CALIFORNIA 

ENERGY 

COMMISSION 

BASELINE GREENHOUSE GAS 

EMISSIONS AND REMOVALS FOR FOREST 

AND AGRICULTURAL LANDS IN OREGON 

PIER

COLLABORATIVE 

REPORT

                                      

November 2007 

CEC-500-2007-025 

California Climate Change Center 

Report Series Number 2007-009 

Prepared By: 

Winrock International 

Ecosystems Services Unit 

Sandra Brown and John Kadyszewski 

Arlington, Virginia 22209 

Commission Contract No. 500-02-004 

Commission Work Authorization No. 021

Prepared For: 

Public Interest Energy Research (PIER) Program 

California Energy Commission 

Guido Franco 

Project Manager 

Beth Chambers 

Contract Manager 

Kelly Birkinshaw 

Program Area Lead 

Energy-Related Environmental Research 

Laurie ten Hope 

Office Manager 

Energy Systems Research 

Martha Krebs 

Deputy Director 

ENERGY RESEARCH & DEVELOPMENT 

DIVISION 

Melissa Jones 

Executive Director 

DISCLAIMER 

This report was prepared as the result of work sponsored by the California Energy Commission. It does not necessarily represent

the views of the Energy Commission, its employees or the State of California. The Energy Commission, the State of California, its 

employees, contractors and subcontractors make no warrant, express or implied, and assume no legal liability for the information 

in this report; nor does any party represent that the uses of this information will not infringe upon privately owned rights. This 

report has not been approved or disapproved by the California Energy Commission nor has the California Energy Commission 

passed upon the accuracy or adequacy of the information in this report. 

i

Acknowledgments 

We thank Mark Tilton for access and advice on National Resources Inventory data.  Scott Sweet

provided preliminary analysis on subject of the fire baseline.   Sarah Walker, Melanie Lenart,

Mark Tilton, Bruce Franssen, Kristina Vogt, and Daniel Vogt provided helpful comments on a

draft of the report.

Several faculty and extension specialists of the Oregon State University (OSU) system provided

valuable information to derive the estimates of biomass for Pacific Northwest agriculture and

horticulture used in this report. For information on hazelnuts, we wish to thank Dr. Shawn

Mehlenbacher of the OSU Department of Horticulture and Jeff Olsen, extension horticulturalist,

OSU Extension Service; for pears, Steve Castagnoli, extension horticulturalist for the OSU Hood

River County Extension Service; for cherries, Jeff Olsen and Lynn E. Long, extension

horticulturist, OSU Extension Service; for apples, Tom Darnell, horticulturalist/county agent for

the OSU Extension Service; for berry crops, Dr. Bernadine Strik, OSU Department of

Horticulture; for viticulture, Anne Connelly, OSU Horticulture/Viticulture Extension; and Paul

Schreiner, research plant physiologist, USDA‐Agricultural Research Service. Dr. Les Fuchigami,

Dr. Russell Karow, Dr. Anita Azarenko, and Dr. Gail Achterman, also of OSU, also provided

initial guidance.

This research was co‐funded by the U.S. Department of Energy under contract no. DE‐FC26‐

03NT41984.

Please cite this report as follows:  

Pearson, Timothy, Sandra Brown, Nicholas Martin, Sebastián Martinuzzi, Silvia Petrova, Ian

Monroe, Sean Grimland, and Aaron Dushku. 2007. Baseline Greenhouse Gas Emissions and

Removals for Forest and Agricultural Lands in Oregon. California Energy Commission, PIER

Energy‐Related Environmental Research Program. CEC‐500‐2007‐025.  
ii
iii

Preface 

The Public Interest Energy Research (PIER) Program supports public interest energy research

and development that will help improve the quality of life in California by bringing

environmentally safe, affordable, and reliable energy services and products to the marketplace.

The PIER Program, managed by the California Energy Commission (Energy Commission),

conducts public interest research, development, and demonstration (RD&D) projects to benefit

California’s electricity and natural gas ratepayers. The PIER Program strives to conduct the

most promising public interest energy research by partnering with RD&D entities, including

individuals, businesses, utilities, and public or private research institutions.

PIER funding efforts are focused on the following RD&D program areas:

• Buildings End‐Use Energy Efficiency

• Energy‐Related Environmental Research

• Energy Systems Integration  

• Environmentally Preferred Advanced Generation

• Industrial/Agricultural/Water End‐Use Energy Efficiency

• Renewable Energy Technologies

• Transportation

In 2003, the California Energy Commission’s Public Interest Energy Research (PIER) Program

established the California Climate Change Center to document climate change research

relevant to the states. This Center is a virtual organization with core research activities at

Scripps Institution of Oceanography and the University of California, Berkeley, complemented

by efforts at other research institutions. Priority research areas defined in PIER’s five‐year

Climate Change Research Plan are: monitoring, analysis, and modeling of climate; analysis of

options to reduce greenhouse gas emissions; assessment of physical impacts and of adaptation

strategies; and analysis of the economic consequences of both climate change impacts and the

efforts designed to reduce emissions.

The California Climate Change Center Report Series details ongoing Center‐sponsored

research. As interim project results, the information contained in these reports may change;

authors should be contacted for the most recent project results. By providing ready access to

this timely research, the Center seeks to inform the public and expand dissemination of climate

change information; thereby leveraging collaborative efforts and increasing the benefits of this

research to California’s citizens, environment, and economy.

Baseline Greenhouse Gas Emissions and Removals for Forest and Agricultural Lands in Oregon is a

final report for the West Coast Regional Carbon Sequestration Partnership project (contract

number 500‐02‐004, work authorization 021), conducted by Winrock International. The

information from this project contributes to PIER’s Energy‐Related Environmental Research

Program.
iv

  

For more information on the PIER Program, please visit the Energy Commission’s website

www.energy.ca.gov/pier/ or contract the Energy Commission at (916) 654‐5164.
v

Table of Contents 

Preface.................................................................................................................................................. iii

Abstract ............................................................................................................................................... xi

Executive Summary........................................................................................................................... 1

1.0 Introduction and Background Information ..................................................................... 7

1.1. General Approach.......................................................................................................... 7

1.2. Datasets Used in the Analysis ...................................................................................... 7

1.2.1. The National Resources Inventory ......................................................................... 7

1.2.2. The Forest Inventory and Analysis Database ....................................................... 8

1.3. Geographical Subdivision of the State ........................................................................ 8

2.0 Baselines for Forest Lands in Oregon............................................................................... 11

2.1. Introduction .................................................................................................................... 11

2.2. General Forests Baseline................................................................................................ 11

2.2.1. State Level Analysis for All Forestlands................................................................ 11

2.2.2. Changes in Forest Area on Private Land............................................................... 12

2.2.3. Conclusions................................................................................................................ 15

2.3. Development Baseline ................................................................................................... 15

2.3.1. General Approach..................................................................................................... 15

2.3.2. Changes in Area at the State and County Level ................................................... 16

2.3.3. Carbon Stocks ............................................................................................................ 19

2.3.4. Carbon Emissions from Development................................................................... 19

2.3.5. Additional Considerations ...................................................................................... 22

2.3.6. Conclusions................................................................................................................ 23

2.4. Fire Baseline .................................................................................................................... 24

2.4.1. Methods for Assessing Biomass Volatilized ......................................................... 25

2.4.2. Methods for Assessing Area Impacted by Fire and Fire Intensity..................... 29

2.4.3. Results......................................................................................................................... 32

2.4.4. Uncertainties.............................................................................................................. 38

2.4.5. Conclusions................................................................................................................ 39

3.0 Baselines for Agricultural Lands in Oregon.................................................................... 41

3.1. General Approach.......................................................................................................... 41

3.1.1. Classification of Agricultural Land ........................................................................ 41
vi

3.1.2. Limitations of the NRI Database............................................................................. 43

3.1.3. Area and Change in Area of Agricultural Land................................................... 44

3.1.4. Carbon Density of Agricultural Land .................................................................... 44

3.1.5. Uncertainty ................................................................................................................ 46

3.2. Results.............................................................................................................................. 47

3.2.1. Statewide Land Use and Land Use Change 1987–1997....................................... 47

3.2.2. Changes in Specific Land‐use Type........................................................................ 49

3.2.3. County‐Level Estimate of Agricultural Land Area.............................................. 52

3.2.4. Carbon Stock of Agricultural Land During 1987–1997........................................ 57

3.2.5. Carbon Stocks of Agricultural Land by County................................................... 59

3.3. Non‐CO2 Greenhouse Gas Emissions.......................................................................... 62

3.4. Conclusions..................................................................................................................... 62

4.0 References............................................................................................................................. 64

5.0 Glossary ................................................................................................................................ 66
vii

List of Figures 

Figure 1‐1. Oregon counties Source: Digital Map Store, http://county‐map.digital‐topo‐

maps.com/oregon.shtml.................................................................................................................... 9

Figure 2‐3‐1. Loss in non‐federal forest area between 1987 and 1997 as a percentage of total non‐

federal forest area in the county..................................................................................................... 17

Figure 2‐3‐2. Carbon emissions under the two scenarios at the county level across the state...... 20

Figure 2‐4‐1. Flow diagram illustrating the various destinations of pre‐burn carbon after a fire 25

Figure 2‐4‐2. Illustration of the mapping methodology.  In (a), the  point location from the state

or federal database is established, a  fire boundary is then created and compared to the fire

area reported   with the point location; in (b), the fire intensity through the burn area   is

calculated using NDVI values........................................................................................................ 32

Figure 2‐4‐3. The location and extent of fires in Oregon between 1990 and 1996........................... 33

Figure 2‐4‐4. Area affected by fire and estimated emissions from  fire across the study period . 34

Figure 2‐4‐5. Area burned (in acres), at the county level, between 1990    and 1996 (excluding

1994) ................................................................................................................................................... 35

Figure 2‐4‐6. Metric tons of carbon dioxide emitted, at the county  level, between 1990 and 1996

(excluding 1994) ............................................................................................................................... 36

Figure 3‐1. Proportional area for land uses in Oregon in 1997, based on  NRI data (non‐federal

lands only)......................................................................................................................................... 47

Figure 3‐2. Proportional change in area between 1987 and  1997 for broad land uses in Oregon48

Figure 3‐3. Proportional coverage of each agricultural land use in Oregon in 1997 ...................... 49

Figure 3‐4. Proportional change in area between 1987 and 1997 for agricultural    land uses in

Oregon ............................................................................................................................................... 50

Figure 3‐5. Distribution of woody (a) and non‐woody (b) cropland by county in Oregon in 1997.

Values indicate the percentage of total land area in each county occupied by agricultural

land..................................................................................................................................................... 53

Figure 3‐6. Distribution of change in area in woody (a) and non‐woody (b) cropland in Oregon

by county. Values indicate change in hectares; minus sign indicates a loss in area from 1987

to 1997, and a plus sign indicates a gain in area in the same period. ....................................... 54

Figure 3‐7. County‐scale change of carbon stocks in Oregon, 1987 to 1997, in (a) high‐carbon

crops (orchards and vineyards, and (b) in low‐carbon (non‐woody) crops. Values in tons of

carbon. ............................................................................................................................................... 60
viii

List of Tables 

Table 1‐1. Five Oregon regions with the component counties detailed............................................. 8

Table 2‐2‐1. Change in area (ha) between 1987 and 1997 for non‐federal  forestland in Oregon. 13

Table 2‐2‐2. Area (ha) of non‐federal forestland in Oregon in 1987 and 1997    and change

between the two dates..................................................................................................................... 14

Table 2‐2‐3. Area (ha) of non‐federal forestland in 1987  and 1997 and the change between the

two dates ........................................................................................................................................... 15

Table 2‐3‐1. Non‐federal forest area (ha) between 1987 and 1997  in Oregon................................. 16

Table 2‐3‐2. County‐level data on area of non‐federal forest in 1997, area of forest    lost to

development between 1987 and 1997, and % of losses that were small‐scale......................... 18

Table 2‐3‐3. Mean above‐ground tree carbon stock (from 1992 FIA data) for each region of

Oregon with the number of plots and the confidence interval around the stock estimate... 19

Table 2‐3‐4. County‐level estimates on the emissions between 1987 and 1997 due to

development.  Scenario 2 is more conservative, assuming that trees are not clear‐cut  during

small‐scale development................................................................................................................. 21

Table 2‐3‐5. Region‐level summary of loss in area and carbon  emissions between 1987 and 1997

due to development.   Scenario 2 is more conservative, assuming that trees are  not clear‐cut

during small‐scale development.................................................................................................... 22

Table 2‐3‐6. Region‐level summary of annual loss in area and    CO2 equivalent emissions

between 1987 and 1997 due to  development.  Scenario 2 is more conservative, assuming

that  trees are not clear‐cut during small‐scale development.................................................... 22

Table 2‐4‐1. Forest types for fire baseline analysis cross‐walked with FIA forest type ................. 27

Table 2‐4‐2. Mean biomass stock by forest type and region .............................................................. 27

Table 2‐4‐3. Mean emissions (in t CO2e/ha) from a high‐,    mid‐, and low‐intensity fire in the

Coastal Region of Oregon ............................................................................................................... 28

Table 2‐4‐4. Mean emissions (in t CO2e/ha) from a high‐,    mid‐, and low‐intensity fire in the

Central Region of Oregon ............................................................................................................... 28

Table 2‐4‐5. Mean emissions (in t CO2e/ha) from a high‐,    mid‐, and low‐intensity fire in the

Eastern Region of Oregon............................................................................................................... 29

Table 2‐4‐5. Estimates of pre‐fire biomass stocks in non‐tree vegetation ........................................ 29
ix

Table 2‐4‐6. Area burned and carbon emissions  in forests and in rangeland across the analysis

period................................................................................................................................................. 33

Table 2‐4‐7. Area burned and carbon emissions  per year across the analysis period................... 34

Table 2‐4‐8. Area burned and carbon emissions by  ownership across the analysis period ......... 34

Table 2‐4‐9. Area burned and carbon emissions per  county across the analysis period .............. 37

Table 2‐4‐10. Relative increase in stocks that would result from adding each of  the additional

carbon pools to live above‐ground trees....................................................................................... 38

Table 3‐1. NRI categories and subcategories in Oregon..................................................................... 42

Table 3‐2. Estimates of the average carbon stock  (t C/ha) for each of the crop types in Oregon. 45

Table 3‐3. The estimated average biomass carbon  accumulation after five years of growth for

fruit and nut  orchards in Oregon (t C/ha) ................................................................................... 46

Table 3‐4. The estimated average biomass carbon    accumulation over 10 years of growth for

fruit and nut  orchards in Oregon (t C/ha).  This growth rate is for existing  orchards—that

is, for areas unaffected by land‐use change.................................................................................. 46

Table 3‐5. Estimated ranges in average carbon stock  for each crop type in Oregon (t C/ha)....... 47

Table 3‐6. Areas (ha) and changes in areas (ha) for land use types in Oregon   from the NRI

dataset................................................................................................................................................ 48

Table 3‐7. Land‐use change transition matrix, showing the source and direction of changes in

Oregon 1987–1997.  A negative sign indicates a net loss of area from the land use in the row

to the land use in the column. ........................................................................................................ 51

Table 3‐8. The county‐level coverage (ha) for specific agricultural land uses and the change in

coverage in Oregon 1987 to 1997 ................................................................................................... 55

Table 3‐9. Carbon stocks (t C) and changes in carbon stocks (t C)  for land use types in Oregon57

Table 3‐10. The land use origins and destinations of changes in carbon stocks in agriculture in

Oregon between 1987 and 1997. A negative sign indicates a net loss of carbon stocks from

the land use in the row to the land use in the column................................................................ 58

Table 3‐11. Carbon stocks on agricultural land in Oregon and their change  (in MMtCO2e) ....... 59

Table 3‐12. Change in carbon stocks (t C) between 1987 and 1997 across crop types in Oregon . 61

Table 3‐13. Summary of agricultural land area and changes in area,  carbon stocks and changes

in stocks, for Oregon 1987–1997..................................................................................................... 63
x
xi

Abstract 

The project described in Baseline Greenhouse Gas Emissions and Removals for Forest and Agricultural

Lands in Oregon sought to quantify the baseline of changes in carbon stocks on forest and

agricultural lands in Oregon for the 1990s. These baselines provide an estimate of the emissions

and removals of greenhouse gases attributable to changes in the use and management of land

and are useful for identifying where major opportunities could exist in Oregon for enhancing

carbon stocks and/or reducing carbon sources to potentially reduce greenhouse gas emissions.  

