and Moskal L.M Integrating LIDAR with Hyperspectral Data for Tree Species Classification in Urban Ecosystems.
SGM as an Affordable Alternative to LiDAR
Citation: Moskal, L. M., D. M. Styers, J. Richardson and M. Halabisky, Seattle Hyperspatial Land use/land cover (LULC) from LiDAR and Near Infrared.
Citation: Kato, A.., L. M. Moskal., P. Schiess, M. Swanson, D. Calhoun and W. Stuetzel, LiDAR based tree crown surface reconstruction. Factsheet.
U NIVERSITY OF J OENSUU F ACULTY OF F ORESTRY Introduction to Lidar and Airborne Laser Scanning Petteri Packalén Kärkihankkeen ”Multi-scale Geospatial.
Airborne LiDAR requires purchase, but offers a number of advantages; Airborne LiDAR requires purchase, but offers a number of advantages; Spatial resolution.
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
Tree Modelling TLS data, QSMs, applications Pasi Raumonen, Markku Åkerblom, Mikko Kaasalainen Department of Mathematics, Tampere University of Technology.
Lidar Point Clouds for Developing Canopy Height Models (CHM) for Bankhead National Forest Plots By: Soraya Jean-Pierre REU Program at Alabama A & M University.
Remote sensing technologies that utilize lasers are becoming increasingly available to researchers and can quickly provide landscape level coverage of.
Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.8 Overview.
Citation: Halabisky, Meghan A, Moskal, LM Monitoring Wetlands Dynamics Across Spatial Scales and Over Time. Factsheet # 7. Remote Sensing and Geospatial.
Factsheet # 26 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS CREATING LIDAR-DRIVEN MODELS TO IMPROVE.
IFSAR and terrestrial LIDAR for vegetation study in Sonora, Texas
Factsheet # 17 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Estimating Tree Species Diversity.
Factsheet # 8 Predicting an Invasive Species’ Distribution with
PADMA ALEKHYA V V L, SURAJ REDDY R, RAJASHEKAR G & JHA C S
Retrieval of Information from Different Optical 3D Remote Sensing Sources for Use in Forest Inventory (3D-FORINVENT) HRVATSKI ŠUMARSKI INSTITUT CROATIAN.
Factsheet # ? Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Title Goes Here, Capitalize Each Word,
Factsheet # 20 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Title Goes Here, Capitalize Each Word,
Factsheet # 23 Study Area Methods
Remote Sensing and Avalanches
Lecture 21: GIS Analytical Functionality (V)
Factsheet # 12 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Land use/land cover (LULC) from high-resolution.
Factsheet # 19 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Hyperspectral Remote Sensing of Urban.
Figure 3 An example plot with high error index values for the estimated diameter distributions: ML-estimation (48.5), parameter recovery (70.7), ALS-based.
Factsheet # 9 Decadal Analysis of Wetland & Agricultural Change
Factsheet #11 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Small Stream Mapping Method: Local.
Factsheet # 2 Leaf Area Index (LAI) from Aerial & Terrestrial LiDAR
Factsheet # 12 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Monitoring Ecological Restoration.
REMOTE SENSING & GEOSPATIAL ANALYSIS LABORATORY
Factsheet # 6 Spatiotemporal Analysis of Mountain Goat Habitat
Factsheet # 21 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Quantifying Vertical and Horizontal.
Semi-arid Ecosystem Plant Functional Type and LAI from Small Footprint Waveform Lidar Nayani Ilangakoon, Nancy F. Glenn, Lucas.
Precision Forestry Cooperative Lidar Projects
Integrating Airborne LiDAR and Terrestrial Laser Scanner for Accurate Estimation of Above-ground Biomass/Carbon of Tropical Forests Accuracy Matters Muluken.
Integrating Airborne LiDAR and Terrestrial Laser Scanner for Accurate Estimation of Above-ground Biomass/Carbon of Tropical Forests Accuracy Matters Muluken.
Integrating Airborne LiDAR and Terrestrial Laser Scanner for Accurate Estimation of Above-ground Biomass/Carbon of Tropical Forests Accuracy Matters Muluken.
