HomeMy WebLinkAboutFort Yukon Biomass Resource Assessment 2010
Fort Yukon Biomass Resource Assessment
Will Putman, Forestry Director
Tanana Chiefs Conference, Forestry Program
Fairbanks, Alaska
September 30, 2010
EXECUTIVE SUMMARY
As part of an effort to develop a woody biomass energy system in Fort Yukon, Alaska, a
biomass resource assessment was conducted. The purpose of the assessment was to build
a model that would serve to estimate biomass stocking, growth, sustainability, and cost
using a geographic information system (GIS) and relational database technology with
available information. Cover type was interpreted from high-resolution satellite imagery for
an area within a 5-mile radius of Fort Yukon, and combined with ownership data,
interpreted site class information, defined management restrictions, forest inventory
information, cost parameters, and an array of parameters and assumptions used to
estimate growth. The model as initially established produced an estimate of 462,958 green
tons of woody biomass within the project area, with an estimated annual allowable harvest
of 9,517 tons. The model is intended to be used as a land management planning tool, and
was designed to be as flexible as possible, allowing future information developments and
refinements to be used to evaluate biomass supply, cost, and sustainability under a variety
of scenarios.
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TABLE OF CONTENTS
INTRODUCTION..................................................................................................................... 1
DATA COMPONENTS ........................................................................................................... 2
Forest Inventory Data ................................................................................................... 2
Remotely-sensed imagery .......................................................................................... 4
Cover Type Data ................................................................................................................ 4
Site Class ............................................................................................................................... 7
Ownership Data ................................................................................................................ 7
Management Concerns or Restrictions ............................................................. 10
DATA PROCESSING ........................................................................................................... 10
Spatial Data Intersection .......................................................................................... 10
Proximity to village....................................................................................................... 10
Assigning stocking figures to stands ................................................................. 10
Estimating AAC and assigning rotation and growth parameters to
stands.................................................................................................................................... 12
Cost modeling .................................................................................................................. 16
ANALYSIS AND RESULTS ............................................................................................... 16
ACKNOWLEDGEMENTS .................................................................................................... 21
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LIST OF TABLES
Table 1. Wood density of tree species of interior Alaska........................ 12
Table 2. TCC Forest Inventory Strata, Associated Project Cover Types, and
woody biomass green tons/acre. .......................................... 13
Table 3. Maturity codes assigned to Cover Types................................. 15
Table 4. Cost parameters used in the analysis..................................... 15
Table 5. Woody biomass tonnage and Annual Allowable Cut (AAC) by cover
type ................................................................................. 17
Table 6. Woody biomass tonnage and Annual Allowable Cut (AAC)......... 19
Table 7. Woody biomass tonnage and Annual Allowable Cut (AAC)......... 19
Table 8. Woody biomass tonnage and Annual Allowable Cut (AAC)......... 20
Table 9. Woody biomass tonnage by species....................................... 20
Table 10. Woody biomass tonnage and Annual Allowable Cut (AAC)....... 21
LIST OF FIGURES
Figure 1. Location of Fort Yukon in Alaska................................................................. 2
Figure 2. Fort Yukon project area as defined by Ikonos imagery extent....... 5
Figure 3. Fort Yukon project area cover type classes............................................ 6
Figure 4. Fort Yukon project area site classes.......................................................... 8
Figure 5. Fort Yukon project area land ownership.................................................. 9
Figure 6. Fort Yukon project area management designations......................... 11
Figure 7. Fort Yukon project area woody biomass tons/acre........................... 18
Figure 8. Fort Yukon project area woody biomass cost of harvest,
transport, and management........................................................................ 22
INTRODUCTION
Rapidly increasing fossil fuel costs have resulted in a heightened sense of urgency when
considering the ability of small communities to absorb these costs and maintain some sense
of community sustainability. There are few places where this is more severe than rural
communities in interior Alaska, where fossil fuel dependence, energy costs, and remoteness
are conspiring to produce an energy crisis that is becoming increasingly difficult for these
small communities to deal with. These conditions have resulted in increased interest in any
available form of alternative energy that may possibly be deployed. In interior Alaska, the
presence of apparently large amounts of woody biomass has increased the consideration of
biomass energy systems to help address this crisis.
Fort Yukon is a community in interior Alaska located near the confluence of the Porcupine
and Yukon Rivers in the Yukon Flats in Northeastern interior Alaska (Figure 1). Fort Yukon
is not located on a contiguous highway system and is accessible only by air and river, with
resultant high costs of imported energy. To help remedy this, the residents of Fort Yukon
have been considering the installation of biomass energy systems for some time. Planning
efforts have included several organizations, including the Council of Athabascan Tribal
Governments (CATG), the Gwitchyaa Zhee Corporation (Fort Yukon’s Native village
corporation), and Alaska Village Initiatives (AVI), with funding provided at various phases of
the project by the U.S Department of Energy (DOE) and the Alaska Energy Authority (AEA).
