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HomeMy WebLinkAboutTanana Biomass Resource Assessment Phase2 Tanana Biomass Resource Assessment Phase 2 Will Putman, Forestry Director Tanana Chiefs Conference, Forestry Program Fairbanks, Alaska March, 2012 Tanana Biomass Resource Assessment, Phase 2 i EXECUTIVE SUMMARY As part of an effort to support the implementation of woody biomass energy system s in Tanana, Alaska, a biomass resource assessment was conducted. Phase 1 of this assessment was designed to summarize and report on woody biomass relying solely on existing archival forest inventory information collected and processed for lands selected by Tozitna Ltd., the local ANCSA village corporation. Phase 2, conducted in the immediate future following Phase 1, was to include the interpretation and use of newer satellite imagery to compile better information for current land cover, consider additional land ownerships in the vicinity of Tanana and involve a more in -depth analysis, including estimat es of growth, sustainability, and costs used to derive annual allowable cut figures and biomass cost estimates. The assessment resulted in woody biomass stocking and allowable cut figures stored and maintained in a geodatabase, with the ability to query and report data by land cover type, ownership, site class, biomass growth, management restrictions, biomass cost, distance from village, and other paramet ers. Highlights of the resulting data analysis include: • Woody biomass stocking on the entire project area of 236,089 acres is estimated at 3,274,112 dry tons. • 93% of the biomass in the project area is located on land owned by Tozitna, Ltd., with 5% on Native allotments, and the remainder on Doyon, BLM, or State land. • An estimated 62% of the standing stock of woody biomass is in white spruce, 30% is paper birch, 4% is aspen, 2% is black spruce, and 2% is balsam poplar. • Standing stock estimates and modeled growth rates indicate a total Annual Allowable Cut of 71,000 tons on all areas over all ownerships. • Seventy -four percent of the biomass is more than six miles from the village. • Estimates from biomass cost modeling indicates that only 38% of the total biomass is available at less than $70/ton. • Detailed field data collection on Long Island, a proposed wood harvesting area near the village, produced estimates of 14,572 tons. While the biomass stocking on Long Island appears capable of supplying village needs in the near future (< 20 years), depending on required harvest levels, the island does not appear to be capable of sustaining those harvest levels indefinitely. Tanana Biomass Resource Assessment, Phase 2 ii TABLE OF CONTENTS INTRODUCTION..................................................................................................................... 1 DATA COMPONENTS ........................................................................................................... 2 Geographic scope ................................................................................................................. 3 Forest Inventory data ......................................................................................................... 3 Woody Biomass Units ......................................................................................................... 6 Land Cover .............................................................................................................................. 7 Site Class ................................................................................................................................. 8 Ownership ............................................................................................................................... 8 Management Concerns or Restrictions ........................................................................ 9 Road access .......................................................................................................................... 10 DATA PROCESSING ........................................................................................................... 11 Spatial Data Intersection ................................................................................................ 11 Proximity to village and road distances .................................................................... 12 Assigning stocking figures to stands .......................................................................... 13 Estimating AAC and assigning rotation and growth parameters to stands . 14 Cost modeling ...................................................................................................................... 16 ANALYSIS AND RESULTS ............................................................................................... 18 Long Island ........................................................................................................................... 24 ACKNOWLEDGEMENTS .................................................................................................... 26 LIST OF TABLES Table 1. Land cover classification and coding system used. ............................... 5 Table 2. Wood density of tree species of interior Alaska. ..................................... 