HomeMy WebLinkAboutMcGrath Biomass Resource Assessment 2011
McGrath Biomass Resource Assessment
Will Putman, Forestry Director
Tanana Chiefs Conference, Forestry Program
Fairbanks, Alaska
March, 2011
McGrath Biomass Resource Assessment
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EXECUTIVE SUMMARY
As part of an effort to develop a woody biomass energy system in McGrath, 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. Land cover was interpreted from high-resolution satellite imagery for
an area with ranging from 3.3 to 5.7 miles from McGrath, defined as Phase 1 of the
assessment. In addition, archived land cover from a forest inventory conducted on McGrath
ANCSA village corporation lands (MTNT, Ltd.) was included in the assessment, defined as
Phase 2. The land cover data was compiled with local forest inventory data to determine
woody biomass stocking levels, and combined with ownership data, interpreted site class
information, defined management restrictions, cost parameters, and an array of parameters
and assumptions used to estimate growth.
The model as initially established produced an estimate of 390,909 green tons of woody
biomass within the Phase 1 of the project area, with an estimated annual allowable harvest
of 10,800 tons. The analysis of Phases 1 and 2 combined yielded estimates of 1,566,094
green tons of woody biomass, with an annual allowable harvest of 44,142 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
Geographic scope ................................................................................................................. 3
Remotely-sensed imagery ................................................................................................ 4
Cover Type Data ................................................................................................................... 4
Forest Inventory Data ........................................................................................................ 7
Site Class ................................................................................................................................. 9
Ownership Data .................................................................................................................. 11
Management Concerns or Restrictions ...................................................................... 13
Road access .......................................................................................................................... 13
DATA PROCESSING ........................................................................................................... 16
Spatial Data Intersection ................................................................................................ 16
Proximity to village ............................................................................................................ 16
Assigning stocking figures to stands .......................................................................... 16
Estimating AAC and assigning rotation and growth parameters to stands . 18
Cost modeling ...................................................................................................................... 21
ANALYSIS AND RESULTS ............................................................................................... 23
ACKNOWLEDGEMENTS .................................................................................................... 35
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LIST OF TABLES
Table 1. Land cover classification and coding system used................................ 8
Table 2. Wood density of tree species of interior Alaska.................................... 17
Table 3. TCC forest inventory strata, associated project cover types,
and woody biomass green tons/acre....................................................... 17
Table 4. Maturity codes assigned to Cover Types .................................................. 20
Table 5. Cost parameters used in the analysis....................................................... 22
Table 6. Woody biomass green tonnage (GT) and Annual Allowable Cut
(AAC) by cover type ........................................................................................ 25
Table 7. Woody biomass tonnage and Annual Allowable Cut (AAC)
by cover type class.......................................................................................... 27
Table 8. Woody biomass tonnage and Annual Allowable Cut (AAC) by
ownership and management, Phase 1..................................................... 28
Table 9. Woody biomass tonnage and Annual Allowable Cut (AAC) by
ownership and management, Phase 2..................................................... 29
Table 10. Woody biomass tonnage and Annual Allowable Cut (AAC) by
ownership and management, Phases 1 and 2...................................... 30
Table 11. Woody biomass tonnage by species, Phase 1.................................... 31
Table 12. Woody biomass tonnage by species, Phase 2.................................... 31
Table 13. Woody biomass tonnage by species....................................................... 31
Table 14. Woody biomass tonnage and Annual Allowable Cut (AAC)
by distance from boiler site.......................................................................... 32
Table 15. Woody biomass tonnage and Annual Allowable Cut (AAC)
by cost threshold, Phase 1........................................................................... 32
Table 16. Woody biomass tonnage and Annual Allowable Cut (AAC)
by cost threshold, Phase 1 and 2.............................................................. 32
LIST OF FIGURES
Figure 1. Location of McGrath in Alaska...................................................................... 2
Figure 2. Location of McGrath Project Phases........................................................... 3
Figure 3. Ikonos imagery and Phase 1 project area.............................................. 5
Figure 4. Phase 1 and 2 land cover classes and recent wildfire perimeters.6
Figure 5. McGrath project area site classes............................................................. 10
Figure 6. McGrath project area land ownership..................................................... 12
Figure 7. McGrath project area management designations.............................. 14
Figure 8. McGrath area roads........................................................................................ 15
Figure 9. McGrath Phase 1 project area woody biomass tons/acre............... 25
Figure 10. McGrath Phase 1 and 2 project area
woody biomass tons/acre............................................................................. 26
Figure 11. McGrath project area woody biomass cost of harvest,
transport, and management, Phase 1..................................................... 33
Figure 12. McGrath project area woody biomass cost of harvest,
transport, and management, Phase 1 and 2........................................ 34
McGrath Biomass Resource Assessment
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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.
McGrath is a community in Southwestern interior Alaska located on the Kuskokwim River at
the mouth of the Takotna River (Figure 1), approximately 220 air miles northwest of
Anchorage and 275 air miles southwest of Fairbanks. Although it serves as a hub for the
Upper Kuskokwim region, McGrath 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 McGrath have been considering the installation of biomass
energy systems. Planning efforts have included several organizations, including MTNT Ltd.