Much planning and analysis has taken place concerning the size, type, and placement of
biomass energy facilities, and techniques and equipment for harvesting and transporting the
biomass required. However, available information has been lacking to answer basic
questions concerning the amount, availability, and sustainability of the biomass resources in
the vicinity of the community. These questions include:
• How much biomass is there in the vicinity of the community?
• What are the characteristics of the biomass (size, species, quality)?
• Where is the resource located?
• Who owns the resource?
• What are the costs associated with getting the resource to an energy facility?
• What management restrictions are there are on the resource?
• Considering growth rates, cover type conversions, and other factors, what is the
sustainability of the resource?
• How large a biomass energy facility could be economically supported on a
sustainable basis by the local biomass resource?
This report is an attempt to document an approach to answer these questions with
available information, using information management tools such as a geographic
information system (GIS) and relational databases. The process described here is meant to
present a model for the handling of information to answer these questions, and in that
regard does not constitute an end product. In those cases where information is lacking or
unavailable, assumptions have been made and documented, with the idea that improved
information in the future can be used to improve the model. It is intended that the model
itself be a useful tool in the land management required to support a proposed biomass
energy project.
In an attempt to keep the geographic extent of this project within some reasonable and
practical scope, this project will focus on that area within about a 5 mile radius of Fort
Yukon. This decision is driven by the desire to focus on those resources most easily
available to the community, and by the availability of current relevant information such as
high-resolution satellite imagery. Within that geographic extent, the analysis will consider
all land ownerships.
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Figure 1. Location of Fort Yukon in Alaska.
DATA COMPONENTS
This biomass assessment relied heavily on a computerized geographic information system
(GIS) and relational database technology to store, process, query, and analyze data. The
GIS software used was ArcGIS 9.3 from ESRI, Inc., and the relational database software
used was Microsoft Access. The GIS was used to spatially define the location of various
attributes of the landscape, the combination of those attributes for any given location on the
landscape, and the acreage associated with each combination of attributes. A relational
database was used to combine the attribute and area information produced by the GIS with
other tabular information to calculate and derive information such as biomass stocking,
growth, and annual allowable harvest. Some of the derived information was then stored
back in the GIS layers to facilitate mapping of the derived data. The spatial data used in
the GIS consisted of layers of vector data (points, lines or polygons defined by X,Y
coordinates) with associated attribute records, or raster data in the form of digital imagery
used to produce some of the vector data. The vector data was stored and managed as
feature classes in a personal geodatabase, which uses the format of a MS Access database
to store the spatial data.
Forest Inventory Data
In interior Alaska, as in many places, woody biomass is a forest resource. The process of
trying to assess the amount and location of forest resources falls under the purview of
forest inventories, a traditional and essential component of forestry and forest
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management. Forest inventories cover a wide range of projects; they can be very broad or
quite specific, they can be intensive or extensive, they can cover broad landscapes or a very
specific land base, and they can include any one of a large number of sampling techniques,
data processing options, and analyses. This project is essentially a form of forest inventory,
with particular interests and requirements that are driven by the land management required
to support proposed biomass energy projects. Any current or past forest inventory
information for Fort Yukon’s biomass resources would be an important data source for the
current effort.
Previous forest inventory projects conducted in the vicinity of Fort Yukon include projects
conducted by the Tanana Chiefs Conference (TCC) Forestry Program in the 1980s and
1990s. A project was conducted on Fort Yukon’s ANCSA village corporation lands
(Gwitchyaa Zhee lands) in 1982, and an inventory was conducted on Native allotments in
that part of the region in 1988. TCC also conducted village inventories elsewhere in the
Yukon Flats region at Beaver (1985) and Circle (1999).
In the TCC projects, the areas included were interpreted for land cover type using high-
altitude color-infrared aerial photographs dating from the late 1970s. The Fort Yukon
village corporation inventory only included those ANCSA village selections within one-half
mile of the Yukon River, and the allotment inventory was restricted to Native allotment
parcels, no larger than 160 acres each, in the region. Within each project, forested cover
types covering the highest proportion of area were selected for field sampling by randomly
selecting accessible stands within those types. Field sampling was accomplished by visiting
the selected stands on the ground and installing a series of variable radius plots and
conducting tree measurements. Sample trees were measured for species, tree diameter,
tree height, and percent defect, and a small number of white spruce trees were measured
for radial growth and age. The collected field data were processed and compiled in the
office with a computer to produce timber volume per acre figures by species and size class
within strata defined as groupings of similar cover types. The volume per acre figures were
then extrapolated to all forested areas within the extent of the projects.
