HomeMy WebLinkAboutIRHA_biomass_assessments 2012 Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska: Koyukuk, Nulato, Kaltag, Anvik, Holy Cross, Hughes, Ruby, and Nikolai Presented to: Interior Regional Housing Authority 828 27th Ave. Fairbanks, AK 99701 By: Will Putman Tanana Chiefs Conference, Forestry Program 122 First Ave., Suite 600 Fairbanks, AK 99701 October, 2012
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska i Executive Summary As part of an effort to assess the feasibility of proposed biomass energy projects at a number of villages in Interior Alaska, an assessment of woody biomass resources was conducted for the vicinities of the villages of Koyukuk, Nulato, Kaltag, Anvik, Holy Cross, Hughes, Nikolai, and Ruby. The assessments attempt to leverage existing information as much as possible, including forest inventory information compiled by Tanana Chiefs Conference for previous projects and classified satellite imagery. The area considered for each community was defined by a 25-mile radius from the community (~1.25 million acres), with the area additionally constrained to exclude areas closer to neighboring communities. A number of cost parameters were assumed and used to estimate costs of harvesting, transporting, and managing biomass resources across the landscape. The assessments result in woody biomass stocking and annual allowable cut estimates stored and maintained in geodatabases, with the ability to query and report data by land cover type, ownership, biomass growth, biomass cost, distance from village, and other parameters. Highlights of the resulting data analysis include: • The percentage of land area determined to be associated with forested timber-bearing strata in the area of each community ranged from 28% at Nikolai and Holy Cross to 55% at Ruby, and averaged 37% overall. • Total biomass associated with each community ranged from 4,091,397 tons at Nulato to 15,743,931 tons at Ruby; the relatively lower numbers at Nulato are the result of the project area being constrained by the presence of nearby villages up and downriver from Nulato, and the relatively higher numbers at Ruby are partially the result of a higher percentage of forested land on the landscape. • Using some simple growth modeling and estimates of existing stocking, estimates of Annual Allowable Cut (AAC) were generated. Total AAC for each community ranges from 131,996 tons/year at Nulato to 427,338 tons/year at Ruby. • Using the cost parameters assumed in the analysis, the cost of harvesting, transporting and managing the woody biomass was determined to range from $37 to $256 per ton. Not surprisingly, the most expensive biomass is farthest from the communities because of the effect of the estimated transportation cost parameters. • There are extensive biomass stocks on Federal, State, and ANCSA corporation land holdings, but the areas closest to the villages are dominated by ANCSA corporation ownerships. • The data indicate the presence of significant amounts of recoverable woody biomass, particularly when viewed in terms of supporting relatively modest-sized thermal heating projects. Larger -scale projects, more demanding economic thresholds, and information demands required by more detailed planning will require the collection and analysis of additional data.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska ii Table of Contents INTRODUCTION........................................................................................ 1 DATA COMPONENTS ................................................................................ 5 Land Cover ................................................................................................. 5 Forest Inventory data ................................................................................. 6 Woody Biomass Units ................................................................................. 8 Land Ownership ......................................................................................... 9 Site class 10 Estimating AAC and assigning rotation and growth parameters...................... 10 Cost modeling .......................................................................................... 13 DATA PROCESSING AND ANALYSIS .................................................. 15 RESULTS ................................................................................................... 19 Overall Results ......................................................................................... 19 Koyukuk ................................................................................................. 21 Nulato ................................................................................................. 27 Kaltag ................................................................................................. 33 Anvik ................................................................................................. 39 Holy Cross ................................................................................................ 45 Hughes ................................................................................................. 51 Nikolai ................................................................................................. 57 Ruby ................................................................................................. 63 FUTURE STEPS ........................................................................................ 69
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska iii List of Figures Figure 1: Location of project communities in Alaska.................................................. 2 Figure 2: Location of 25-mile radius community project areas.................................... 3 Figure 3: Land ownership, Koyuku k project area. ................................................... 24 Figure 4: Woody biomass dry ton stocking, Koyukuk. ............................................. 25 Figure 5: Woody biomass cost, Koyukuk. .............................................................. 26 Figure 6: Land ownership, Nulato project area. ...................................................... 30 Figure 7: Woody biomass dry ton stocking, Nulato. ................................................ 31 Figure 8: Woody biomass cost, Nulato. ................................................................. 32 Figure 9: Land ownership, Kaltag project area. ...................................................... 36 Figure 10: Woody biomass dry ton stocking, Kaltag. ............................................... 37 Figure 11: Woody biomass cost, Kaltag. ................................................................ 38 Figure 12: Land ownership, Anvik project area. ...................................................... 42 Figure 13: Woody biomass dry ton stocking, Anvik. ................................................ 43 Figure 14: Woody biomass cost, Anvik. ................................................................. 44 Figure 15: Land ownership, Holy Cross project area. .............................................. 48 Figure 16: Woody biomass dry ton stocking, Holy Cross. ......................................... 49 Figure 17: Woody biomass cost, Holy Cross. .......................................................... 50 Figure 18:: Land ownership, Hughes project area. .................................................. 54 Figure 19: Woody biomass dry ton stocking, Hughes. ............................................. 55 Figure 20: Woody biomass cost, Hughes. .............................................................. 56 Figure 21: Land ownership, Nikolai project area. .................................................... 60 Figure 22: Woody biomass dry ton stocking, Nikolai. .............................................. 61 Figure 23: Woody biomass cost, Nikolai. ............................................................... 62 Figure 24: Land ownership, Ruby project area. ...................................................... 66 Figure 25: Woody biomass dry ton stocking, Ruby.................................................. 67 Figure 26: Woody biomass cost, Ruby. ................................................................. 68
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska iv List of Tables Table 1: Biomass assessment project communities ....................................... 3 Table 2: TCC forest inventories near project communities. ............................. 7 Table 3: Wood density of tree species in Interior Alaska................................. 9 Table 4: Cost parameters used in the analyses. .......................................... 13 Table 5: Forest area and biomass by village. .............................................. 20 Table 6: Biomass dry tons by ownership and village. ................................... 20 Table 7: Biomass by Land Ownership, Koyukuk. ......................................... 21 Table 8: Biomass by Village Proximity, Koyukuk. ........................................ 21 Table 9: Biomass by Estimated Cost, Koyukuk. ........................................... 22 Table 10: Biomass Dry Tons by Ownership and Village Proximity, Koyukuk. ... 23 Table 11: Biomass by species, Koyukuk. .................................................... 23 Table 12: Biomass by Land Ownership, Nulato. ........................................... 27 Table 13: Biomass by Village Proximity, Nulato. .......................................... 27 Table 14: Biomass by Estimated Cost, Nulato. ............................................ 28 Table 15: Biomass Dry Tons by Ownership and Village Proximity, Nulato. ...... 29 Table 16: Biomass by species, Nulato. ....................................................... 29 Table 17: Biomass by Land Ownership, Kaltag. ........................................... 33 Table 18: Biomass by Village Proximity, Kaltag ........................................... 33 Table 19: Biomass by Estimated Cost, Kaltag. ............................................ 34 Table 20: Biomass Dry Tons by Ownership and Village Proximity, Kaltag. ...... 35 Table 21: Biomass by species, Kaltag. ....................................................... 35 Table 22: Biomass by Land Ownership, Anvik. ............................................ 39 Table 23: Biomass by Village Proximity, Anvik. ........................................... 39 Table 24: Biomass by Estimated Cost, Anvik. ............................................. 40 Table 25: Biomass Dry Tons by Ownership and Village Proximity, Anvik. ........ 40 Table 26: Biomass by species, Anvik. ........................................................ 41 Table 27: Biomass by Land Ownership, Holy Cross. ..................................... 45 Table 28: Biomass by Village Proximity, Holy Cross. .................................... 45 Table 29: Biomass by Estimated Cost, Holy Cross. ...................................... 46 Table 30: Biomass Dry Tons by Ownership an d Village Proximity, Holy Cross. 46 Table 31: Biomass by species, Holy Cross. ................................................. 47 Table 32: Biomass by Land Ownership, Hughes .......................................... 51 Table 33: Biomass by Village Proximity, Hughes. ........................................ 51 Table 34: Biomass by Estimated Cost, Hughes. ........................................... 52 Table 35: Biomass Dry Tons by Ownership and Village Proximity, Hughes. ..... 53 Table 36: Biomass by species, Hughes. ..................................................... 53 Table 37: Biomass by Land Ownership, Nikolai. .......................................... 57 Table 38: Biomass by Village Proximity, Nikolai. ......................................... 57 Table 39: Biomass by Estimated Cost, Nikolai. ............................................ 58 Table 40: Biomass Dry Tons by Ownership and Village Proximity, Nikolai. ...... 58 Table 41: Biomass by species, Nikolai. ....................................................... 59 Table 42: Biomass by Land Ownership, Ruby. ............................................. 63 Table 43: Biomass by Village Proximity, Ruby. ............................................ 63 Table 44: Biomass by Estimated Cost, Ruby. .............................................. 64 Table 45: Biomass Dry Tons by Ownership and Village Proximity, Ruby. ........ 65 Table 46: Biomass by species, Ruby. ......................................................... 65
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 1 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. Funding provided through the State of Alaska’s Renewable Energy Fund, administered by the Alaska Energy Authority, has resulted in the Interior Regional Housing Authority being tasked with assessing the feasibility of potential woody biomass energy projects at the villages of Koyukuk, Nulato, Kaltag, Anvik, Holy Cross , Hughes, Nikolai, and Ruby, all located in Interior Alaska. These feasibility assessments, which are preliminary in nature, are to be composed of 2 major components – an assessment of the needs, economics, and human resources of the communities themselves, and an assessment of the woody biomass resources in the vicinity of the villages. Dalson Energy, Inc., a consulting firm based in Anchorage, Alaska, was retained to assess the community resources and needs and to produce recommendations on appropriate biomass heating systems, and has summarized that information in a series of separate reports. The biomass resource assessment component of the feasibility assessments was to be produced by the Forestry Program at Tanana Chiefs Conference, a non-profit tribal organization based in Fairbanks, Alaska, and is the subject of this report . With any proposed woody biomass energy project, a number of basic questi ons arise concerning the biomass supply, including: • 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 a ssociated 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 an array of bi omass energy facilities 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
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 2 Figure 1: Location of project communities in Alaska. 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 proposed biomass energy project s. The communities addressed in this report are located in western Interior Alaska (Figure 1). All are rural, not located on a contiguous highway system, and are accessible only by air, water, or overland trails. Hughes i s located on the Koyukuk River; Ruby, Koyukuk, Nulato, Kaltag, Anvik, and Holy Cross are located on the Yukon River, and Nikolai is located on the Upper Kuskokwim River. The largest nearby urban centers providing goods and services are Fairbanks and Anchorage. Air distances and community populations are summarized in Table 1. The geographic extent of each community’s assessment was defined as a radius of 25 miles surrounding each village. In addition, only those areas that were closer to a community than an adjacent community were included in that community’s assessment extent (Figure 2).