(the local Alaska Native Claims Settlement Act (ANCSA) village corporation), the McGrath
Native Village Council (MNVC), Alaska Village Initiatives (AVI), and Tanana Chiefs
Conference (TCC). Funding for the phase of the project that includes this biomass resource
assessment effort has been provided by the Alaska Energy Authority (AEA) through the
State of Alaska’s Renewable Energy Fund.
Although planning efforts have taken place for a potential biomass energy facility in
McGrath, 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.
Figure 1. Location of McGrath 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, annual allowable harvest, and cost information. 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 (areas)
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.
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Figure 2. Location of McGrath Project Phases.
Geographic scope
The geographic scope of this assessment was originally defined to be the extent of an
available high-resolution satellite image scene on file at Tanana Chiefs Conference, covering
an area of 122 square miles within a radius of 4.5 to 9.3 miles of the center of McGrath.
However, because of difficulty encountered when interpreting that imagery, a different
image with a smaller extent was used, covering 56 square miles over an area 3.3 to 5.7
miles from the center of McGrath. This extent was further adjusted by expanding the scope
of the analysis by 203 square miles to include archived forest inventory data covering the
extent of McGrath village corporation lands selected by MTNT Ltd. In this report, the initial
extent of the project defined by the extent of the satellite imagery used will be referred to
as “Phase 1”, and the extension of the scope defined by the inclusion of the MTNT lands will
be referred to as “Phase 2” (Figure 2). Since the area covered by Phase 2 is primarily in
one ownership (MTNT Ltd.) and the source of the interpreted land cover is from a different
source (late 1970’s aerial photography), the nature and comprehensiveness of the data and
the analysis differs somewhat between the 2 phases.
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Remotely-sensed imagery
This project is reliant on the existence of some means to identify the land cover on areas in
the vicinity of McGrath. 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. This
detail can take the form of higher spatial resolution (smaller image pixels) and/or
higher spectral resolution (multiple narrower spectral bands).
• 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, processing, query, and analysis.
In the case of McGrath, these criteria were met by the existence of QuickBird high-
resolution satellite imagery, collected in 2002 by DigitalGlobe, Inc. and made available
through a civil organization license acquired by TCC. The proposal that generated the
funding made available for this project was based on the existence of this imagery.
However, difficulties were encountered while processing and interpreting the imagery to
generate the land cover data that is the foundation of this assessment. Specifically, the
image had been acquired early in the growing season (May 20, 2002), and differences in
the timing of leaf emergence in vegetation across the image made the digital classification
portion of the process inconsistent. As an alternative, a different image was made available
by the State of Alaska, Division of Forestry and used instead. This image is an Ikonos
image from Space Imaging, acquired June 27, 2002. The image is orthorectified, has 4
bands (red, green, blue, near-infrared), has a horizontal spatial resolution of 1 meter, and
has a footprint of 56 square miles centered over McGrath (Figure 3).
Cover type data
For Phase 1 of the project, 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. 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.
Phase 2 of the project used land cover information generated for MTNT Ltd. lands as part of
a forest inventory conducted by TCC Forestry in 2000 and 2001. This land cover
information was created by stereo interpretation of high-altitude color-infrared aerial
photography dating from the late 1970’s and digitization of the delineated stand information
into a GIS. Land cover as interpreted for both Phase 1 and Phase 2 is shown in Figure 4.
Figure 3. Ikonos imagery and Phase 1 project area.
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Figure 4. Phase 1 and 2 land cover classes and recent wildfire perimeters. McGrath Biomass Resource Assessment 6
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For both phases, forested stands were attributed with a cover type code that included a
determination of primary tree species, primary tree size class (dwarf, reproduction,
poletimber, sawtimber), secondary tree species, secondary tree size class, and overall tree
density (low, medium, and high crown closure) (Table 1). Non-forested areas were
attributed for cover types such as water, tall shrub, bog, barren/cultural, etc.
There have been several forest fires in the McGrath area whose effects were not captured
on the satellite imagery used in the Phase 1 analysis or the older aerial photography used in
the Phase 2 analysis. These include the Broken Snowshoe fire of 2009 on the north side of
the Kuskokwim River upstream of McGrath, and the Vinisale fire of 2002 that burned large
areas east and south of the McGrath. The spatial data defining the perimeter of those 2
fires were acquired from the BLM/Alaska Fire Service and overlain on the land cover data in
the GIS (Figure 4).
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
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 McGrath’s biomass resources would be an important data source for the
current effort.
Previous forest inventory projects conducted in the vicinity of McGrath include a project
conducted by the Tanana Chiefs Conference (TCC) Forestry Program in 2000 and 2001 on
McGrath village corporation lands (MTNT Ltd.), and a very limited project conducted on
Native allotments in the region in 1992. The McGrath village corporation inventory was
used in this project to drive the biomass stocking estimates. Elsewhere in the upper
Kuskokwim region, TCC Forestry has also conducted an inventory for village corporation
lands at Nikolai (1985), and projects for the village corporation lands at Takotna and Telida
that consisted only of interpreted land cover from aerial photography with no field sampling.
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 village
corporation inventories included those ANCSA village selections as determined at the time of
the projects, and the Native allotment inventory was restricted to Native allotment parcels,
no larger than 160 acres each. 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.
Tree Species Codes
WS White spruce
BS Black spruce
HW Hardwood (Paper birch or Aspen)*
CW Balsam poplar (cottonwood)
* difficulties in differentiating between birch and aspen on satellite imagery and
aerial photography cause these species to be combined into one species code.