The timber volume per acre figures calculated in the inventories included both board-foot
and cubic-foot estimates. For the purposes of evaluating a forest resource as an energy
source, it is most appropriate to focus on the cubic-foot estimates, since they represent the
total woody biomass volume on the main stem of trees below a minimum top diameter
(usually 4”), and not just the amount of recoverable wood when processing trees for
lumber.
There are a number of serious limitations in this available forest inventory data that need to
be considered. The inventories are quite “extensive”, that is, the geographic scope was
relatively large and the intensity of the field sampling was relatively low. Forest cover types
with relatively low acreages were not field sampled at all, but were lumped into similar
types that were sampled, with resulting inaccuracies in the volume estimates. The
photography used to produce the land cover typing was a nearly a decade old at the time
the inventories were conducted, and is now 30 years old or more, and does not take into
account the changes that have no doubt occurred on the landscape. Only village
corporation selections or Native allotments are included in the inventories, with no
consideration given to the land cover on other ownership classes. The data collection was
focused on the standing stock, and what little growth information was collected is difficult to
apply in any meaningful way with regards to estimates of site and forest growth. Only the
biomass represented by the main boles of trees is included in the volume estimates, with no
attention paid to whole tree biomass or non-timber species such as alder or willow. That
being said, the data contained in those old inventory projects still provide a useful starting
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point for evaluation of biomass energy resources. The primary contribution of these data
lies in the per-acre stocking estimates that can be applied to similar cover types in the area.
Remotely-sensed imagery
This project is reliant on the existence of some means to identify the land cover on areas in
the vicinity of Fort Yukon. In the available older forest inventories, this was accomplished
with the use of 1:63,360 and 1:31,680 scale color-infrared aerial photography collected in
the late 1970s. The dynamic nature of boreal forest landscapes, with disturbances
commonly resulting from forces such as river erosion and wildfires, requires the use of more
current data sources if available. The requirements of this data would include:
• Detailed enough to be useful in determining land cover at a suitable scale.
• Extensive enough to cover an area large enough to conduct a meaningful analysis
• Available in digital, georeferenced forms to be able to be included in modern
geographic information systems for data storage, query, and analysis.
In the case of Fort Yukon, these criteria are met by the existence of Ikonos high-resolution
satellite imagery, collected in 2005 by Space Imaging and made available through a non-
commercial unrestricted license. The available imagery is actually a mosaic of several
images, and covers an area approximately 9x9 miles centered over Fort Yukon (Figure 2).
The imagery has 4 bands (red, green, blue, near-infrared), and has a spatial resolution of 1
meter. The extent of this imagery defines the extent of this biomass assessment project,
although the model represented by this project can be expanded to cover additional
imagery in the future.
Cover Type Data
The Ikonos imagery was classified to identify homogenous cover type areas. In forestry
terminology, these areas can be thought of as “stands”, and are created and stored as
polygons in a GIS (Figure 3). For this project, delineation and attributing of the polygons
was accomplished using a combined automated and manual approach. In a cooperative
arrangement with the Fairbanks Area Office of the State of Alaska Division of Forestry, an
attempt was made to process the imagery with eCognition software to delineate and
attribute cover type areas. However, it was determined that there were practical limitations
as to how detailed the classification could be as determined by the software, so the decision
was made to use the software to delineate polygons, attribute them for broad cover type
designations for species, then manually interpret the imagery in an attempt to attribute the
polygons to a more detailed level for size class and density. At both levels, existing cover
type calls from the older inventories and ground truth data from sampled forest stands were
referenced whenever possible to guide the classification.
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Figure 2. Fort Yukon project area as defined by Ikonos imagery extent.
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Figure 3. Fort Yukon project area cover type classes.
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Site Class
It was assumed that site productivity is a critical factor when attempting to determine the
growth of biomass on the landscape, a key factor when evaluating biomass sustainability.
For the purposes of this analysis, 4 broad site classes were defined and a GIS layer was
created to describe the location of site class areas in the project area. The 4 site classes
defined were:
• Site Class 0 – areas incapable of producing woody biomass such as rivers, lakes,
seasonally submerged sandbars, wetland bogs, etc.
• Site Class 1 – areas of relatively poor site in terms of woody biomass production,
such as poorly drained areas and north-facing slopes with underlying continuous
permafrost. These sites may have cover types such as tall shrubs, dwarf shrubs
(dwarf birch, etc.), black spruce or other slow-growing unproductive cover types.
• Site Class 2 – areas of intermediate productivity such as lower slopes adjacent to
wetlands, areas underlain by permafrost but with some productive tree cover, etc.
• Site Class 3 – Areas of relatively high productivity such as south-facing slopes, well-
drained benchlands, and productive riparian sites.
In the GIS, all areas of the project area were classified into one of the 4 site classes, using
the cover type polygons as the basis for classification and interpreting the areas on the
satellite imagery. This information exists as a feature class in the geodatabase, and is
displayed as a layer in the GIS (Figure 4).