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 3 Figure 2: Location of 25-mile radius community project areas . Table 1: Biomass assessment project communities Village Distance to Anchorage (miles) Distance to Fairbanks (miles) Population Project Area Acreage Hughes 362 206 79 1,256,509 Ruby 301 229 173 1,256,509 Koyukuk 352 294 97 651,422 Nulato 354 307 275 729,169 Kaltag 352 329 205 1,113,249 Nikolai 190 239 101 1,256,509 Anvik 347 411 79 642,839 Holy Cross 328 414 176 1,067,849
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 4
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 5 DATA COMPONENTS These biomass assessments relied heavily on computerized geographic information system (GIS) and relational database technologies to store, process, query, and analyze data. The GIS software used was ArcGIS 10.0 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 to produce acreages and biomass stocking estimates associated with any combination of attributes. A relational database was used to relate the attribute information stored in GIS data layers to tabular datasets such as biomass stocking information derived from existing forest inventory datasets, cost parameters, and lookup tables, allowing the generation of GIS layers of derived information such as biomass stocking, annual allowable cut, and biomass cost estimates. The basic data input to the biomass assessment models consisted of land cover data, forest inventory data, and land ownership. Additional data components were derived from the basic input datasets, including raster datasets for site class, biomass stocking, biomass annual allowable cut, village proximity, and biomass cost estimates. Land Cover Typically, land cover is characterized from sources of remotely sensed image data such as aerial photography or satellite imagery. For the villages within the scope of this project, high resolution QuickBird satellite imagery (spatial resolution 0.6m) was available for 6 of the 8 villages, and there was the possibility of medium resolution (spatial resolution 2.5m) Spot 5 imagery becoming available through the Alaska Statewide Digital mapping Initiative (SDMI) for portions of the project area . However, the time and funding required to classify the imagery into classified land cover data layers was prohibitive given the scope of the project, and in any case those image sources were not available everywhere. As a result, it was decided instead to attempt to rely on classified image layers made available through the LandFire program, an interagency vegetation, fire and fuel characteristics mapping program sponsored by the U.S. Department of the Interior and the U.S. Forest Service (http://www.landfire.gov). LandFire data products consist of up to 50 data layers generated for all land areas within the United S tates, including Alaska. Within Alaska, the data layers are generated from classified LandSat satellite imagery at a spatial resolution of 30 meters. Existing vegetation is described in 3 layers; existing vegetation type (evt), existing vegetation height (evh) and existing vegetation cover, or density (evc). In addition, a layer for biophysical settings (bps) showed potential for attempting to model potential productivity of a site. The advantages of the LandFire data include the comprehensive coverage of the data over the entire country, and the apparent detailed vegetation classification that appeared to be relatable to forest inventory stocking data from old forest inventory data on file at TCC. Potential disadvantages of use of the LandFire data include it’s relatively coars e spatial resolution (30m), and anecdotal and objective evidence that would lead one to question the accuracy of the LandFire classifications. In either case, the landscape-level nature of these biomass assessments, the preliminary nature of the assessments, and the lack of better options led to the decision to utilize the LandFire datasets.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 6 The LandFire data layers are provided as raster datasets, with classifications provided for individual pixels, or cells in an image. This is in contrast to vector datasets, which define areas as polygons defined by line segments running between x-y coordinat es. Previous analyses compiled by TCC have used land cover data in vector formats, and relied on standard vector overlay techniques to analyze biomass stocking with ownership, proximity to a village, etc. Using land cover data in a raster format dictated that the analyses for these assessments be based on raster techniques and processing. Forest Inventory data In I nterior 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. 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. The most prominent forest inventor y effort s to date in the vicinities of the communities in this project are a number of inventories conducted by the Forestry Program at Tanana Chiefs Conference on village corporation lands and Native allotments. The village corporation inventories were conducted on individual village ANCSA corporation lands on lands with a selected status at the time of the inventory. The Native allotment inventories were conducted on Native allotment parcels with a status of pending or better at the time of the inventories. For the allotment inventories, the entire TCC region was subdivided into 8 subunits, and a separate inventory project was conducted for each subunit, with the overall work occurring from 1987 to 1993. Table 2 summarizes the TCC inventory projects and the year they were conduct ed in the vicinities of the project communities. The protocols and processes used in the corporation and allotment inventories were very similar, and utilized a process that included the following steps: 1. The area included in the inventory was interpreted f or land cover type using high -altitude color-infrared aerial photographs dating from the late 1970s. 2. Forested stands delineated on the aerial photographs 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). Non-forested areas were attributed for cover types such as water, tall shrub, bog, barren/cultural, etc. 3. Forested cover types covering the highest proportion of area were selected for field sampling by randomly selecting accessible stands within those types.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 7 Table 2: TCC forest inventories nea r project communities. Village Forest Inventory Hughes Hughes village corporation lands, 1987 Doyon -Koyukuk Native allotments, 1987-1993 Ruby Ruby village corporation lands, 1987 Doyon -Melozitna Native allotments, 1987-1993 Koyukuk Koyukuk village corporation lands, 1987 Doyon -Melozitna Native allotments, 1987-1993 Doyon -Koyukuk Native allotments, 1987-1993 Nulato Nulato-Galena corporation lands, 1990 Doyon -Melozitna Native allotments, 1987-1993 Kaltag Kaltag village corporation lands, 1985 Doyon -Middle Yukon Native allotments, 1987 -1993 Nikolai Nikolai village corporation lands, 1987 Doyon -McGrath Native allotments, 1987-1993 Anvik Doyon -Middle Yukon Native allotments, 1987 -1993 Holy Cross Doyon -Middle Yukon Native allotments, 1987-1993 4. 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 5. 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. 6. The volume per acre figures were then extrapolated to all forested areas withi n the extent of the project. 7. Some years after the completion of the inventories in the early ‘90s, the spatial data represented by th e cover type maps prepared in the inventory were digitized into a GIS, and the processed timber volume data was incorporated into a digital relational database. The most important component provided to the biomass assessments as a result of the forest inventor ies are the tabular timber volume and stocking estimates. The stocking data generated from the field measurements are used to produce estimates of the amount of woody biomass present in each forested cover type. The tree data processing produced est imates of board -foot and cubic-foot t imber volumes per acre by tree species and size class. 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.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 8 There are a number of serious limitations in this available forest inventory data that need to be considered. The inventor ies are quite “extensive”, that is, the geographic scope was relatively large and the intensity of the field sampling was relatively low, particularly for the allotment inventories . 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 village corporation inventory at Nulato was not field sampled at all, but instead used stocking figures from strata from other nearby inventories to generate overall timber volumes. The photography used to produce the land cover typing was less than 15 years old at the time the inventories wer e conducted, but is now more than 30 years old, and does not take into account the changes that have no doubt occurred on the landscape. 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 th ese old inventory project still provide a useful starting point for evaluation of woody biomass energy resources. Woody Biomass Units As mentioned previously, the cubic-foot (CF) estimates of wood volume that are one of the products of a forest inventory analysis are appropriate when evaluating the volume of woody biomass as an energy source. However, the energy value of wood per unit volume varies somewhat by species because of varying wood densities, so it is common to report woody biomass in units of weight, commonly tons (1 ton=2,000 lbs). This matter is further complicated by the variability of wood weight per unit volume because of moisture levels in the wood. There are three units commonly used to report woody biomass by weight: Green tons, or the weight of the wood in tons at moisture levels found when the material is freshly cut, often in the neighborhood of 50% moisture by weight; air dry tons, or the weight of the wood when enough moisture has been removed from the wood to make it feasible to efficiently recover energy from the wood through combustion, commonly in the neighborhood of 20% moisture by weight; and bone-dry tons, the weight of the wood with all moisture removed. For the purposes of this analysis, the unit of air-dry tons (also referred to in this document as “dry tons”) is used, the weight of the wood in the form most likely to be used in a heating project. The literature is inconsistent in terms of wood density values for the species found in Interior Alaska, but representative values (and their sources) are presented in Table 3.