The analysis revealed that forests were responsible for a net removal of carbon dioxide from the

atmosphere of 27 million metric tons of carbon dioxide per year between 1987 and 2003, and

that agricultural lands were responsible for a net emission of 0.06 million metric tons of carbon

dioxide per year. On non‐federal lands, emissions from forests caused by development were

estimated at 1.4–1.5 million metric tons of carbon dioxide per year, and between 1990 and 1996

(excluding 1994) 0.81 million acres of forest and rangeland were burned by fires, with an

estimated emission of 1.03 million metric tons of carbon dioxide per year. Nitrous oxide and

methane emissions (in carbon dioxide equivalents) from agricultural lands are more than 100

times higher than carbon emissions attributable to land use change.  

Keywords: Carbon sequestration, carbon storage, carbon dioxide, greenhouse gas, emissions,

forest fire, agriculture, Oregon, WESTCARB, Regional Carbon Sequestration Partnership
xii
1

Executive Summary 

Introduction 

This study is one of a series of carbon sequestration research projects conducted by the West

Coast Regional Carbon Sequestration Partnership (WESTCARB), which is managed and

co‐funded by the California Energy Commission.  

Purpose 

The purpose of this WESTCARB project was to derive a carbon sequestration baseline for

Oregon’s forest and agricultural lands.

Project Objective 

This project sought to establish baseline carbon stocks and changes in stocks for the forest and

agricultural sectors in Oregon during the most recent 10‐year period for which data are

available (generally the decade of the 1990s). Such baselines can assist in identifying

opportunities where carbon removals (sequestration) in each sector might be increased, or

carbon emissions decreased, through changes in land use and management.

Project Outcomes 

Baseline for Forest Lands 

The forest baseline is separated into three components: a general forests baseline, a baseline

effect of development, and a baseline effect from fire. The general forests baseline is presented

at the state level for all forest lands, based on U.S. Department of Agriculture (USDA) Forest

Service data, detailing change in forest area and change in carbon stocks, but with no attribution

to the causes for the change. Using additional databases, the specific cases of emissions

associated with development and emissions associated with fire are further examined.  

General Forestlands Baseline

Between 1987 and 2003 there was an estimated increase in forest area in Oregon of 2.1 million

acres (ac) (850,000 hectares, ha), or 94,000 ac per year (ac/yr) (38,000 ha/yr) between 1987 and

1997, and 175,000 ac/yr (71,000 ha/yr) between 1997 and 2003.

This increase is equivalent to an estimated increase of 431 million metric tons carbon dioxide

(CO2) equivalent between 1987 and 2003, or 23.0 million metric tons CO2 equivalent per year

between 1987 and 1997, and 34.4 million metric tons CO2 equivalent per year between 1997 and

2003.
2

The total emissions for Oregon (excluding forests) for 2000 were estimated as 67.7 million metric

tons CO2 equivalent (from the Governor’s Advisory Group on Global Warming). Forest sinks,

therefore, potentially can offset as much as 50 percent of the state’s emissions.  

Baseline Effect of Development on Forest Lands

The baseline for emissions from development on non‐federal lands was estimated using land

use data from the National Resources Inventory of the USDA and carbon data derived from the

USDA Forest Service’s Forest Inventory and Analysis Database, for the period 1987 to 1997.

Because of data limitations the analysis is limited to non‐federal lands and to the gross CO2

emissions from above‐ground live tree biomass on conversion of non‐federal forestland to

developed land uses. Because the focus is on non‐federal lands, the analyses should only be

used to explore decisions on private lands.

Between 1987 and 1997, 69,000 ac (28,000 ha) of non‐federal forest were converted to

development. This is equal to 6,900 ac per year. Large losses were concentrated in the coastal

regions. For gross carbon emissions, two scenarios were considered. Under Scenario 1 all tree

biomass in the converted area was immediately emitted as carbon dioxide. Under Scenario 2 for

developed areas of less than 10 ac (4 ha), it was assumed that 50 percent of the carbon was

retained in the form of residual trees.

Under Scenario 1 an estimated 15.4 million metric tons CO2 equivalent were emitted due to

development, or 1.54 million metric tons CO2 equivalent per year. Under Scenario 2,

13.9 million metric tons CO2 equivalent were emitted, or 1.39 million metric tons CO2 equivalent

per year. Development was concentrated in the northwest region of the state where the major

city of Portland is located. In this region 56 percent of the emissions under Scenario 1 or

58 percent of the emissions under Scenario 2 occurred, although the region represents only 9

percent of the state’s area.

The emissions from development on non‐federal lands of 1.39–1.54 million metric tons CO2

equivalent per year represent about 2.3 percent of the total gross emissions for the state of

63 million metric tons carbon dioxide equivalent per year in 1995 (from Governor’s Advisory

Group on Global Warming). Oregon’s forests have a net sequestration of 22 million metric tons

CO2 equivalent per year between 1987 and 1997, after accounting for the emissions from

development of about 1.4 million metric tons CO2 equivalent per year.  

Baseline Effect of Fire on Forest Lands

The emissions from fire were examined through overlaying the wildfire database for Oregon on

the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High

Resolution Radiometer satellite imagery showing change in normalized differential vegetation

index. (The normalized differential vegetation index measures ”greenness” of landscapes;

greenness decreases immediately after fire). This process determined the location, size, and

intensity of fires between 1990 and 1996 (although 1994 was excluded due to poor image

quality). Carbon values were applied to these fires using data from the Forest Inventory and

Analysis Database and proportional emissions from the detailed baseline fire analysis for
3

Northern California. The analysis considered all forests and rangelands in Arizona, federal and

non‐federal.

Across the six years analyzed, fires with a total area of 328,000 ha (0.81 million ac) were

recorded. This is equivalent to an average 54,700 ha per year (135,100 ac/yr) for the period

studied. Emissions totaling 25.0 million metric tons CO2 equivalent were estimated to have

occurred from fire during the six‐year period. On an average annual basis this is equal to

1.03 million metric tons CO2 equivalent per year.

Forty‐eight percent of the burned area and 83 percent of the emissions were in forest rather than

rangeland. Fire incidence varied by year, with high emissions in 1996 and low emissions in 1993

and 1995. Seventy‐nine percent of area burned, and 83 percent of the emissions were from

federally owned land; 18 percent of the area burned and 13 percent of the emissions were from

private land. Emissions from fire occurred throughout the state but were markedly lower in the

northwest.

The emissions from fire of 1.03 million metric tons CO2 equivalent per year during the six‐year

period represented about 1.6 percent of the total gross emissions for the state of 63 million

metric tons CO2 equivalent per year in 1995, according to the Governor’s Advisory Group on

Global Warming.

Baseline for Agricultural Lands 

Agricultural land area in Oregon amounts to about 6 percent of the total land area. The state

lost agricultural land area from 1987–1997 through conversion to other land uses, in particular

to urban development/transportation and from retiring agricultural land from cultivation. In

some counties, the area of woody cropland actually increased, but these increases were more

than offset by decreases in non‐woody cropland. Accompanying these losses in area were losses

in standing carbon stocks on agricultural land, so that conversion of agricultural land to other

uses was responsible for a net annual source (emission) of CO2 to the atmosphere. Losses of

agricultural carbon stocks over the 1987–1997 period were estimated at 160,000 tons. The

estimated net annual source from Oregon agricultural lands was 0.06 million metric tons CO2

equivalent.  

Emissions of CO2 from agricultural land conversion, while the primary focus of this report,

represent only a portion of the total greenhouse gas emissions attributable to the agricultural

sector. The primary non‐CO2 greenhouse gases associated with agricultural activities are nitrous

oxide (N2O) and methane (CH4). Nitrous oxide is emitted from agricultural soils, especially after

fertilizer application, and has approximately 296 times the global warming potential of CO2.

Methane is emitted by livestock and through manure management, and has approximately 23

times the global warming potential of CO2. Examination of data from Oregon indicated that

greenhouse gas emissions from N2O and CH4 in the agricultural sector dwarf the annual CO2

source from agricultural land conversion: CO2 emissions from land conversion represented

about 1 percent of the total CO2 and non‐CO2 greenhouse gas emissions attributable to the

agricultural sector.
4

Conclusions 

General Forests Baseline 

• An estimated 383,000 ha (946,000 ac) of forest were gained on federal and non‐federal

lands in Oregon between 1987 and 1997 at a rate of 38,326 ha/yr (94,704 ha/yr). These

gains are equivalent to 0.33 percent of the forest area per year between 1987 and 1997

and 0.59 percent of the forest area per year between 1997 and 2003.

• A gross sequestration of an estimated 229.9 million metric tons CO2 equivalent

(23 million metric tons CO2 equivalent per year) occurred between 1987 and 1997, and

204.6 million metric tons CO2 equivalent (34 million metric tons CO2 equivalent per year)

occurred between 1997 and 2003.

• The emissions for Oregon (excluding forests) for 2000 were estimated as 67.7 million

metric tons CO2 equivalent. Sinks, therefore, potentially can offset as much as 50 percent

of the state’s emissions.

• For just non‐federal forested lands, there is a net loss of 5,000 ha (12,000 ac).  

Development Baseline 

• An estimated 28,000 ha (69,000 ac) were lost to development in Oregon State between

1987 and 1997 at a rate of 2,788 ha (6,889 ac) per year. This forest loss is equivalent to a

gross emission of between 13.9 and 15.4 million metric tons CO2 equivalent, or 1.39 to

1.54 million metric tons CO2 equivalent per year.

• The emissions were concentrated in the coastal region and in particular in the

Northwest—the region in which Portland is located.

• These emissions compare with the estimated gross sequestration from forests in Oregon

of 23 million metric tons CO2 equivalent per year between 1987 and 1997 and gross

emissions for the state of 63 million metric tons CO2 equivalent per year in 1995.

Emissions from deforestation therefore offset between 6 percent and 7 percent of the

forest sequestration and represent between 2.2 percent and 2.4 percent of the total

emissions in the state.

Fire Baseline 

• Across the six years analyzed, researchers recorded fires with a total area of 328,000 ha

(0.81 million ac)—equivalent to an average 54,700 ha per year (135,100 ac/yr). Emissions

totaling 6.8 million tons of carbon, or 25.0 million metric tons CO2 equivalent, were

estimated to have occurred from fire during that period. Averaged annually, this is

equal to 0.28 million tons of carbon per year (1.03 million metric tons CO2 equivalent per

year).

• Forty‐eight percent of the burnt area and 83 percent of the emissions were in forest as

opposed to rangeland. Fire incidence varied by year, with high emissions in 1996 and

low emissions in 1993 and 1995.
5

• The emissions from fire of 1.03 million metric tons CO2 equivalent per year during the

six‐year period represented about 1.6 percent of the total gross emissions for the state of

63 million metric tons CO2 equivalent year in 1995.  

Agricultural Baseline 

• In 1997, agricultural land represented 5.9 percent of the total land area. Non‐woody

crops were about 96 percent of all agricultural land. Woody crops are concentrated

primarily west of the Cascades, but in most counties constitute less than 2 percent of the

total land area, while non‐woody crops are concentrated primarily in the northern

counties along the Columbia River and constitute up to 24 percent of the land area.  

• Statewide, there was a loss of agricultural land of 4.8 percent between 1987 and 1997,

coming in approximately equal proportions from conversion of woody and non‐woody

cropland.   

• Total carbon stocks in all agricultural land in Oregon were estimated at 3.2 million tons.

Between 1987 and 1997, there was a total loss of about 160,000 tons of carbon, or

4.8 percent of the carbon stored in agricultural lands in 1987.

• Non‐CO2 greenhouse gas emissions from N2O (emitted from agricultural soils after

fertilizer application) dwarf the annual CO2 source from agricultural land conversion in

Oregon.
6
7

1.0 Introduction and Background Information 

1.1. General Approach 

This baseline document’s purpose is to examine changes in land use and the associated

emissions or sequestration of carbon for forest and agricultural lands in the State of Oregon.  

Separate baseline analyses are included here for forestlands and agricultural lands. The

agricultural land study followed the same principles as the California baseline study (Brown et

al. 2004).  For forestlands, the California baseline study was based on California‐specific

interpreted satellite imagery that detailed the scale of change, vegetation type, and cause of

change.  Because no comparable data is available for Oregon, the research team instead relied

predominantly on two national datasets (see Section 1.2).  The consequence of using generalized

broad‐scale datasets is that the outcome is less certain than that achieved for California.

The forest baseline includes a state‐level analysis on the change in area and carbon stocks in all

forestland, plus a county‐level analysis of changes on non‐federal forestland.  Also included are

specific case studies on emissions due to development and fire.

1.2. Datasets Used in the Analysis 

Two datasets are used repeatedly through the baseline analyses: the National Resources

Inventory (NRI) database and the U.S. Forest Service Forest Inventory and Analysis (FIA)

database.

1.2.1. The National Resources Inventory 

The National Resources Inventory is conducted by the U.S. Department of Agriculture ‐ 

National Resources Conservation Service (NRCS). The NRI is a scientifically designed survey of

the nation’s soil, water, and other related resources with the purpose of assessing conditions

and trends.  The NRI contains data only on non‐federal lands and water bodies.  As noted in the

users’ manual (NRCS 2000), the NRI data are useful in developing estimates of natural resource

conditions and in conducting geospatial and temporal analyses of these conditions (however,

the location of the survey plots is not given in the database). In these baseline analyses, NRI

data were used for estimates of area because NRI data is available across the WESTCARB states,

wide in coverage, and available for multiple points in time and multiple classes of land use.   