Ownership Data
A key component of the analysis is the determination of which individual or organization
owns or has management responsibilities for the lands on which the biomass resource is
found. This is accomplished through the use of a GIS layer that defines land ownership in
the vicinity of Fort Yukon. Several data sources were used to compose this layer:
• Conveyed ANCSA corporation lands, acquired from Doyon, Ltd. This serves to
identify lands owned by Gwitchyaa Zhee, the local Fort Yukon ANCSA village
corporation, and Doyon, Ltd., the regional ANCSA corporation.
• Generalized land status, acquired from State of Alaska Department of Natural
Resources. This dataset identifies land ownerships, but only to the section level. In
other words, a section (square mile) is the smallest land area identified by these
data; multiple ownerships within a section, and overlapping selections make this a
potentially confusing dataset to employ. Nonetheless, it is useful for identifying
various forms of State and Federal land status.
• Native allotments, acquired from the Bureau of Land Management (BLM). These are
relatively small parcels (up to 160 acres) applied for and transferred to individual
Alaska Natives through the authority of the Allotment Act of 1906. Native allotments
are usually retained in a restricted federal trust status, where they are owned by
individual allottees or their heirs, but are managed in trust by the Federal
government. In most cases, this management responsibility has been compacted or
contracted to tribal organizations, including Tanana Chiefs Conference or, in the case
of many of the parcels near Fort Yukon, the Gwichyaa Zhee Gwich’in Tribal
Government. These data were created by BLM by digitizing Native allotment parcels
from township Master Title Plats (MTPs) or from survey data created and stored in
BLM’s Spatial Data Management System (SDMS).
• Other ownerships, including military reserves, as identified on the township Master
Title Plat at Fort Yukon.
These data were combined into one comprehensive ownership layer and stored in the GIS
(Figure 5). No attempt was made to research the detail of private land and townsite lots
within and immediately adjacent to the community of Fort Yukon itself, since it was felt that
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Figure 4. Fort Yukon project area site classes.
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Figure 5. Fort Yukon project area land ownership.
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most of the biomass management and harvesting would be focused on lands beyond the
immediate vicinity of the village. That information could be researched and incorporated
into the model at any point in the future, if necessary.
Management Concerns or Restrictions
An attempt was made to include the ability to identify areas of different management
restrictions or concerns. This can be anything that is of importance to the land owners or
the community related to the potential of harvesting and transporting biomass. Culturally
important sites, areas of subsistence use or other resource use, aesthetic concerns, barriers
to operations, management restrictions, or any other factor that may affect the availability
of the biomass resources for energy use could be incorporated into this layer. Additional
community input and changing social conditions could make this a very dynamic dataset, so
it is important to retain the ability to change this information over time and reassess
biomass resource availability. As a starting point, this layer was created by identifying 3
broad land designations and creating a GIS layer (Figure 6):
• Areas that define the village itself.
• Areas of potential all-season road access.
• Areas requiring crossing of rivers to access.
DATA PROCESSING
Starting with the basic datasets described above, there were several data processing steps
that were conducted to prepare for data analysis.
Spatial Data Intersection
The GIS polygon layers for cover type, ownership, site class, and management were
“intersected”. Intersection is a GIS overlay process that combines features from multiple
overlapping layers into one layer that contains all the attributes of the input layers. In this
case 3,662 cover type polygons, 159 ownership polygons, 1,585 site class polygons, and 3
management polygons were intersected to produce a feature class with 4,407 polygons
attributed for cover type, ownership, site class, and management, referred to hereafter as
the “intersected layer”.
Proximity to village
The distance of each stand from the proposed boiler site location in the village will affect the
cost of transporting the resource. This can easily be determined in the GIS through a
number of techniques. The existence of a transportation plan with proposed access routes
would be an important information source to help evaluate these transportation costs, but
the lack of such a plan forced the use of a much simpler method. The centroid point of each
polygon in the intersected layer was determined and stored in a feature class, and the
distance in miles from each centroid point to the proposed boiler site was calculated using
geoprocessing tools in the GIS software. The village proximity distance value was stored as
an attribute in the intersected layer for further analysis.
Assigning stocking figures to stands
The TCC inventory database contains a table for stand stocking that consists of records with
timber volume data by species and size class for individual sampled stands and defined
strata. A query was developed that summarized the cubic-foot volume per acre figures in
this table for individual strata and converted them into green tons per acre figures using
researched conversion data (Table 1). A list of the cover types present in the cover type
GIS layer was prepared, and each cover type was associated with a stratum in the TCC
inventory database. Strata from projects conducted at Fort Yukon, Circle and Upper Yukon
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Figure 6. Fort Yukon project area management designations.
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Tree Species Green Density (lbs/cubic
foot)
Air-dry density (lbs/cubic
foot)
White spruce 36 31
Black spruce 32 28
Paper birch 48 38
Aspen 43 27
Balsam poplar 38 24
Tamarack 47 37
Table 1. Wood density of tree species of interior Alaska.