Tree Size Class Codes
D Dwarf (< 4.5” DBH, relatively mature)
R Reproduction (< 4.5” DBH, young)
P Poletimber (4.5”-9.0” DBH)
S Sawtimber (> 9.0” DBH)
Tree Density Codes
1 Low density (<30% crown closure)
2 Medium density (30%-60% crown closure)
3 High density (> 60% crown closure)
Other land cover codes
R River
W Water
Cu, Cu97, Cu98 Cultural, human development
Ba Barren
B Bog
TS Tall shrub
TSW Tall shrub wet
DS Dwarf shrub
DSW Dwarf shrub wet
DM Dry meadow
WM Wet meadow
Br Burned
When a second tree species is coded, this indicates that the second species
constitutes at least 30% of the stand. A size class is coded for each tree species
coded, but only one overall density is coded. Density is only coded for stands with
poletimber or sawtimber sized trees. Forested and non-forested combinations may
be combined. The “Br” burned code is used as a prefix descriptor on another code.
Examples:
BSP1 Black spruce poletimber, low density.
HWP/WSS2 Hardwood poletimber, white spruce sawtimber, medium density.
WSS2/TS White spruce medium density sawtimber mixed with tall shrub.
BrBSD Burned black spruce dwarf.
HWR Hardwood reproduction.
CWS1/TS Cottonwood sawtimber low density mixed with tall shrub
Table 1. Land cover classification and coding system used.
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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 at least 20 years old at the time the
McGrath village corporation inventory was conducted, 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 were 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
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.
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 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 and site location as the basis for classification and interpretation.
This information was stored in the geodatabase by attributing the land cover polygons for
site (Figure 5).
Figure 5. McGrath project area site classes. McGrath Biomass Resource Assessment 10
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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 McGrath. Several data sources were used to compose this layer:
• Conveyed ANCSA corporation lands, acquired from Doyon, Ltd. This serves to
identify lands owned by MTNT Ltd. including lands selected as the ANCSA village
corporation selections for McGrath, and Doyon, Ltd., the regional ANCSA corporation.
• 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 for allotments
in the vicinity of McGrath. 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).
• Land status data compiled by the State of Alaska Division of Forestry in an effort to
describe land ownership comprehensively across Alaska for wildfire management and
planning purposes. The data acquired from elsewhere and used to construct this
dataset include generalized land status acquired from BLM and processed and
maintained by State of Alaska DNR, private land parcels identified on State land
status plats, Borough tax lot information where it exists, and other sources.
These data were combined into one comprehensive ownership layer and stored in the GIS
(Figure 6). No attempt was made to research the detail of private land and townsite lots
within and immediately adjacent to the community of McGrath itself, since it was felt that
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.
Land status research for the area covered by Phase 1 revealed the existence of several
ownership classes, including regional and village ANCSA land, State land, Native allotments,
and a relatively small amount of private land. This is not surprising, since the extent of
Phase 1 was determined by the satellite image coverage in the immediate vicinity of
McGrath regardless of ownership status. By contrast, the extent of Phase 2 was determined
by the scope of the TCC McGrath forest inventory, which was restricted to village
corporation selections at the time of the inventory. As a result, fewer ownership classes
exist in Phase 2, including MTNT land, BLM land, and Native allotments. The BLM land
exists in Phase 2 as a result of the difference between village corporation selections and
actual conveyances as of 2010, and the Native allotments were included in the TCC
inventory data as inholdings in the corporation land.
Figure 6. McGrath project area land ownership. McGrath Biomass Resource Assessment 12
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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 harvesting and transporting of 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 a
number of land designations and creating a GIS layer (Figure 7):
• Areas in and near the village itself; the “core” village areas, areas “near” the village,
and areas a “mid” distance out.
• Areas of potential all-season road access.
• Areas of potential winter road access.
• Areas of potential winter road access across rivers, requiring ice bridges.
• Areas to be treated for hazardous fuel reduction as specified in the community
CWPP.
• Areas within 66 feet of a riverbank; affected by riparian buffer strip regulations as
specified by the Alaska Forest Resources Practices Act.
Road access
Existing roads in the McGrath vicinity were digitized and stored in a GIS layer for use in the
analysis (Figure 8). The most prominent feature of the road network at McGrath is an all-
season road stretching 13 miles east of the village to Noir Hill across MTNT lands. This road
network information was used in the analysis to help estimate the transportation component
of the costs of supplying woody biomass to a facility in McGrath.
Figure 7. McGrath project area management designations. McGrath Biomass Resource Assessment 14
McGrath Biomass Resource Assessment 15Figure 8. McGrath area roads.
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DATA PROCESSING
Starting with the basic datasets described above, there were several data processing steps
that were conducted to prepare for data analysis and reporting. The spatial data
intersection and proximity determination steps described below used geoprocessing tools in
the GIS software. Subsequent steps for assigning stocking figures, estimating annual
allowable cut figures, and determining biomass supply costs were conducted using a series
of database queries in MS Access.
Spatial data intersection
The GIS polygon layers for cover type, ownership, fire history 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, 996 cover type polygons for Phase 1, 1,577 cover type polygons for Phase 2, 103
ownership polygons, 6 polygon features for recent fire history, and 29 management
polygons were intersected to produce a feature class with 4,034 polygons attributed for
cover type, ownership, site class, fire history and management, referred to hereafter as the
“intersected layer”.