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 9 Table 3: Wood density of tree species in Interior Alaska. White spruce, Paper bi rch, 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.htm); 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). Tree Species Green Density (lbs/cubic foot) Air -dry density (lbs/cubic foot) Air -dry tons/cord White spruce 36 31 1.31 Black spruce 32 28 1.19 Paper birch 48 38 1.62 Aspen 43 27 1.15 Balsam poplar 38 24 1.02 Tamarack 47 37 1.57 Another unit used to measure wood is the “cord”, traditionally used to measure fuelwood. A cord is defined as the amount of minimally processed wood (bucked, split) that can be stacked in a space measuring 4’x4’x8’. Because of the airspace and inconsistency inherent in stacking cordwood, the cord is a relatively imprecise measure, but is n onetheless in common use in fuelwood transactions. The volume space of a cord, 128 cubic feet, is sometimes thought to contain roughly 100 cubic feet of wood (a “cunit”) when the air space between wood chinks in the stacked wood is considered. Other esti mates put the conversion at 85 cubic-feet of roundwood per cord. Using the conversion factors presented in Table 3 at 85 CF/cord, the number of air-dry tons in a cord varies from approximately 1.0 tons for balsam poplar to 1.6 tons for paper birch. Land Ownership 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. In these analyses, this is accomplished through the use of a GIS layer that defines land ownership in the vicinity of the project communities . Spatial data of land ownership were acquired from several sources and combined into the ownership layer: • Generalized land status, from the Bureau of Land Management (BLM) • Native Allotments, from BLM • ANCSA Corporation conveyed lands, from Doyon, Ltd. The data from the various sources vary in quality and precision; specifically, the generalized land status data is available statewide, but only shows categories of land ownership to the nearest section (square mile). Because of that, allotment lands and ANCSA corporation lands as defined in the other sources were given priority over the generalized land status when combining the land ownership data. The data acquired from Doyon for conveyed ANCSA lands allows the land status to be defined for the regional corporation (Doyon Ltd.) and the village corporations, but the ANCSA land as coded in the BLM generalized land
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 10 status made no distinction between regional and village corporations. As a result, any listing of individual ANCSA corporations in the results in this report refers to the location of conveyed ANCSA corporation lands as defined in the Doyon data, and any reference to “ANCSA misc.” refers to ANCSA selected or patented lands as defined in the BLM generalized land status data with no distinction between individual corporations. 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, four broad site classes were defined to describe the location of site class areas in the project area. The four 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 parts of the project areas were classified into one of the four site classes, using the LandFire biophysical settings (bps) layer and a lookup table in the database assigning a site class to each bps classification, creating site class raster datasets for covering the project area. Estimating AAC and assigning rotation and growth parameters 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.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 11 • 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 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 j uvenile 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. Fully stocked stands will show best realization of potential growth . 2. Lower site quality will result in longer rotations and slower growth.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 12 The first assumption of stand stocking levels influencing relative growth can be dealt with most directly using the stand density component of the cover type calls coming from the LandFire evc layer. Each of the evc codes related to dens ity of a tree canopy were assigned a relative growth rate expressed as a proportion of optimum growth: LandFire evc Class Growth Proportion 151 (Tree Canopy >= 10 and < 25%) 0.3 152 (Tree Canopy >= 25 and < 60%) 0.6 153 (Tree Canopy >= 60 and <= 100%) 1.0 Similarly, the second assumption of relative growth varying by site quality was 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 was 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 was determined for the project areas; since this was a raster analysis, this was done on a pixel -by-pixel basis based on the LandFire datasets. Growth for each pixel was determined by multiplying the optimum growth rate (0.5 tons/acre/year) by the growth proportion number assigned to the stand density of the pixel , and multiplied again by the growth proportion assigned to the site class of the pixel . Rotation length for each pixel was determined by applying the rotation length assigned to the site class of the area . The resulting figures for growth and rotation were used with the overall stocking of each pixel in the Hanzlik formula to generate an AAC for each pixel . The resulting AAC figures for each pixel are not me ant to mean that some calculated portion of every pixel is a portion of the volume cut in any given time frame, but refers to the contribution that the resource represented by area of that pixel contributes to the harvestable volume of biomass over the project as a whole. Through the other attributes assigned to each pixel through the creation of overlaid raster datasets, both standing stock and AAC figures can be broken out by ownership, proximity to the village, or other area attributes.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 13 Table 4: Cost parameters used in the analyses. Cost Type Cost Stumpage (payments to owner), cost per ton $ 5 Harvest Costs Costs per acre $300 Costs per ton of woody biomass $ 10 Transportation costs Cost/ton/mile off -road $ 6 Reforestation – cost per acre $100 Misc. Admin – cost per acre $ 20 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 were converted into costs per ton. Per acre cost parameters such as harvest costs per acre and reforestation costs per acre have the effect of driving up relative costs per ton of woody biomass for low volume areas. Estimated transportation costs were driven solely by distances from the vill age, with the off-road transportation cost parameter of $6/ton/mile being applied. Harvest costs are broken into two components, cost per ton and cost per acre (Table 4). This is an attempt to recognize that some costs associated with harvesting will rem ain 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 mod ified 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 per ton is determined by area, as is the annual allowable cut, one interesting ramification of this is that it is possible to evaluate AAC based on different cost thresholds.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 14
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 15 DATA PROCESSING AND ANALYSIS Starting with the basic datasets described above, there were several data processing steps that were conducted to prepare and analyze the data and prepare for the generation of tables and maps showing the analysis results. The spatial data raster processing steps described below used geoprocessing tools in the GIS software, with the use of the tools bein g automated somewhat through the creation of script tools written in Python, a scripting language used with ArcGIS software. The data processing steps implemented for each project area were: 1. Data were downloaded from the LandFire website for the LandFire data layers to be used in the analysis. A spatial extent defining an area including a 25-mile radius around each project village was used to define each download, with a separate download for each village except where villages were near enough to each oth er that their areas overlap, in which case there was a single download covering the area of several villages simultaneously. For example, Koyukuk, Nulato and Kaltag (all Gana -A’Yoo Ltd. villages) had overlapping areas, and were included in the same download area and processed together . Likewise, Holy Cross and Anvik were included in the same download area. Data were downloaded for the LandFire evt, evc, evh, and bps layers in ArcInfo GRID format. 2. A geodatabase was created, and the downloaded data were imported into it as raster datasets. All resulting datasets, both raster and vector, were also stored in the geodatabase, which was created as an ArcGIS personal geodatabase in MS Access format, and which also served as the repository for the other database structures in the analysis; lookup tables, strata stock tables, queries, data entry forms, reports, etc. 3. The evt, evc, and evh layers were combined into a new raster layer (called lf_tch) containing the combined attributes of vegetation type, vegetation cover (density) and vegetation height. This produced a VAT (value attribute table) describing all possible combinations of the attributes from the combined raster layers. In the case of the 3 Gana-A’Yoo villages being processed together, this produced a t able of 374 combinations of vegetation type, coverage, and height classes. 4. The VAT was exported into a database table, (called tch_classes), and a column was added to the table to hold information on strata ID. 5. Each row in the tch_classes table was assigned a strata ID from the TCC forest inventories . Non-forested vegetation types (shrubland, wetland, water, barren, etc.) were assigned to non -forested strata not associated with any timber volume. Forested vegetation types were subjectively assigne d to the most appropriate strata from nearby TCC forest inventory projects. To aid in this rather complex, manual, and very subjective process, a form was developed in MS Access. 6. The database contained a table called strata_biomass that had been processed to contain biomass stocking values (in tons and cords) for all strata defined in the TCC inventories. In ArcGIS, the tch_classes table and the strata_biomass table were joined and the tch_classes table and the lf_tch VAT were joined to associate each cel l in the combined vegetation raster with strata biomass stocking values. This joined raster is used to create a series of raster datasets of biomass stocking with the ArcGIS Spatial Analyst “lookup” command. Raster datasets were created for overall dry t on stocking, dry tons by species, and cords by species. 7. Similarly, a site class raster was created for the project area. The LandFire biophysical settings raster (lf_bps) VAT was exported to a database table (bps_classes) and a column for site class code was added to the bps_classes table, and each row of the bps_classes table was coded for site class using the codes 0