Native allotments were used, with strata being subjectively associated with cover types
giving consideration to the actual stocking data that comprise the strata summaries and
other factors (Table 2). Using the polygon cover type codes, the GT/acre figures were
related to polygons in the intersected GIS layer and stored in the attribute table.
Multiplying the GT/acre figure by the acreage for each stand produces an estimate of total
green tons/acre for all areas in the project.
Estimating AAC and assigning rotation and growth parameters to stands
In order to assess sustainability, the traditional forestry concept of Annual Allowable Cut
(AAC) was applied. AAC is deemed to be the maximum level of annual harvest that is
possible in perpetuity without diminishment of the level of harvest or the amount and
quality of the resource. There are a variety of techniques used to calculate AAC, including
the “Hanzlik formula”, which was designed to attempt to deal with areas still in an
unmanaged “old-growth” state. The Hanzlik formula uses mature standing volume, rotation
length, and growth (increment) as parameters required to calculate AAC:
Allowable cut (AAC) = (Mature Standing Volume / Rotation ) + Growth
Standing volume is determined from the inventory data as described above, but figures for
rotation length and growth are more difficult to determine.
“Rotation”, or “rotation length” refers to the hypothetical length of time required for a forest
stand to reforest, grow, and replace itself after harvest. At first glance this appears quite
simple, but there are a number of complicating factors, including:
• What species the stand regenerates to – different species will grow at different rates
and mature at different time intervals.
• Site potential may vary over time; in fact, in interior Alaska, the act of harvesting (or
other disturbances, such as fire) may change the growth potential of a site.
• Anything other than even-aged management may complicate the determination of
rotation length, particularly if it involves multiple tree species and multiple stand
entries in a rotation.
• Differing economic conditions or other factors may dictate a different array of forest
products requiring material to reach different sizes or ages to be marketable.
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TCC Forest Inventory Stratum Included Cover types Green Tons/Acre
Circle HP3 HWP3 29.8
Circle SP3 WSP3 50.0
Circle SS/CWS1 WSS/CWS1 8.2
Circle SS/CWS3 WSS/HWP2, WSS/CWP2,
WSS/CWP3, WSS/CWS3,
WSS/HWS2, WSS/CWS2
25.2
Circle SS/HP3 WSS/HWP3 42.9
Fort Yukon CWP1 CWP/WSR1 16.2
Fort Yukon CWP2 CWP2, CWP3, CWP2/TS 15.6
Fort Yukon SP1 WSP1/DS, WSP1/HWR,
WSP1, WSP1/TS
21.4
Fort Yukon SP2 WSP2, WSP2/HWR,
WSP2/TS
24.9
Fort Yukon SS1 WSS1, WSS1/TS 18.6
Fort Yukon SS2 WSS2/TS, WSS2 32.6
UY Native Allotments BSP1 BSP1, BSP2, BSP3 8.5
UY Native Allotments CWP1/TS CWP1, CWP1/TS, CWS1 5.7
UY Native Allotments CWS2 CWS2, CWS3 17.4
UY Native Allotments HP/SP2 HWP/WSP1, HWP/WSP2 16.1
UY Native Allotments HP1 HWP1 11.8
UY Native Allotments HP2 HWP2 19.4
UY Native Allotments SP/CWP2 CWP/WSP2, CWP/WSP3,
CWS/WSP2, CWS/WSS2,
CWS/WSS3, WSP/CWP2,
WSP/CWP3
27.2
UY Native Allotments SP/HP2 WSP/HWP1, WSP/HWP2 29.9
UY Native Allotments SP/HP3 HWP/WSP3, WSP/HWP3 31.3
UY Native Allotments SS3 WSS3 55.7
Table 2. TCC Forest Inventory Strata, Associated Project Cover Types, and woody biomass
green tons/acre.
Similarly, “growth” can be a concept that may be simple to visualize, but involves a number
of factors that make it difficult to determine with any precision. The ability to gauge the
capacity of woody biomass to grow and replace itself after harvest is a critical component of
any assessment that would attempt to evaluate the sustainability of the resource.
Unfortunately, this is one area where hard data to drive the analysis is in short supply. It is
an exceedingly complex situation that is being modeled – growth rates of individual trees
and the stands they grow in vary by site, species, tree age, stand age, stand density,
reproductive capacity, disturbance regime, and other factors, and all in cumulative and
interactive ways. Growth models for the boreal forest are in development at the University
of Alaska Fairbanks and with the U.S. Forest Service and may prove to be useful. In the
meantime, this effort applies some broad and exceedingly gross assumptions in an attempt
to get a handle on growth and sustainability.
