Spatial Mapping of Forest Aboveground Biomass (ABG) using Remote Sensed Data
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Transcript of Spatial Mapping of Forest Aboveground Biomass (ABG) using Remote Sensed Data
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Bob Pliszka, VP- Operations & Forestry, ImageTree Corporation
Advisor- Dr. Wayne Myers, Professor of Forest Biometrics; Director, Office for Remote Sensing and Spatial Information Services
September 23rd, 2008
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Hardwood forests of East Rockhill township, Bucks County, Pennsylvania
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Significant increase during the last ~15 years in Greenhouse Gas Emissions (GHG) in the US (and globally)
From Environmental Protection Agency website. http://www.epa.gov/climatechange/emissions/downloads/08_Trends.pdf
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Cumlative Change in GHG (US)
GHG (Terragrams)
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Currently there’s an ongoing global effort to reduce GHG levels
In the US, voluntary trading of forest carbon creditso Carbon emitters can purchase “credits”
US working towards a mandatory cap and trade system o Will require an effective means for measuring and
mapping biomass levels
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o Net increase in biomass from year to year is used to calculate CO₂ available for trading
o One such example is the Chicago Climate Exchange (CCX)
o Global system, the Kyoto Protocol, only recognizes afforestation credits
One area of focus is the mapping of forest aboveground biomass (ABG)o ABG provides an input into calculations of CO₂o ABG provides insight into GHG emission levels
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‣Traditional ground based forest inventory methods (Brown et al)
o Translates directly into forest biomass
(Photo taken from the Food and Agricultural Organization of the United Nations Website) http://www.fao.org/
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Low resolution imagery (Hall et al)
o High resolution imagery (Neilson et al)
o (LiDAR) (Bortolot and Wynne, Van Aardt et al, Patenaude et al)
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Combination of traditional ground methods and remote sensed data (Brown et al)
+ = ABG
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Current commercial ABG solutions are:o Can be less cost effective for small woodlots
• Potential lack of incentive for small, non-industrial private owners
o Coarse resolution and targeted for large scale; not always optimal for small ownerships
• High resolution remote sensed data may help….o Readily available and current in certain caseso Provides reliable spatial information
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Spatially map forest biomass in East Rockhill township, Bucks County, Pennsylvaniao Use of both:
Low resolution Landsat imagery (20-30 meter pixel) High resolution NAIP imagery (1 meter pixel)
Compare and contrast the estimated biomass maps from the two datasets
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Remote Sensed Datao Landsat data (30 meter
pixels)o National Agricultural
Imagery Program (NAIP) imagery (1 meter pixel)
Shapefiles and coverages:o East Rockhill township
boundarieso Hydrology layers
Forestry toolso Diameter tapeo Clinometero 10 BAF prism
Misc.o Applicable biomass
equationso USDA FIA Datao Arcview GIS software
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Digitization of Forest BoundariesAcquire Data Field Data Collection
Estimation of BiomassSpatial AnalysisResults
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Digitization of forest stand boundaries (homogenous areas)o Classification (forest vs. non-forest, mature vs.
young, etc.)
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Collection of low intensity tree measurement data
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For every forested standoEstimation of tree volume and biomass (per stand
basis) for both remote sensed data sets
Quantification of differenceso Forested acresoTotal estimated biomassoSpatial distribution of biomass
Creation of Thematic Maps and final write up
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Task ~ Start Date ~ End Date
Acquire all data 9/29/2008 10/03/2008
Delineate forest stands in both data sets 10/03/2008 10/016/2008
Classify forest stands 10/17/2008 10/17/2008
Collect and process ground data 10/17/2008 10/24/2008
Obtain regional volume and biomass equations
10/25/2008 10/29/2008
Calculate volume and biomass for all forested stands
11/01/2008 11/07/2008
Create and analyze raster layers depicting biomass
11/07/2008 11/14/2008
Create thematic maps depicting results 11/15/2008 11/22/2008
Final write up 11/23/2008 12/04/2008
Submission for White Paper 12/05/2008 12/05/2008
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Relative ABG maps for East Rockhill township
Analysis and visualization of estimated ABG differences using high vs. low resolution remote sensed data
Potentially lead to further efforts to develop efficient and cost effective methods for small private woodlot owners