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 16 through 3 described above. In ArcGIS, the bps_classes table was joined to the lf_bps raster layer, and the “lookup” command was used to create a site class raster for the project area. 8. A raster of annual allowable cut (AAC) was created by first creating rasters of growth adjustment by density values, growth adjustment by site values, and rotation adjustment by site values, and then executi ng a map algebra raster calculation for AAC using an application of the Hanzlik formula with the rasters for biomass stocking, growth as determined from the growth adjustment rasters, and rotation as determined from the rotation adjustment raster. The growth by density adjustment raster was created in a process similar to that used to create the stocking and site class rasters by joining the LandFire vegetation density raster (lf_evc) to a growth_by_density table in the database to relate the evc codes to a density adjustment factor and creating a growth by density adjustment raster with a lookup command. Similarly, the growth adjustment by site and rotation adjustment by site rasters were created by joining the site class raster to lookup tables in the database (growth_by_site, rotation_by_site) and creating the adjustment rasters with lookup commands. 9. A raster dataset was created defining the proximity to the nearest village in miles up to a 25 mile radius using ArcGIS spatial analyst commands. In addition, a raster dataset defining which village was closest to each pixel in the dataset was created , to account for those villages whose 25-mile radii overlap. 10. A raster dataset of biomass costs per ton was created by applying the cost parameters described above to previously created raster datasets. A harvest cost raster was created by dividing the harvest per acre parameter by the biomass stocking per acre raster, and adding the result to the harvest cost per ton parameter. A transportation cost raster was created by multiplying the village proximity raster and multiplying it by the off-road transportation cost parameter. A total cost per ton raster was created by adding the harvest cost raster, the transportation cost raster, the reforestation parameter and the administration cost parameters divided by the biomass stocking raster (to convert those parameters to per -ton units), and the stumpage parameter. 11. A vector layer of land ownership was created for each project area by overlaying generalize d land status (from BLM) with conveyed ANCSA land data (from Doyon, Ltd.), and Native allotment locations (from BLM). These are overlapping datasets, but a unique ownership was identified for all areas through the overlay commands applied, with a priority given to the location of Native allotment parcels, the next lowest priority given to the conveyed ANCSA data, and the least priority given to the generalized land status. The resulting polygons were attributed for owner and owner class. Native allotments were coded with the BLM serial number as the owner and “Native allotment” as the owner class. ANCSA conveyed lands were coded with the name of the ANCSA corporation as the owner (usually either the local village corporation or Doyon, Ltd.) and an owner class of “ANCSA corp”. The remaining lands were identified from the generalized land status data with some level of agency ownership; State lands were identified as “State patented” or “State selected” as the owner and “State of Alaska” as the owner Class; federal lands identify the agency (USFWS, NPS, BLM) as the owner and “Federal” as the owner class. To be compatible with the raster analysis used in these analyses, the tools used to query the data convert the vector ownership layer to a raster dataset f or processing. 12. The layers described above for ownership, village, village proximity, and biomass cost were combined together into a single raster layer, called the “combined parameters layer”, attributed for all parameters. To do this, vector layers such as ownership were converted to rasters, and to keep the number of parameter
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 17 combinations to a reasonable number, layers containing continuous data (biomass cost and distance to village) were converted into class categories; for example, instead of using the calculated biomass cost numbers directly, the biomass costs were grouped into increments of $20/ton ($20 -40/ton, $40-60/ton, etc.), and the distances in the village proximity raster were converted to 1-mile classes (1-2 miles, 2-3 miles etc.). 13. Using spatial analyst commands in ArcGIS, tables of statistics were generated by analyzing the stocking rasters with the combined parameters layer. Each table generated summarized one component of biomass stocking with all combinations of the parameters. Tables were generated for summary statistics for overall dry tons, dry ton annual allowable cut, dry tons by species, and cords by species. Once the statistics table were generated, it was possible to produce summary tables of biomass stocking by various attributes using standard database reporting tools. The datasets resulting from the process described above allow querying and displaying the data with multiple combinations of attributes. For example, one can query the data to show those areas and the biomass stocking amounts for a particular ownership and under a particular cost threshold. Or, perhaps one would want to query the data show the estimated annual allowable cut on a particular ownership within a specified distance of the village. Two tools were prepared as ArcGIS Python script tools to facilitate querying the data: 1. A GIS interactive query tool allows a user to interactively specify query parameters for village, ownership, owner class, and maximum biomass cost per ton, view the calculated values for total biomass and annual allowable cut in a brief tabular display, and have the areas in question highlighted on the map in ArcGIS. 2. A GIS statistics generation t ool generates a table of statistics that is stored in the database and can be used to drive reports showing biomass stocking and annual allowable cut by distance class, cost class, owner, owner class, and village.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 18
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 19 RESULTS Following are selected results of the analysis by village, with tabular results produced from the statistical summaries generated by the statistics generation tool described above, and sample maps of the generated spatial data. As indicated above, these results as displayed constitute only a portion of the possible combinations and ways to view the data, both in tabular form or on maps. “Forested area” refers to those portions of the project areas that have been associated with a forest inventory stratum that have woody biomass estimates. It does not include those areas that have a LandFire classification not associated with any woody biomass stocking estimates, including low-volume types such as dwarf black spruce or shrubland types. As determined in this analysis, forested area ranges from 28% at Nikolai and Holy Cross to 55% at Ruby (Table 5). Overall Results The amount of biomass found on ANCSA corporation lands (both regional and village) ranged from 29% to 40% of the totals by village (Table 6). Perhaps more importantly, a range of 53% to 86% (average of 72%) of the biomass within 10 miles of the village was found on ANCSA lands, highlighting the importance of the ANCSA corporations, particularly the village corporati ons, in the ownership of the most accessible, least expensive biomass resources. Over half of the estimated biomass stocking was found to be white spruce at 6 of the villages (59.3% to 75.3%), but Nikolai and Ruby showed a higher proportion of hardwoods (birch, cottonwood, aspen; 52% at Nikolai, and 49% at Ruby). This may be due to the inherent difference in the landscapes at those communities, but these determinations are also sensitive to the subjective assignment of inventory strata to the LandFire land classifications. For example, cottonwood stockings are extremely low (~1%) for several of the Yukon River villages, despite the known presence of extensive productive riparian cottonwood stands; the nature of the LandFire classifications did not readily associate themselves with forest inventory cottonwood strata, and as a result, may be underestimated in the analysis.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 20 Table 5: Forest area and biomass by village. Table 6: Biomass dry tons by ownership and village. Land Ownership : Native State of Village ANCSA Corp. Allotments Federal Alaska Total Koyukuk 1,811,844 46,830 2,423,907 216,699 4,499,280 Nulato 2,008,240 111,767 1,857,234 114,156 4,091,397 Kaltag 2,950,911 74,723 2,274,429 2,341,749 7,641,812 Anvik 2,750,225 118,083 3,792,036 311,696 6,972,039 Holy Cross 3,029,788 113,696 3,681,992 793,182 7,618,657 Hughes 2,749,873 10,224 6,450,437 321,555 9,535,036 Nikolai 3,048,703 59,246 458,457 4,770,585 8,336,991 Ruby 5,359,655 119,829 3,579,498 6,684,949 15,743,931 Village Forested acres Forested % of project area Biomass (dry tons) Annual Allowable Cut (dry tons/year) Koyukuk 226,001 35% 4,499,280 148,618 Nulato 215,423 30% 4,091,397 131,996 Kaltag 436,256 39% 7,641,812 252,749 Anvik 270,219 42% 6,972,039 196,019 Holy Cross 303,595 28% 7,618,657 220,155 Hughes 496,483 40% 9,535,036 301,032 Nikolai 346,648 28% 8,336,991 223,732 Ruby 692,623 55% 15,743,931 427,338