For both growth and rotation, the approach taken was to establish an optimal value for
each, then adjust the values based on other conditions. Based on TCC inventory data,
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maximum biomass stocking in high-volume spruce stands, presumably on good sites, is in
the neighborhood of 60 tons/acre. Employing the concept of mean annual increment (MAI),
and assuming a stand age of 120 years to produce this volume, this would indicate a
maximum of 0.5 tons/acre/year on the best sites. Interestingly, roughly similar rates can
be arrived at with productive hardwood stands; TCC’s inventory data indicates total biomass
tons of well-stocked cottonwood, birch, or aspen stands to be in a somewhat lower range
(~20-50 tons/acre), with lower stand ages to be expected to produce those volumes (~50-
80 years). Based on this, a value of 0.5 tons/acre/year is assumed as an optimum mean
growth rate.
Optimal rotation length is assumed to be 50 years, based on a hypothetical rotation length
for the deciduous broadleaf tree species (birch, aspen, and balsam poplar). Although white
spruce has traditionally been the favored species for timber management in interior Alaska,
it is assumed that managing for hardwoods is desirable from a woody biomass perspective
because of faster juvenile growth rates, shorter rotations, ease in regenerating, importance
in wildlife habitat, and desirability from a community wildfire protection perspective.
Several key assumptions were made to facilitate adjusting the optimum growth and rotation
figures based on the availability of existing information. The assumptions used in this
analysis to estimate growth and rotation include:
1. Relatively mature stands will show less current increment (growth).
2. Fully stocked stands will show best realization of potential increment.
3. Lower site quality will result in longer rotations
Implementing the first assumption relies on coming up with a method of determining
maturity for a forest stand. Maturity is an attribute that would be a function of stand age
which, since it is lacking in the available data, can only be arrived at for a stand indirectly
through interpretation of the other cover type attributes of species, size class, and density.
All cover types in the project were assigned a maturity code from 0 to 4 using the above
guidelines in a VERY subjective way (Table 3). Each of the 4 maturity codes were assigned
a relative growth rate expressed as a proportion of optimum growth; these proportions
could be greater than 1.0 for those ages (maturity level) where growth may be greater, and
less for those ages where growth may be less, such as early in the establishment of a stand
or in an older decadent stand. The optimum growth proportions assigned to each maturity
level were:
Maturity Growth proportion
1 0.3
2 1.2
3 1.2
4 0.5
The second assumption of stand stocking levels influencing relative growth can be dealt with
more directly using the stand density component of the cover type calls. Each of the 3
density codes were assigned a relative growth rate expressed as a proportion of optimum
growth in the same manner as the maturity codes:
Stand Density Growth Proportion
1 (0-30% crown closure) 0.3
2 (30-60% crown closure) 0.6
3 (60-100% crown closure) 1.0
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Maturity Code Cover Types
0 DS, TS/W, TS, R, B, W, DS/W, Cu, Ba
1 HWR/WSR, CWR/WSR, CWR, HWR/DS, BSD/TS, BSD, HWR,
WSR/CWR, HWR/BSD, WSR, WSR/HWR, WSR/DS
2 HWP1, HWP/WSP1, WSS1, CWP/WSP2, WSS/CWS1, CWP1,
CWP1/TS, CWP2, CWP2/TS, CWS1, CWP/WSR1, WSP1/DS,
WSP3, WSS2/TS, WSS2, WSP2, WSP1/HWR, WSP/CWP2,
WSP1, WSS1/TS, BSP1, WSP/HWP1, BSP2, WSP1/TS
3 CWP/WSP3, BSP3, CWS/WSP2, CWP3, WSS/HWS2,
WSS/CWS2, WSP2/TS, WSP2/HWR, CWS2, WSP/HWP3,
WSS/HWP2, HWP/WSP2, WSP/HWP2, HWP2, CWS/WSS2,
WSP/CWP3, WSS/CWP2
4 WSS3, WSS/CWS3, HWP3, HWP/WSP3, CWS3, WSS/HWP3,
CWS/WSS3, WSS/CWP3
Table 3. Maturity codes assigned to Cover Types.
Cost Type Cost
Stumpage (payments to owner), cost per ton $ 5
Harvest Costs
Costs per acre $200
Costs per ton of woody biomass $ 10
Transportation costs
Cost/ton/mile all-season access $ 2
Cost/ton/mile winter access $ 4
Reforestation – cost per acre $100
Table 4. Cost parameters used in the analysis.
