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Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho www.cnr.uidaho.edu/remotesensing

Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

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Page 1: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Digital Data Sources For Family Forests

Paul GesslerRemote Sensing & GIS Research Laboratory

College of Natural Resources, University of Idahowww.cnr.uidaho.edu/remotesensing

Page 2: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Talk Outline:• Geographic Information Systems (GIS)

1. Definitions, tools2. Software packages3. Digital data sources4. Training courses

• Global Positioning Systems (GPS)(generating your own digital data)

• Images – Aerial and Satellite, LIDAR(purchasing cooperatives)

• Web-based Mapping and Data Management

Page 3: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Talk Outline:• Geographic Information Systems (GIS)

1. Definitions, tools2. Software packages3. Digital data sources4. Training courses

• Global Positioning Systems (GPS)(generating your own digital data)

• Images – Aerial and Satellite, LIDAR(purchasing cooperatives)

• Web-based Mapping and Data Management

Page 4: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

A Geographic Information System (GIS) is defined as:

A system for capturing, storing, checking, integrating, manipulating, analyzing, and displaying data which are spatially referenced to the Earth.

Computer & software

Page 5: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Why Use GIS?• Improve organizational integration

• Make better decisions, analyze landscape trends & patterns

• Make maps

GIS tools provide the “big picture” about the resources under your care and assist you in developing long-term supply strategies, forecasting silvicultural stock, determining harvesting system options, where to plant certain species for optimal returns etc.

Page 6: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 7: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Landscape Structure

Georeferenced 3-D Visualization

Page 8: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Rick LovellSpatialTec

Page 9: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Where to start? • Basic hardware, software: $1350 - $10,000

• Expertise training: short courses

• Private Contractors: provide hardcopy maps, digital data, Internet Map Services (IMS)

www.mines.uidaho.edu/geography/ - UI GIS Certificate Program

www.gis.com – ESRI (Arc/Info, ArcGIS, ArcView…)

www.consulting-foresters.com - Northwest Management, private company

www.digitalgrove.net - Private site with lots of useful resources

www.digitshare.org - Sentry Dynamics, private company located at UI Research Park in Post Falls

www.cnr.uidaho.edu/rsgis/education.htm - UI College of Natural Resources

www.cnr.uidaho.edu/remotesensing - UI Remote Sensing & GIS Research Lab

Page 10: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Software Packages – many!

Page 11: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Where to get data?

Many places: local, state, federal government agencies, Universities…

inside.idaho.edu

wagda.lib.washington.edu/

www.ecy.wa.gov/services/gis/data/data.htm

www.wsdot.wa.gov/mapsdata/geodatacatalog/default.htm

www.dnr.wa.gov/dataandmaps/

Page 12: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Talk Outline:• Geographic Information Systems (GIS)

1. Definitions, tools2. Software packages3. Digital data sources4. Training courses

• Global Positioning Systems (GPS)(generating your own digital data)

• Images – Aerial and Satellite, LIDAR(purchasing cooperatives)

• Web-based Mapping and Data Management

Page 13: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

GPS - Global Positioning Systems

$20 billion US Taxpayer Investment

Page 14: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

GPS Equipment - Receivers

Mounted on a roof, a pole, a truck, or a person.GPS Equipment - Antennas

Price range:

$150 - $25,000

Page 15: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

GPS references

www.trimble.com

www.aero.org/publications/GPSPRIMER/

www.colorado.edu/geography/gcraft/notes/gps/gps_f.html

Page 16: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Laser Rangefinders

• Distance and Height Measurement

• GPS Offset Mapping & GIS Data Capture (many units interface directly with GPS through built-in RS232 interfaces)

• Timber Cruising • GIS data capture• Wildlife locations

Wireless integration w/GPS & use with data dictionaries

Page 17: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Basic Features to look for:

• Level of accuracy required: differential corrections needed?

• Integrated beacon: FM, or Omnistar…

• Integrated WAAS/EGNOS/GLONASS capability

• Data storage capacity, logging (RTCM, NMEA)

• Multipath rejection – Everest

• Data dictionaries

• Wireless integration

• Weight, battery life, vehicle kit

Page 18: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 19: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 20: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Talk Outline:• Geographic Information Systems (GIS)

1. Definitions, tools2. Software packages3. Digital data sources4. Training courses

• Global Positioning Systems (GPS)(generating your own digital data)

• Images – Aerial and Satellite, LIDAR(purchasing cooperatives)

• Web-based Mapping and Data Management

Page 21: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Traditional film-based aerial image capture will shortly become a historical technology – replaced by

digital cameras

Reasons: resolution, digital format convenience, inexpensive good quality printers, satellite capabilities

Page 22: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 23: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 24: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

• Aerial photo/image is NOT A MAP! – distortions due to camera lens, aircraft movement,

topography

• Various levels of geometric correction and georeferencing

• If desire is to use for mapping, seek ortho-rectified images – advantages to satellite based

images

• Ortho-rectified means corrections for all distortions – tools that warp images using a few control points are NOT ortho-rectified – requires digital elevation model and more sophisticated photogrammetry software