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 21 Koyukuk Table 7: Biomass by Land Ownership, Koyukuk. Annual Allowable Cut Forested Ownership Air -dry Tons Cords (AAC, tons/year) Acres Gana-A' Yoo Ltd. 902,767 662,484 29,937 43,817 Doyon, Ltd. 909,077 659,666 30,139 44,932 Native allotments 46,830 34,346 1,562 2,217 BLM 1,246,190 898,634 41,711 63,463 USFWS 1,177,717 849,482 38,138 60,731 State of Alaska 216,699 159,140 7,130 10,841 All ownerships: 4,499,280 3,263,752 148,618 226,001 Table 8: Biomass by Village Proximity, Koyukuk. Proximity to Annual Allowable Cut Forested village (miles) Air-dry Tons Cords (AAC, tons/year) Acres 0 - 1 16,401 12,210 563 764 1 - 2 55,518 40,930 1,865 2,543 2 - 3 89,760 65,849 3,012 4,243 3 - 4 114,372 83,869 3,857 5,539 4 - 5 150,759 110,263 5,086 7,098 5 - 6 178,792 131,112 6,067 8,213 6 - 7 198,736 145,617 6,587 9,607 7 - 8 249,848 182,616 8,284 12,256 8 - 9 299,777 218,831 9,938 14,191 9 - 10 251,790 183,758 8,200 12,232 10 - 11 239,479 174,585 7,845 12,101 11 - 12 236,309 171,997 7,876 12,123 12 - 13 217,886 158,090 7,238 11,001 13 - 14 195,137 141,638 6,424 10,241 14 - 15 184,968 133,918 6,108 9,669 15 - 16 184,950 134,029 6,154 9,359 16 - 17 177,732 128,624 5,872 9,059 17 - 18 160,810 115,929 5,290 8,266 18 - 19 167,414 121,117 5,553 8,622 19 - 20 188,568 135,823 6,273 9,523 20 - 21 203,150 146,057 6,867 10,194 21 - 22 194,349 139,445 6,527 9,785 22 - 23 191,534 137,222 6,328 9,713 23 - 24 161,676 115,334 5,108 8,957 24 - 25 189,565 134,889 5,693 10,703 Totals: 4,499,280 3,263,752 148,618 226,001
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 22 Table 9: Biomass by Estimated Cost, Koyukuk. Biomass Cost Annual Allowable Cut Forested ($/ton) Air-dry Tons Cords (AAC, tons/year) Acres 20 - 40 91 68 2 2 40 - 60 87,124 65,039 2,763 2,963 60 - 80 397,026 291,683 13,242 16,615 80 - 100 712,033 520,558 23,307 31,190 100 - 120 790,695 575,014 26,288 38,980 120 - 140 662,567 480,673 21,986 34,155 140 - 160 579,237 419,046 19,039 30,182 160 - 180 641,483 462,259 21,392 33,039 180 - 200 482,463 344,801 16,079 27,995 200 - 220 141,761 101,049 4,331 10,102 220 - 240 3,550 2,636 138 564 240 - 260 1,251 926 51 213 Totals: 4,499,280 3,263,752 148,618 226,001
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 23 Table 10: Biomass Dry Tons by Ownership and Village Proximity, Koyukuk. Land Ownership : Proximity to Native State of village (miles) ANCSA Corp. Allotments Federal Alaska Total 0 - 1 15,077 1,324 16,401 1 - 2 51,723 3,795 55,518 2 - 3 88,495 1,067 197 89,760 3 - 4 105,310 3,214 5,847 114,372 4 - 5 117,553 1,864 31,342 150,759 5 - 6 122,401 10,328 45,013 1,050 178,792 6 - 7 134,361 7,558 40,937 15,880 198,736 7 - 8 118,499 4,389 98,347 28,613 249,848 8 - 9 123,848 1,752 143,338 30,838 299,777 9 - 10 124,439 1,354 94,996 31,000 251,790 10 - 11 139,294 3,527 56,383 40,275 239,479 11 - 12 153,000 2,073 35,543 45,692 236,309 12 - 13 162,855 480 33,431 21,120 217,886 13 - 14 152,248 265 40,420 2,204 195,137 14 - 15 112,920 1,876 70,146 26 184,968 15 - 16 62,986 160 121,805 184,950 16 - 17 16,528 181 161,023 177,732 17 - 18 6,521 874 153,415 160,810 18 - 19 3,329 119 163,966 167,414 19 - 20 457 188,111 188,568 20 - 21 203,150 203,150 21 - 22 626 193,723 194,349 22 - 23 4 191,531 191,534 23 - 24 161,676 161,676 24 - 25 189,565 189,565 Totals: 1,811,844 46,830 2,423,907 216,699 4,499,280 Table 11: Biomass by species, Koyukuk. Tree Species Air -dry Tons Cords % of Total White Spruce 2,743,655 2,082,471 61.0% Black Spruce 265,696 223,274 5.9% Birch 1,371,070 848,959 30.5% Aspen 68,671 59,844 1.5% Cottonwood 50,188 49,204 1.1% All Species 4,499,280 3,263,752 100.0%
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 24 Figure 3: Land ownership, Koyukuk project area.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 25 Figure 4: Woody biomass dry ton stocking, Koyukuk.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 26 Figure 5: Woody biomass cost, Koyukuk.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 27 Nulato Table 12: Biomass by Land Ownership, Nulato. Annual Allowable Cut Forested Ownership Air -dry Tons Cords (AAC, tons/year) Acres Gana-A' Yoo Ltd. 1,244,942 910,404 40,969 55,766 Doyon, Ltd. 763,298 552,907 24,834 47,447 Native allotments 111,767 82,332 3,475 4,901 BLM 1,489,694 1,079,789 48,372 80,213 USFWS 367,539 263,708 10,583 21,771 State of Alaska 114,156 83,992 3,763 5,324 All ownerships: 4,091,397 2,973,133 131,996 215,423 Table 13: Biomass by Village Proximity, Nulato. Proximity to Annual Allowable Cut Forested village (miles) Air-dry Tons Cords (AAC, tons/year) Acres 0 - 1 7,069 5,342 226 297 1 - 2 43,030 32,179 1,403 1,766 2 - 3 77,094 56,779 2,472 3,441 3 - 4 118,889 87,275 3,916 5,668 4 - 5 138,036 101,011 4,547 6,773 5 - 6 178,863 130,827 5,920 8,866 6 - 7 182,557 133,832 6,035 9,182 7 - 8 227,490 166,758 7,452 11,496 8 - 9 232,510 169,316 7,714 11,893 9 - 10 242,861 176,213 8,042 12,645 10 - 11 251,405 182,513 8,312 13,450 11 - 12 235,814 171,378 7,720 12,695 12 - 13 236,100 171,118 7,767 12,824 13 - 14 248,855 180,276 8,117 13,457 14 - 15 263,852 191,101 8,435 13,883 15 - 16 235,716 170,894 7,575 12,391 16 - 17 197,773 142,802 6,341 10,622 17 - 18 170,799 122,989 5,438 9,501 18 - 19 148,475 107,088 4,534 8,122 19 - 20 133,464 96,193 4,068 7,381 20 - 21 120,498 87,778 3,635 6,613 21 - 22 109,943 80,168 3,241 5,850 22 - 23 106,714 76,869 3,327 5,885 23 - 24 95,535 68,962 2,988 5,551 24 - 25 88,055 63,472 2,770 5,170 Totals: 4,091,397 2,973,133 131,996 215,423
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 28 Table 14: Biomass by Estimated Cost, Nulato. Biomass Cost Annual Allowable Cut Forested ($/ton) Air-dry Tons Cords (AAC, tons/year) Acres 20 - 40 65 49 2 1 40 - 60 78,684 58,873 2,480 2,552 60 - 80 335,985 246,443 10,728 13,330 80 - 100 631,647 461,992 20,775 29,232 100 - 120 744,235 539,571 24,523 37,914 120 - 140 815,835 591,944 26,308 43,018 140 - 160 641,455 463,886 20,637 36,928 160 - 180 470,716 341,216 14,428 27,848 180 - 200 291,791 210,133 9,437 17,973 200 - 220 76,863 55,946 2,534 6,067 220 - 240 3,256 2,439 112 411 240 - 260 863 641 33 149 Totals: 4,091,397 2,973,133 131,996 215,423
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 29 Table 15: Biomass Dry Tons by Ownership and Village Proximity, Nulato. Land Ownership : Proximity to Native State of village (miles) ANCSA Corp. Allotments Federal Alaska Total 0 - 1 5,123 1,946 7,069 1 - 2 32,032 10,998 43,030 2 - 3 57,450 19,644 77,094 3 - 4 109,042 9,846 118,889 4 - 5 124,488 10,666 2,883 138,036 5 - 6 138,394 12,426 20,802 7,241 178,863 6 - 7 127,673 10,418 30,649 13,818 182,557 7 - 8 164,307 4,529 37,569 21,085 227,490 8 - 9 137,445 3,914 81,190 9,960 232,510 9 - 10 157,541 5,560 79,760 242,861 10 - 11 164,124 4,489 82,792 251,405 11 - 12 168,574 82 67,158 235,814 12 - 13 193,950 245 41,905 236,100 13 - 14 170,373 2,392 72,176 3,914 248,855 14 - 15 125,814 2,942 119,720 15,377 263,852 15 - 16 74,233 2,937 135,924 22,621 235,716 16 - 17 40,126 2,698 135,266 19,683 197,773 17 - 18 11,457 158,885 457 170,799 18 - 19 5,545 55 142,875 148,475 19 - 20 551 2,210 130,704 133,464 20 - 21 2,433 118,065 120,498 21 - 22 664 109,279 109,943 22 - 23 380 106,334 106,714 23 - 24 292 95,243 95,535 24 - 25 88,055 88,055 Totals: 2,008,240 111,767 1,857,234 114,156 4,091,397 Table 16: Biomass by species, Nulato. Tree Species Air -dry Tons Cords % of Total White Spruce 2,505,067 1,901,379 61.2% Black Spruce 275,169 231,235 6.7% Birch 1,216,231 753,084 29.7% Aspen 51,715 45,067 1.3% Cottonwood 43,214 42,367 1.1% All Species 4,091,397 2,973,133 100.0%
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 30 Figure 6: Land ownership, Nulato project area.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 31 Figure 7: Woody biomass dry ton stocking, Nulato.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 32 Figure 8: Woody biomass cost, Nulato.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 33 Kaltag Table 17: Biomass by Land Ownership, Kaltag. Annual Allowable Cut Forested Ownership Air-dry Tons Cords (AAC, tons/year) Acres Gana-A' Yoo Ltd. 1,349,940 980,705 44,669 74,074 Doyon, Ltd. 1,600,971 1,154,853 52,771 89,258 Native allotments 74,723 54,416 2,445 3,869 BLM 1,921,919 1,375,939 61,376 106,818 USFWS 352,509 258,248 10,445 19,788 State of Alaska 2,341,749 1,684,626 81,044 142,449 All ownerships: 7,641,812 5,508,787 252,749 436,256 Table 18: Biomass by Village Proximity, Kaltag. Proximity to Annual Allowable Cut Forested village (miles) Air-dry Tons Cords (AAC, tons/year) Acres 0 - 1 9,011 6,695 250 470 1 - 2 35,497 26,355 1,036 1,966 2 - 3 72,594 53,220 2,218 3,835 3 - 4 106,516 77,785 3,334 5,852 4 - 5 152,710 110,694 4,943 8,417 5 - 6 222,493 161,137 7,266 12,120 6 - 7 263,767 190,927 8,661 14,472 7 - 8 278,973 202,323 9,029 15,358 8 - 9 324,412 234,165 10,625 17,805 9 - 10 346,512 250,394 11,178 18,921 10 - 11 379,190 273,928 12,420 20,745 11 - 12 387,415 280,110 12,813 21,037 12 - 13 425,412 307,617 14,123 23,242 13 - 14 447,246 322,908 14,952 25,182 14 - 15 473,540 341,058 15,895 26,892 15 - 16 481,993 347,760 16,066 27,575 16 - 17 423,384 304,340 14,201 25,510 17 - 18 371,882 265,913 12,557 22,341 18 - 19 366,234 262,089 12,163 21,780 19 - 20 385,401 275,772 12,941 22,181 20 - 21 370,020 265,138 12,506 21,554 21 - 22 325,936 233,388 11,009 19,694 22 - 23 316,180 227,522 10,447 19,342 23 - 24 333,639 240,425 10,986 19,627 24 - 25 341,854 247,126 11,131 20,336 Totals: 7,641,812 5,508,787 252,749 436,256
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 34 Table 19: Biomass by Estimated Cost, Kaltag. Biomass Cost Annual Allowable Cut Forested ($/ton) Air-dry Tons Cords (AAC, tons/year) Acres 40 - 60 45,397 34,019 1,032 1,530 60 - 80 319,114 233,083 9,545 14,501 80 - 100 816,767 590,622 26,182 40,656 100 - 120 1,148,248 829,778 37,443 59,448 120 - 140 1,387,524 1,001,529 46,085 73,934 140 - 160 1,340,992 962,001 45,495 78,780 160 - 180 1,219,877 872,995 41,152 73,052 180 - 200 1,041,863 749,112 34,864 65,201 200 - 220 289,405 211,336 9,750 24,334 220 - 240 26,409 19,697 966 3,749 240 - 260 6,215 4,616 236 1,070 Totals: 7,641,812 5,508,787 252,749 436,256