Similarly, the third assumption of relative growth varying by site quality is handled by
taking the site class codes as assigned to areas on the landscape and adjusting the optimal
rotation of 50 years upwards for poorer site classes:
Site Class Rotation (years)
0 none
1 90
2 70
3 50
By applying queries in the database, allowable cut figures were calculated for every stand in
the project area. Growth for each individual stand is determined by multiplying the
optimum growth rate (0.5 tons/acre/year) by the growth proportion number assigned to the
maturity code associated with that stand, and multiplied again by the growth proportion
number assigned to the stand density of the stand. Rotation length for each stand is
determined by applying the rotation length assigned to the site class of the stand. The
resulting figures for growth and rotation are used with the stocking of each stand in the
15
Hanzlik formula to generate an AAC for each stand. The resulting AAC figures for each stand
are not meant to mean that some calculated portion of every stand is a portion of the
volume cut in any given time frame, but refers to the contribution that the resource
represented by that stand contributes to the harvestable volume of biomass over the
project as a whole. Through the other attributes assigned to each stand by creating the
intersected layer, both standing stock and AAC figures can be broken out by ownership,
management option, proximity to the village, or other stand attributes.
Cost modeling
In addition to estimates of the amount and growth of the woody biomass resource, it is also
useful to estimate the costs involved in making the biomass available to an energy facility.
This estimation could include the modeling of costs associated with harvesting, transport,
reforestation, stumpage, and other costs. At this stage of the project, much is unclear in
terms of type of harvest and equipment to be used, the nature and extent of the
transportation network to be established, and other cost factors, but all of these factors can
be modeled in the GIS and reported back from the database. Table 4 presents a list of cost
factors used in this analysis as an example of how these costs could be modeled.
Per acre costs are converted into costs per ton for each polygon. The parameter for harvest
costs per acre functions to drive harvest costs up for low volume stands. Transportation
costs are driven by distances from the village, which at this point are defined only by
straight-line proximity distance measurements determined in the GIS, and by the access
type as defined by the management option layer. All of these costs could be adjusted,
refined, ignored, other costs added, etc., and the overall cost scenarios recalculated. One
interesting ramification of this is that it is possible to evaluate AAC based on different cost
thresholds.
ANALYSIS AND RESULTS
Woody biomass green tonnage and AAC figures were summarized from the database by
cover type, cover type class, ownership, management option, and distance from the village
(Tables 5, 6, 7, and 8). It must be considered that any combination of these attributes can
be queried from the database, and can also be displayed in the GIS to evaluate graphically
the location of the resource. What is shown here is merely a sample of how the data may
be summarized and displayed.
Total standing stock of woody biomass for the extent of the project is 462,958 green tons
(Table 5, Figure 7). The bulk of that is on village corporation land (283,484 tons, 61%),
followed by regional corporation land (102,663 tons, 22%) and Native allotments (76,840
tons, 17%). The majority of the standing stock (72%) is found in white spruce poletimber
and mixed species poletimber cover types (Table 6). Of particular importance is the result
that 92% of the standing stock is in the “water access” management designation, indicating
additional costs and logistical concerns for accessing most of the biomass resources in the
project area (Table 7). 83% of the standing stock is greater than 3 miles from the boiler
site in the village (Table 8). When broken out by species, 84% of the standing stock is
white spruce (Table 9). It may be worth considering that the highest value of the resource
may be as dimension lumber when available; for that reason, the tonnage of white spruce
sawtimber board feet was extracted from the database and estimated to be 15% of the
standing stock (Table 9). This calculation considers only the board foot volume of white
spruce greater than 9 inches DBH, and does not include recoverable board foot volume in
smaller material or the slabwood and other “waste” that could be recovered as woody
biomass when logs are sawn up for lumber.
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Standing Green Tons
Cover Type Acres Green Tons AAC
BSD 177 0 27
BSD/TS 116 0 17
BSP1 9 74 1
BSP2 19 160 2
BSP3 74 627 9
CWP/WSP2 137 3,743 75
CWP/WSP3 172 4,688 93
CWP/WSR1 57 927 19
CWP1 401 2,303 40
CWP1/TS 273 1,568 29
CWP2 1,119 17,498 339
CWP2/TS 139 2,171 43
CWP3 364 5,698 111
CWR 1,295 0 194
CWR/WSR 5 0 1
CWS/WSP2 6 174 3
CWS/WSS3 9 256 5
CWS1 5 27 1
CWS2 148 2,579 51
CWS3 74 1,290 26
HWP/WSP1 20 329 5
HWP/WSP2 458 7,377 105
HWP/WSP3 363 11,370 166
HWP1 35 410 6
HWP2 152 2,947 40
HWP3 24 707 9
HWR 1,935 0 290
HWR/BSD 202 0 30
HWR/DS 1,396 0 209
HWR/WSR 914 0 137
WSP/CWP2 182 4,960 84
WSP/CWP3 108 2,939 58
WSP/HWP1 900 26,911 384
WSP/HWP2 920 27,503 422
WSP/HWP3 455 14,261 220
WSP1 946 20,226 322
WSP1/DS 133 2,833 40
WSP1/HWR 260 5,552 78
WSP1/TS 521 11,126 179
WSP2 4,060 101,242 1,934
WSP2/HWR 270 6,736 96
WSP2/TS 39 962 18
WSP3 1,626 81,294 1,466
WSR 817 0 123
WSR/CWR 74 0 11
WSR/DS 341 0 51
WSR/HWR 1,177 0 177
WSS/CWP2 80 2,009 40
WSS/CWP3 29 741 15
WSS/CWS1 26 216 4
WSS/CWS2 37 941 19
WSS/CWS3 23 578 12
WSS/HWP2 16 404 8
WSS/HWP3 49 2,091 42
WSS/HWS2 4 108 2
WSS1 30 561 10
WSS1/TS 642 11,965 238
WSS2 1,710 55,672 1,099
WSS2/TS 5 169 3
WSS3 252 14,037 276
Totals: 25,829 462,958 9,517
Table 5. Woody biomass tonnage and Annual Allowable Cut (AAC) by cover type
17
Figure 7. Fort Yukon project area woody biomass tons/acre.