Proximity to village and road distances
The distance of each polygon, or stand, from the proposed boiler site location in the village
will affect the cost of transporting the resource. This can 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
this information is currently lacking beyond what is known about the existing road network,
as captured in the roads GIS layer described above. Using geoprocessing tools in the GIS
software, 3 values were generated for each polygon in the intersected layer; straight-line
distance from the polygon to the village center, straight-line distance from each polygon to
the nearest point on the road network, and the road distance from that nearest road point
to the village center.
These distances were determined for “centroid” points determined for each polygon in the
the intersected layer. A centroid is a point that defines the “center of gravity” for an area,
or a point that represents the average horizontal location of an area. The centroid point of
each polygon in the intersected layer was determined and stored in a feature class, and the
distance from each centroid point to the village center and to the nearest point on the road
network was calculated using proximity analysis geoprocessing tools in the GIS software.
The road distance from the nearest point on the road network to the village center was
determined for each centroid using network analyst tools in the GIS software. The 3
distances were calculated in miles and stored as attributes for the intersected layer for
further processing in the cost analysis described below.
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 (GT/acre) using
researched conversion data (Table 2). 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 and cover type associations from the TCC McGrath forest
inventory were used (Table 3); those cover type associations not defined in the McGrath
inventory were subjectively assigned considering the species and stocking levels generated
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 2. Wood density of tree species of interior Alaska. White spruce, Paper birch, Aspen
and Balsam poplar figures are from the State of Alaska, Department of Commerce
(http://www.commerce.state.ak.us/ded/dev/forest_products/forest_products5.ht
m); Black spruce figures are from a Canadian website maintained by Lakehead
University in Ontario (http://www.borealforest.org/); Tamarack figures are from
an engineering website (http://www.engineeringtoolbox.com/weigt-wood-
d_821.html).
TCC Forest Inventory Stratum Included Cover types Green Tons/Acre
BSP1 BSP1, BSP1/DS, BSP1/TS,
BSP2, BSP2/DS, BSP2/TS,
BSP/HWP2, BSP/HWP3, BSP3
9.6
CWS1/TS CWP2, CWP2/TS, CWP3,
CWS1/TS, CWS2, CWS3
33.1
HWP/BSP1 HWP/BSP1, HWP/BSP2,
HWP/BSP3, HWP1/BSD
25.9
HWP/WSP3 HWP/WSP3 45.1
HWP2 HWP1, HWP1/TS, HWP2,
HWP2/BSD, HWP2/DS,
HWP2/TS
26.1
HWP3 HWP3, HWP3/BSD 49.1
WSP/HWP2 WSP/HWP1, WSP/HWP2 28.7
WSP/HWP3 WSP/HWP3 38.4
WSP2 WSP1, WSP1/TS, WSP2,
WSP2/TS
24.4
WSP3 WSP3 28.5
WSS/HWP2 HWP/WSP2, WSP/CWP2,
WSP/CWP3, WSS/CWP2,
WSS/CWP3, WSS/CWS2,
WSS/CWS3, WSS/HWP1,
WSS/HWP2
32.7
WSS/HWP3 WSS/HWP3 50.2
WSS1/TS WSS1/TS 30.7
WSS2 WSS2, WSS2/TS 31.6
WSS3 WSS3 60.6
Table 3. TCC forest inventory strata, associated project cover types, and woody biomass
green tons/acre.
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McGrath Biomass Resource Assessment
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from the inventory stocking data. Using the polygon cover type codes, the GT/acre figures
for the strata were related to polygons in the intersected GIS layer and stored in the
attribute table. Acreage for each stand in the intersected layer was calculated using tools in
the GIS software and stored in the attribute table. Multiplying the GT/acre figure by the
acreage for each stand in the intersected layer produces an estimate of total green tons of
woody biomass for each stand.
The biomass stocking estimates for those stands that were identified as recently burned
from the inclusion of the recent fire history data were reduced by 50%. This was done
because the land cover data as interpreted from the satellite imagery (Phase 1) or the aerial
photography (Phase 2) predated the fires and the recognition that not all standing woody
biomass is consumed in a wildfire. The applied 50% reduction is very subjective, but is
based on professional observation and can be adjusted in future analyses if better data
becomes available.
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 or estimate.
“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, and as a
result, the anticipated rotation length.
• 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.
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
McGrath Biomass Resource Assessment
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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,
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 mean annual increment 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 annual 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 and slower growth.