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 35 Table 20: Biomass Dry Tons by Ownership and Village Proximity, Kaltag. Land Ownership : Proximity to Native State of village (miles) ANCSA Corp. Allotments Federal Alaska Total 0 - 1 6,750 2,261 9,011 1 - 2 30,682 4,815 35,497 2 - 3 71,261 1,333 72,594 3 - 4 98,623 3,131 4,762 106,516 4 - 5 135,476 6,420 10,814 152,710 5 - 6 146,081 8,336 68,076 222,493 6 - 7 186,354 2,583 74,830 263,767 7 - 8 183,463 4,006 91,504 278,973 8 - 9 205,418 6 118,988 324,412 9 - 10 207,323 4,189 134,926 74 346,512 10 - 11 243,419 3,210 123,429 9,132 379,190 11 - 12 270,058 3,113 88,082 26,162 387,415 12 - 13 281,278 7,446 87,320 49,369 425,412 13 - 14 223,206 5,099 116,630 102,311 447,246 14 - 15 174,293 5,860 128,695 164,692 473,540 15 - 16 144,652 5,981 120,462 210,897 481,993 16 - 17 119,061 1,144 88,728 214,452 423,384 17 - 18 28,891 128,793 214,198 371,882 18 - 19 5,606 1,328 136,656 222,643 366,234 19 - 20 21,057 1,970 121,373 241,001 385,401 20 - 21 51,061 1,695 102,286 214,978 370,020 21 - 22 40,501 94,891 190,545 325,936 22 - 23 14,323 100,350 201,507 316,180 23 - 24 23,456 142,680 167,503 333,639 24 - 25 38,618 796 190,154 112,286 341,854 Totals: 2,950,911 74,723 2,274,429 2,341,749 7,641,812 Table 21: Biomass by species, Kaltag. Tree Species Air -dry Tons Cords % of Total White Spruce 4,530,772 3,438,916 59.3% Black Spruce 411,654 345,928 5.4% Birch 2,527,750 1,565,170 33.1% Aspen 87,184 75,978 1.1% Cottonwood 84,452 82,796 1.1% All Species 7,641,812 5,508,787 100.0%
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 36 Figure 9: Land ownership, Kaltag project area.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 37 Figure 10: Woody biomass dry ton stocking, Kaltag.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 38 Figure 11: Woody biomass cost, Kaltag.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 39 Anvik Table 22: Biomass by Land Ownership, Anvik. Annual Allowable Cut Forested Ownership Air -dry Tons Cords (AAC, tons/year) Acres Deloy Ges Inc. 1,563,983 1,172,996 46,101 60,005 Hee-yea -lingde Corp. 19,051 13,837 634 763 Doyon Ltd. 1,123,038 834,135 32,635 46,398 ANCSA misc. 44,153 32,928 1,251 1,688 Native allotments 118,083 88,408 3,455 4,510 BLM 3,792,036 2,854,343 103,132 144,980 State of Alaska 311,696 232,374 8,812 11,875 All ownerships: 6,972,039 5,229,021 196,019 270,219 Table 23: Biomass by Village Proximity, Anvik. Proximity to Annual Allowable Cut Forested village (miles) Air-dry Tons Cords (AAC, tons/year) Acres 0 - 1 25,821 19,327 769 987 1 - 2 92,945 69,923 2,760 3,552 2 - 3 136,125 102,897 3,983 5,155 3 - 4 208,939 157,741 6,110 8,101 4 - 5 290,288 218,041 8,439 11,643 5 - 6 276,844 208,396 7,977 11,257 6 - 7 266,533 199,442 7,752 11,353 7 - 8 361,124 267,048 10,796 14,974 8 - 9 383,942 283,031 11,555 15,432 9 - 10 342,521 255,491 10,043 13,318 10 - 11 291,981 220,002 8,375 11,540 11 - 12 275,583 208,310 7,673 10,745 12 - 13 246,906 186,395 6,820 9,794 13 - 14 307,757 233,641 8,463 12,265 14 - 15 361,661 273,546 10,030 14,229 15 - 16 364,591 274,778 10,019 13,782 16 - 17 408,585 306,491 11,323 15,318 17 - 18 389,755 292,131 10,714 14,777 18 - 19 293,099 220,896 7,688 10,710 19 - 20 225,569 169,988 5,847 8,319 20 - 21 226,489 170,480 5,884 8,449 21 - 22 251,065 188,172 6,736 9,445 22 - 23 287,885 214,586 7,970 10,811 23 - 24 334,820 248,609 9,519 12,471 24 - 25 321,210 239,659 8,777 11,793 Totals: 6,972,039 5,229,021 196,019 270,219
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 40 Table 24: Biomass by Estimated Cost, Anvik. Biomass Cost Annual Allowable Cut Forested ($/ton) Air-dry Tons Cords (AAC, tons/year) Acres 40 - 60 177,684 133,777 5,284 6,438 60 - 80 765,238 576,001 22,268 28,173 80 - 100 1,059,286 787,017 31,410 41,899 100 - 120 1,032,853 775,698 29,525 42,555 120 - 140 1,119,033 846,716 30,748 42,917 140 - 160 1,211,044 908,086 32,991 45,574 160 - 180 790,968 594,433 20,989 29,688 180 - 200 793,762 590,094 22,237 30,975 200 - 220 22,171 17,199 567 2,000 Totals: 6,972,039 5,229,021 196,019 270,219 Table 25: Biomass Dry Tons by Ownership and Village Proximity, Anvik. Land Ownership : Proximity to Native State of village (miles) ANCSA Corp. Allotments Federal Alaska Total 0 - 1 23,864 1,957 25,821 1 - 2 87,670 5,276 92,945 2 - 3 128,175 7,950 136,125 3 - 4 186,023 13,714 9,202 208,939 4 - 5 226,548 12,383 51,357 290,288 5 - 6 197,017 13,038 66,788 276,844 6 - 7 220,064 598 45,871 266,533 7 - 8 286,105 995 74,024 361,124 8 - 9 322,680 4,048 57,215 383,942 9 - 10 287,638 3,778 51,105 342,521 10 - 11 222,589 5,129 49,279 14,984 291,981 11 - 12 191,166 3,071 45,474 35,872 275,583 12 - 13 148,858 3,924 56,712 37,412 246,906 13 - 14 127,931 7,885 152,050 19,891 307,757 14 - 15 58,309 3,220 286,780 13,352 361,661 15 - 16 20,247 7,216 323,120 14,009 364,591 16 - 17 8,790 11,713 360,008 28,075 408,585 17 - 18 6,369 1,704 361,510 20,172 389,755 18 - 19 183 1,117 291,799 293,099 19 - 20 224 225,345 225,569 20 - 21 2,069 224,420 226,489 21 - 22 3,011 248,054 251,065 22 - 23 261,818 26,067 287,885 23 - 24 1,154 281,874 51,792 334,820 24 - 25 2,911 268,228 50,072 321,210 Totals: 2,750,225 118,083 3,792,036 311,696 6,972,039
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 41 Table 26: Biomass by species, Anvik. Tree Species Air -dry Tons Cords % of Total White Spruce 5,182,452 3,933,550 74.3% Black Spruce 141,131 118,598 2.0% Birch 1,194,892 739,871 17.1% Aspen 70,398 61,349 1.0% Cottonwood 383,166 375,653 5.5% All Species 6,972,039 5,229,021 100.0%
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 42 Figure 12: Land ownership, Anvik project area.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 43 Figure 13: Woody biomass dry ton stocking, Anvik.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 44 Figure 14: Woody biomass cost, Anvik.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 45 Holy Cross Table 27: Biomass by Land Ownership, Holy Cross. Annual Allowable Cut Forested Ownership Air -dry Tons Cords (AAC, tons/year) Acres Deloycheet 1,612,466 1,215,246 47,850 62,912 Doyon Ltd. 1,326,622 985,639 39,245 52,652 ANCSA misc. 90,700 67,044 2,282 3,701 Native allotments 113,696 84,571 3,422 4,504 BLM 3,292,801 2,452,681 92,351 132,702 USFWS 389,191 289,098 11,525 15,122 State of Alaska 793,182 593,481 23,480 32,001 All ownerships: 7,618,657 5,687,759 220,155 303,595 Table 28: Biomass by Village Proximity, Holy Cross. Proximity to Annual Allowable Cut Forested village (miles) Air-dry Tons Cords (AAC, tons/year) Acres 0 - 1 29,612 21,946 934 1,149 1 - 2 68,652 51,955 2,052 2,657 2 - 3 127,264 95,501 3,822 4,973 3 - 4 183,749 136,626 5,545 7,236 4 - 5 218,702 164,622 6,454 8,608 5 - 6 258,842 192,814 7,652 10,200 6 - 7 287,083 214,471 8,382 11,305 7 - 8 274,172 206,430 7,947 10,817 8 - 9 292,230 219,073 8,523 11,484 9 - 10 329,910 246,711 9,698 13,057 10 - 11 302,208 227,927 8,882 11,824 11 - 12 348,732 262,256 10,277 13,714 12 - 13 360,086 270,341 10,622 14,130 13 - 14 367,069 273,197 10,796 14,570 14 - 15 386,943 288,131 11,259 15,465 15 - 16 430,961 318,718 12,677 17,100 16 - 17 451,739 333,746 13,365 18,138 17 - 18 431,443 319,603 12,531 17,271 18 - 19 395,803 295,111 11,177 15,729 19 - 20 383,476 286,997 10,632 15,418 20 - 21 370,244 276,875 10,299 15,166 21 - 22 352,850 264,196 9,667 14,392 22 - 23 350,131 261,473 9,709 14,090 23 - 24 328,627 244,540 9,185 13,318 24 - 25 288,126 214,498 8,067 11,783 Totals: 7,618,657 5,687,759 220,155 303,595
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 46 Table 29: Biomass by Estimated Cost, Holy Cross. Biomass Cost Annual Allowable Cut Forested ($/ton) Air-dry Tons Cords (AAC, tons/year) Acres 40 - 60 145,913 110,255 4,420 5,274 60 - 80 658,387 493,088 19,547 25,428 80 - 100 935,617 701,463 27,311 36,356 100 - 120 1,117,109 840,294 32,802 43,727 120 - 140 1,271,117 946,232 37,214 48,931 140 - 160 1,423,714 1,053,730 41,408 55,602 160 - 180 1,181,123 882,284 32,807 46,769 180 - 200 835,807 621,651 23,369 36,981 200 - 220 49,870 38,763 1,277 4,528 Totals: 7,618,657 5,687,759 220,155 303,595 Table 30: Biomass Dry Tons by Ownership and Village Proximity, Holy Cross. Land Ownership : Native Native State of Allotments Federal Allotments Federal Alaska Total 0 - 1 29,184 428 29,612 1 - 2 68,652 68,652 2 - 3 127,264 127,264 3 - 4 176,098 2,877 4,774 183,749 4 - 5 199,244 4,647 14,811 218,702 5 - 6 237,574 4,596 16,672 258,842 6 - 7 251,954 9,255 22,567 3,307 287,083 7 - 8 219,049 7,000 30,815 17,308 274,172 8 - 9 194,964 11,580 66,252 19,434 292,230 9 - 10 193,815 8,164 94,484 33,446 329,910 10 - 11 174,324 2,215 102,462 23,206 302,208 11 - 12 198,382 6,375 112,587 31,387 348,732 12 - 13 186,289 9,432 125,556 38,810 360,086 13 - 14 187,407 5,437 162,562 11,664 367,069 14 - 15 155,611 4,944 213,217 13,171 386,943 15 - 16 122,436 6,772 264,142 37,611 430,961 16 - 17 84,732 6,097 250,623 110,288 451,739 17 - 18 59,872 6,431 258,776 106,364 431,443 18 - 19 30,553 2,253 276,755 86,243 395,803 19 - 20 30,813 283,939 68,724 383,476 20 - 21 23,743 4,855 302,368 39,277 370,244 21 - 22 36,495 3,762 288,042 24,551 352,850 22 - 23 19,016 6,059 289,080 35,975 350,131 23 - 24 15,792 114 269,761 42,960 328,627 24 - 25 6,525 401 231,744 49,456 288,126 Totals: 3,029,788 113,696 3,681,992 793,182 7,618,657
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 47 Table 31: Biomass by species, Holy Cross. Tree Species Air -dry Tons Cords % of Total White Spruce 5,288,287 4,013,880 69.4% Black Spruce 142,748 119,956 1.9% Birch 1,603,439 992,841 21.0% Aspen 106,922 93,178 1.4% Cottonwood 477,262 467,904 6.3% All Species 7,618,657 5,687,759 100.0%
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 48 Figure 15: Land ownership, Holy Cross project area.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 49 Figure 16: Woody biomass dry ton stocking, Holy Cross.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 50 Figure 17: Woody biomass cost, Holy Cross .