Factsheet # 15 Coastal Wetlands: Monitoring Estuarine Topographic Change with Terrestrial Laser Scanning (TLS) Understanding multiscale dynamics of landscape.
Presentation transcript:

Factsheet # 27 Canopy Structure From Aerial and Terrestrial LiDAR Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Canopy Structure From Aerial and Terrestrial LiDAR This research was funded by the Bureau of Land Management Introduction: Measuring crown parameters in the field can be a challenging and time consuming task, often prone to high measurement error. Technological advancements in the field of remote sensing including the development and implementation of hyperspatial Light Detection and Ranging (LiDAR) and hyperspectral remote sensing are driving the discipline to new frontiers of forestry applications. LiDAR is one of the active optical remote sensing technologies and is a great tool for extracting information about vertical and horizontal canopy structure. The three dimensional nature of LiDAR data makes it possible to detect and isolate individual and clusters of trees (Hyyppa et al., 2001; Persson et al., 2002) Figure 3. NMDS ordination of plot canopy structure metrics by canopy slice, where slice 2, 3, and 4 represent height gradients from low, medium and high, respectively. Calibrating Aerial LiDAR with canopy metrics derived from Terrestrial LIDAR Due to the scanning time and the bulkiness of the equipment the TLS is great at quantifying the canopy structure at the plot level. However the big question of accurately quantifying canopy structure on a landscape level still needs to be answered. We took the calibration approach to investigate the relationship between TLS derived canopy structure and metrics derived from aerial lidar (ALS). 2m 300002 300001 300005 200108 200209 TLS 2011 ALS 2010 Figure 1. Left: Graphic respresenting the point cloud slicing technique. Right: Image of a terrestrial lidar scanner while collecting scans of canopy. Methods: We used a terrestrial lidar scanner (TLS)(Fig1.) to obtain three dimensional point cloud representations of forest plots for further analysis and extraction of canopy metrics. Please note the methods for scanner field set up, point cloud processing and canopy slicing techniques are described in Factsheet #21. We proceeded to extract canopy metrics from the point clouds by applying the patch metrics to quantify the canopy of each plot (Fig.2). The patch metrics in this case were used to characterize the configuration of individual canopy patches and the distribution and density of those patches on the landscape, which in the case of this study is study plot. In use of patch metrics in describing the forest canopy structure three major components were focused on: metrics describe size and shape, patch composition and the spatial configuration of the canopy patches. Figure 4. The stark differences in point cloud density between ALS and TLS Can simple ALS metrics describe canopy structure in a similar way? After extracting ALS point cloud metrics for the same set of plots we were able to model the canopy structure metrics derived from TLS. The two lidar datasets connect between slices 3 and 4 (middle of the canopy height gradient). The best model that described canopy shape, size and structure utilized the percentage of ALS 3rd and 4th returns. Results: Canopy structure metrics that represent canopy shape, size and distribution varied significantly along the height gradient (Fig. 3). The canopy metrics were significantly correlated with stand parameters such as trees per hectare (TPH), above-ground biomass, stand density index (SDI), basal area, volume and quadratic mean diameter (QMD). Figure 2. Canopy distribution maps for one of the study plots Figure 5. Average point distribution along the height gradient. THE ISSUE: Remote sensing techniques can be used to quantify forest stand level structure. We developed a method that uses terrestrial laser scanning (TLS) to accurately quantify canopy structure. To get at canopy structure at landscape level we investigate the possibility of modeling TLS canopy structure with metrics derived from aerial LiDAR. THE KEY QUESTIONS: Can we calibrate ALS with canopy structure metrics derived from TLS? ⓒ RSGAL 2013 Citation: Kazakova A.N., L.M. Moskal, 2013. Canopy structure from aerial and terrestrial LiDAR. Factsheet # 27. Remote Sensing and Geospatial Application Laboratory, University of Washington, Seattle, WA. Digital version of the fact sheet can be downloaded at: http://dept.washington.edu/rsgal/