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 4), with 1 indicating young stands in early
stages of development, ranging up to 4, assigned to stands interpreted to be mature or
possibly decadent stands in a late development stage. All stands that had been burned in
recent fires (since 2002) were assigned a maturity of “1”. Each of the 4 maturity codes
were assigned a relative growth rate expressed as a proportion of optimum mean annual
increment; 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
Maturity Code Cover Types
0 B, Ba, Cu, DM, DS, DS/W,DSw, R, TS, TS/W, TSw, W, WM
1 BrBSD, BrHWP3, BrWSP/HWP3, BSD, BSD/B, BSD/DS, BSD/HWD,
BSD/TS, CWR, HWD, HWD/BSD, HWD/DS, HWD/TS, HWR, WSR,
WSR/CWR
2 BSP/HWP1, BSP/HWP2, BSP1, BSP1/DS, BSP1/TS, BSP2, BSP2/DS,
BSP2/TS, CWP1/TS, CWP2, CWP2/TS, CWS1/TS, CWS2, HWP/BSP1,
HWP1, HWP1/BSD, HWP1/TS, WSP/CWP2, WSP/HWP1, WSP/HWP2,
WSP1, WSP1/TS, WSP2, WSP2/TS, WSS/HWP1, WSS1/TS
3 BSP/HWP3, BSP3, CWP3, CWS3, HWP/BSP2, HWP/BSP3, HWP/WSP2,
HWP2, HWP2/BSD, HWP2/DS, HWP2/TS, WSP/CWP3, WSP/HWP3,
WSP3, WSS/CWP2, WSS/CWS2, WSS/HWP2, WSS2, WSS2/TS,
HWP/WSP3, HWP3, HWP3/BSD, WSS/CWP3, WSS/CWS3, WSS/HWP3,
WSS3
4 HWP/WSP3, HWP3, HWP3/BSD, WSS/CWP3, WSS/CWS3, WSS/HWP3,
WSS3
Table 4. Maturity codes assigned to Cover Types.
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
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, as well as assigning degraded growth
proportions for lower sites:
Site Class Growth proportion Rotation (years)
0 0 none
1 0.3 90
2 0.6 70
3 1.0 50
Using this approach, annual allowable cut will be seriously degraded for those areas
interpreted to be of poor site quality, by calculating a lower current growth and by using a
longer rotation in the AAC formula.
By applying a series of update 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, and multiplied again
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McGrath Biomass Resource Assessment
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by the growth proportion assigned to the site class 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
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 5 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. Per acre cost parameters
such as harvest costs per acre and reforestation costs per acre have the effect of driving up
relative costs/ton of woody biomass for low volume stands.
Estimated transportation costs were driven by distances from the village, distances from the
nearest road, and road distances, which were calculated and stored for each stand in the
intersection layer. Each type of distance had a cost/ton/mile assigned to it (Table 5). A
series of queries is executed in the database to calculate a total cost/ton for each stand in
the intersection layer:
• Stands with a distance to the village (village proximity) less than a distance
to the nearest road (road proximity) use the village proximity distance to
calculate costs:
o If a stand is located in a winter-only access area, then the winter off-
road cost parameter is multiplied by the village proximity to get a
transportation cost per ton.
o If stand is located in an all-season access area, then the all-season
off-road cost parameter is multiplied by the village proximity to get a
transportation cost.
• Stands that are closer to the nearest road than they are to the village use the
road proximity and road distance values to calculate costs:
o If a stand is located in a winter-only access area, then the winter off-
road cost parameter is multiplied by the road proximity and added to
the product of the road distance and the all-season road cost
parameter to get a transportation cost per ton.
o If a stand is located in an all-season access area, then the all-season
off-road cost parameter is multiplied by the road proximity and added
to the product of the road distance and the all-season road cost
parameter to get a transportation cost per ton.
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 road $ 2
Cost/ton/mile all-season off-road $ 4
Cost/ton/mile winter off-road $ 4
Cost/ton ice bridging costs $ 5
Reforestation – cost per acre $100
Table 5. Cost parameters used in the analysis.
In addition, those areas requiring winter road access across rivers and ice bridges are
assigned an additional fixed amount per ton to accommodate that cost (Table 5).
Harvest costs are broken into 2 components, cost/ton and cost/acre (Table 5). This is an
attempt to recognize that some costs associated with harvesting will remain relatively fixed
per ton, while other costs associated with mobilization, equipment movement, etc. may
remain relatively fixed per unit area.
Other costs associated with biomass supply could include reforestation costs and other
management costs, and stumpage payments made to a landowner. The reforestation costs
initially used in this analysis are based on a lowering of known planting costs, assuming that
some level of natural regeneration or other techniques may be used.
This cost modeling can be modified in the future with changes to the cost parameters,
modification of the modeling used to assign costs, etc. to create updated cost scenarios.
Since the cost/ton is determined by stand, as is the annual allowable cut, one interesting
ramification of this is that it is possible to evaluate AAC based on different cost thresholds.
McGrath Biomass Resource Assessment
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McGrath Biomass Resource Assessment
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ANALYSIS AND RESULTS
Woody biomass green tonnage and AAC figures were summarized from the database by
cover type, cover type class, ownership, management option, distance from the village, and
cost threshold (Tables 6 through 15). 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 Phase 1 of the project is 390,909
green tons (Table 6, Figure 9). The bulk of that is on MTNT land (289,028 tons, 74%),
followed by State land (67,538 tons, 17%), with less than 3% each on Doyon, Ltd. land,
Native allotments, BLM land, and private lands (Table 8). The majority of the standing
stock is found in mixed species poletimber cover types (43%) and hardwood poletimber
types (30%) (Table 7). When broken out by species, 50% of the standing stock is birch
and 41% is white spruce (Table 11).
When the additional acreage from the McGrath forest inventory is added (Phase 1 and 2),
total standing stock of woody biomass increases to 1,566,094 green tons (Table 6, Figure
10). The bulk of the stock is still in mixed species poletimber types (41%) and hardwood
poletimber types (25%), but there is a higher proportion of stocking found in sawtimber
types (15%) (Table 7). Despite this difference in sawtimber cover types between Phase 1
and 2, the amount of biomass in spruce sawtimber remained at about the same proportion
(~8%), an important concern when considering that the highest value of the resource may
be as dimension lumber when available (Tables 11, 12, 13).
