High-resolution Satellite Imagery Applications Denis Collins Research Section Vancouver Forest Region.

Slides:



Advertisements
Similar presentations
SCHOOL OF ENVIRONMENT Translating satellite images into meaningful geospatial information: The data fusion approach Mr. Amit A. Kokje PhD candidate, School.
Advertisements

Galina N. Fet Image Processing for Conifer Forest Detection in Tien-Shan Mountains. Marshall University, Department of Physical Sciences.
Oil spill off NW coast of Spain IKONOS image Oil reaching shore.
Image classification in natural scenes: Are a few selective spectral channels sufficient?
New modules of the software package “PHOTOMOD Radar” September 2010, Gaeta, Italy X th International Scientific and Technical Conference From Imagery to.
SDMI Source Imagery Collection Updating Alaska’s image maps Drew Hopwood February 24, 2011.
Use of Remote Sensing and GIS in Agriculture and Related Disciplines
Land Use Change and Effects on Water Quality in the Lake Tahoe Basin: Applications of GIS Christian Raumann Research and Technology Team USGS Western Geographic.
Multispectral Remote Sensing Systems
Remote sensing in meteorology
Modeling Digital Remote Sensing Presented by Rob Snyder.
Introduction, Satellite Imaging. Platforms Used to Acquire Remote Sensing Data Aircraft Low, medium & high altitude Higher level of spatial detail Satellite.
Session 131 Hazard Mapping and Modeling Supporting Emergency Response Operations using GIS and Modeling.
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.
Geosynchronous Orbit A satellite in geosynchronous orbit circles the earth once each day. The time it takes for a satellite to orbit the earth is called.
Aerial photography and satellite imagery as data input GEOG 4103, Feb 20th Adina Racoviteanu.
A.K.M. Saiful Islam Associate Professor, IWFM, BUET December 2010
REMOTE SENSING and AERIAL PHOTOGRAPHY Roger Wheate NREM100 Fall 2010.
Satellite Images. Similar idea as aerial photos Similar idea as aerial photos Have some of the same uses Have some of the same uses Basis for mappingBasis.
Carolyn J. Merry NCRST-Flows The Ohio State University.
Satellite Imagery Data Products & Services November 2011Pacific Geomatics Ltd.1 Farida Raghina Manager, Sales & Customer Relations November 29, 2011.
Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module.
An Object-oriented Classification Approach for Analyzing and Characterizing Urban Landscape at the Parcel Level Weiqi Zhou, Austin Troy& Morgan Grove University.
Copyright © 2003 Leica Geosystems GIS & Mapping, LLC Turning Imagery into Information Suzie Noble, Product Specialist Leica Geosystems Denver, CO.
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
MODIS: Moderate-resolution Imaging Spectroradiometer National-Scale Remote Sensing Imagery for Natural Resource Applications Mark Finco Remote Sensing.
U.S. Department of the Interior U.S. Geological Survey Assessment of Conifer Health in Grand County, Colorado using Remotely Sensed Imagery Chris Cole.
Pollution Monitoring  Defense / Intelligence Planning  Yield Forecasting  Pesticide Applications Transportation Planning  Delivery Routing  Watershed.
Introduction to Remote Sensing. Outline What is remote sensing? The electromagnetic spectrum (EMS) The four resolutions Image Classification Incorporation.
Inventory Presentation to VFR Regional Management Team July 2001 Regional TEM and VRI Status and Issues Arrowsmith TSA Inventory Update Audit IKONOS Satellite.
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.
U.S. Department of the Interior U.S. Geological Survey Multispectral Remote Sensing of Benthic Environments Christopher Moses, Ph.D. Jacobs Technology.
Remotely Sensed Data EMP 580 Fall 2015 Dr. Jim Graham Materials from Sara Hanna.
Resolution A sensor's various resolutions are very important characteristics. These resolution categories include: spatial spectral temporal radiometric.
Data Sources Sources, integration, quality, error, uncertainty.
10/12/2015 GEM Lecture 10 Content Other Satellites.
Robert E. Crippen, Ph.D. NASA Jet Propulsion Laboratory California Institute of Technology Pasadena, California USA
Károly Róbert College The GREEN College. Remote sensing applications in disaster management Tibor Bíró dean Károly Róbert College Faculty of Natural Resources.
Geographic Information Systems in Water Science Unit 4: Module 16, Lecture 3 – Fundamental GIS data types.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Terra Launched December 18, 1999
Fundamentals of Remote Sensing: Digital Image Analysis.
USA Select Briefing to The Federal Geographic Data Committee November 6, 2001 Glenn Geoghegan SPOT Image Corporation Reston, VA
Commercial High Resolution Sensors SatelliteResolutionSwath Width Pricing (%$USD)Applications Quickbird-20.61m Pan 2.44m MS 16.5 kmNew:$22/km 2 (min 64km.
Environmental Remote Sensing GEOG 2021 Lecture 8 Observing platforms & systems and revision.
Remote Sensing and Urban Disaster Management Jie Chang Laurence Clinton 11/02/2006.
Updated Cover Type Map of Cloquet Forestry Center For Continuous Forest Inventory.
Geosynchronous Orbit A satellite in geosynchronous orbit circles the earth once each day. The time it takes for a satellite to orbit the earth is called.
LANDSLIDE INVENTORIES THE KEY TO SEISMIC LANDSLIDE HAZARD ANALYSIS.
Remote Sensing Imagery Types and Sources GIS Management and Implementation GISC 6383 October 27, 2005 Neil K. Basu, Janice M. Jett, Stephen F. Meigs Jr.,
Electro-optical systems Sensor Resolution
Commercial Space-based Synthetic Aperture Radar (SAR) Application to Maritime Domain Awareness John Stastny SPAWAR Systems Center Pacific Phone:
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Satellite Image Pixel Size vs Mapping Scale
Satellite based Sensors for Agricultural Applications
Introduction to Remote Sensing of the Environment Bot/Geog 4111/5111
Tae Young Kim and Myung jin Choi
Hyperspectral Sensing – Imaging Spectroscopy
Satellite Image Pixel Size vs Mapping Scale
Why LiDAR makes hyperspectral imagery more valuable for forest species mapping OLI 2018 Andrew Brenner, Scott Nowicki & Zack Raymer.
This week’s earth observatory: false colour image
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.
CNES’s SPOT 5 (Satellite Pour l’Observation de la Terre)
IKONOS ~Derived from the Greek term eikōn, meaning image~
Planning a Remote Sensing Project
Landsat (same day) data viewer
Worldview-1 The DigitalGlobe constellation of very high resolution
Image fusion Goal: Combine higher spatial information in one band with higher spectral information in another dataset to create ‘synthetic’ higher resolution.
Presentation transcript:

High-resolution Satellite Imagery Applications Denis Collins Research Section Vancouver Forest Region

Project Goals Determine feasibility & effectiveness of locating/mapping resource features using new satellite data Utility of fusion with radar for winter monitoring Linkage with change detection projects

Team/Resources Collaboration between –Research –LIM –Interfor –DSI –FRBC –Inventory branch

Description Acquired 1m b/w and 4m MSS IKONOS imagery 3 different Radarsat-1 images Geocoded images Visual interpretation using Natural, NIR, and PCA analysis FOR MORE INFO...

Radarsat-1/IKONOS coverage Radarsat-1 F1 scene covers 50 x 50 km IKONOS scene covers 55 km 2. AOI Escalante and Hesquiat watersheds

Radarsat-1 image Large landslides difficult to differentiate

Radarsat-1 Findings: –weather independent but requires expert knowledge not available throughout organization –resolution not sufficient for mapping large landslides

Ikonos specs Revisit schedule varies from 1-3 days 11 km swath width Orbits at altitude of 680 km

IKONOS Strengths: –Provides good input for mapping, inventorying, monitoring, surveying –Information captured in digital format plus provides NIR and spectral information –Allows digital processing/interpretation –11 bit data better for shadowed areas Weaknesses –Cost –Not stereo (Quickbird will have capability) –Cloud cover

DEM capability

Vegetation monitoring MSS provides spectral information in visible and NIR parts of electromagnetic spectrum Differences in reflectance denote different vegetation –green-up –free growing –Forest health

Vegetation

Ikonos - RGB image

Ikonos Imagery Examples NIR image of landslide area –revegetation –landslide classification –incipient landslides

Predictive mapping

Tenure Mapping

NIR for NAR of VR block

CWD volume estimation

Roads Deactivation monitoring Inventory Infrastructure calculations for input to TSR’s

Accuracy Image overlain with GPS points 1.1 to 11.5 m Some distortion at edges of image

Smearing due to Ortho software

Imagery Cost

Costs Image acquisition cost –Current price = $0.21/ha –$3360/1:20,000 map sheet –Image will have < 20% cloud cover Image processing/orthorectification cost ($1500 for average scene)

Current Status Barkley Sound TFL to TSA conversion project –Very little current data –235 km 2 costing $5272 Quickbird-2 will have stereo capability, 0.61m Pan/2.5m MSS Hyperspectral capabilities with 256 channels eg. Aster

CONCLUSIONS High-resolution satellite imagery is a new tool that can be used cost effectively. For aerial coverage - the sky is no longer the limit !