7 Table 3. TCC forest inventory strata, associated project cover types, and woody biomass dry tons/acre. .................................................................... 13 Table 4. Cost parameters used in the analysis. ...................................................... 17 Table 5. Woody biomass tonnage by cover type class. ....................................... 20 Table 6. Woody biomass tonnage by species. ......................................................... 20 Table 7. Woody biomass dry tonnage and Annual Allowable Cut (AAC) by ownership and management option. ........................................................ 21 Table 8. Woody biomass dry tonnage by Management Unit and ownership. ................................................................................................................................. 21 Table 9. Woody biomass tonnage and Annual Allowable Cut (AAC) by ownership and distance from village. ....................................................... 22 Table 10. Woody biomass dry tonnage by cost/ton threshold. ........................ 23 Table 11. Cubic -foot volume and woody biomass dry ton estimates calculated from Long Island 2011 plot data. ......................................... 25 Tanana Biomass Resource Assessment, Phase 2 iii LIST OF FIGURES Figure 1. Location of Tanana in Alaska. ....................................................................... 2 Figure 2. Extent of Tozitna Ltd. land selections as determined for 1987 forest inventory and extent of 2003 QuickBird satellite imagery. .. 3 Figure 3. Tanana project Phase 2 area land cover classes. ................................. 6 Figure 4. Tanana project Phase 2 area site classes. .............................................. 9 Figure 5. Tanana area land ownership. ..................................................................... 10 Figure 6. Tanana area management designations. ............................................... 11 Figure 7. Tanana area roads. ......................................................................................... 12 Figure 8. Tanana project area woody biomass tons/acre. ................................. 19 Figure 9. Tanana project area woody biomass biomass cost per ton. .......... 23 Figure 10. Location of Long Island inventory plots. ............................................. 25 Tanana Biomass Resource Assessment, Phase 2 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 al ternative 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. Tanana is a community in interior Alaska located on the Yukon River at the mouth of the Tanana River (Figure 1), approximately 290 air miles north of Anchorage and 130 air miles west of Fairbanks. Although it serves as a hub for central Interior Alaska, Tanana is not located on a contiguous h ighway system and is accessible only by air and river, with resultant high costs of imported energy. To help remedy this, the City of Tanana has installed two wood -fired boilers heating a washeteria and offices in the community, and has plans to install additional wood -fired facilities to heat a number of public buildings in the community. Planning efforts have been led by the City of Tanana, with funding assistance fro m the U.S. Department of Energy and the Alaska Energy Authority. The Tanana Tribal Council and Tozitna, Ltd. are involved as cooperators in these efforts, and technical assistance is being provided by Tanana Chiefs Conference and the State of Alaska Division of Forestry. Although great progress has been made in actually installing and implementing wood -fired facilities in Tanana, available information has been lacking to answer basic questions concerning the amount, availability, and sustainability of the biomass resources in the vicinity of the community. These questions include: • How mu ch biomass is there in the vicinity of the community? • What are the characteristics of the biomass (size, species, quality)? • Where is the resource located? • Who owns the resource? • What are the costs associated with getting the resource to an energy facility? • What management restrictions are there are on the resource? • Considering growth rates, cover type conversions, and other factors, what is the sustainability of the resource? • How large an array of biomass 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 the future can be used to improve the model. It is intended that the model itself be a useful tool in the land management required to support a proposed biomass energy project. Tanana Biomass Resource Assessment, Phase 2 2 Fig ure 1. Location of Tanana in Alaska. DATA COMPONENTS This biomass assessment relied heavily on a computerized geographic information system (GIS) and relational database technology to store, process, query, and analyze data. The GIS software used was ArcGIS 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 the acreage associated with each combination of attributes. A relational database was used to combine the attribute and area information produced by the GIS with other tabular information to calculate and derive information such as biomass stocking, growth, annual allowable harvest, and cost information. Some of the derived information was then stored back in the GIS