Key points/issues to be aware of:

Page 25: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Some providers:

http://www.cnr.uidaho.edu/remotesensing/rs_courses/for375/375.htm

www.asprs.org - American Society for Photogrammetry and Remote Sensing

http://www.possys.com/

http://edcsns17.cr.usgs.gov/EarthExplorer/ - USGS Earth Explorer

http://www.nima.mil/ - National Geospatial Intelligence Agency

Many value-added image products are being developed and more are on the horizon

Page 26: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Forest Health/Status Index Pilot StudyPotlatch Corp and Forest Capital – initial end user pilot study

Page 27: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 28: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Normalized Difference Vegetation Index: NDVI = NIR + R

NIR - R

Page 29: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 30: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Image purchasing cooperatives:

Potlatch Corp., County Govt., AVISTA, Coeur d’Alene Tribe

Page 31: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

LIght Detection And Ranging (LIDAR)LIght Detection And Ranging (LIDAR)

• 11stst developed in 1960 by Hughes developed in 1960 by Hughes Aircraft inc.Aircraft inc.

• Modern computers and DGPS make Modern computers and DGPS make it practical: requires extremely it practical: requires extremely accurate timingaccurate timing

• Measures distance to surfaces by Measures distance to surfaces by timing a laser pulse and it’s timing a laser pulse and it’s corresponding return(s)corresponding return(s)

• Lidar data are the X, Y, Z positions of Lidar data are the X, Y, Z positions of each returneach return

•Typically used in very accurate Typically used in very accurate mapping of topographymapping of topography

Page 32: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Discrete return lidar: Millions of X,Y,Z pointsDiscrete return lidar: Millions of X,Y,Z points

Area of 1.0 X 0.75mi. includes Area of 1.0 X 0.75mi. includes ~ 440,000 returns (canopy)~ 440,000 returns (canopy)

Page 33: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Lidar terrain modelingLidar terrain modeling

Page 34: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Lidar captures 3D informationLidar captures 3D information

Page 35: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Talk Outline:• Geographic Information Systems (GIS)

1. Definitions, tools2. Software packages3. Digital data sources4. Training courses

• Global Positioning Systems (GPS)(generating your own digital data)

• Images – Aerial and Satellite, LIDAR(purchasing cooperatives)

• Web-based Mapping and Data Management

Page 36: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 37: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 38: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 39: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

SUMMARY

• Many digital data sources and tools – touched on several (GIS, GPS, Imagery, Web-based)

• Should private landowners invest in these tools? Web mapping is on the horizon, subscription based – will require high speed internet connections

• Value added products are becoming available (fire fuels, forest health/status index…)

• Many training opportunities exist – some online

• New mapping technologies on the horizon for assessing forests

Lidar, value-added image products – forest health

Page 40: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 41: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho
Page 42: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Using spatial 3-PG to predict carbon sequestration over the Priest River

Experimental Forest

Page 43: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Empirical vs. Process models:

• empirical - based on statistical relationships, predicting outcomes (i.e. Predicting forest growth based on past growth)

• process - based on processes, understanding relationships(i.e. Predicting forest growth based on physiological processes)

Process models can incorporate empirical models

3-PG (Physiological Principles Predicting Growth) is forest growth process model based upon physiological processes

Page 44: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

Structure and Causal InfluencesP AR

Interc eptedP AR

G P P

N P P

LAI

P hys M od

F R T av

Res piration

VP D

S LA

Rain

S oilw ater

In terc eption Evaporation

Conduc -tanc e

+

+

++

+

+

+

+

+

+

Water balance Canopy production

Rootbiom as

F oliagebiom as s

Above groundw oody biom as s

D BH

pF S

s temvolum e

w ooddens ity

branc h &bark ratio

LitterfallRootturnover

+ +

+

+

+

M ortality

S tem m as s S toc king

+ +

+ B iomass partitioning

Stem mortalityV olume growth

Input data

S tate var iable

In ternal var iable

F ixed param eter

Explic itly tim e-dependentparam eter

Caus al inf luenc e:+ = causes increase = causes decrease = has optimum

P hys ic al f low w ith rate

M e a n in g o f e le m e n ts

Page 45: Digital Data Sources For Family Forests Paul Gessler Remote Sensing & GIS Research Laboratory College of Natural Resources, University of Idaho

3-PGS Final Outputs

100 yrs without alteration

200 yrs without alteration

100 yrs, 20% reduction ASW

100 yrs, 40% reduction ASW

Maximum Total Biomass

200 yrs without alteration

100 yrs, 20% reduction ASW

100 yrs, 40% reduction ASW

100 yrs without alteration

Maximum Total Biomass Allocated to Highest Value Species

ABGRABGR ABLAABLA PIMOPIMO TSHETSHE THPLTHPL Grand Fir Subalpine Fir Western White Pine Western Hemlock Western Red Cedar