Because NRI data come from sample surveys, it is important to have a sufficient sample size for

a reliable estimate.  The NRI users’ manual does not recommend that the data be used for

county‐level analysis because of sample size issues.  To be conservative, here analyses are

reported at the state level.  County‐level results are given for illustrative purposes only.

National Resources Inventory analyses are for the time period 1987 to 1997.  More recently the

NRI has switched to annual reporting, but these data are not yet publicly available.
8

1.2.2. The Forest Inventory and Analysis Database 

Forest biomass was estimated using the U.S. Forest Service Forest Inventory and Analysis

database.  Following Acts of Congress in 1928 and 1974, the USFS has been systematically

collecting data on U.S. forests via the FIA.  

The FIA data is composed of a hierarchy of the following nine tables: SURVEY, COUNTY,

PLOT, SUBPLOT, CONDITION, TREE, SEEDLING, SITETREE, and BOUNDARY. Examples of

plot‐level records include: State, County, Plot number, Owner, Forest type, Stand age, Site

productivity, and Slope.  Examples of tree‐level records include:  State, County, Plot number,

Tree number, Diameter at breast height (DBH), Crown class, Volume, Growth, and Expansion

Factors (which allow extension from values per plot to per acre).  Diameters are included in the

database for all trees with DBH > 1 inch. Creating links between the different hierarchies of the

database and utilizing the expansion factors allows the user to explore a variety of topics related

to biomass stocks in trees.

In this baseline study, data were downloaded from the FIA website on the scale of individual

trees within plots within each county within each state.  Using the biomass regressions of

Jenkins et al. (2003), DBH was converted to biomass for each tree. Area expansion factors (plot

to acre), metric conversions and summation were used to calculate biomass in metric tons per

hectare (ha).  In the fire baseline, forests are consolidated by forest type, which is a plot‐level

characteristic.  

1.3. Geographical Subdivision of the State 

In this forest baseline study, Oregon was subdivided into five regions. These regions were

based on FIA ”units”’ but are convenient due to climatic, topographic, and vegetation

similarities within units (Table 1‐1). Both the forest and agricultural baselines include county‐

level analyses; counties in Oregon are shown in Figure 1‐1.

Table 1-1. Five Oregon regions with the component counties detailed 

Region Counties

Northwest Clackamas, Clatsop, Columbia, Hood River,

Marion, Multnomah, Polk, Tillamook,

Washington, Yamhill

West Central Benton, Lane, Lincoln, Linn

Southwest Coos, Curry, Douglas, Jackson, Josephine

Central Crook, Deschutes, Gilliam, Jefferson,

Klamath, Lake, Sherman, Wasco, Wheeler

Blue Mountains Baker, Grant, Harney, Malheur, Morrow,

Umatilla, Union, Wallowa
9

Figure 1-1. Oregon counties 

Source: Digital Map Store, http://county‐map.digital‐topo‐maps.com/oregon.shtml
10
11

2.0 Baselines for Forest Lands in Oregon 

2.1. Introduction 

This chapter presents a baseline for emissions and sequestration in the forests of Oregon.  Forest

is defined here (as it is in the FIA and NRI) as land with a greater than 10% stocking of trees.

The forest baseline chapter is presented in three sections.   

Section 2.2 presents a general forest baseline, detailing changes forest area and in carbon stocks

in Oregon’s forests with an estimate of annual sequestration/emissions.  A state‐level total is

presented for all forests with county level detail only for non‐federal lands.

The remaining sections present case studies of individual causes of emissions from forests.  

These case studies should not be considered as an addition to the general baseline (Section 2.2)

but as a subset of it.  Emissions from fire or development will have formed part of the total

emissions from forests that are presented, or alternatively will have decreased the total

estimated sequestration presented from forests.

Section 2.3 presents the case study of emissions caused by development on forestland.

Section 2.4 presents the case study of emissions caused by fire on forestland.

2.2. General Forests Baseline 

2.2.1. State Level Analysis for All Forestlands 

1987–1997 

The USDA Forest Service published a baseline for forests in Oregon between 1987 and 1997

(Birdsey and Lewis 2003).  Estimates are based on forest inventory data collected by the Forest

Service’s Forest Inventory and Analysis Unit.  Determination of the location of tree

measurement plots and changes in land area were assessed using high altitude photography.  

Where forest inventory was not available, estimates of land use change were derived from the

National Resources Inventory.

Between 1987 and 1997 Birdsey and Lewis (2003) estimated a net change in forest area for

Oregon from 11.6 million ha in 1987 to 12.0 million ha in 1997.  This is a total gain of 383,245 ha

(an increase of 3.3%), which averages out to 38,326 ha per year (an increase of 0.33%/yr).

Across the state Birdsey and Lewis calculated a mean forest carbon stock density of 77.9 tons of

carbon per hectare (t C/ha) in 1987 and 80.7 t C/ha in 1997—a gain of 2.8 t C/ha over the ten

years.  

Combining the area data with the carbon stock density data gives a total stock on forestland in

Oregon in 1987 and 1997 and a change in stock between the two dates.

The stock in 1987 was estimated as 907 million t C, and this grew to 970 million t C in 1997.   

This is equal to a total gain of 62.7 million t C (a 6.9% gain), which averages out to

6.3 million t C per year (a gain of 0.69%/yr).
12

Beyond 1997 

Using the Forest Inventory Mapmaker (version 2.1),1 researchers estimated the total area of

forestland for the year 2003.  This area was 12.4 million ha, which is equal to a gain of 71,021 ha

per year since 1997 (an increase of 0.59%/yr).  The rate of gain in forest area has therefore

increased by 85% between 1997 and 2003 over the rate between 1987 and 1997.  

To attain carbon stock densities to apply to the areas, an extrapolation was made from the

change in stock density between 1987 and 1997. The research team extrapolated from the values

of 77.9 t C/ha in 1987 and 80.7 t C/ha in 1997 to arrive at 82.4 t C/ha for 2003.

This results in a 2003 carbon stock of 1,025 million tons of carbon, or an additional

55.8 million t C above the total in 1997 (a 5.8% gain), which equals a mean accumulation of

9.3 million t C per year (a gain of 0.96%/yr).  The rate of accumulation increased by 48%

between 1997 and 2003 in comparison with 1987–1997.  

2.2.2. Changes in Forest Area on Private Land 

This section provides a detailed baseline at the county level for the change in area in privately

owned forests in Oregon. Changes are only examined on private land.  It is not expected that

widespread deforestation is occurring on public lands, though some afforestation may be

overlooked.

Researchers analyzed the change in land use associated with forests on private lands in Oregon

from the NRI. Two dates were used that reported data at the county scale of resolution: the

most recent publicly available data for 1997 and for 1987.  At the state level, all forested land

was estimated for 1987 and 1997, as well as the broad destination or origin of land that changed

from or to forest in the same time period (Table 2‐2‐1).

                                                     

1

 See www.ncrs2.fs.fed.us/4801/fiadb/fim21/wcfim21.asp.
13

Table 2-2-1. Change in area (ha) between 1987 and 1997 for non-federal 

forestland in Oregon 

Category Unchanged1 Lost to2 Gained from3

Unchanged 5,040,619    

Development    27,884 1,902  

Pasture/Rangeland    7,163 55,322  

Farmland/Agriculture    6,435 1,578  

Other    26,022 3,764  

1987 Total 5,108,123  

1997 Total     5,103,186  

1 Unchanged refers to areas remaining forest between 1987 and 1997.

2 Lost to refers to areas lost from forest to other land use categories between 1987 and 1997.

3 Gained from refers to areas becoming forest between 1987 and 1997.

In Oregon, forest area decreased by 4,937 ha in the ten years between 1987 and 1997, or an

average of 494 ha/yr.  Of the total area of forest in 1987, 98.7% experienced no change in land

use, remaining as forest ten years later.  There was a loss of 67,504 ha, principally to

development and other factors (including barren land and water), and a gain of 62,567 ha, of

which 88% was from pasture/rangeland.

County-Level Changes in Forest Area 

National Resources Inventory data is not designed for use at the county‐level; results are given

here for illustrative purposes. Across the State 58% of counties lost forest area during the

10‐year period and 22% gained forest area.  Large losses (> 4,000 ha) occurred in Lane,

Washington, and Harney counties.  Gains in area of a similar magnitude occurred in Lake,

Wheeler, and Douglas counties.  In Douglas County there was an increase of almost 16,000 ha

over the ten‐year period (Table 2‐2‐2).
14

Table 2-2-2. Area (ha) of non-federal forestland in Oregon in 1987 and 1997 

and change between the two dates 

County

Population County

Area

(ha) 1987 1997 Change

Baker 16,741 794,639 45,610 45,569   (40)

Benton 78,153 175,203 91,017 89,074   (1,943)

Clackamas 338,391 483,855 154,595 152,734   (1,862)

Clatsop 35,630 214,251 187,376 187,174   (202)

Columbia 43,560 170,090 129,018 129,706   688  

Coos 62,779 414,522 261,962 261,517   (445)

Crook 19,182 771,656 95,347 95,307   (40)

Curry 21,137 421,490 133,308 135,332   2,024  

Deschutes 115,367 781,698 36,302 35,330   (971)

Douglas 100,399 1,304,480 534,568 550,513   15,945  

Gilliam 1,915 311,852  ‐   ‐   ‐  

Grant 7,935 1,172,903 158,481 158,400   (81)

Harney 7,609 2,624,779 73,979 69,932   (4,047)

Hood River 20,411 135,287 33,347 32,578   (769)

Jackson 181,269 721,362 268,357 265,524   (2,833)

Jefferson 19,009 461,221 99,961 99,961   ‐  

Josephine 75,726 424,652 137,719 135,817   (1,902)

Klamath 63,775 1,539,537 479,124 478,679   (445)

Lake 7,422 2,107,150 170,783 176,692   5,909  

Lane 322,959 1,179,482 376,816 371,029   (5,787)

Lincoln 44,479 253,706 150,346 147,999   (2,347)

Linn 103,069 593,671 220,642 219,590   (1,052)

Malheur 31,615 2,560,745 19,587 9,587   ‐  

Marion 284,834 306,643 70,054 68,961   (1,093)

Morrow 10,995 526,384 42,170 42,129   (40)

Multnomah 660,486 112,724 33,347 30,838   (2,509)

Polk 62,380 191,925 96,076 96,359   283  

Sherman 1,934 213,210  ‐   ‐   ‐  

Tillamook 24,262 285,454 201,298 198,667   (2,631)

Umatilla 70,548 832,749 144,235 144,235   ‐  

Union 24,530 527,465 98,828 98,828   ‐  

Wallowa 7,226 814,639 130,840 130,840   ‐  

Wasco 23,791 616,688 129,018 130,920   1,902  

Washington 445,342 187,450 110,321 105,262   (5,059)

Wheeler 1,547 444,162 115,825 119,953   4,128  

Yamhill 84,992 185,330 77,864 78,148   283  
15

The largest losses in area occurred in the Northwest and West Central regions.  In the

Southwest and Central regions there was a gain in area (Table 2‐2‐3). For identification of the

regions, see Table 1‐1.

Table 2-2-3. Area (ha) of non-federal forestland in 1987 

and 1997 and the change between the two dates 

Change

Region 1987 1997 in Area

Northwest 1,093,297 1,080,428 (12,869)

West Central 838,822 827,692 (11,129)

Southwest 1,335,915 1,348,703 12,789  

Central 1,126,361 1,136,843 10,482  

Blue Mountains 713,729 709,520 (4,209)

  

2.2.3. Conclusions 

An estimated 383,000 ha of forest were gained on federal and non‐federal lands in Oregon

between 1987 and 1997 at a rate of 38,326 ha/yr.  Between 1997 and 2003 an estimated 426,000

additional hectares were gained at a rate of 71,021 ha/yr.  These gains are equivalent to 0.33% of

the forest area per year between 1987 and 1997 and 0.59% of the forest area per year between

1997 and 2003.

A gross sequestration of an estimated 229.9 million metric tons CO2 equivalent (MMTCO2e)  

(23 MMTCO2e/yr) occurred between 1987 and 1997, and 204.6 MMTCO2e (34 MMTCO2e/yr)

occurred between 1997 and 2003.

The emissions for Oregon (excluding forests) for the year 2000 were estimated as 67.7

MMTCO2e (Governor’s Advisory Group on Global Warming 2004).  Sinks, therefore, potentially

can offset as much as 50% of the state’s emissions.

For just non‐federal forested lands, there is a net loss of 5,000 ha.  This loss is composed of

decreases in forest area in the Northwest and West Central (coastal) regions and in the Blue

Mountains, balanced by gains in forest area in the Southwest (coastal) and in the Central

(inland) region.

2.3. Development Baseline 

2.3.1. General Approach 

This section provides a baseline for the emissions of carbon attributable to development of

Oregon’s forest lands. This analysis should be considered a subset of the general forest baseline:

the emissions due to development will form part of wider changes in the state’s carbon stocks.  

If this analysis is added to the analysis of the general forest baseline, then double counting will

occur.
16

Forest land development is examined only for private lands; it is not expected that widespread

development is occurring on public land. Changes in stocks are only changes in above‐ground

tree biomass, due to uncertainties surrounding both the absolute level of carbon in other carbon

pools and whether or not development will cause emissions from these pools.

As in the general forest baseline, changes in forest area due to development were based on NRI

data for changes in land use. Carbon stocks and changes in those stocks were derived from FIA

data. For the purposes of this study, development includes three NRI categories:

• Urban/10 acres or larger.

• Urban/small built‐up (< 10 acres). The category ”Urban/small built‐up” will be referred

to as “small‐scale development.”

• Transportation (such as roads and airports).

Statistical confidence can only be maintained in results given at the state level, because of the

design of the NRI database.   

2.3.2. Changes in Area at the State and County Level 

National Resources Inventory data is not designed for use at the county level; results are given

here for illustrative purposes. Between 1987 and 1997 almost 28,000 ha of non‐federal forest

were lost in Oregon to development, or 2,788 ha per year.  The loss over ten years is equivalent

to 0.5% of the total forest area present in the State in 1987. Of the total area lost to development,

20% could be considered small‐scale development (Table 2‐3‐1).

Table 2-3-1. Non-federal forest area (ha) between 1987 and 1997 

in Oregon 

Category Unchanged1 Lost to2 Gained from3

Unchanged 5,040,619      

Development    27,884 1,902  

% small scale      20%                    0%

Pasture/Rangeland    7,163 55,322  

Farmland/Agriculture    6,435 1,578  

Other    26,022 3,764  

1987 Total     5,108,123  

1997 Total     5,103,186  

1 Unchanged refers to areas remaining forest between 1987 and 1997.

2 Lost to refers to areas lost from forest to other land use categories between 1987 and 1997.

3 Gained from refers to areas becoming forest between 1987 and 1997.

Losses in excess of 2,000 ha occurred in Clackamas, Douglas, Lane, Multnomah, and

Washington counties, all of which are in the coastal region of Oregon (Figure 2‐3‐1, Table 2‐3‐2).  