Of particular importance is the result that for Phase 1, 75% of the standing stock is in the
“winter access – cross water” management designation, indicating additional costs and
logistical concerns for accessing most of the biomass resources in the project area (Table
8). This proportion drops to 58% when considering Phase 1 and 2.
Calculated Annual Allowable Cut (AAC) totals 10,800 tons per year for Phase 1, and 44,141
tons per year for Phase 1 and 2. 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.
Almost 60% of the standing stock is greater than 6 miles from the village (Table 14). This
is also reflected in the high proportion of the standing stock that has relatively high costs
due to modeled transportation costs. For Phase 1 and 2, only 21% (329,376 tons) of the
standing stock has estimated delivered costs less than $50/ton (Table 16). Not
surprisingly, since Phase 1 focused on areas closer to the village, 72% of the standing stock
in Phase 1 is estimated to be available for less than $50/ton (Table 15). 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.
McGrath Biomass Resource Assessment
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Phase 1 Phase 2 Total
Cover Type Acres GT GT AAC Acres GT GT AAC Acres GT GT AAC
BrBSD 2,122 0 95 0 0 0 2,122 0 95
BrHWP/WSP3 315 0 47 0 0 0 315 0 47
BrHWP3 282 0 40 0 0 0 282 0 40
BrWSP/HWP3 6 0 1 0 0 0 6 0 1
BSD 5,275 0 237 32,930 0 1,482 38,205 0 1,719
BSD/B 0 0 0 162 0 7 162 0 7
BSD/DS 2,412 0 109 2,472 0 111 4,883 0 220
BSD/HWD 0 0 0 94 0 4 94 0 4
BSD/TS 729 0 33 3,582 0 161 4,312 0 194
BSP/HWP1 0 0 0 113 0 4 113 0 4
BSP/HWP2 130 1,254 28 137 871 27 267 2,125 55
BSP/HWP3 76 731 38 0 0 0 76 731 38
BSP1 2,357 22,703 380 21,471 113,220 2,362 23,828 135,923 2,742
BSP1/DS 93 900 15 0 0 0 93 900 15
BSP1/TS 2 23 0 24 235 6 27 257 6
BSP2 223 2,105 61 25 122 3 248 2,227 64
BSP2/DS 4 38 1 0 0 0 4 38 1
BSP2/TS 55 528 12 0 0 0 55 528 12
BSP3 53 446 13 0 0 0 53 446 13
CWP1/TS 0 0 0 89 0 10 89 0 10
CWP2 182 6,018 174 284 9,388 275 465 15,406 450
CWP2/TS 8 260 5 0 0 0 8 260 5
CWP3 27 903 29 33 1,105 42 61 2,008 71
CWR 0 0 0 65 0 6 65 0 6
CWS1/TS 289 9,576 244 454 15,026 354 743 24,602 597
CWS2 0 0 0 82 2,726 84 82 2,726 84
CWS3 0 0 0 141 4,674 178 141 4,674 178
HWD 0 0 0 1,172 0 53 1,172 0 53
HWD/BSD 0 0 0 810 0 36 810 0 36
HWD/DS 0 0 0 245 0 11 245 0 11
HWD/TS 0 0 0 1,949 0 88 1,949 0 88
HWP/BSP1 94 2,437 36 2,274 30,719 508 2,368 33,156 544
HWP/BSP2 295 7,650 145 213 3,221 63 508 10,871 208
HWP/BSP3 44 1,130 32 0 0 0 44 1,130 32
HWP/WSP2 482 15,731 332 481 13,841 329 962 29,572 661
HWP/WSP3 1,307 42,373 1,095 2,483 92,901 2,331 3,790 135,275 3,426
HWP1 48 1,259 22 9 121 2 57 1,380 24
HWP1/BSD 230 5,955 79 1,018 25,879 474 1,247 31,834 553
HWP1/TS 19 495 7 720 18,849 347 739 19,344 354
HWP2 708 18,524 501 1,035 26,408 646 1,743 44,932 1,147
HWP2/BSD 170 4,461 73 0 0 0 170 4,461 73
HWP2/DS 5 128 2 0 0 0 5 128 2
HWP2/TS 7 190 3 0 0 0 7 190 3
HWP3 1,821 85,973 2,105 4,388 209,608 5,163 6,209 295,582 7,268
HWP3/BSD 67 3,267 50 0 0 0 67 3,267 50
HWR 90 0 12 970 0 87 1,060 0 98
WSP/CWP2 85 2,789 81 429 14,014 343 515 16,803 423
WSP/CWP3 213 6,960 267 26 836 32 239 7,795 299
WSP/HWP1 22 630 11 0 0 0 22 630 11
WSP/HWP2 1,471 42,247 1,011 4,226 120,577 3,338 5,696 162,824 4,349
WSP/HWP3 1,610 61,525 2,168 6,429 246,281 8,581 8,038 307,806 10,749
WSP1 15 355 5 0 0 0 15 355 5
WSP1/TS 105 2,016 33 0 0 0 105 2,016 33
WSP2 293 7,053 210 642 15,488 482 935 22,540 692
WSP2/TS 8 193 4 0 0 0 8 193 4
WSP3 53 1,525 52 321 9,142 375 374 10,668 427
WSR 0 0 0 16 0 2 16 0 2
WSR/CWR 0 0 0 17 0 2 17 0 2
WSS/CWP2 40 1,302 40 183 5,965 185 223 7,267 225
WSS/CWP3 248 8,086 224 10 316 9 257 8,402 232
WSS/CWS2 106 3,454 107 47 1,519 47 152 4,973 154
WSS/CWS3 11 358 10 40 1,319 36 51 1,676 46
WSS/HWP1 49 1,605 41 0 0 0 49 1,605 41
WSS/HWP2 285 9,309 289 235 7,663 187 520 16,973 476
WSS/HWP3 0 0 0 1,972 98,002 2,423 1,972 98,002 2,423
WSS1/TS 0 0 0 388 11,882 224 388 11,882 224
WSS2 178 5,629 175 588 18,564 499 766 24,193 674
WSS2/TS 2 54 2 0 0 0 2 54 2
WSS3 13 763 18 904 54,704 1,320 917 55,467 1,338
Totals: 24,832 390,909 10,800 96,396 1,175,185 33,342 121,229 1,566,094 44,142
Table 6. Woody biomass green tonnage (GT) and Annual Allowable Cut (AAC) by cover
type.
