Technology Transfer Ideas from the Private Sector John Paul McTague Rayonier, Inc. NCASI – Biometrics Working Group, Chairman SAF National FIA User Group.

Slides:



Advertisements
Similar presentations
FOREST INVENTORY PREDICTIONS FROM INDIVIDUAL TREE CROWNS - Regression Modeling Within a Sampling Framework Jim Flewelling in association with ImageTree.
Advertisements

U.S. Department of the Interior U.S. Geological Survey USGS/EROS Data Center Global Land Cover Project – Experiences and Research Interests GLC2000-JRC.
Lecture 7 Forestry 3218 Forest Mensuration II Lecture 7 Forest Inventories Avery and Burkhart Chapter 9.
Carbon Information Needed to Support Forest Management Bob Davis, Director Of Planning, Watershed And Air, USDA Forest Service 0.
Centre for Integrated Petroleum Research University of Bergen & Unifob, Norway Walter Wheeler & Simon Buckley Unifob, UiB Bergen, Norway Lidar laser scanning.
FIA Data and Data Gaps Elizabeth LaPoint - NRS FIA Durham, NH June 2011.
The use of airborne laser scanner data (LIDAR) for forest measurement applications Hans-Erik Andersen Precision Forestry Cooperative University of Washington.
FOREST INVENTORY BASED ON INDIVIDUAL TREE CROWNS Jim Flewelling Western Mensurationist Meeting June 18-20, 2006.
Cruising Approaches  Area Based Methods  Tree Based Methods.
Brian S. Keiling Program Head – Forest Management Dabney S.Lancaster Community College.
Imagery for Forest R&D General requirements –Sub-crown spatial resolution –Fine spectral resolution (~10nm FWHM) –Season –Image collection coincident with.
FOR 474: Forest Inventory Plot Level Metrics from Lidar Heights Other Plot Measures Sources of Error Readings: See Website.
ESRM 304 Final Exam Hints The exam will contain up to 7 sections of : Short Answers, Calculations, Definitions, Multiple Choice, and/or True and False.
Forest site in Järvselja, Estonia: Summary of ground-based LAI measurements Tiit Nilson, Tartu Observatory.
Remote sensing is up! Inventory & monitoring Inventory – To describe the current status of forest Landcover / landuse classification Forest structure /
Lecture 17 – Forest remote sensing  Reading assignment:  Ch 4.7, 8.23,  Kane et al., Interpretation and topographic correction of conifer forest.
Compton Tucker, GSFC Sassan Satchi, JPL Jeff Masek, GSFC Rama Nemani, ARC Diane Wickland, HQ Terrestrial Biomass Pilot Product: Estimating Biomass and.
The Potential for Integration of Lidar into FIA Operations Joseph E. Means Forest Science Department Oregon State University Kenneth C. Winterberger PNW.
Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.
Mapping Forest Vegetation Structure in the National Capital Region using LiDAR Data and Analysis Geoff Sanders, Data Manager Mark Lehman, GIS Specialist.
An overview of Lidar remote sensing of forests C. Véga French Institute of Pondicherry.
USDA Forest Service Remote Sensing Applications Center Forest Inventory and Analysis New Technology How is FIA integrating new technological developments.
Introduction OBJECTIVES  To develop proxies for canopy cover and canopy closure based on discrete-return LiDAR data.  To determine whether there is a.
The Value of FIA Remeasurement Data Paul Van Deusen NCASI Sacramento, CA March 9, 2011.
ESTIMATING WOODY BROWSE ABUNDANCE IN REGENERATING CLEARCUTS USING AERIAL IMAGERY Shawn M. Crimmins, Alison R. Mynsberge, Timothy A. Warner.
A Decision Support Demonstrator for Abiotic Damage to Trees, using a WWW Interface Ari Talkkari Roger Dunham.
Using the SINK Hydrologic Function to Identify Treetops in Small-Footprint Multiple-Return Lidar Data Jason Stoker Lidar Remote Sensing Scientist USGS.
MMS/MLS – Mobile Mapping and Mobile Laser Scanning System 4th ISPRS SC and WG VI/5 Summer School, Warsaw 2009.
Quantitative Estimates of Biomass and Forest Structure in Coastal Temperate Rainforests Derived from Multi-return Airborne Lidar Marc G. Kramer 1 and Michael.
Planning for Inventory & Monitoring Chip Scott National Inventory & Monitoring Applications Center (FIA-NIMAC) Northern Research Station U.S. Forest Service.
Assessing Urban Forests Top-down Bottom-up. Assessing Urban Forests Top-down Produces good cover estimates Can detail and map tree and other cover locations.
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.
How should forest landowners estimate carbon growth and yield from FIA data? Paul Van Deusen NCASI New Orleans March 3,2009 Le Pavillon Hotel.
Spatial Analysis of Large Tree Distribution of FIA Plots on the Lassen National Forest Tom Gaman, East-West Forestry Associates, Inc Kevin Casey, USDA-FS.
__________. 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.
LIDAR Technology Everett Hinkley USDA Forest Service Geospatial Management Office Prepared for Congressman Allan Mollahan's Office.
NCASI -Biometrics Working Group Meeting Le Pavillon Hotel New Orleans, LA March 4, 2009.
February 25-26, 2003 San Diego, California Inventory & Monitoring Technology Development USDA Forest Service, Remote Sensing Application Center,
AN IMPROVED VOLUME, BIOMASS, AND CARBON DATABASE FOR U.S. TREE SPECIES James A. Westfall U.S. Forest Service Forest Inventory and Analysis.
The Effects of Spatial Patterns on Canopy Cover Estimated by FVS (Forest Vegetation Simulator) A Thesis Defense by Treg Christopher Committee Members:
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.
Airborne LiDAR requires purchase, but offers a number of advantages; Airborne LiDAR requires purchase, but offers a number of advantages; Spatial resolution.
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.
Forest ecological applications of ALS ALS provide 3D information where each point has height (and intensity) value Even with low pulse density data, say,
Counting the trees in the forest
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 # 27 Canopy Structure From Aerial and Terrestrial LiDAR
A LiDAR Processing Toolkit
Factsheet # 17 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Estimating Tree Species Diversity.
PADMA ALEKHYA V V L, SURAJ REDDY R, RAJASHEKAR G & JHA C S
Other Cruise Methods.
Tools for individual tree analysis
Factsheet # 12 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Land use/land cover (LULC) from high-resolution.
LiDAR and Habitat Identification
Factsheet # 2 Leaf Area Index (LAI) from Aerial & Terrestrial LiDAR
REMOTE SENSING & GEOSPATIAL ANALYSIS LABORATORY
Light Detection & Ranging (LiDAR) – Enhanced Forest Inventory
Factsheet # 21 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Quantifying Vertical and Horizontal.
Precision Forestry Cooperative Lidar Projects
Why LiDAR makes hyperspectral imagery more valuable for forest species mapping OLI 2018 Andrew Brenner, Scott Nowicki & Zack Raymer.
National Forest Inventory for Great Britain
Big data for Global Change Ecology (Biogeography)
Presentation transcript:

Technology Transfer Ideas from the Private Sector John Paul McTague Rayonier, Inc. NCASI – Biometrics Working Group, Chairman SAF National FIA User Group Meeting March 9, 2011 Sacramento, CA

Three goals of Precision Forestry and individual stem mapping  Using geospatial information to assist forest management and planning  Site specific silvicultural operations (site prep, regeneration, stand mgt)  Efficient harvesting practices (merchandizing and transportation systems)

Auburn Univ. is researching new techniques to create tree volume maps based on ground- based and remote sensing LiDAR data

Value map based on sensor measurements mounted on a tree feller

The UW Precision Forestry Cooperative states that ‘spatial & temporal resolution (of FIA plots) is inadequate for answering important questions’

Remote sensing LiDAR and stem mapping of Precision Forestry Coop

What are the problems in matching individual tree crowns of remote sensing data and ground-based stem maps? Individual stand on LiDAR image after tree polygon creation. A polygon now surrounds every visible tree crown. Sample Frame - Ground or Map? (rarely will the two coincide)

As reported by J. Flewelling in 2006, 81% of the individual tree crowns are the same as interpreted crowns

Ground-based mapping with 3-D laser scanning

Ground-based mapping with 3-D laser scanning (New Zealand and Ireland) The link between these two operations is not widely divulged

Murphy (2008) reports that hidden trees and false positive trees equal ≈ 12% from a single point Scanners cost $40,000 and can complete a 360° sweep in 80 sec.

The stem measurements are not error free Arc centers are estimated The 10 th percentile of the cloud data estimates upper-stem dob well

Other ground-based mapping approaches

Other ground-based mapping approaches (photographs in New Brunswick)

Other ground-based mapping approaches (Haglöf Postex sonic measurements) Can be used in stands with dense understory vegetation

Interior West – FIA has experience with stem mapping

The inference appears limited to estimates of canopy cover

Terrestrial laser scanning is expensive; and reliance upon aerial LiDAR is still imperative There is tremendous room for improvement in the matching algorithms and techniques

Potential role for FIA plots? Create a pilot project to perfect the techniques of matching ground and aerial LiDAR Stem mapped FIA plots

Create value contour maps at macro level (county – state) Stem maps and better merchandising into multiple products can improve values by $160/acre

Role of terrestrial laser scanning  Equipment similar to the FARO scanner is probably not needed for FIA plots  A single upper-stem measurement however could be used to localize a taper function for each measured tree, improving immensely the ability to merchandize and correctly value a tree into multiple products