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 51 Hughes Table 32: Biomass by Land Ownership, Hughes. Annual Allowable Cut Forested Ownership Air -dry Tons Cords (AAC, tons/year) Acres K'oyitl'ots'ina Ltd. 328,128 244,499 11,591 20,061 Doyon Ltd. 2,285,475 1,695,809 71,781 113,348 ANCSA misc. 136,269 100,788 4,383 6,746 Native allotment 10,224 7,667 379 715 Private 2,947 2,179 92 161 BLM 5,036,979 3,748,495 158,172 261,290 USFWS 1,302,296 956,653 41,196 72,557 Military 111,162 82,064 3,500 5,435 State of Alaska 321,555 238,440 9,938 16,170 All ownerships: 9,535,036 7,076,594 301,032 496,483 Table 33: Biomass by Village Proximity, Hughes. Proximity to Annual Allowable Cut Forested village (miles) Air-dry Tons Cords (AAC, tons/year) Acres 0 - 1 8,115 6,055 303 551 1 - 2 33,292 24,581 1,137 1,780 2 - 3 69,378 50,963 2,347 3,562 3 - 4 128,600 94,241 4,267 6,326 4 - 5 162,439 119,348 5,272 8,233 5 - 6 172,264 126,707 5,529 9,133 6 - 7 194,013 142,862 6,124 10,451 7 - 8 214,280 157,335 6,881 11,463 8 - 9 271,749 200,728 8,643 14,157 9 - 10 261,322 193,629 8,357 13,803 10 - 11 256,407 190,603 8,165 13,081 11 - 12 291,211 216,588 9,377 15,092 12 - 13 320,091 238,516 10,170 16,395 13 - 14 414,588 308,897 13,061 20,412 14 - 15 453,341 338,532 14,344 23,039 15 - 16 506,343 376,920 15,931 25,611 16 - 17 519,682 384,290 16,403 26,959 17 - 18 617,836 458,635 19,023 30,390 18 - 19 672,203 498,840 20,824 33,605 19 - 20 626,598 466,023 19,703 32,645 20 - 21 674,008 502,040 20,963 34,894 21 - 22 617,596 460,266 19,319 33,485 22 - 23 662,429 491,500 20,814 35,941 23 - 24 678,059 502,848 21,504 37,195 24 - 25 709,191 525,649 22,572 38,279 Totals: 9,535,036 7,076,594 301,032 496,483
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 52 Table 34: Biomass by Estimated Cost, Hughes. Biomass Cost Annual Allowable Cut Forested ($/ton) Air-dry Tons Cords (AAC, tons/year) Acres 20 - 40 286 214 8 5 40 - 60 51,653 38,553 1,473 1,370 60 - 80 345,957 254,737 10,953 14,574 80 – 100 645,353 474,394 20,277 31,191 100 - 120 953,540 704,129 29,922 45,425 120 - 140 1,265,392 939,078 39,279 58,437 140 - 160 1,842,956 1,366,810 56,993 86,492 160 - 180 2,039,404 1,511,987 63,873 103,851 180 - 200 1,876,963 1,390,298 59,986 105,354 200 - 220 443,123 329,453 15,310 34,006 220 - 240 31,711 30,098 1,424 5,656 240 - 260 16,428 15,534 693 3,449 260 - 280 9,037 8,648 330 2,708 280 - 300 12,529 11,989 481 3,755 300 - 320 703 673 31 211 Totals: 9,535,036 7,076,594 301,032 496,483
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 53 Table 35: Biomass Dry Tons by Ownership and Village Proximity, Hughes. Land Ownership : Proximity to Native State of village (miles) ANCSA Corp. Allotments Federal Alaska Total 0 - 1 7,035 812 268 8,115 1 - 2 26,821 310 274 5,887 33,292 2 - 3 39,332 24 17,950 12,072 69,378 3 - 4 58,129 130 66,698 3,643 128,600 4 - 5 77,022 546 84,871 162,439 5 - 6 77,291 873 94,099 172,264 6 - 7 99,616 132 94,264 194,013 7 - 8 125,423 1,637 82,880 4,340 214,280 8 - 9 156,519 974 98,085 16,171 271,749 9 - 10 143,129 88,616 29,578 261,322 10 - 11 124,488 99,840 32,079 256,407 11 - 12 152,137 1,029 109,427 28,618 291,211 12 - 13 176,650 312 124,747 18,383 320,091 13 - 14 206,460 499 196,135 11,494 414,588 14 - 15 219,560 227,495 6,286 453,341 15 - 16 242,831 60 262,050 1,402 506,343 16 - 17 212,518 568 297,967 8,629 519,682 17 - 18 182,121 255 397,611 37,850 617,836 18 - 19 126,710 433 473,602 71,458 672,203 19 - 20 71,424 521,777 33,397 626,598 20 - 21 56,410 177 616,832 674,008 21 - 22 44,200 327 570,712 617,596 22 - 23 44,218 881 617,329 662,429 23 - 24 42,779 219 635,061 678,059 24 - 25 37,051 27 672,114 709,191 Totals: 2,749,873 10,224 6,450,437 321,555 9,535,036 Table 36: Biomass by species, Hughes. Tree Species Air -dry Tons Cords % of Total White Spruce 7,183,921 5,452,691 75.3% Black Spruce 158,640 133,311 1.7% Birch 1,796,965 1,112,672 18.8% Aspen 90,288 78,683 0.9% Cottonwood 305,222 299,238 3.2% All Species 9,535,036 7,076,594 100.0%
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 54 Figure 18:: Land ownership, Hughes project area.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 55 Figure 19: Woody biomass dry ton stocking, Hughes .
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 56 Figure 20: Woody biomass cost, Hughes.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 57 Nikolai Table 37: Biomass by Land Ownership, Nikolai. Annual Allowable Cut Forested Ownership Air -dry Tons Cords (AAC, tons/year) Acres MTNT 423,971 382,717 11,587 18,447 Doyon Ltd. 2,614,262 2,008,618 73,210 108,457 ANCSA misc. 10,470 7,663 286 389 Native allotments 59,246 53,792 1,587 2,208 BLM 458,457 415,087 10,510 17,146 State of Alaska 4,770,585 3,821,741 126,553 200,000 All ownerships: 8,336,991 6,689,618 223,732 346,648 Table 38: Biomass by Village Proximity, Nikolai. Proximity to Annual Allowable Cut Forested village (miles) Air-dry Tons Cords (AAC, tons/year) Acres 0 - 1 6,858 6,117 191 467 1 - 2 22,837 20,031 594 1,219 2 - 3 40,040 35,167 1,075 2,117 3 - 4 52,875 45,357 1,458 2,786 4 - 5 55,646 47,026 1,592 2,972 5 - 6 60,529 53,351 1,686 2,861 6 - 7 80,391 74,185 2,211 3,537 7 - 8 131,363 123,567 3,486 5,214 8 - 9 163,850 153,816 4,253 6,387 9 - 10 166,028 150,351 4,253 6,804 10 - 11 230,410 199,681 5,965 9,485 11 - 12 266,084 229,049 6,898 10,485 12 - 13 314,715 266,185 8,040 12,052 13 - 14 392,093 325,085 10,215 14,664 14 - 15 494,510 397,207 13,382 19,009 15 - 16 585,415 465,659 15,864 22,753 16 - 17 642,780 498,659 17,695 25,462 17 - 18 605,175 470,370 16,656 24,707 18 - 19 614,911 476,954 16,736 24,924 19 - 20 647,547 501,373 17,527 26,033 20 - 21 599,118 466,064 16,060 25,212 21 - 22 561,191 435,095 15,225 25,052 22 - 23 535,065 416,356 14,291 23,754 23 - 24 485,316 378,005 12,913 22,740 24 - 25 582,243 454,908 15,466 25,954 Totals: 8,336,991 6,689,618 223,732 346,648
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 58 Table 39: Biomass by Estimated Cost, Nikolai. Biomass Cost Annual Allowable Cut Forested ($/ton) Air-dry Tons Cords (AAC, tons/year) Acres 40 - 60 42,217 38,182 1,006 1,398 60 - 80 151,233 131,710 3,964 5,325 80 – 100 417,854 391,082 10,951 15,952 100 - 120 816,348 708,087 21,077 30,485 120 - 140 1,530,722 1,239,570 40,493 54,402 140 - 160 2,007,995 1,552,482 54,435 74,062 160 - 180 1,856,030 1,435,189 49,602 69,979 180 - 200 1,189,532 925,237 31,865 52,808 200 - 220 184,860 152,442 5,973 24,011 220 - 240 140,200 115,637 4,366 18,226 Totals: 8,336,991 6,689,618 223,732 346,648 Table 40: Biomass Dry Tons by Ownership and Village Proximity, Nikolai. Land Ownership : Proximity to Native State of village (miles) ANCSA Corp. Allotments Federal Alaska Total 0 - 1 6,858 6,858 1 - 2 16,870 5,966 22,837 2 - 3 33,889 6,151 40,040 3 - 4 50,538 2,337 52,875 4 - 5 52,441 3,205 55,646 5 - 6 54,211 1,165 5,154 60,529 6 - 7 46,999 1,647 31,745 80,391 7 - 8 73,507 2,014 23 55,820 131,363 8 - 9 98,858 1,658 1,147 62,187 163,850 9 - 10 81,266 6,579 10,378 67,806 166,028 10 - 11 118,885 14,497 30,138 66,889 230,410 11 - 12 151,491 5,393 25,414 83,786 266,084 12 - 13 155,797 3,825 16,490 138,604 314,715 13 - 14 180,079 2,651 24,235 185,129 392,093 14 - 15 201,726 5,912 26,415 260,457 494,510 15 - 16 203,233 2,813 44,529 334,838 585,415 16 - 17 271,257 3,771 39,809 327,944 642,780 17 - 18 275,703 409 31,158 297,904 605,175 18 - 19 234,176 1,730 27,324 351,681 614,911 19 - 20 198,984 2,052 23,080 423,432 647,547 20 - 21 137,545 2,329 21,218 438,026 599,118 21 - 22 97,108 24,548 439,536 561,191 22 - 23 87,261 729 35,426 411,649 535,065 23 - 24 70,769 68 37,463 377,016 485,316 24 - 25 149,253 3 39,662 393,325 582,243 Totals: 3,048,703 59,246 458,457 4,770,585 8,336,991
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 59 Table 41: Biomass by species, Nikolai. Tree Species Air -dry Tons Cords % of Total White Spruce 3,368,461 2,556,707 40.4% Black Spruce 541,611 455,136 6.5% Birch 1,819,053 1,126,349 21.8% Aspen 48,690 42,431 0.6% Cottonwood 2,559,176 2,508,996 30.7% All Species 8,336,991 6,689,618 100.0%