Figure 9. McGrath Phase 1 project area woody biomass tons/acre.
McGrath Biomass Resource Assessment
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Figure 10. McGrath Phase 1 and 2 project area woody biomass tons/acre. McGrath Biomass Resource Assessment 26
Phase 1 Phase 2 Total
Cover Class Acres GT GT AAC Acres GT GT AAC Acres GT GT AAC
Barren/
cultural 718 0 0 503 0 0 1,221 0 0
Black spruce
poletimber 2,765 26,636 480 2,062 19,865 507 4,828 46,502 986
Black spruce
woodland 8,228 0 370 30,049 0 1,352 38,277 0 1,722
Burned 3,923 20,957 737 33,763 155,167 3,519 37,686 176,124 4,256
Cottonwood
poletimber 217 7,181 208 406 10,493 327 623 17,674 535
Cottonwood
sawtimber 289 9,576 244 678 22,426 616 967 32,002 860
Hardwood
poletimber 2,936 116,843 2,753 6,835 273,822 6,480 9,771 390,664 9,232
Hardwood
woodland 0 0 0 3,366 0 152 3,366 0 152
Mixed
poletimber 5,081 168,679 4,798 13,223 470,110 14,499 18,304 638,789 19,296
Mixed
sawtimber 738 24,114 711 2,447 113,810 2,862 3,186 137,924 3,573
Mixed
woodland 0 0 0 895 0 40 895 0 40
Reproductio
n 90 0 12 1,049 0 96 1,138 0 108
Shrubland 2,478 0 0 10,167 0 0 12,645 0 0
Water 3,587 0 0 9,762 0 0 13,348 0 0
Wetland 4,540 0 0 11,869 0 0 16,410 0 0
White spruce
poletimber 420 10,477 293 946 24,434 852 1,366 34,911 1,145
White spruce
sawtimber 193 6,446 195 1,876 85,060 2,040 2,069 91,505 2,235
Totals: 36,203 390,909 10,800 129,896 1,175,185 33,342 166,099 1,566,094 44,142
Table 7. Woody biomass tonnage and Annual Allowable Cut (AAC) by cover type class.
McGrath Biomass Resource Assessment
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Standing Green
Ownership Management Acres Green Tons Tons AAC
ANCSA Regional Corp. Riparian buffer 15 167 3 Winter access - cross water 1,356 10,664 249
ANCSA Regional Corp. Totals: 1,371 10,831 252
ANCSA Village Corp. All-season access 609 6,875 197 HFR 259 4,902 110 Riparian buffer 488 5,883 166 Village - Mid 813 14,908 306 Village - Near 262 5,571 138 Village Core 922 12,476 316 Winter access 2,549 41,461 995 Winter access - cross water 16,889 196,952 5,937
ANCSA Village Corp. Totals: 22,791 289,028 8,165
BLM Riparian buffer 155 3,544 78 Winter access - cross water 3,110 6,909 209
BLM Totals: 3,266 10,453 287
Native Allotment All-season access 138 2,362 63 HFR 22 233 8 Riparian buffer 8 79 2 Winter access - cross water 308 6,087 153
Native Allotment Totals: 475 8,762 226
Private Winter access - cross water 170 4,297 110
Private Totals: 170 4,297 110
State of Alaska Riparian buffer 67 693 19 Winter access - cross water 8,063 66,846 1,741
State of Alaska Totals: 8,129 67,538 1,760
Phase 1 Totals: 36,203 390,909 10,800
Table 8. Woody biomass tonnage and Annual Allowable Cut (AAC) by ownership and
management, Phase 1.
McGrath Biomass Resource Assessment
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Standing Green
Ownership Management Acres Green Tons Tons AAC
ANCSA Village Corp. All-season access 22,691 141,259 3,085 Riparian buffer 1,095 17,795 509 Winter access 27,654 317,204 9,118 Winter access - cross water 43,568 430,993 13,358
ANCSA Village Corp. Totals: 95,008 907,251 26,071
BLM All-season access 7,564 36,724 812 Riparian buffer 159 3,728 102 Winter access 2,628 31,331 865 Winter access - cross water 23,210 173,088 4,914
BLM Totals: 33,562 244,871 6,693
Native Allotment All-season access 422 7,187 138 Riparian buffer 39 685 22 Winter access 176 1,413 38 Winter access - cross water 689 13,778 380
Native Allotment Totals: 1,325 23,063 578
State of Alaska Winter access - cross water 1 0 0
State of Alaska Totals: 1 0 0
Phase 2 Totals: 129,896 1,175,185 33,342
Table 9. Woody biomass tonnage and Annual Allowable Cut (AAC) by ownership and
management, Phase 2.