18
Standing reen Tons
Cover Type Class Acres Green Tons AAC
Black spruce 395 860 56
Cottonwood poletimber 2,296 29,238 562
Cottonwood sawtimber 227 3,895 78
Hardwood poletimber 211 4,063 55
Mixed poletimber 3,773 105,010 1,631
Mixed sawtimber 281 7,516 150
Reproduction 8,155 0 1,223
White spruce poletimber 7,853 229,971 4,134
White spruce sawtimber 2,639 82,404 1,627
Totals: 25,829 462,958 9,517
Table 6. Woody biomass tonnage and Annual Allowable Cut (AAC) by cover type class.
Standing Green Tons
Ownership Management Acres Green Tons AAC
ANCSA
Regional Corp.
Water access 9,093 102,633 1,760
Total: 9,093 102,633 1,760
ANCSA
Village Corp.
All-season 6,756 27,333 1,044
Water access 29,993 256,152 5,045
Total: 36,749 283,484 6,089
Native
Allotments
All-season 1,910 9,120 289
Water access 4,167 67,720 1,380
Total: 6,077 76,840 1,669
Grand Total: 51,919 462,958 9,517
Table 7. Woody biomass tonnage and Annual Allowable Cut (AAC) by ownership and
management.
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Distance from Standing Green Tons
Boiler Site Acres Green Tons AAC
0-1 miles 130 2,428 47
1-2 miles 2,158 25,704 652
2-3 miles 3,547 50,714 1,190
3-4 miles 6,854 132,415 2,708
4-5 miles 8,336 163,706 3,170
5-6 miles 4,456 81,780 1,629
6-7 miles 349 6,211 120
Totals: 25,829 462,958 9,517
Table 8. Woody biomass tonnage and Annual Allowable Cut (AAC)
by distance from boiler site.
Total Sawtimber Board Foot
Species Green Tons Green Tons
White Spruce 387,281 67,995
Black Spruce 1,683
Birch 24,337
Aspen 7,441
Balsam Poplar 42,215
Totals: 462,958 67,995
Table 9. Woody biomass tonnage by species.
Calculated Annual Allowable Cut (AAC) totals 9,517 tons per year. There are a number of
“reproduction” cover types that show no standing stock volume, but do contribute to the
AAC figures. It is implied that the reproduction cover types do not currently have standing
volume, or at least are not affiliated with timber-bearing strata in the inventory database,
but do have growing stock in those stands that can be considered when estimating the
growth potential of the project area.
Table 10 and Figure 8 display available green tons of woody biomass by cost threshold,
using the assumed cost parameters described above. Using those parameters, 90% of the
woody biomass is available for $50 per green ton or less. Annual Allowable Cut is also
calculated for these cost thresholds in an attempt to describe what the AAC might be if
operations were limited by costs, although it is difficult to compare this across the board
since the reproduction areas that contribute to AAC because of growth occurring on those
sites do not have a standing stock of woody biomass that is possible to assign cost
estimates to.
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Total Green Tons
Total Cost per Ton Available AAC Available
< $30 13,236 226
< $40 182,566 3,298
< $50 416,819 7,424
< $60 457,984 8,164
< $70 458,470 8,172
< $80 461,090 8,218
< $90 462,958 8,250
Table 10. Woody biomass tonnage and Annual Allowable Cut (AAC)
by cost threshold.
ACKNOWLEDGEMENTS
Many thanks are owed to the staff at the Fairbanks Area Office of the State of Alaska
Division of Forestry, especially Doug Hanson for repeated conversations and consultations,
and Dan LaBarre for the eCognition classification work. Fabian Keirn, TCC Forester, did the
manual cover type classification refinement work. And, thanks to Alaska Village Initiatives
and their representatives, especially Bill Wall and Peter Olsen, for supporting funding for this
work and for promoting the development of alternative renewable energy in rural Alaska.
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Figure 8. Fort Yukon project area woody biomass cost of harvest, transport, and
management.
22