layers to facilitate mapping of the derived data. The spatial data used in the GIS consisted of layers of vector data (points, lines or polygons (areas) defined by X,Y coordinates) with associated attribute records, or raster data in the form of digital imagery used to produce some of the vector data. The vector data was stored and managed as feature classes in a personal geodatabase, which uses the format of a MS Access database to store the spatial data. Tanana Biomass Resource Assessment, Phase 2 3 Figure 2. Extent of Tozitna Ltd. land selections as determined for 1987 forest inventory, and extent of 2003 QuickBird satellite imagery . Geographic scope The geographic scope of this phase of the assessment is defined as the extent of land cover information that was available for the project. This land cover data came from two sources; 1.) a forest inventory conducted for Tozitna Ltd. lands in 1987 by the Bureau of Indian Affairs, and 2.) interpreted from a high-resolution QuickBird satellite image acquired in 2003 and covering an area approximately 10 miles square centered over Tanana . The extent of those two portions of the project are shown in Figure 2. Forest Inventory data In interior Alaska, as in many places, woody biomass is a forest resource. The process of trying to assess the amount and location of forest resources falls under the purview of forest inventories, a traditional and essential component of forestry and forest management. This project is essentially a form of forest inventory, with particular interests and requirements that are driven by the land management required to support proposed biomass energy projects. Any current or past forest inventory information for Tanana’s resources are potentially an important data source for the current effort. Tanana Biomass Resource Assessment, Phase 2 4 The most prominent forest inventory effort to date in the vicinity of Tanana is the project conducted by the Bureau of Indian affairs in 1987 on Tozitna Ltd. selected lands. The process used in this inventory project included the following steps: 1. The area included in the inventory was interpreted for 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) (Table 1). Non-forested areas were attributed for cover types such as water, tall shrub, bog, barren/cultu ral, etc. 3. Forested cover types covering the highest proportion of area were selected for field sampling by randomly selecting accessible stands within those types. 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 inventory in 1987, the spatial data represented by the 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 for est inventory yields two important components for use in the biomass assessment, the mapped land cover data and the tabular timber volume and stocking estimates. The spatial land cover data is used to determine where the woody biomass resources are located . 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 estimates 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. There are a number of serious limitations in this available forest inventory data that need to be considered. The inventor y is quite “extensive”, that is, the geographic scope was relatively large and the intensity of the field sampling was relatively low. Forest cover types with relatively low acreages were not field sampled at all, but were l umped into similar types that were sampled, with resulting inaccuracies in the volume estimates. The photography used to produce the land cover typing was less than 10 years old at the time the inventory was conducted, but is now 30 years old or more, and does not take into account the changes that have no doubt occurred on the landscape. Only village corporation selections were included in the inventories, with no consideration given to the land cover on other ownership classes. The data collection was focused on the standing Tanana Biomass Resource Assessment, Phase 2 5 Table 1. Land cover classification and coding system used in the 1987 Tozitna inventory . Tree Species Codes WS White spruce BS Black spruce HW Hardwood (Paper birch or Aspen)* CW Balsam poplar (cottonwood) * difficulties in differentiating between birch and aspen on satellite imagery and aerial photography cause these species to be combined into one species code. Tree Size Class Codes D Dwarf (< 4.5” DBH, relatively mature) R Reproduction (< 4.5” DBH, young) P Poletimber (4.5”-9.0” DBH) S Sawtimber (> 9.0” DBH) Tree Density Codes 1 Low density (<30% crown closure) 2 Medium density (30%-60% crown closure) 3 High density (> 60% crown closure) Other land cover codes R River W Water Cu, Cu97, Cu98 Cultural, human development Ba Barren B Bog TS Tall shrub TSW Tall shrub wet DS Dwarf shrub DSW Dwarf shrub wet DM Dry meadow WM Wet meadow Br Burned When a second tree species is coded, this indicates that the second species constitutes at least 30% of the stand. A size class is coded for each tree species coded. Density is coded for stands with poletimber or sawtimber sized trees , and repro sized tree s from the satellite imagery classification. Forested and non-forested combinations may be combined. The “Br” burned code is used as a prefix descriptor on another code. Examples: BSP1 Black spruce poletimber, low density. HWP/WSS2 Hardwood poletimber, white spruce sawtimber, medium density. WSS2/TS White spruce medium density sawtimber mixed with tall shrub. BrBSD Burned black spruce dwarf. HWR Hardwood reproduction. Tanana Biomass Resource Assessment, Phase 2 6 Figure 3. Tanana project Phase 2 area land cover classes. 