McGrath Biomass Resource Assessment
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Standing Green
Ownership Management Acres Green Tons Tons AAC
ANCSA Regional Corp. Riparian buffer 15 167 3 Winter access - cross water 1,356 10,664 249
ANCSA Regional Corp. Totals: 1,371 10,831 252
ANCSA Village Corp. All-season access 23,300 148,134 3,282 HFR 259 4,902 110 Riparian buffer 1,582 23,677 676 Village - Mid 813 14,908 306 Village - Near 262 5,571 138 Village Core 922 12,476 316 Winter access 30,204 358,665 10,113 Winter access - cross water 60,457 627,945 19,295
ANCSA Village Corp. Totals: 117,799 1,196,279 34,236
BLM All-season access 7,564 36,724 812 Riparian buffer 315 7,272 180 Winter access 2,628 31,331 865 Winter access - cross water 26,320 179,997 5,123
BLM Totals: 36,828 255,325 6,980
Native Allotment All-season access 559 9,549 201 HFR 22 233 8 Riparian buffer 46 765 24 Winter access 176 1,413 38 Winter access - cross water 997 19,865 533
Native Allotment Totals: 1,800 31,825 804
Private Winter access - cross water 170 4,297 110
Private Totals: 170 4,297 110
State of Alaska Riparian buffer 67 693 19 Winter access - cross water 8,064 66,846 1,741
State of Alaska Totals: 8,130 67,538 1,760
Phase 1 and 2 Totals: 166,099 1,566,094 44,142
Table 10. Woody biomass tonnage and Annual Allowable Cut (AAC) by ownership and
management, Phases 1 and 2.
McGrath Biomass Resource Assessment
30
Sawtimber Board Foot
Species Total Green Tons Green Tons White Spruce 160,779 33,126
Black Spruce 28,420
Birch 194,918
Aspen 5,738
Balsam Poplar 22,011
Totals: 390,909 33,126
Table 11. Woody biomass tonnage by species, Phase 1.
Sawtimber Board Foot
Species Total Green Tons Green Tons
White Spruce 527,625 107,631
Black Spruce 196,615
Birch 536,328
Aspen 23,282
Balsam Poplar 46,503
Totals: 1,175,185 107,631
Table 12. Woody biomass tonnage by species, Phase 2.
Sawtimber Board Foot
Species Total Green Tons Green Tons
White Spruce 688,404 140,757
Black Spruce 225,035
Birch 731,246
Aspen 29,020
Balsam Poplar 68,514
Totals: 1,566,094 140,757
Table 13. Woody biomass tonnage by species Phase 1 and 2.
McGrath Biomass Resource Assessment
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Standing Green Tons
Distance from Boiler Site Acres Green Tons AAC
0-2 miles 4,030 84,355 2,294
2-4 miles 16,522 230,899 6,511
4-6 miles 18,332 291,711 9,253
6-8 miles 30,208 263,212 6,894
8-10 miles 22,461 205,441 5,735
10-12 miles 15,423 225,273 6,301
12-14 miles 7,616 125,398 3,473
14-16 miles 5,635 120,341 3,156
16-18 miles 1,002 19,463 523
Totals: 121,229 1,566,094 44,142
Table 14. Woody biomass tonnage and Annual Allowable Cut (AAC) by distance from boiler
site.
Total Green Tons
Total Cost per Ton Available AAC Available
< $30 16,046 401
< $40 104,944 2,758
< $50 272,029 7,454
< $60 345,876 9,190
< $70 370,713 9,763
< $80 380,438 9,960
< $90 380,438 9,960
< $100 380,438 9,960
< $125 380,543 9,962
Table 15. Woody biomass tonnage and Annual Allowable Cut (AAC) by cost threshold,
Phase 1.
Total Green Tons
Total Cost per Ton Available AAC Available
< $30 16,046 401
< $40 108,051 2,836
< $50 329,376 9,207
< $60 582,115 16,478
< $70 812,544 22,596
< $80 938,879 25,840
< $90 1,091,510 29,847
< $100 1,235,605 33,543
< $125 1,518,407 40,250
< $150 1,533,520 40,614
Table 16. Woody biomass tonnage and Annual Allowable Cut (AAC) by cost threshold,
Phase 1 and 2.
McGrath Biomass Resource Assessment
32
Figure 11. McGrath project area woody biomass cost of harvest, transport, and
management, Phase 1.
McGrath Biomass Resource Assessment
33
Figure 12. McGrath project area woody biomass cost of harvest, transport, and management, Phase 1 and 2. McGrath Biomass Resource Assessment 34
McGrath Biomass Resource Assessment
35
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. Many thanks to Tilly Dull, Tribal
Administrator, and the McGrath Native Village Council, for support of this project. And,
thanks to the Alaska Energy Authority and its representatives for supporting funding for this
work and for promoting the development of alternative renewable energy in rural Alaska.