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 60 Figure 21: Land ownership, Nikolai project area.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 61 Figure 22: Woody biomass dry ton stocking, Nikolai.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 62 Figure 23: Woody biomass cost, Nikolai.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 63 Ruby Table 42: Biomass by Land Ownership, Ruby. Annual Allowable Cut Forested Ownership Air -dry Tons Cords (AAC, tons/year) Acres Dineega Corporation 2,029,349 1,524,067 57,465 82,931 Doyon Ltd. 3,330,306 2,533,386 89,889 144,001 Native allotment 119,829 89,509 3,445 4,811 BLM 2,038,745 1,499,636 56,853 87,855 USFWS 1,540,752 1,250,323 36,474 80,227 State of Alaska 6,684,949 5,007,512 183,212 292,798 All ownerships: 15,743,931 11,904,433 427,338 692,623 Table 43: Biomass by Village Proximity, Ruby. Proximity to Annual Allowable Cut Forested village (miles) Air-dry Tons Cords (AAC, tons/year) Acres 0 - 1 26,106 19,199 769 1,069 1 - 2 77,967 59,978 2,127 3,591 2 - 3 151,771 116,815 4,138 6,600 3 - 4 217,293 165,215 5,995 9,482 4 - 5 292,367 220,014 8,300 12,308 5 - 6 392,605 289,774 11,545 15,769 6 - 7 462,991 339,815 13,581 18,282 7 - 8 453,027 337,552 12,932 19,134 8 - 9 496,964 370,634 14,012 21,068 9 - 10 529,892 397,768 14,711 22,754 10 - 11 572,431 428,356 16,101 23,955 11 - 12 578,222 433,652 16,078 25,710 12 - 13 642,505 481,494 17,780 27,560 13 - 14 682,614 513,881 18,625 29,377 14 - 15 745,194 561,676 20,360 32,602 15 - 16 771,341 580,996 20,935 33,965 16 - 17 790,542 600,459 21,295 34,849 17 - 18 806,510 613,087 21,487 36,141 18 - 19 842,744 641,075 22,520 38,171 19 - 20 908,812 690,297 24,097 40,607 20 - 21 958,667 729,580 25,258 42,894 21 - 22 999,063 763,404 26,310 45,972 22 - 23 1,067,935 813,314 28,154 47,905 23 - 24 1,112,226 848,775 29,309 50,178 24 - 25 1,164,143 887,623 30,919 52,680 Totals: 15,743,931 11,904,433 427,338 692,623
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 64 Table 44: Biomass by Estimated Cost, Ruby. Biomass Cost Annual Allowable Cut Forested ($/ton) Air-dry Tons Cords (AAC, tons/year) Acres 20 - 40 32 24 1 1 40 - 60 143,220 105,488 4,129 4,710 60 - 80 892,148 661,483 25,751 34,022 80 - 100 1,428,862 1,058,725 41,142 57,888 100 - 120 1,850,193 1,382,961 51,725 77,426 120 - 140 2,306,720 1,732,651 63,146 97,451 140 - 160 2,611,077 1,974,746 70,324 112,888 160 - 180 3,175,870 2,414,688 83,906 139,024 180 - 200 2,971,905 2,276,806 77,908 138,470 200 - 220 319,551 263,059 7,927 22,609 220 - 240 21,089 16,094 598 3,766 240 - 260 23,264 17,709 782 4,367 Totals: 15,743,931 11,904,433 427,338 692,623
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 65 Table 45: Biomass Dry Tons by Ownership and Village Proximity, Ruby. Land Ownership : Proximity to Native State of village (miles) ANCSA Corp. Allotments Federal Alaska Total 0 - 1 24,060 2,046 26,106 1 - 2 73,491 4,476 77,967 2 - 3 140,020 11,189 563 151,771 3 - 4 191,014 9,655 16,623 217,293 4 - 5 214,156 4,358 73,854 292,367 5 - 6 233,178 17,662 105 141,660 392,605 6 - 7 262,060 12,187 24,129 164,615 462,991 7 - 8 316,378 3,897 41,007 91,745 453,027 8 - 9 344,797 7,291 35,269 109,607 496,964 9 - 10 340,583 852 39,967 148,490 529,892 10 - 11 414,885 555 34,167 122,824 572,431 11 - 12 404,426 567 46,766 126,463 578,222 12 - 13 365,589 496 51,222 225,199 642,505 13 - 14 274,752 3,581 122,666 281,615 682,614 14 - 15 238,307 1,644 163,489 341,753 745,194 15 - 16 204,846 2,350 202,774 361,372 771,341 16 - 17 214,599 6,253 237,073 332,617 790,542 17 - 18 210,555 1,427 244,198 350,330 806,510 18 - 19 168,967 5,487 265,371 402,919 842,744 19 - 20 186,007 6,312 263,132 453,360 908,812 20 - 21 169,650 2,802 308,486 477,729 958,667 21 - 22 129,003 3,461 339,715 526,883 999,063 22 - 23 86,756 3,965 364,307 612,908 1,067,935 23 - 24 88,073 2,297 370,790 651,066 1,112,226 24 - 25 63,505 5,019 424,866 670,753 1,164,143 Totals: 5,359,655 119,829 3,579,498 6,684,949 15,743,931 Table 46: Biomass by species, Ruby. Tree Species Air -dry Tons Cords % of Total White Spruce 5,283,702 4,010,400 33.6% Black Spruce 2,810,741 2,361,967 17.9% Birch 5,148,791 3,188,106 32.7% Aspen 988,725 861,634 6.3% Cottonwood 1,511,973 1,482,327 9.6% All Species 15,743,931 11,904,433 100.0%
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 66 Figure 24: Land ownership, Ruby project area.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 67 Figure 25: Woody biomass dry ton stocking, Ruby.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 68 Figure 26: Woody biomass cost, Ruby.
Assessment of Woody Biomass Energy Resources for Rural Villages in Interior Alaska 69 FUTURE STEPS As plans for proposed biomass heating projects move forward, what steps need to be taken to implement effective and sustained use of forest resources as a woody biomass supply at villages in Interior Alaska? This report constitutes a first-look assessment designed to assist in determining if the potential supply of woody biomass warrants pursuing the development of proposed biomas s energy projects. Additional steps that will need to be considered as proposed projects move forward include: • Develop agreements with major landowners. As owners of the resource required to fuel a biomass energy project, any proposed project needs to have the commitment and participation of the landowners involved. In many cases, this means the required participation of the local ANCSA village corporation as the owner of the bulk of the lands in the immediate vicinity of a community. • With the involved landowners, develop forest management plans. The forest stewardship program, administered by the State of Alaska with federal funds, is one option for a landowner to receive planning assistance. A project involving multipl e landowners would require coordinated planning among the landowners to best serve the project and the affected community. Included in the issues to be addressed by these plans would be: Managing the biomass resources in a sustainable manner through reforestation and other forestry Best management Practices (BMPs), and ensuring compliance with the Alaska Forest Resource Practices Act (FRPA); Preparation of a transportation and access plan; Detailed harvest plans; Ensuring that the harvest of biomass for energy does not interfere with normal subsistence wood gathering and other forest products utilization by community residents; Work to avoid the natural tendency to harvest the most available resource first, with the resultant effect of making fuel costs prohibitively more expensive in the future; Coordinate biomass harvesting with other land management activities such as hazardous fuel mitigation, wildlife habitat enhancement, etc. • Work to develop local capacity for technical land management tasks, biomass harvesting and transportation, and other contractable services and small businesses required to make a biomass energy project functional. • Attempt to develop better biomass supply and growth data. This can include the development of more precise and accur ate land cover mapping using higher-resolution imagery or aerial photography, and the installation of ground plots to determine more accurate estimates of biomass stocking. This work can be quite expensive, but can be scaled to fit the demands of a proposed project. For example, a combined heat and power project (CHP) projected to consume relatively large amounts of woody biomass would require tighter biomass stocking and sustainability estimates and more detailed planning than would a relatively small co rdwood thermal heating projec t. The Alaska Energy Authority has recently worked to develop standards for required information for projects of varying size, complexity, resource demands, and stage of development. • As projects come on line, develop monitorin g programs to collect information on harvest and transportation costs to better inform decisions made for current and future projects.