These five counties represent just 24% of the area of non‐federal forest in 1997, but 53% of the

total area lost to development over the ten‐year study period.  Across these five counties the

mean proportion of small‐scale development is 14%, as opposed to 20% across the entire state.
17

Figure 2-3-1. Loss in non-federal forest area between 1987 and 1997 as a percentage of total 

non-federal forest area in the county 
18

Table 2-3-2. County-level data on area of non-federal forest in 1997, area of forest 

lost to development between 1987 and 1997, and % of losses that were small-scale 

County

Population County

Area (ha)

Non‐Fed

Forest

Area 1997

(ha)

Area Lost to

Development

(ha)  

%

small

scale

Baker 16,741 794,639 45,569 40   100

Benton 78,153 175,203 89,074 364   11

Clackamas 338,391 483,855 152,734 2,307   18

Clatsop 35,630 214,251 187,174 324   63

Columbia 43,560 170,090 129,706 81   100

Coos 62,779 414,522 261,517 1,295   41

Crook 19,182 771,656 95,307 0    

Curry 21,137 421,490 135,332 162   100

Deschutes 115,367 781,698 35,330 1,012   0

Douglas 100,399 1,304,480 550,513 2,064   6

Gilliam 1,915 311,852 0    

Grant 7,935 1,172,903 158,400 0    

Harney 7,609 2,624,779 69,932 0    

Hood River 20,411 135,287 32,578 121   33

Jackson 181,269 721,362 265,524 931   39

Jefferson 19,009 461,221 99,961 0    

Josephine 75,726 424,652 135,817 1,781   14

Klamath 63,775 1,539,537 478,679 364   78

Lake 7,422 2,107,150 176,692 0    

Lane 322,959 1,179,482 371,029 2,671   38

Lincoln 44,479 253,706 147,999 1,659   20

Linn 103,069 593,671 219,590 1,052   69

Malheur 31,615 2,560,745 19,587 0    

Marion 284,834 306,643 68,961 1,093   7

Morrow 10,995 526,384 42,129 40   0

Multnomah 660,486 112,724 30,838 2,792   6

Polk 62,380 191,925 96,359 486   25

Sherman 1,934 213,210 0    

Tillamook 24,262 285,454 198,667 1,578   10

Umatilla 70,548 832,749 144,235 0    

Union 24,530 527,465 98,828 0    

Wallowa 7,226 814,639 130,840 0    

Wasco 23,791 616,688 130,920 283   0

Washington 445,342 187,450 105,262 4,816   3

Wheeler 1,547 444,162 119,953 0    

Yamhill 84,992 185,330 78,148 567   57
19

2.3.3. Carbon Stocks 

Estimates of the carbon stocks in live tree biomass are derived from the FIA database.  The FIA

databases for 1991 and 1992 were used because these dates represent a midpoint between 1987

and 1997.  The FIA data were consolidated at the FIA Unit level.  Biomass carbon estimates were

derived from the measurements of tree DBH for all trees in inventory plots using the allometric

equations of Jenkins et al. (2003), scaled up to a per‐hectare basis using the plot‐area expansion

factors (Table 2‐3‐3).    

To be conservative, above‐ground tree biomass alone was considered.  The rate of emission of

carbon stored in roots and soil organic matter is slow and poorly understood, especially when it

is considered that some of the developed areas will be capped with concrete.  Wood products

are also not included, because it is not clear what proportion of the cut trees would be harvested

for products, nor what products would be produced (firewood and even paper can be rapidly

emitted).

Table 2-3-3. Mean above-ground tree carbon stock (from 1992 FIA data) for each region of Oregon 

with the number of plots and the confidence interval around the stock estimate 

Region Mean

t C/ha

95% CI # plots Counties included in region

Northwest 165.6 103.9 309 Clackamas, Clatsop, Columbia, Hood River,

Marion, Multnomah, Polk, Tillamook,

West Central 151.8 108.6 220 Benton, Lane, Lincoln, Linn

Southwest 136.4 102.7 353 Coos, Curry, Douglas, Jackson, Josephine

Central 83.4 70.7 251 Crook, Deschutes, Gilliam, Jefferson,

Klamath, Lake, Sherman, Wasco, Wheeler

Blue Mountains 79.1 49.8 148 Baker, Grant, Harney, Malheur, Morrow,

Umatilla, Union, Wallowa

CI = confidence interval

2.3.4. Carbon Emissions from Development 

Two carbon emission scenarios are considered here.  In both cases FIA data from federal and

non‐federal forests were applied to NRI land cover estimates for non‐federal forests.

• Scenario 1 assumes that all carbon present on the land in above‐ground tree biomass is

lost when development occurs.

• Scenario 2 assumes that when small‐scale development occurs, a significant proportion

of the trees remain during and after the process of development.  As examples, these

may be trees surrounding residential properties or trees on golf courses.  Therefore, in

this scenario it is assumed that for Transportation and Urban/10 acres or larger, all

carbon is lost, but for Urban/small built‐up, only 50% of the carbon stocks are emitted.

Emissions discussed here for conversion of forestland to development are gross emissions from

above‐ground tree biomass only.  Total emissions from development over the ten‐year period

were estimated as 4.2 million t C under Scenario 1 and 3.8 million t C under Scenario 2.  This is
20

equivalent to 421,249 and 380,206 t C per year. The difference is small because only 20% of the

total development change is attributed to small‐scale development.  Emissions by county are

summarized in Figure 2‐3‐2 and Table 2‐3‐4.

Figure 2-3-2. Carbon emissions under the two scenarios at the county level across the state 
21

Table 2-3-4. County-level estimates on the emissions between 1987 and 1997 due to 

development. Scenario 2 is more conservative, assuming that trees are not clear-cut 

during small-scale development. 

County

Population County

Area

Non‐Fed

Forest Area

1997 Carbon emissions (t C)

(ha) (ha) Scenario 1 Scenario 2

Baker 16,741 794,639 45,569 3,199   1,600  

Benton 78,153 175,203 89,074 55,272   52,202  

Clackamas 338,391 483,855 152,734 381,956   348,451  

Clatsop 35,630 214,251 187,174 53,608   36,855  

Columbia 43,560 170,090 129,706 13,402   6,701  

Coos 62,779 414,522 261,517 176,680   140,792  

Crook 19,182 771,656 95,307  ‐     ‐    

Curry 21,137 421,490 135,332 22,085   11,043  

Deschutes 115,367 781,698 35,330 84,392   84,392  

Douglas 100,399 1,304,480 550,513 281,584   273,302  

Gilliam 1,915 311,852    ‐     ‐    

Grant 7,935 1,172,903 158,400  ‐     ‐    

Harney 7,609 2,624,779 69,932  ‐     ‐    

Hood River 20,411 135,287 32,578 20,103   16,752  

Jackson 181,269 721,362 265,524 126,989   102,143  

Jefferson 19,009 461,221 99,961  ‐     ‐    

Josephine 75,726 424,652 135,817 242,935   226,372  

Klamath 63,775 1,539,537 478,679 30,381   18,566  

Lake 7,422 2,107,150 176,692  ‐     ‐    

Lane 322,959 1,179,482 371,029 405,330   328,563  

Lincoln 44,479 253,706 147,999 251,796   227,230  

Linn 103,069 593,671 219,590 159,675   104,403  

Malheur 31,615 2,560,745 19,587  ‐     ‐    

Marion 284,834 306,643 68,961 180,926   174,225  

Morrow 10,995 526,384 42,129 3,199   3,199  

Multnomah 660,486 112,724 30,838 462,367   448,965  

Polk 62,380 191,925 96,359 80,412   70,360  

Sherman 1,934 213,210    ‐     ‐    

Tillamook 24,262 285,454 198,667 261,338   247,936  

Umatilla 70,548 832,749 144,235  ‐     ‐    

Union 24,530 527,465 98,828  ‐     ‐    

Wallowa 7,226 814,639 130,840  ‐     ‐    

Wasco 23,791 616,688 130,920 23,630   23,630  

Washington 445,342 187,450 105,262 797,416   787,364  

Wheeler 1,547 444,162 119,953  ‐     ‐    

Yamhill 84,992 185,330 78,148 93,814   67,010  
22

The carbon emissions as a result of development mirror the loss in forest area.  The majority of

the emissions occurred in the Northwest region—56% of total emissions from development

under Scenario 1 and 58% under Scenario 2 (Table 2‐3‐5)—even though these counties only

represent 9% of the state’s area (but include the major city of Portland).   

Table 2-3-5. Region-level summary of loss in area and carbon 

emissions between 1987 and 1997 due to development. 

Scenario 2 is more conservative, assuming that trees are 

not clear-cut during small-scale development. 

Region Area lost  

(ha)

Carbon emissions (t C)

Scenario 1 Scenario 2

Northwest 14,165 2,345,341 2,204,621  

West Central 5,747 872,074 712,398  

Southwest 6,232 850,274 753,652  

Central 1,659 138,403 126,588  

Blue Mountains 81 6,399 4,799  

This loss to development is equal to an annual loss in area across the state of almost 3,000 ha

with annual CO2 equivalent emissions of between 1.4 and 1.5 MMTCO2e (Table 2‐3‐6).

Table 2-3-6. Region-level summary of annual loss in area and 

CO2 equivalent emissions between 1987 and 1997 due to 

development. Scenario 2 is more conservative, assuming that 

trees are not clear-cut during small-scale development. 

Region Area lost 

per yr 

(ha/yr)

Annual carbon emissions 

(MMTCO2e) 

Scenario 1 Scenario 2

Northwest 1,416 0.86 0.81 

West Central 575 0.32 0.26 

Southwest 623 0.31 0.28 

Central 166 0.051 0.046 

Blue Mountains   8 0.002 0.002 

TOTAL 2,788 1.54 1.39 

2.3.5. Additional Considerations 

Emissions discussed here for conversion of forestland to development are gross emissions from

above‐ground tree biomass only.

Gross versus Net Emissions 

The analysis presented above represents gross emissions.  The only consideration was of

emissions from losses of forest to development.   

Where gains of forest were made from development, this was not considered.   
23

The destination of biomass upon development was also not considered.  The assumption was

made that all carbon is immediately emitted.  In reality this is unlikely to be the case.  Some of

the wood is likely to ultimately become firewood, some will be left to decompose, and some

may be used as timber and will have a longer existence as wood products.  Regardless, all trees

cut for development will ultimately be emitted to the atmosphere as CO2 or CO2 equivalents.  

Instead of including any delay here, the assumption is made of immediate emission.

Other Carbon Pools 

Above‐ground tree biomass was the only carbon pool considered in this analysis.  This decision

was made because of the uncertainty involved in other pools generally, and specifically in the

case of development.   

Soil carbon is particularly uncertain.  If the land is capped by concrete it is unlikely that soil

carbon will be affected at all.  If grasses are planted there is even the possibility that

development could lead to an increase in soil carbon.

For similar reasons roots are also uncertain.  The rate at which roots decompose is very poorly

known and even less is known about the diminished rate if the roots are buried beneath

concrete or tarmac.

Dead wood and litter are likely to be emitted either immediately upon development, or through

time as decomposition occurs.  However, there is no clear relationship between above‐ground

tree biomass and these pools, and the uncertainty involved with any assumption would be very

large.

Non‐ CO2 greenhouse gas (GHG) emissions are also unknown.  If site preparation occurs

through burning, there will be emissions of methane and nitrous oxide (both potent GHGs).  If

site preparation involves drainage there will be emissions of methane.  Without specific site‐by‐

site information, it is not possible to make these estimations.

2.3.6. Conclusions 

An estimated 28,000 ha were lost to development in Oregon State between 1987 and 1997 at a

rate of 2,788 ha per year.  This forest loss is equivalent to a gross emission of between 13.9 and

15.4 MMTCO2e, or 1.39 to 1.54 MMTCO2e per year.

The emissions were concentrated in the coastal region and in particular in the Northwest—the

region in which Portland is located.

These emissions compare with the estimated gross sequestration from forests in Oregon of

23 MMTCO2e/yr between 1987 and 1997 (Section 2.2) and gross emissions for the state of

63 MMTCO2e/yr in 1995 (Governor’s Advisory Group on Global Warming 2004).  Emissions

from deforestation therefore offset between 6% and 7% of the forest sequestration and represent

between 2.2% and 2.4% of the total emissions in the state.
24

Comparison with the Oregon Department of Forestry Report on Development 

The Oregon Department of Forestry and the U.S. Forest Service produced reports on land use

change on non‐federal land in Oregon (Lettman et al. 2002; 2004).  The authors conducted their

own sampling based on aerial photographs across the state.  The study was split into two

reports: one for Western Oregon covering 1973–2000; and one for Eastern Oregon 1975–2001.

The authors report losses in area, from ”Wildland Forest” and ”Mixed Forest Agriculture” to

”Low Density Residential” and ”Urban” land uses, of 56,000 ac in Western Oregon between

1982 and 2000, and of 23,000 ac in Eastern Oregon between 1986 and 2001.  This produces a total

of 79,000 ac of land lost from forest or mixed forest.

The present study details a loss of 68,901 ac from forest to developed land uses between 1987

and 1997.  This loss is approximately 10,000 ac fewer (but includes eight fewer years) in the

Western region and five fewer years in the Eastern region.   

It is not easy to directly compare the results of each study because of the overlapping time

periods.  However, three of the additional years in the Western region and four of the years in

the Eastern region are after 1997.  The years after 1997 are likely not to be responsible for a

significant proportion of deforestation for development, as Lettman et al. (2002) showed that the

rate of forest loss to development was 90% lower between 1994 and 2000 in the Western region

than between 1982 and 1994.  If the pattern of decreasing deforestation through time is

consistent, then the five years between 1982 and 1987 in the Western region may, in contrast, be

responsible for a significant proportion of the total deforestation for development.  If this is the

case then the analysis of the NRI database may be producing an overestimate relative to the

Oregon‐specific study, but without a more direct comparison it is not possible to know.

2.4. Fire Baseline 

In this fire analysis the emissions caused by fire between 1990 and 1996 are estimated.  These

emissions are part of the general forest baseline, as discussed in Section 2.2. Without emissions

from fire, the general forest baseline would be raised by an amount equal to these emissions.

This baseline—unlike the general forest baseline and the development emissions baseline—

contains an analysis of rangelands as well as forests.

There are two components to a fire emissions analysis.  It is necessary to know both the area

that is burnt and the amount of biomass that is volatilized into GHGs per area.  Knowledge of

these components permits an estimation of total fire‐derived emissions.  

The period 1990–1996 was chosen for this analysis, for (although a partial dataset exists for

1997–2003) these study dates represent the most recent, consistent complete coverage.  

Complete coverage is essential in order to be able to make state‐level conclusions on the impact

of fire.
25

2.4.1. Methods for Assessing Biomass Volatilized 

Background 

The effects of fire on carbon stocks are dependent on the intensity of the fire. An intense fire will

destroy biomass and release a great proportion of the carbon to the atmosphere, while a less

intense fire will even fail to kill the majority of the trees. Here fires are divided into three

potential intensities: high, medium, and low.  