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 this old inventory project still provides a useful starting poin t for evaluation of biomass energy resources. Woody Biomass Units As mentioned previously, the cubic-foot 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 a t 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 Tanana Biomass Resource Assessment, Phase 2 7 Table 2. Wood density of tree species of interior Alaska. White spruce, Paper birch, Aspen and Balsam poplar figures are from the State of Alaska, Department of Commerce (http://www.commerce.state.ak.us/ded/dev/forest_products/forest_products5.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) White spruce 36 31 Black spruce 32 28 Paper birch 48 38 Aspen 43 27 Balsam poplar 38 24 Tamarack 47 37 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 2. 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 nonetheless in common use in fuelwood transactions. The volume space of a cord, 1 28 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 estimates put the conversion at 85 cubic-feet of roundwood per cord. Using the conversion factors presented in Table 2 at 85 CF/cord, the number of air-dry tons in a cord varies from approximately 1.0 ton s for balsam poplar to 1.6 tons for paper birch. Land Cover As described, the 1987 Tozitna inventory provides land cover data for approximately 223,000 acres of land defined as having been selected by Tozitna, Ltd. at the time the inventory was conducted. In addition, a QuickBird high-resolution satellite image, acquired in 2003 from DigitalGlobe, provided the opportunity to determine land cover on an additional 10,000 acres. This image was classified and interpreted by personnel at the State of Alaska Division of Forestry (DOF), Northern Region Office, providing classified land cover data on those additional 10,000 acres. Aerial photography was used as a reference while interpreting the QuickBird imagery, resulting in some areas being classified that extend slightly beyond the extent of the satellite imagery. The areas covered by the 1987 land cover typing and the newer image classification were combined together into one dataset of overall land cover types (Figure 3). Tanana Biomass Resource Assessment, Phase 2 8 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 a reas 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 areas of the project area were classified into one of the four site classes, using the cover type polygons and subjective interpretation of landform as the basis for classification and interpret ation . This information was stored in the geodatabase by attributing the land cover polygons for site (Figure 4). 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. This is accomplished through the use of a GIS layer that defines land ownership in the vicinity of Tanana. Land ownership spatial data from several sources were acquired 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 precisi on; 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 combined ownership data for the Tanana vicinity is shown in Figure 5. Tanana Biomass Resource Assessment, Phase 2 9 Figure 4. Tanana project Phase 2 area site classes. Management concerns or restrictions An attempt was made to include the ability to identify areas of different management restrictions or concerns. This can include anything that is of importance to the land owners or the community related to the potential managing, harvesting and transporting of woody biomass. Culturally important sites, areas of subsistence use or other resource use, aesthetic concerns, ba rriers to operations, management restrictions, or any other factor that may affect the availability of the biomass resources for energy use could be incorporated into this layer. Additional community input and changing and economic social conditions could make this a very dynamic dataset, so it is important to retain the ability to change this information over time and reassess biomass resource availability. As a starting point, this layer was created by identifying a number management units in a GIS layer (Figure 6): • “North Unit”, North of the Yukon River and East of the Tozitna River. • “West Unit”; North of the Yukon River and West of the Tozitna River. • “South Unit”; South of the Yukon and Tanana Rivers. • “East Unit”; Northeast of the Tanana River and Sou theast of the Yukon River. • “Islands”; islands in the Yukon and Tanana Rivers. • “Long Island”; Island in the Yukon near Tanana, of interest for potential biomass harvesting and management in the near future. Tanana Biomass Resource Assessment, Phase 2 10 Figure 5. Tanana area land ownership. In addition, portions of these management units were identified for special consideration. Areas in and immediately adjacent to the village itself and areas of riparian buffers along the major waterways were delineated in this management layer because of probable harvest restrictions that would