Imagery for Forest R&D General requirements –Sub-crown spatial resolution –Fine spectral resolution (~10nm FWHM) –Season –Image collection coincident with.

Slides:



Advertisements
Similar presentations
Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Satellite Remote Sensing for Land-Use/Land-Cover.
Advertisements

Remote sensing, promising tool of the future Mária Szomolányi Ritvayné – Gabriella Frombach VITUKI CONSULT MOKKA Conference, June
Ann Johnson Associate Director
Remote Sensing GIS/Remote Sensing Workshop June 6, 2013.
U.S. Department of the Interior U.S. Geological Survey USGS/EROS Data Center Global Land Cover Project – Experiences and Research Interests GLC2000-JRC.
Scaling Biomass Measurements for Examining MODIS Derived Vegetation Products Matthew C. Reeves and Maosheng Zhao Numerical Terradynamic Simulation Group.
Hyperspectral Image Acquisition and Analysis PECORA 15 Workshop 7 Airborne Remote Sensing: A Fast-track Approach to NEPA Streamlining for Transportation.
Toward Near Real Time Forest Fire Monitoring in Thailand Honda Kiyoshi and Veerachai Tanpipat Space Technology Applications and Research, School of Advanced.
Carbon dynamics at the hillslope and catchment scale Greg Hancock 1, Jetse Kalma 1, Jeff McDonnell 2, Cristina Martinez 1, Barry Jacobs 1, Tony Wells 1.
Resolution Resolving power Measuring of the ability of a sensor to distinguish between signals that are spatially near or spectrally similar.
Bushfire CRC Grassland Curing Project Ian Grant Bureau of Meteorology.
Technology Transfer Ideas from the Private Sector John Paul McTague Rayonier, Inc. NCASI – Biometrics Working Group, Chairman SAF National FIA User Group.
Brian S. Keiling Program Head – Forest Management Dabney S.Lancaster Community College.
Modeling Digital Remote Sensing Presented by Rob Snyder.
Prevention - Containment - Safety Unmanned Aerial System Support to the Pikes Peak Wildfire Protection Partners.
Remote Sensing What can we do with it?. The early years.
o What were we looking at? o The Pit Crew studied soil patterns throughout the landscape.
Remote sensing is up! Inventory & monitoring Inventory – To describe the current status of forest Landcover / landuse classification Forest structure /
Remote Sensing Part 1.
Meteorological satellites – National Oceanographic and Atmospheric Administration (NOAA)-Polar Orbiting Environmental Satellite (POES) Orbital characteristics.
Near surface spectral measurements of the land surface Heidi Steltzer Plant and Ecosystem Ecologist Natural Resource Ecology.
Introduction to Digital Data and Imagery
Thank you 2 3D Elevation Program 3D data include surface elevations and natural and constructed features 3DEP increases the quality level of lidar being.
Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
AGRO 500 Special Topics in Agronomy Remote Sensing Use in Agriculture and Forestry Lecture 6 Biomass Estimation Junming Wang Department of Plant and.
U.S. Department of the Interior U.S. Geological Survey Assessment of Conifer Health in Grand County, Colorado using Remotely Sensed Imagery Chris Cole.
Introduction to Remote Sensing. Outline What is remote sensing? The electromagnetic spectrum (EMS) The four resolutions Image Classification Incorporation.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Assessment of Regional Vegetation Productivity: Using NDVI Temporal Profile Metrics Background NOAA satellite AVHRR data archive NDVI temporal profile.
Quantitative Estimates of Biomass and Forest Structure in Coastal Temperate Rainforests Derived from Multi-return Airborne Lidar Marc G. Kramer 1 and Michael.
The role of remote sensing in Climate Change Mitigation and Adaptation.
Resolution A sensor's various resolutions are very important characteristics. These resolution categories include: spatial spectral temporal radiometric.
Fuzzy Entropy based feature selection for classification of hyperspectral data Mahesh Pal Department of Civil Engineering National Institute of Technology.
Chapter 5 Remote Sensing Crop Science 6 Fall 2004 October 22, 2004.
West Hills College Farm of the Future. West Hills College Farm of the Future Precision Agriculture – Lesson 4 Remote Sensing A group of techniques for.
Remote Sensing. Vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including.
Remote Sensing Introduction to light and color. What is remote sensing? Introduction to satellite imagery. 5 resolutions of satellite imagery. Satellite.
Károly Róbert College The GREEN College. Remote sensing applications in disaster management Tibor Bíró dean Károly Róbert College Faculty of Natural Resources.
© July 2011 Linear and Nonlinear Imaging Spectrometer Denoising Algorithms Assessed Through Chemistry Estimation David G. Goodenough 1,2, Geoffrey S. Quinn.
The Use of Red and Green Reflectance in the Calculation of NDVI for Wheat, Bermudagrass, and Corn Robert W. Mullen SOIL 4213 Robert W. Mullen SOIL 4213.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Systematic Terrestrial Observations: a Case for Carbon René Gommes with C. He, J. Hielkema, P. Reichert and J. Tschirley FAO/SDRN.
USGS - California Fire Response -Hyperspectral Remote Sensing
Beyond Spectral and Spatial data: Exploring other domains of information: 4 GEOG3010 Remote Sensing and Image Processing Lewis RSU.
Measuring Vegetation Characteristics
Understory Growth Dynamics following High Severity Burn in a Mixed-Conifer Forest Daniel Wilcox, Space Grant Intern Dr. Shirley A. Papuga, Faculty Advisor.
A Remote Sensing Sampler. Typical reflectance spectra Remote Sensing Applications Consultants -
Flow prediction accuracy given DEM resolution  Model accuracy for snow-rain transition watersheds was more sensitive to DEM resolution than for snow-dominated.
EPA HWI Comments on CA Assessment June 26, 2013 HSP Call 2 major categories of comments: – Report writing (we will work on this) – Content/Analysis/Discussion.
Figure 1. (A) Evapotranspiration (ET) in the equatorial Santarém forest: observed (mean ± SD across years of eddy fluxes, K67 site, blue shaded.
A Remote Sensing Approach for Estimating Regional Scale Surface Moisture Luke J. Marzen Associate Professor of Geography Auburn University Co-Director.
Data Models, Pixels, and Satellite Bands. Understand the differences between raster and vector data. What are digital numbers (DNs) and what do they.
High Spatial Resolution Land Cover Development for the Coastal United States Eric Morris (Presenter) Chris Robinson The Baldwin Group at NOAA Office for.
Landsat Satellite Data. 1 LSOS (1-ha) 9 Intensive Study Areas (1km x 1km) 3 Meso-cell Study Areas (25km x 25km) 1 Small Regional Study Area (1.5 o x 2.5.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
26. Classification Accuracy Assessment
IFSAR and terrestrial LIDAR for vegetation study in Sonora, Texas
Introduction to Remote Sensing of the Environment Bot/Geog 4111/5111
Using vegetation indices (NDVI) to study vegetation
Factsheet # 19 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Hyperspectral Remote Sensing of Urban.
Light Detection & Ranging (LiDAR) – Enhanced Forest Inventory
Why LiDAR makes hyperspectral imagery more valuable for forest species mapping OLI 2018 Andrew Brenner, Scott Nowicki & Zack Raymer.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Paulo Gonçalves1 Hugo Carrão2 André Pinheiro2 Mário Caetano2
By: Paul A. Pellissier, Scott V. Ollinger, Lucie C. Lepine
Planning a Remote Sensing Project
Resolution.
Sources of Variability in Canopy Spectra and the Convergent Properties of Plants Funding From: S.V. Ollinger, L. Lepine, H. Wicklein, F. Sullivan, M. Day.
Presentation transcript:

Imagery for Forest R&D General requirements –Sub-crown spatial resolution –Fine spectral resolution (~10nm FWHM) –Season –Image collection coincident with field data collection –Accurate ground control and radiometric corrections

Potential NAFE Projects Three objectives: 1.Forest water use 2.Productivity prediction – catchment-scale soil water holding capacity 3.The condition of crowns that are severely affected by agents that reduce crown visibility in images

Forestry: Water Use Forest water use an important issue –Post fire –Pasture to plantations –Effects on streamflow/groundwater Canopy transpiration rate High resolution thermal imagery - midday Field site near Mt.Gambier (SA) –Blue gum (E.globulus) –Calibrate models to sap flow sensor data Calibration of imagery?

Forestry: Productivity Prediction Plantation productivity prediction at catchment scale –Current work focuses on recharge and discharge areas, break of slope –Maximum benefit requires understanding of catchment- specific hydrological processes Images to calibrate/validate catchment-scale soil water holding capacity prediction models –Point samples and temporal prediction Imagery requirements – moderate resolution radar? Subcatchments of the S.W. Goulburn-Broken

Forestry: Invisible Crowns Several damaging agents increase crown transparency Essigella californica aphid defoliates crowns –Reduces crown visibility Increased light penetration causes higher vigour of understorey vegetation –Pixels over the most affected trees have high NIR High resolution lidar and hyperspectral imagery Field study area in Carabost, southern NSW (P.radiata).