Pre‐fire carbon has five potential destinations during and after a fire (Fig. 2‐4‐1). The first

proportion will survive the fire to continue as live vegetation, a second proportion will be

volatilized during the fire and immediately be released to the atmosphere, and the remainder

will be divided between the pools of dead wood, soot, and charcoal. Soot and charcoal are

stable forms of carbon and can remain unchanged for hundreds of years; in contrast, dead wood

decomposes over time.  

Figure 2-4-1. Flow diagram illustrating the various destinations of pre-burn carbon after a fire 

The basis for this baseline analysis was the detailed study conducted for California (Brown et al.

2004).  Under the California baseline analysis changes in canopy coverage (measured from

satellite imagery) were recorded through time for forest types and causes (including fire) were

assigned.  The research team assumed (based on expert opinion) that the three intensities are

associated with the magnitude of change in crown cover, so that a large decrease in crown cover

would be due to a high‐intensity fire or a small decrease would be caused by a low‐intensity

fire.

The midpoint of each decrease in canopy coverage class was assumed to be the proportion of

the vegetation killed by the fire. The proportion volatilized is dependent on fire intensity (60%

Census 1 FIRE Census 2 

 Not Severely Live Vegetation 

Carbon in Damaged 

Forests / 

Woodlands 

 Volatilized 

 Soot Soot 

 Charcoal Charcoal 

 Dead Wood Dead Wood 

 Decomposed/ 

Oxidized 
26

high‐intensity fire, 40% mid‐intensity fire, and 20% low‐intensity fire) (McNaughton et al. 1998;

Carvalho et al. 2001). If the volatilized proportion is subtracted from the midpoint of the

decrease, then the remaining fraction is the dead wood, soot, and charcoal pool.   This fraction

was divided using the following proportions: 22% charcoal, 44% soot, and 32% dead wood

(Comery 1981; Raison et al. 1985; Fearnside et al. 1993; Neary et al. 1996).  

Approach for Calculations 

This study’s aim was to determine biomass loss as a result of fire in Oregon.  In California the

research team had data on the area affected by fire in classes of initial and post‐fire crown cover

and forest type.  The degree of reduction in crown cover was used to indicate the intensity of

the fire.  Researchers also knew the biomass associated with each crown cover class, so a change

between two cover classes could be represented as a loss in carbon.  In contrast, in Oregon,

available data included only forest type and an indication of fire intensity from fire extent and

change in spectral reflectance.

The approach for this study is therefore to use the California data to determine the percentage

loss in biomass that occurs as a result of a high‐, medium‐, or low‐intensity fire in each of the

forest types.  The percentage loss is then applied to Oregon‐specific biomass numbers for

comparable forest types.

The source of biomass values is the Oregon 2004 inventory of the FIADB (forest inventory and

analysis database).  These were split between forest types.  In all cases, Oregon FIA data was

divided by the five forest/woodland types (Douglas Fir, Fir‐Spruce, Other Conifer, Hardwood

Forest, Hardwood Rangeland; Table 2‐4‐1) at the county level.  The division by forest/woodland

type occurred to align the Oregon analysis with the original California study (Brown et al.

2004).
27

Table 2-4-1. Forest types for fire baseline analysis cross-walked with FIA forest type 

California‐analysis

forest type

FIA forest type

Douglas fir Douglas fir

Fir Spruce White fir, Red fir, Noble fir, Pacific silver fir,

Engelmann spruce, Engelmann spruce/Subalpine fir,

Grand fir, Subalpine fir, Blue spruce, Sitka spruce

Other Conifer Port‐Orford cedar, Ponderosa pine, Western white

pine, Jeffrey pine/Coulter pine/big cone Douglas‐fir,

Mountain hemlock, Lodgepole pine, Western hemlock,

Western redcedar, Alaska yellow cedar, Western larch,

Misc. western softwoods

Hardwoods ‐ forest Cottonwood, Willow, Oregon Ash, Aspen, Red alder,

Bigleaf maple, Tanoak, Giant chinkapin, Pacific

Madrone

Hardwood ‐ rangeland Western juniper, California black oak, Oregon white

oak, Canyon live oak/Interior live oak, California

laurel, Misc. western hardwood woodlands,

Intermountain maple woodland, Juniper woodland,

Pinyon juniper woodland, Rocky mountain juniper,

Deciduous oak woodland, Mesquite woodland

The FIA data was further split into regions—Eastern, Central, and Coastal—with the

assumption that the climatic variation would lead to variation in biomass that would refine the

estimates.  The split of counties among regions is shown in Table 1‐1, though here Northwest,

West Central, and Southwest are combined into a single Coastal region.

The mean biomass stocks were calculated from Oregon FIA data by region and forest type

(Table 2‐4‐2).

Table 2-4-2. Mean biomass stock by forest type and region 

Forest Type Mean biomass

(t biomass/ha)

Coastal Central Eastern

Douglas Fir 326.1 178.0 132.8

Fir Spruce 310.1 242.8 175.0

Other Conifer 285.5 101.1 95.9

Hardwood 158.2 65.7 36.8

Range Hardwood 138.2 26.9 29.8
28

Biomass Loss through Fire 

Researchers used results from the California analysis (Brown et al. 2004) to calculate the

emissions through fire.  The estimated stocks for each forest type at each of the four canopy

density classes was taken from the California analysis, plus the net emissions for each forest

type/canopy density class/fire intensity class.  Finally the emissions were calculated as a

proportion of the original biomass, and the results were expressed as a percentage.

Because no canopy cover class data exists for Oregon, a mean emission percentage that excludes

canopy cover is required.  This was achieved by weighting the emission percentages by the

proportion of forest in each canopy class in the most representative region of California (North

Coast for Coastal regions and Cascades Northeast for Central and Eastern regions).

The proportions by forest type by region by fire intensity were then multiplied by the biomass

by forest type by region to give estimated biomass lost through emissions from fire, as shown in

Tables 2‐4‐3, 2‐4‐4, and 2‐4‐5.

Table 2-4-3. Mean emissions (in t CO2e/ha) from a high-, 

mid-, and low-intensity fire in the Coastal Region of Oregon 

Forest type High Mid Low

Douglas Fir 259.8 111.8 45.1

Fir Spruce 298.8 130.9 52.6

Other Conifer 206.6 137.0 68.2

Hardwood 138.2     59.6 24.0

Range Hardwood 121.0     52.1 20.9

  

Table 2-4-4. Mean emissions (in t CO2e/ha) from a high-, 

mid-, and low-intensity fire in the Central Region of Oregon 

Forest type High Mid Low

Douglas Fir 137.0    58.9 23.7

Fir Spruce 231.9 102.3 41.3

Other Conifer   69.7    46.2 23.1

Hardwood   57.2    24.6    9.9

Range Hardwood   30.1    13.6    5.5
29

Table 2-4-5. Mean emissions (in t CO2e/ha) from a high-, 

mid-, and low-intensity fire in the Eastern Region of Oregon 

Forest type High Mid Low

Douglas Fir 102.1 43.8 17.6

Fir Spruce 167.2 73.7 29.7

Other Conifer   66.2 43.8 21.8

Hardwood   32.1 13.8    5.5

Range Hardwood   33.4 15.0    6.1

Non-Tree Vegetation 

Biomass numbers for non‐tree vegetation (primarily shrubs and grasses in rangelands) are

taken from the literature and Winrock International experience (Table 2‐4‐5).

Table 2-4-5. Estimates of pre-fire biomass stocks in non-tree vegetation 

Vegetation type Biomass carbon (t C/ha) Source

Wet Grasslands 5.9 Prichard et al. 2000

Mesic Grasslands 2.4 Brown and Archer 1999

Xeric Grasslands 0.6 Winrock unpublished data

Shrublands 5.1 Martin et al. 1981

Desert scrub 2.6 Winrock unpublished data

Here the conservative assumption is made that 50% of the pre‐fire biomass in non‐tree

vegetation is volatilized to be emitted as carbon dioxide.  

2.4.2. Methods for Assessing Area Impacted by Fire and Fire Intensity 

Satellite‐based analysis is a practical method of quantifying area burned, primarily due to the

dangerous nature and the wide geographic extent of wildfires.  The State reports the location

and size of recorded fires but with no measure of fire intensity, nor with the location of the

boundaries of the fire.  It is necessary to know fire intensity to estimate emissions and the

precise location is necessary for a correlation with a database of vegetation species.  The

approach for this analysis was to estimate the extent of fires at known fire locations, through

delineating areas with a change in reflectance on multiple satellite images—that is, pre‐fire and

post‐fire images.

A common measurement of vegetation from satellite imagery is the Normalized Difference

Vegetation Index (NDVI).  Very low values of NDVI (0.1 and below) correspond to barren areas

of soil without vegetation or of sand, rock, or snow.  Moderate values (0.2 to 0.3) represent

shrub and grassland, while high values (0.6 to 0.8) indicate forests.
30

Databases 

The NDVI was calculated from 1.1 kilometer (km) pixel resolution NOAA Advanced Very

High Resolution Radiometer (AVHRR) 10‐day composite images.  The temporal frameset

covered the month of September and spanned 1990–2003 (except 1994).  This encompassed the

NOAA 11, 14, and 16 satellite data.  September was chosen for the analysis time frame because

it is toward the end of the fire season, and the burned areas are not yet affected by regrowth.  

Only one September 1994 composite was produced for 1994 due to the failure of the AVHRR

sensor aboard NOAA 11.  As a result, the imagery and fire data from 1994 was dropped from

the analysis due to data inconsistencies in image values and incomplete temporal coverage from

sensor failures.   

The wildfire database for Oregon encompassed a total of 21,625 occurrences that vary from less

than one acre to many thousand acres. Fires for the study period with a final size greater than

2,000 ac were identified for NDVI post‐fire burn detection analysis to quantify area burned.  

Each fire record included a unique identification with a global positioning system (GPS) point

location, date, and final extent in acres.  There was no geographic information system (GIS)

polygon representing the extent of the fire in the original database so it was not possible to

precisely locate the extent of the fire from these records, so the research team used the approach

described below.    

Mapping Methods 

Fire Identification

This analysis used a post‐fire burn detection method to quantify area burned by wildfires.  The

NDVI was calculated from the water vapor‐corrected band 10 (visible, 0.58–0.68 μm) and band

11 (near infrared, 0.725–1.10 μm).

NDVI = (ch 11 – ch 10)/(ch 11 + ch 10).   

In order to obtain a single September NDVI for each year of the study period, three (or in some

years four) 10‐day composites were averaged into a single image (NDVIy).  These September

images were then averaged into a 13‐year historical NDVI reference image (NDVIm).   

The NDVI reflectance values are bimodal, ranging from ‐1.0 to 1.0.  Positive values reflect

vegetation or ”greenness,” and negative values are indicative of soil or non‐vegetated areas.  

Values close to 1 are ”greener” than values close to 0, and values close to ‐1 are more barren

than values close to 0.  When vegetation is burned, a rise in channel 10 reflectance and a

decrease in channel 11 reflectance occurs.  The degree of change (NDVId) was measured by

subtracting NDVIy from NDVIm  

NDVIy ‐ NDVIm = NDVId

Each individual annual September image was subtracted from the reference image, and

potential fire locations were identified.  In NDVI difference imagery, positive values indicate an

increase in ”greenness” from NDVIm and negative values indicate a decrease.  For burned

area‐identification purposes, all positive values were removed, along with negative values
31

greater than ‐0.05.  The result was an image containing areas of concentrated vegetation

decrease.  The fire location data was then overlaid to confirm the changes as potential fires.   

Fire Extent

The extent of fires listed as having over 2,000 ac in final size were mapped by visual

interpretation from the changes seen in NDVId, with assistance from the fire’s GPS location and

extent information (Figure 2‐4‐2).

The wildfire mapping process consisted of creating polygons that represent the extent of the

burn area.  Fires were first divided into big and small based on final extent.  Fires with a final

extent of < 2,000 ac or 8 pixels were labeled as small fires.  For AVHRR imagery, 1 pixel = 100 ha

= 247.5 ac.  Areas of vegetation decrease in NDVId greater than 8 pixels and with a

corresponding fire greater than 2,000 ac were digitized using the ”heads up method.”2  The area

digitized was then compared with the reported fire extent.

All fires with less than 2,000 ac burned were classified as too small to display a change in the

AVHRR imagery.  For these fires, a buffer was calculated and added to the fire point based on

the GPS point (which was considered the center of the fire) and the radius (which was derived

from the size reported in the original record).   

Additionally, if a fire that was larger than 2,000 ac could not be mapped by visual

interpretation, it was mapped by the buffering method.

Fire Severity

For the fires that occurred in forested lands, three classes of burn severity were identified: low,

medium, and high (Figure 2‐4‐2).  Again, the intensity was evaluated separately depending on

the fire mapping method.  For the fires that were identified using the imagery, the value of burn

severity corresponded with the value of the difference in NDVI.  The rationale is that the more

negative the difference between the actual NDVI and the mean NDVI, the more severe is the

fire.  As a result, one fire can include areas with different burn severities.  Small fires (< 2,000 ac)

were arbitrarily considered to experience a low‐burn fire severity, since there was no image

data to consistently support the estimation.  

                                                     

2

 “Heads up” digitizing refers to on-screen digitizing. It is referred to as “heads up” because the 

analyst focuses on the screen as opposed to a digitizing tablet. 
32

Figure 2-4-2. Illustration of the mapping methodology. In (a), the 

point location from the state or federal database is established, a 

fire boundary is then created and compared to the fire area reported 

with the point location; in (b), the fire intensity through the burn area 

is calculated using NDVI values. 

Land Cover Affected by Fire

Finally, the fire maps were crossed with the land cover maps, making it possible to estimate the

amount of land cover type/forest type that was affected by fires.

2.4.3. Results 

Across the six years analyzed, researchers recorded fires with a total area of 328,000 ha

(0.81 million ac), as illustrated in Figure 2‐4‐3.  This area is equivalent to an average 54,700 ha

per year (135,100 ac/yr) for the period studied.

Emissions totaling 6.8 million tons of carbon, or 25.0 MMTCO2e, were estimated to have

occurred from fire during the analysis period.  On an averaged annual basis this is equal to

0.28 million tons of carbon per year (1.03 MMTCO2e/yr).
33

Figure 2-4-3. The location and extent of fires in Oregon between 1990 and 1996 

Forty‐eight percent of the fires occurred in forests and 46% in rangeland during the study

period (Table 2‐4‐6).3  Due to the higher biomass loss from forests during fire, more than 94% of

the total emissions from fire originated in the 48% of fire area that was in forest.

Table 2-4-6. Area burned and carbon emissions 

in forests and in rangeland across the analysis period 

Type

Area 

burned (ha) 

Emissions

(t C) 

Forest 392,296 5,859,967 

Rangeland 187,129 363,826 

Fires occurred in every year studied, but the area burned and the total carbon emissions varied

greatly between years, as shown in Table 2‐4‐7 and Figure 2‐4‐4.  For example, 186,000 ha were

burned in 1996, but just 5,118 ha burned in 1993.  The calculated carbon emissions were as

variable as the area burned, with 23,500 tons of carbon emitted in 1993 but 1.0 million tons of

carbon emitted in 1996.  In 1994 no fires were examined due to poor quality imagery.

                                                     

3

 The remaining fire area was on developed, agricultural, or barren land. 
34

Table 2-4-7. Area burned and carbon emissions 

per year across the analysis period 

Year 

Area burned 

(ha) 

Emissions 

(t C) 

1990 45,891 176,199 

1991 12,610 111,113 

1992 52,560 321,680

1993 5,118 23,538

1994 

1995 26,083 87,434

1996 185,746 1,001,547

0

40,000

80,000

120,000

160,000

200,000

Hectares of fire

a

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1990 1991 1992 1993 1994 1995 1996

Estimated emissions

 (t C)

Year

b

Figure 2-4-4. Area affected by fire and estimated emissions from 

fire across the study period 

The greatest area of fire and the greatest emissions occurred on federal lands with 79% of the

burned area and 83% of the released carbon (Table 2‐4‐8).  Eighteen percent of the area burned

and 13% of the emissions were from private lands.

Table 2-4-8. Area burned and carbon emissions by 

ownership across the analysis period 

Ownership 

Area burned 

(ha)

Emissions 

(t C) 

County Government 4 34 

Private 110,525 224,237 

Private - Conservation 179 863 

State Government 5,594 5,997 

Tribal 10,655 47,979 

U.S. Federal Government 481,209 1,410,487 
35

Emissions from fire occurred throughout the State of Oregon; however, areas burned and

carbon emitted were lowest in the counties in the Northwest of the state, as shown in Figure

2‐4‐5 and Figure 2‐4‐6, and Table 2‐4‐9.  In each of Clatsop, Columbia, Tillamook, Washington,

Polk, Lincoln, Benton, Marion, Yamhill, and Clackamas the area burned was less than 500 ha

over the six years of study (1994 was not examined), and the emissions were less than 7,000 tons

of carbon.  In contrast, the highest emissions were in Grant County in the East where 40,000 ha

burned with 250,000 tons of carbon emitted and in Douglas County in the Southwest where

14,000 burned hectares coincided with high carbon stocks producing 105,000 tons of carbon

emission.   

Figure 2-4-5. Area burned (in acres), at the county level, between 1990 

and 1996 (excluding 1994) 
36

Figure 2-4-6. Metric tons of carbon dioxide emitted, at the county 

level, between 1990 and 1996 (excluding 1994) 
37

Table 2-4-9. Area burned and carbon emissions per 

county across the analysis period 

County County 

Area (ha)

Area 

burned 

(ha)

Emissions 

(t C) 

Baker 794,639 4,541 15,238 

Benton 175,203 130 347 

Clackamas 483,855 433 6,409 

Clatsop 214,251 52 503 

Columbia 170,090 27 431 

Coos 414,522 655 9,581 

Crook 771,656 2,285 12,550 

Curry 421,490 1,543 22,279 

Deschutes 781,698 30,686 90,465 

Douglas 1,304,480 14,137 205,327 

Gilliam 311,852 352 420 

Grant 1,172,903 39,815 250,308 

Harney 2,624,779 47,126 116,020 

Hood River 135,287 32 496 

Jackson 721,362 6,472 71,701 

Jefferson 461,221 5,160 16,837 

Josephine 424,652 1,270 17,022 

Klamath 1,539,537 33,837 202,307 

Lake 2,107,150 8,585 39,618 

Lane 1,179,482 9,434 156,502 

Lincoln 253,706 90 1,253 

Linn 593,671 137 1,114 

Malheur 2,560,745 58,496 121,124 

Marion 306,643 109 1,759 

Morrow 526,384 516 1,320 

Multnomah 112,724 1,062 17,217 

Polk 191,925 32 81 

Sherman 213,210 605 1,432 

Tillamook 285,454 55 739 

Umatilla 832,749 22,133 179,122 

Union 527,465 4,697 22,496 

Wallowa 814,639 11,929 10,584 

Wasco 616,688 13,291 57,639 

Washington 187,450 16 257 

Wheeler 444,162 8,221 38,729 

Yamhill 185,330 46 373 
38

2.4.4. Uncertainties 

The carbon values to which percentage emission factors are applied are averaged values across

all FIA plots in a forest type/region combination.  Consequently, the same average value is used

to represent forests with very high carbon stocks or very low carbon stocks.  Fires will occur in

forests regardless of starting carbon stock, yet it is possible that the forests with the very lowest

carbon stocks (for example in the year immediately after clear‐cut logging) may not have

enough biomass to sustain a fire.  The emissions reported here may therefore be a small

overestimate, for if the very lowest biomass plots are excluded from the FIA analysis, the mean

will be raised and consequently, so will the estimated emissions.

The calculated emissions presented here are conservatively limited to just above‐ground tree

biomass and therefore represent an underestimation of total emissions.  Carbon stored in other

pools will combust and be emitted through fire.  However, the research team has no detailed

source that will link the region and forest type‐specific FIA data on above‐ground tree biomass

with similar data on other carbon pools.

Fire will directly impact dead wood, litter, shrubs and herbs (though even these pools may not

be completely volatilized in low‐severity fires [e.g., Skinner 2002]).  The influence of fire on soil

carbon or the carbon stored in roots is less clear.  When a tree is killed, the roots will not be

burned but will become dead material that will decompose at a rate that is not well understood.  

A very intense fire will affect soil carbon though it is not fully understood what proportion of

soil carbon is volatilized, nor to what depth the impact penetrates.  

To give an indication of the scale of potential additional emissions for pools not included here,

the literature was consulted.  Smithwick et al. (2002) took measurements of all carbon pools

across 43 stands at seven sites in Washington and Oregon.  The authors divided their

measurements into three regions: Coastal, Cascades, and Eastern.  Values for roots were not

taken from Smithwick et al. (2002); roots were estimated more directly by using the temperate

forest allometric equation of Cairns et al. (1997), which calculates belowground biomass from

above‐ground biomass.  Table 2‐4‐10 shows the amount of additional biomass carbon as a

percentage of above‐ground live tree biomass carbon stocks.

Table 2-4-10. Relative increase in stocks that would result from adding each of 

the additional carbon pools to live above-ground trees 

Region Litter Dead Wood Shrubs Herbs Roots Soil Carbon

Coastal 8% 20% 0.30% 0.05% 23%–26% 79%

Cascades 8% 21% 0.24% 0.07% 24%–31% 28%

Eastern 22% 23% 0.38% 0.09% 25%–31% 43%

   

The measurements of Smithwick et al. (2002) were in old‐growth forests.  In younger forests,

lower absolute amounts of dead wood might be expected, together with similar quantities of

litter, shrubs, and herbs.  Therefore a lower proportion of dead wood and a higher proportion of

litter, shrubs, and herbs might be expected in younger forests.
39

Here, as an indication of potential additions, the values of Smithwick et al. (2002) were used.  

An addition of litter, dead wood, shrubs, and herbs (assuming that these pools are volatilized at

the same proportion as live above‐ground trees) results in an additional emission over the study

period equal to 1,984,952 tons of carbon, or an additional 29.6%.

2.4.5. Conclusions 

Across the six years analyzed,4 researchers recorded fires with a total area of 328,000 ha

(0.81 million ac).  This is equivalent to an average 54,700 ha per year (135,100 ac/yr) for the

period studied.  Emissions totaling 6.8 million tons of carbon, or 25.0 MMTCO2e, were

estimated to have occurred from fire during the analysis period.  On an averaged annual basis

this is equal to 0.28 million tons of carbon per year (1.03 MMTCO2e/yr).

Forty‐eight percent of the burnt area and 83% of the emissions were in forest as opposed to

rangeland.  Fire incidence varied by year, with high emissions in 1996 and low emissions in

1993 and 1995. Seventy‐nine percent of area burned and 83% of the emissions were from

federally owned land; 18% of the area burned and 13% of the emissions were from private land.  

Emissions from fire occurred throughout the State but were markedly lower in the Northwest.

The emissions from fire of 1.03 MMTCO2e/yr during the six‐year period represented about 1.6%

of the total gross emissions for the state of 63 MMTCO2e/yr in 1995 (from the Governor’s

Advisory Group on Global Warming).   

                                                     

4 As previously mentioned, 1994 was excluded due to poor image quality. From analysis of state and

federal reporting of fire areas, there is no indication that 1994 was a year of high fire incidence, so its

exclusions will not have dramatically altered the reported results.
40
41

3.0 Baselines for Agricultural Lands in Oregon 

3.1. General Approach 

The goal of this part of the research was to quantify the baseline of changes in carbon stocks in

the Oregon agricultural sector for the decade of the 1990s. Baselines provide an estimate of the

emissions and removals of GHGs caused by changes in the use and management of land.  This

report focuses on emissions and removals of CO2 and not on non‐CO2 greenhouse gases.  

Baselines are useful for identifying where, within the landscape of a state, opportunities exist

for enhancing carbon stocks and/or reducing carbon sources to mitigate GHG emissions.   

The baseline for the agricultural sector depends on two types of data: (1) the total area of

agricultural land, and area of each of the major agricultural land‐use types, through time; and

(2) the carbon stocks in each land‐use type. Areas and changes in area of agricultural lands are

based primarily on the NRI database for the period 1987–1997.  Carbon stock estimates for

various agricultural land‐use types were derived from consultation with experts in local

universities and from the literature in combination with standard methods.  The analysis is

conducted for the entire state of Oregon at the county scale of resolution.

3.1.1. Classification of Agricultural Land 

In this study, NRI data were used for estimates of area because of the NRI’s relative strength in

agricultural surveys compared with other sources of data.  The coverage of NRI data is wider

and is available across the states for multiple points in time and for multiple classes of

agriculture.   

In this analysis, agricultural land is equated to cropland as defined in the NRI (NRCS 2000).  

The NRI recognizes two categories of cropland: cultivated and non‐cultivated.  Cultivated

cropland includes small grains and row crops, hay and pasture with cropping history, and

horticulture with double cropping (meaning horticulture with crops planted under the trees).  

Non‐cultivated cropland includes horticulture without double cropping, and hay without

cropping history.  Grazing lands are included under the analyses of rangelands in Chapter 2.

The distinction between cultivated and non‐cultivated crops is not useful for the purpose of

(above‐ground) carbon analysis, which depends instead on biomass models based on the

growth form of the vegetation. Therefore, the specific land‐use categories from NRI were

regrouped for this analysis into categories related to the growth form of the crop.  All

horticulture lands, with or without double cropping, were reclassified as woody cropland. The

rest of the croplands, including hay, row crops and small grains, were considered to be non‐

woody crops (Table 3‐1).
42

Table 3-1. NRI categories and subcategories in Oregon 

Broad classification Detailed classification NRI classification

INCLUDED AS AGRICULTURE IN THIS CHAPTER

Perennial woody crops Fruit orchards Fruit orchards

   Nut orchards Nut orchards

   Vineyards Vineyards

   Bush crops Bush crops

   Berry crops Berry crops

   Other horticulture Other horticulture

Annual non‐woody crops Row / close crops Row/Corn

      Row/Sorghum

      Row/Soybeans

      Row/Cotton

      Row/Peanuts

      Row/Tobacco

      Row/Sugar beets

      Row/Potatoes

      Row/Other veg/truck crops

      Row/All other row crops

      Row/Sunflower

      Close/Wheat

      Close/Oats

      Close/Rice

      Close/Barley

      Close/All other close grown

      Hay/Grass

      Hay/Legume

      Hay/Legume‐grass

      Other crop/Summer fallow

      Other crop/Aquaculture

      Other crop/Other‐set‐aside, etc.

FOCUS OF CHAPTER 2

Pasture / rangeland Pasture / rangeland Pasture/Grass

     Pasture/Legume

Pasture/Grass‐forbs‐legumes

      Rangeland

Forest Forest Forestland/Grazed

      Forestland/Not grazed
43

Table 3-1 (cont’d) 

Broad classification Detailed classification NRI classification

OTHER CATEGORIES

Urban / transportation Urban / transportation Urban/10 acres or larger

     Urban/Small built‐up

      Transportation

Other Other Other farmland/Farmsteads

     Other farmland/Other land

     Other farmland/CRP land

     Barren/Salt flats

Barren/Bare exposed rock

     Barren/Strip mines

Barren/Beaches

Barren/Sand dunes

Barren/Mixed barren lands

     Barren/Mud flats

Barren/River wash

Barren/Oil wasteland

Barren/Other barren land

     Other rural/Permanent snow‐ice

     Other rural/Marshland

All other land

     Water/Body 2–40 acres

     Water/Body < 2 acres

     Water/Streams per. < 66 ft. wide

     Water/Streams per. 66–660 ft. wide

      Water/Large

CRP = Conservation Reserve Program

3.1.2. Limitations of the NRI Database 

Despite the general acceptance of NRI for agricultural resource analysis, it is important to note

its limitations. First, the samples were taken from non‐federal lands only, while in the West

Coast states, federal lands occupy half or more of the total land area.  Second, the data are not

from a complete census, but rather from a statistically sound sampling design.  Finally, the

NRI’s classification of land cover/land use types may not be consistent with other classification

schemes commonly used in land cover/land use analysis, for example, the classification in the

United States Geological Survey (USGS) National Land Cover Classification system.   
44

For the purposes of this chapter of the report, however, these limitations have virtually no effect

on the analysis, because the data are only being used for the agricultural sector, where lands are

privately owned, easy to classify, and statistically well reported.  

The NRI reports a margin of error for the 1997 reporting (equivalent to a 95% confidence

interval) of ±9% for its sampling of areas of cultivated cropland.

3.1.3. Area and Change in Area of Agricultural Land 

The NRI data for each state were reclassified into the broad classes shown in Table 3‐1.  The

areas for each class for 1987 and 1997 were then calculated.  Although data for 1992 were

available, a similar analysis for California where the change over two 5‐year periods was

included (1987–1992 and 1992–1997) indicated that using two periods did not appear to add any

further insights into the dynamics of land‐use and carbon stock change (Brown et al. 2004).  

Thus, the research team only examined the change over the 10‐year period 1987 to 1997.

3.1.4. Carbon Density of Agricultural Land 

The baseline analysis for the agricultural sector focuses on carbon in vegetation only, including

above‐ and belowground (roots) components. Carbon in vegetation is estimated as 50% of the

biomass of the vegetation.  

Carbon Stocks for Non-Woody and Woody Crops 

A difficulty in estimating the biomass of non‐woody annual crops is caused by the seasonal

change of the vegetation. During the non‐growing season, there is little biomass in annual

crops, while at the peak of the growing season just before harvest, biomass can be high.

Considering that litter production is usually low in these crops, peak biomass is assumed to be

equivalent to the annual primary production of the crops on the land. In many cases the

majority of the biomass (or production) is removed from the field at harvest.  An approximate

temporal average of the biomass was used to derive the carbon stock. The biomass in cultivated

non‐woody crops was estimated based on three data sources: (1) crop biomass from the U.S.

Department of Agriculture – National Agriculture Statistics Service (USDA NASS; see

www.usda.gov/nass/sso‐rpts.htm), (2) length and timing of harvest cycles, and (3) the relative

abundance of each crop type.

Carbon stocks of horticultural crops have less seasonal variation, but data on carbon stocks for

these crops are scarce. Yield data from the USDA NASS represents only the biomass of the

harvest—a useful estimate of peak biomass for non‐woody crops, but only a small portion of

the standing biomass for woody crops. Thus estimates were instead derived from consultation

with extension agents, university researchers, and government officials in combination with

literature searches, principally to determine typical stocking densities (number of trees per unit

area), tree diameters, and tree heights.  Biomass could then be estimated from tree diameter and

height using a regression equation (Winrock unpublished).  The stocking densities were

combined with estimates of biomass per plant to arrive at an estimate of biomass carbon density

in metric t C/ha. For fruit orchards and bush fruits, multiple crop types were included and the

relative abundance of each crop type in the state, derived from USDA NASS, determined the

area‐weighted mean carbon stock that was used in this analysis (Table 3‐2).
45

Table 3-2. Estimates of the average carbon stock 

(t C/ha) for each of the crop types in Oregon 

 Crop type 

Average C stock 

(t C/ha) 

Fruit orchards 26.9 

Nut orchards 36.1 

Vineyards 4.3 

Bush fruits 4.1 

Berry fruits 1.8 

Other horticulture 4.5 

Non-woody crops 1.5 

Soil carbon stocks are not included in this report because the research team assumed that most

agricultural land has been under cultivation long enough that changes in soil carbon would be

minimal to non‐existent under current practices.  The stability of soil carbon on cultivated land

was confirmed by the study of DeClerck and Singer (2003), who showed that the percent change

in soil carbon under row crops in California remained constant over an approximate period of

50 years.  Interestingly, DeClerck and Singer also found the same trend for tree crops, but found

an increase in soil carbon over the past 50 years for soils under viticulture (about a 1.7‐fold

increase) and pasture (about a 1.6‐fold increase).  These results are difficult to apply in baseline

determination because the results were reported as an increase in percent carbon with no

indication of changes in soil bulk density; calculating changes in carbon stocks requires not only

the change in percent carbon but also the change in soil bulk density.   

Estimates of the carbon stocks in non‐agricultural lands (such as urban/transportation, and all

of the “other” class) are assumed to be zero.  This assumption is probably reasonable for

“other,” because this class contains mostly barren lands, but for urban/transportation there is

likely to be more carbon than in non‐woody croplands.  Urban development often contains

significantly more (but unknown) amount of biomass in trees and shrubs that homeowners and

local municipalities plant than in the agricultural lands that they replace.  This is an area of

further research—estimating the amount of carbon in biomass of urban areas as a function of

density, and other factors.  

Change in Stocks 

When a change in agricultural land use occurred it was assumed in this analysis that the entire

carbon stocks in vegetation present before the change would be emitted into the atmosphere as

carbon dioxide.  This is a reasonable assumption, given the necessity to clear the land to plant

alternative crops or initiate urban development.   

Regarding changes in land use to agricultural crops, it is assumed that the change occurred at

the midpoint of the period under analysis (in 1992)—five years before 1997 and five years after
46

1987.  For non‐woody crops such as vineyards, bush, and berry crops and other horticulture

crops, it is reasonable to assume that in five years, these crop types will have reached their

predicted steady‐state biomass.  The same assumption cannot be applied to orchards, which

will take longer than five years to attain their maximal biomass.  Instead, the biomass

accumulation that might have occurred in five years of growth for fruit and nut orchards was

estimated based on conservative estimations of stocking density, tree heights, and diameters at

five years of age (Table 3‐3).

Table 3-3. The estimated average biomass carbon 

accumulation after five years of growth for fruit and nut 

orchards in Oregon (t C/ha) 

 Crop type Average biomass carbon accumulation 

Fruit orchards 2.1

Nut orchards 1.8 

In addition, it can be expected that fruit and nut orchards will continue to accumulate biomass

for many years.  The research team therefore applied an average biomass accumulation to areas

of orchards that remained constant over the ten years of the analysis.  The rate of biomass

accumulation was determined by estimating the stocks at years 40 and 60 and dividing the

difference by 20 to get an annual accumulation.  The annual accumulation was multiplied by 10

to give an accumulation for the ten years 1987 to 1997 (Table 3‐4).   

Table 3-4. The estimated average biomass carbon 

accumulation over 10 years of growth for fruit and nut 

orchards in Oregon (t C/ha). This growth rate is for existing 

orchards—that is, for areas unaffected by land-use change. 

 Crop type Average biomass carbon accumulation 

Fruit orchards 3.4

Nut orchards 5.6 

3.1.5. Uncertainty 

Uncertainty in NRI Data 

The estimated margin of error (95% confidence interval) for the area of cultivated cropland in

1997 is 6.7% for Oregon (NRCS 2000).  For areas presented at finer scales (county level, specific

crop) or for changes in area, the margin of error will be significantly higher.

Uncertainty in Carbon Stock Data 

To evaluate the confidence in the estimated carbon stocks, ranges were determined (Table 3‐5)

based on the ranges in diameter, height, biomass, and planting density provided by the sources

consulted, as described in Section 3.1.4.
47

Table 3-5. Estimated ranges in average carbon stock 

for each crop type in Oregon (t C/ha) 

Crop type Range in C stocks (t C/ha)

Fruit orchards 16.3–44.3 

Nut orchards 17.8–69.0 

Vineyards 2.4–6.7 

Bush fruits 3.2–5.0 

Berry fruits 1.4–2.2 

Other horticulture 3.4–5.7 

Non-woody crops 1.0–2.0 

Weighting the deviations from the mean by area and carbon stock gave a mean deviation value

for carbon stocks of 42%.

3.2. Results 

3.2.1. Statewide Land Use and Land Use Change 1987–1997 

The total area of Oregon is 25.48 million ha, of which 49% is covered by the NRI and the

remainder is federal land falling outside the scope of the NRI.

In 1997 agricultural land in Oregon, including both perennial woody and annual non‐woody

lands, was estimated as 1.51 million ha, or 5.9% of the state’s land area.  The area of woody

cropland was 3.6% of the total area under agricultural cultivation (Figure 3‐1).   

Woody Crops

Non Woody Crops

Pasture/Rangeland

Forest

Urban/Transportation

Other

1997

Figure 3-1. Proportional area for land uses in Oregon in 1997, based on 

NRI data (non-federal lands only) 
48

Table 3-6. Areas (ha) and changes in areas (ha) for land use types in Oregon 

from the NRI dataset 

1987 1997 Change % Change

Woody crops

Fruit orchards 22,218 19,749 ‐2,469 ‐11.1

Nut orchards 10,401 10,846 445 +4.3

Vineyards 809 3,764 2,955 +365.3

Bush crops 1,577 2,388 811 +51.4

Berry crops 8,053 11,697 3,644 +45.3

Other horticulture 13,719 6,032 ‐7,687 ‐56.0

Total woody crops 56,777 54,476 ‐2,301 ‐4.1

Non‐woody crops

Row/Close crops 1,533,813 1,460,281 ‐73,532 ‐4.8

Other land uses

Pasture/Rangeland 4,564,368 4,546,643 ‐17,725 ‐0.4

Forest 5,108,123 5,103,186 ‐4,937 ‐0.1

Urban/Transportation 422,385 494,503 72,118 +17.1

Other 793,495 819,882 26,387 +3.3

TOTAL 12,478,961 12,478,961

Overall, Oregon experienced a 4.8% (75,833 ha) loss in agricultural land area during the 10‐year

period from 1987–1997.  This included a 4.8% loss in area of non‐woody crops and 4.1% loss in

area of woody crops (Table 3‐1).  In the same time period there were small decreases in the area

of pasture/rangeland (0.4%) and non‐federal forest (0.1%) and increases in the area of

urban/transportation (17.1%) and the Other category (3.3%) (Table 3‐6, Figure 3‐2).

-10%

-5%

0%

5%

10%

15%

20%

Woody Crops

Non Woody Crops

Pasture/RangelandForestUrban/Transportation

Other

Change in coverage 1987-1997

Figure 3-2. Proportional change in area between 1987 and 

1997 for broad land uses in Oregon 
49

3.2.2. Changes in Specific Land-use Type 

Agricultural area in Oregon in 1997 was dominated by non‐woody crops (96%; Figure 3‐3).  

Among the woody crops, fruit orchards make up 36%, nut orchards 20%, vineyards 7%, bush

crops 4%, berry crops 22%, and other 11%.

Fruit Orchards

Nut Orchards

Vineyards

Bush Crops

Berry Crops

Other Horticulture

Non Woody Crops

1997

Figure 3-3. Proportional coverage of each agricultural land use in Oregon in 1997 

The 4.1% loss in area of woody crops between 1987 and 1997 was composed of a 11% decrease

in fruit orchards (2,469 ha) and a 56% decrease in other horticulture (7,687 ha), partially

balanced by a 4% increase in nut orchards (445 ha), a 365% increase in vineyards (2,955 ha), a

51% increase in bush fruits (811 ha), and a 45% increase in berry fruits (3,644 ha) (Table 3‐6,

Figure 3‐4).
50

-100%

-50%

0%

50%

100%

150%

200%

250%

300%

350%

400%

Fruit Orchards

Nut Orchards

Vineyards

Bush Crops

Berry Crops

Other Horticulture

Non Woody Crops

Change in coverage 1987-1997

Figure 3-4. Proportional change in area between 1987 and 1997 for agricultural 

land uses in Oregon 

There was a net loss in area in each of the land uses, excluding vineyards, to development. The

largest losses to development were 18,698 ha from non‐woody crops, 22,946 ha from

pasture/rangeland, and 26,063 ha from forest.  Forest regrowth occurred on 48,159 ha of

pasture/rangeland (Table 3‐7).

The large gains in area of vineyards and berry crops were at the expense of nut orchards (805

ha), other horticulture (121 ha), non‐woody crops (3,076 ha) and rangeland (2,630 ha).  The loss

in other horticulture was predominantly to non‐woody crops (58%).
51

Table 3-7. Land-use change transition matrix, showing the source and direction of changes in Oregon 1987–1997. A ne

loss of area from the land use in the row to the land use in the column. 

 Land Use Type Unchanged 

Fruit 

Orchards 

Nut 

Orchards Vineyards Bush Crops Berry Crops

Other 

Horticulture 

Non-Woody 

Crops Rangeland Forest 

Fruit Orchards 19,021 -1,457 -202 Nut Orchards 8,296 -405 -405 1,902 283 Vineyards 809 405 890 1,497 162 Bush Crops 1,416 728 243 Berry Crops 5,180 405 121 2,186 1,133 Other Horticulture 5,504 -121 -4,492 -567 Non-Woody Crops 1,382,698 1,457 -1,902 -890 -728 -2,186 4,492 -8,094 1,457 Rangeland 4,376,304 202 -283 -1,497 -243 -1,133 8,094 -48,159 Forest 5,040,619 -162 567 -1,457 48,159 
52

3.2.3. County-Level Estimate of Agricultural Land Area 

As mentioned previously, NRI data is not designed for use at the county level; results are given here

for illustrative purposes. Woody cropland is primarily concentrated in the western counties (Figure 3‐

5), west of the Cascade Range, but comprises less than 2% of the total land area, with the exception of

four counties (Washington, Yamhill, Polk, and Hood River), where up to 8% of the land area is in

woody cropland  (Figure 3‐5a). The greatest proportion of non‐woody agricultural land is in the

northern part of the state along the Columbia River, with several counties having over 8% and two

counties over 24% of the total land area in non‐woody crops. However, much of the drier, southeast

portion of the state also has some non‐woody agricultural land (2%–8% of the land area) (Figure 3‐5b).

Fifteen Oregon counties recorded net changes in area of woody crops (Figure 3‐6a).  Losses occurred in

eight, with one county (Clackamas) losing more than 4,000 ha of berry crops and other horticulture.  

Most of the conversion was to the Urban/Transportation and Other categories. Seven counties gained in

area of woody crops, with Multnomah, Washington, and Linn counties gaining the most.

As for non‐woody crops, one county (Umatilla) lost more than 15,000 ha, and seven counties lost more

than 4,000 ha. The largest gains in non‐woody cropland were in the western (especially southwestern)

counties, with Douglas, Curry, and Josephine counties gaining more than 1,000 ha each (Figure 3‐6b,

Table 3‐8).
53

0 30 60 90

Miles

Figure 3-5. Distribution of woody (a) and non-woody (b) cropland by county in Oregon in 1997. Values indic

land area in each county occupied by agricultural land.

0.01% - 2%2.01% - 8%8.01% - 16%16.01% - 24%24.01% - 32%0%

a b
54

0 30 60 90

Miles

Figure 3-6. Distribution of change in area in woody (a) and non-woody (b) cropland in Oregon by county. Values indicasign indicates a loss in area from 1987 to 1997, and a plus sign indicates a gain in area in the same period.

-21,894 to -15,000-14,999 to -10,000-9,999 to -4,000-3,999 to -11 to 500501 to 1,0001,001 to 4,000No Change

a

b
55

Table 3-8. The county-level coverage (ha) for specific agricultural land uses and the change in coverage in Oregon 1987 t

County High‐Carbon Crops Low‐Carb

Fruit Orchards Nut Orchards Vineyards Bush Crops Berry Crops Other Horticulture Row / Cl

1987 1997 1987 1997 1987 1997 1987 1997 1987 1997 1987 1997 1987 

Baker 0 0 32,174 Benton 648 648 27,277 Clackamas 1,214 1,214 445 405 2,226 1,093 3,642 648 27,034 Clatsop 324 Columbia 121 121 769 0 162 162 2,631 Coos 1,659 1,619 Crook 27,115 Curry 0 81 11,898 Deschutes Douglas 445 202 0 243 5,747 Gilliam 88,670 Grant 21,975 Harney 75,477 Hood River 6,637 6,273 0 162 243 0 162 162 202 Jackson 3,197 2,954 8,094 Jefferson 34,845 Josephine 0 486 2,631 0 2,711 Klamath 43,262 Lake 60,543 Lane 1,376 931 44,760 Lincoln 324 324 Linn 283 283 324 2,914 83,651 Malheur 122,988 Marion 1,174 769 931 931 809 2,590 3,035 1,862 91,705 Morrow 126,590 Multnomah 728 1,336 5,302 
56

Table 3-8 (cont’d) 

County High‐Carbon Crops Low‐Carb

Fruit Orchards Nut Orchards Vineyards Bush Crops Berry Crops Other Horticulture Row / Cl

1987 1997 1987 1997 1987 1997 1987 1997 1987 1997 1987 1997 1987 

Polk 3,521 3,197 1,457 2,428 809 1,821 283 0 1,497 567 39,701 Sherman 72,886 Tillamook Umatilla 1,174 1,174 240,311 Union 526 526 70,620 Wallowa 24,282 Wasco 3,804 3,764 62,688 Washingto 3,804 3,885 283 283 1,052 3,076 283 0 28,369 Wheeler 6,516 Yamhill 1,740 890 1,336 1,174 0 1,295 364 0 1,659 1,983 43,465 TOTAL 22,218 19,749 10,401 10,846 809 3,764 1,577 2,388 8,053 11,697 13,719 6,032 1,533,813 
57

3.2.4. Carbon Stock of Agricultural Land During 1987–1997 

The vegetation of agricultural lands in Oregon contained about 3.2 million tons carbon in 1997.  

Between 1987 and 1997, conversion of agricultural land was responsible for a total loss of about 160,000

tons of carbon, including 49,800 t C from woody crops and 110,300 t C from non‐woody crops (Table

3‐9).  These losses are equal to 4.9% of the standing stock in 1987 for woody crops and 4.8% for non‐

woody crops, giving a total loss of 4.8% for all crops combined.

Table 3-9. Carbon stocks (t C) and changes in carbon stocks (t C) 

for land use types in Oregon 

Land Use Type 1987 1997 Change

Woody crops

Fruit orchards 597,665 577,862 -19,803

Nut orchards 341,146 323,169 -17,977

Vineyards 3,479 16,185 12,707

Bush crops 6,466 9,791 3,325

Berry crops 14,495 21,055 6,559

Other horticulture 61,736 27,144 -34,592

Total woody crops 1,024,986 975,206 -49,781

Non‐woody crops

Row / Close crops 2,300,720 2,190,422 -110,298

TOTAL 3,325,706 3,165,627 -160,079

Contributing to the loss in carbon stocks from woody crops were 19,800 t C from fruit orchards,

18,000 t C from nut orchards, and 34,600 t C from other horticulture, principally Christmas trees (Table

3‐10).  Losses were partially offset by a gain of almost 13,000 t C in vineyards and smaller gains in bush

and berry fruits, but these gains were still greatly less than the losses due to conversion of orchards and

Christmas tree plantations.

Of the total net loss of carbon from agriculture of 160,000 tons, 63,000 tons were lost to development

(urban/transportation).  These losses came predominantly from fruit orchards, nut orchards and

row/close crops (Table 3‐10).  There were losses to the expansion of rangelands from fruit orchards

(5,400 t C) and row/close crops (12,100 t C), but these were offset by losses in rangeland to nut orchards,

vineyards, berry, and bush crops.  The largest single loss was from row/close crops to the Other

category (72,700 t C), including water, marshland, barren land, and non‐cultivated farmland.  It is

probable that most of this loss in carbon stocks simply represents agricultural land taken out of

cultivation.   

Ninety‐seven percent of the gross gains in carbon in fruit orchards were from the growth of existing

stands as opposed to new plantings; the equivalent percentage for nut orchards was 91%.  For other

crop types it is assumed that there are little to no continued gains in carbon beyond the initial years of

growth.
58

Table 3-10. The land use origins and destinations of changes in carbon stocks in agriculture in Oregon between 1987 aindicates a net loss of carbon stocks from the land use in the row to the land use in the column. 

Growth of

existing 

stands

Fruit 

Orchards 

Nut 

Orchards Vineyards

Bush 

Crops

Berry

Crops

Other 

Horticulture

NonWoody 

Crops Rangeland Forest

Fruit Orchards 64,671 -56,253 -5,446 Nut Orchards 46,460 -13,274 -24,565 3,424 510 Vineyards 1,740 3,828 6,439 696 Bush Crops 2,987 996 Berry Crops 728 219 3,934 2,040 Other Horticulture -547 -20,215 -2,550 Non-Woody Crops 2,186 -2,853 -1,335 -1,092 -3,279 6,738 -12,141 2,186 
59

When converted to carbon dioxide equivalents, the total stocks in 1997 on agricultural land in Oregon

are estimated at 11.6 MMtCO2eq (Table 3‐11).  There was a net loss of 0.6 MMtCO2eq between 1987 and

1997.  This is equal to an annual source of 0.06 MMtCO2eq.  Sixty‐nine percent of the stocks are

estimated to be in non‐woody vegetation and 69% of the estimated carbon source arose from these crop

types.

Table 3-11. Carbon stocks on agricultural land in Oregon and their change 

(in MMtCO2e) 

Year

Agricultural 

Land Woody Non-woody 

1987 12.2 3.8 8.4 

1997 11.6 3.6 8.0 

1987–1997 -0.6 -0.2 -0.4 

3.2.5. Carbon Stocks of Agricultural Land by County 

Three counties (Umatilla, Gilliam, and Jefferson) had a loss of carbon from conversion of non‐woody

cropland of over 10,000 tons in the ten years from 1987 to 1997; Umatilla alone had a loss of almost

33,000 t C.  There were lesser but still significant losses of carbon from conversion of non‐woody

croplands in Crook, Harney, Marion, Morrow, and Washington counties (Table 3‐12).

In all, 25 counties (69%) had a loss in carbon from conversion of non‐woody cropland to other land

uses totaling almost 127,000 t C, partially offset by a total gain of 16,500 t C in seven counties (four

counties’ carbon stocks remained unchanged).  The two counties with the largest carbon accumulation

due to expansion of non‐woody vegetation, Douglas and Josephine, nonetheless had a net loss of

carbon due to losses in area of fruit orchards and other horticulture (such as Christmas trees),

respectively.

As for losses of carbon due to conversion of woody crops, large losses occurred in Yamhill County

(fruit and nut orchards), Marion and Polk counties (fruit orchards and other horticulture), Clackamas

and Josephine counties (other horticulture), and Lane, Washington, and Yamhill counties (nut

orchards).  Large gains occurred in Polk, Yamhill, and Josephine counties(vineyards), Hood River and

Wasco counties (fruit orchards), Multnomah County (bush crops), and Linn, Marion, and Washington

counties (berry crops).

Figure 3‐7a illustrates that the greatest overall losses in carbon stocks from woody croplands in 1987–97

came in Washington, Yamhill, Polk, Clackamas, Lane, and Josephine counties, accounted for primarily

by losses of fruit and nut orchards and other horticulture. The greatest gains were in Hood River,

Wasco, and Linn counties, accounted for by increases in fruit orchards and berry crops. As for changes

in carbon stocks resulting from the conversion of non‐woody croplands, the greatest losses were in

Gilliam, Morrow, Umatilla, Harney, Crook, Jefferson, Marion, and Washington counties. The greatest

gains were in Columbia, Curry, Douglas, Josephine, Linn, Polk, and Wallowa counties, but no county

gained more than 6,000 t C (Figure 3‐7b).
60

0 30 60 90

Miles

Figure 3-7. County-scale change of carbon stocks in Oregon, 1987 to 1997, in (a) high-carbon crops (orchards and vine

(non-woody) crops. Values in tons of carbon.

-33,000 to -12,000-11,999 to -6,000-5,999 to -500-499 to -11 to 500501 to 6,0006,001 to 12,000No Change

a b
61

Table 3-12. Change in carbon stocks (t C) between 1987 and 1997 across crop types in Oregon 

County Woody Crops             Non‐woody

Crops

TOTAL

  

Fruit

Orchards

Nut

Orchards Vineyards

Bush

Crops

Berry

Crops

Other

Horticulture

Row/Close

Crops

Baker 0 0 0 0 0 -305 -305

Benton 0 0 0 0 0 -3,279 -3,279

Clackamas 6,799 0 -164 -2,039 -13,473 -182 -9,059

Clatsop 0 0 0 0 0 -486 -486

Columbia 0 0 0 -1,384 0 1,395 11

Coos 0 0 0 -72 0 0 -72

Crook 0 0 0 0 0 -8,742 -8,742

Curry 0 0 0 146 0 2,126 2,271

Deschutes 0 000 0 00

Douglas 0 0 996 0 0 4,917 69

Gilliam 0 0 0 0 0 -12,081 -12,081

Grant 0 0 0 0 0 -728 -728

Harney 0 0 0 0 0 -9,288 -9,288

Hood River 0 697 0 -437 0 -303 11,486

Jackson 0 0 0 0 0 -2,672 -301

Jefferson 0 0 0 0 0 -10,988 -10,988

Josephine 0 2,090 0 0 -11,840 4,979 -4,771

Klamath 0 0 0 0 0 -60 -60

Lake 0 0 0 0 0 -1,092 -1,092

Lane ‐9,389 0 0 0 0 -3,218 -12,607

Lincoln 0 000 0 00

Linn 1,586 0 04,662 0 1,215 7,463

Malheur 0 0 0 0 0 -5,099 -5,099

Marion 5,212 0 03,206 -5,279 -6,738 -11,871

Morrow 0 0 0 0 0 -6,252 -6,252

Multnomah 0 0 2,493 0 0 -1,823 670

Polk 6,945 4,352 0 -509 -4,185 1,154 -9,484

Sherman 0 0 0 0 0 -4,977 -4,977

Tillamook 0 000 0 00

Umatilla 0 0 0 0 0 -32,841 -28,851

Union 0 0 0 0 0 -2,186 -397

Wallowa 0 0 0 0 0 729 729

Wasco 0 0 0 0 0 -911 10,798

Washington ‐17,062 0 03,643 -1,274 -8,741 -23,433

Wheeler 0 0 0 0 0 -1,700 -1,700

Yamhill ‐12,068 5,569 0 -655 1,458 -2,126 -27,657

TOTAL ‐19,803 ‐17,977 12,707 3,325 6,559 ‐34,592 ‐110,298 ‐160,079
62

3.3. Non-CO2 Greenhouse Gas Emissions 

The primary non‐CO2 greenhouse gas emitted from croplands is nitrous oxide (N2O), with

approximately 296 times the global warming potential of CO2.  Nitrous oxide is emitted from

agricultural soils especially after fertilizer application.  A second important non‐CO2 gas is

methane (CH4), with approximately 23 times the global warming potential of CO2.  Methane is

emitted during manure management and through livestock enteric fermentation.

The Governor’s Advisory Group on Global Warming (2004) for the State of Oregon reports an

annual source of N2O from agricultural soil management in Oregon equal to 3.4 MMTCO2eq in

1990 and 3.8 MMTCO2eq in 1995.  This is about sixty times the emission estimated here for CO2

emissions attributable to agricultural land conversion (0.06 MMTCO2eq/year).  The emissions

from manure management and enteric fermentation totaled 2.4 MMTCO2e in 1990 and

2.6 MMTCO2e in 1995.   

The CO2 emissions from agricultural land conversion are calculated here to be equal to

0.059 MMTCO2e for the ten year period between 1987 and 1997.  Emissions from land

conversion therefore make up approximately 1% of the total emissions for Oregon’s agricultural

sector.

3.4. Conclusions 

Agricultural land in Oregon in 1997 represented 5.9% of the total land area. Non‐woody crops

were about 96% of all agricultural land; among woody cropland, fruit orchards, nut orchards,

and berries represent the largest proportions.  Woody crops are concentrated primarily west of

the Cascades, but in most counties constitute less than 2% of the total land area, while non‐

woody crops are concentrated primarily in the northern counties along the Columbia River and

constitute up to 24% of the land area.  Statewide, there was a loss of agricultural land of 4.8%

between 1987 and 1997, coming in approximately equal proportions from conversion of woody

and non‐woody cropland.  The greatest cause of change was conversion to urban

development/transportation.  Woody cropland increased over the period in seven counties and

decreased in eight, while non‐woody cropland decreased in eight counties with only small

gains in other counties. Vineyards and berry crops increased in area in selected counties, but not

to the extent of offsetting the overall loss in agricultural land.   

Total carbon stocks in all agricultural land in Oregon were estimated at 3.2 million tons.  

Between 1987 and 1997, there was a total loss of about 160,000 tons of carbon, or 4.8% of the

carbon stored in agricultural lands in 1987.  Gains in carbon stocks in vineyards and other

woody crops only partially offset the overall loss in carbon in fruit and nut orchards, other

horticulture, losses to urban development, and losses to the Other category representing

agricultural land taken out of cultivation. In CO2 equivalent terms, total agricultural carbon

stocks in Oregon in 1997 were 11.6 MMTCO2eq, and the net loss 1987–1997 disregarding non‐

CO2 greenhouse gas emissions was 0.6 MMTCO2eq, equivalent to an annual source of

0.06 MMTCO2eq.  Sixty‐nine percent of this annual source came from non‐woody cropland

conversion.  At the county level of analysis, the greatest loss of carbon from non‐woody

cropland came in Umatilla, Gilliam, and Jefferson counties, while the greatest loss of carbon
63

from woody cropland was in Yamhill, Marion, Polk, Clackamas, Josephine, Lane, and

Washington counties. Gains in carbon stocks from increasing area of woody crops in several

counties only partially offset the overall loss of carbon stocks from conversion of non‐woody

cropland.

Non‐CO2 greenhouse gas emissions from N2O (emitted from agricultural soils after fertilizer

application) dwarf the annual CO2 source from agricultural land conversion in Oregon.

Table 3‐13 summarizes changes in agricultural land area and carbon stocks for Oregon between

1987 and 1997.

Table 3-13. Summary of agricultural land area and changes in area, 

carbon stocks, and changes in stocks, for Oregon 1987–1997 

Parameter Units Oregon

Proportion of agricultural land to total land   % 5.9

Change in agricultural land area, 1987–1997

    Change in woody cropland area

    Change in non‐woody cropland area

Hectares

(%)

‐75,833 (4.8%)

‐2301 (4.1%)

‐73,532 (4.8%)

Total carbon stocks in agricultural land, 1997 MMTCO2e 11.6

Change in carbon stocks in agricultural land   MMTCO2e  ‐0.6

Estimated net annual source (emissions) from

agricultural lands, disregarding non‐CO2

greenhouse gas emissions

     From woody cropland

     From non‐woody cropland

MMTCO2e  ‐0.06

‐0.02

‐0.04

Estimated net annual source from non‐CO2

greenhouse gas emissions, 1995

MMTCO2e  ‐3.8
64

4.0 References 

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by‐state estimates. Gen. Tech. Rep. NE‐310. Newtown Square, Pennsylvania: U.S.

Department of Agriculture, Forest Service, Northeastern Research Station. 42 p.

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not regulated by herbaceous biomass or density.” Ecology 80: 2385–2396.

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Jenkins, J. C., D. C. Chojnacky, L. S. Heath, and R. A. Birdsey. 2003. “National‐scalebiomass

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providing regularly updated agricultural statistics.
66

5.0 Glossary 

AVHRR Advanced Very High Resolution Radiometer

CH4 methane

CO2 carbon dioxide

DBH diameter at breast height

FIA U.S. Forest Service Forest Inventory and Analysis

FIADB forest inventory and analysis database

GHG greenhouse gas

GIS geographic information system  

GPS   global positioning system

km kilometer

MMTCO2e million metric tons CO2 equivalent

N2O nitrous oxide

NASS National Agriculture Statistics Service

NDVI normalized differential vegetation index

NOAA National Oceanic and Atmospheric Administration

NRCS U. S. Department of Agriculture ‐ National Resources Conservation Service

NRI National Resource Inventory

OSU Oregon State University

t CO2e tons of CO2 equivalent

μm micrometer

USDA United States Department of Agriculture

USFS U.S. Forest Service

USGS U.S. Geological Survey

WESTCARB West Coast Regional Carbon Sequestration Partnership