be imposed in those areas. In the village, harvesting would be restricted because of complex townsite ownership issues and probable competing land uses, and the riparian buffers would be restricted because of regulatory restrictions imposed by the Alaska Forest Resource Practices Act, which restricts timber harvest in riparian buffer areas in interior Alaska. It can be assumed that additional areas will be i dentified in the future for special management consideration, and it is in this GIS layer that these designations can be introduced into an analysis of biomass availability. Road access Existing roads in the Tanana vicinity were digitized and stored in a GIS layer for use in the analysis (Figure 7). The most prominent feature of the road network at Tanana are all -season roads extending Northeast and ap proximately four miles west across Tozitna lands. This road network information was used in the analysis to help estimate the transportation component of the costs of supplying woody biomass to a facility in Tanana. Tanana Biomass Resource Assessment, Phase 2 11 Figure 6. Tanana area management units. DATA PROCESSING Starting with the basic datasets described above, there were several data processing steps that were conducted to prepare for data analysis and reporting. The spatial data intersection and proximity determination steps described below used geoprocessing tools in the GIS software. Subsequent steps for assigning stocking figures, estimating annual allowable cut figures, and determining biomass supply costs were conducted using a series of database queries in MS Access. Spatial data intersection The GIS polygon layers for cover type, ownership, and management were “intersected”. Intersection is a GIS overlay process that combines features from multiple overlapping layers into one layer that contains all the attributes of the input layers. In this phase of the analysis, 2,190 cover type polygons , 116 ownership polygons, and 104 management polygons were intersected to produce a feature class with 3,337 polygons attributed for cover type, ownership, site class, and management, referred to hereafter as the “intersected layer”. Tanana Biomass Resource Assessment, Phase 2 12 Figure 7. Tanana area roads. Proximity to village and road distances The distance of each polygon, or stand, from the village will affect the cost of transporting the resource. This can be determined in the GIS through a number of techniques. The existence of a transportation plan with proposed access routes would be an important information source to help evaluate these transportation costs, but this information is currently lacking beyond what is known about the existing road network, as captured in the roads GIS layer described above. Using geoprocessing tools in the GIS software, three values were generated for each polygon in the intersected layer; straight -line distance from the polygon to the village center, straight-line distance from each polygon to the nearest point on the road network, and the road distance from that nearest road point to the village center. These distances were determined for “centroid” points determined for each polygon in the the intersected layer. A centroid is a point that defines the “center of gravity” for an area, or a point that represents the average horizontal location of an area. The centroid point of each polygon in the intersected layer was determined and stored in a feature class, and the distance from each centroid point to the village center and to the nearest point on the road network was calculated using proximity analysis geoprocessing tools in the GIS software. The road distance from the nearest point on the road network to the village center was determined for each centroid using network analyst tools in the GIS software. The three Tanana Biomass Resource Assessment, Phase 2 13 Table 3. Tanana forest inventory strata, associated project cover t ypes, and woody biomass dry tons/acre. TCC Forest Inventory Stratum Included Cover types Dry Tons/Acre BSP2 BSP/HR1,BSP1,BSP1/HP1,BSP1/HP2, BSP1/HR1,BSP2,BSP2/HP1,BSP2/HP2, BSP2/HR1, BSP2/HR 2,BSP3,BSP3/HR2 7.4 CWS2 CWP1,CWP1/SP1,CWP2,CWP2/SS1,CWP3, CWS1,CWS1/SS1,CWS1/TS,CWS2,CWS2/SS2, CWS2/TS,CWS3,CWS3/SP2,CWS2/TS, CWS3,CWS3/SP2,CWS3/SS2 15.2 HP2 HP1,HP1/BSP1,SP1/SP1,HP1/SR1,HP1/SS1, HP1/TS,HP2,HP2/BSR2,HP2/SR2,HP2/SR3, HP2/TS 26.0 HP2/SS1 HP1/SS1,HP2/SP2,HP2/SS1,HP2/SS2 37.8 HP3 HP3,HP3/BSR3,HP3/SP1,HP3/SP2,HP3/SP3, HP3/SR2,HP3/SS1,HP3/SS2,HS2,HS3,HS3/SS3 29.3 SP2/HP1 SP2,SP2/CWP2,SP2/HP1,SP2/HP2,SP2/HP3, SP2/HR1,SP2/HR2,SP3/HP1,SP3/HP2,SP3/HP3, SS1/HP1,SP1/HR1,SP1/TS 33.7 SP3 SP2/BSP2,SP2/TS,SP3,SP3/HR1,SP3/HR2,SP3/HR3 37.0 SS1/HP1 SS1,SS1/TS,SS1/HP1,SS1/HR,SS1/HR1, SS1/TS,SS2/HP2 25.5 SS2 SS2/CWP2,SS2/CWS1,SS2/CWS2,SS1/CWS1, SS1/HR1,SS1/TS,SS2,SS2/HR3,SS2/TS 43.9 SS3 SS3,SS3/CWP3,SS3/HS3,SS3/HP1,SS3/TS 43.0 distances were calculated in miles and stored as attributes for the intersected layer for further processing in the cost analysis described below. Assigning stocking figures to stands The TCC inventory database contains a table for stand stocking that consists of records with timber volume data by species and size class for individual sampled stands and defined strata. A query was developed that summarized the cubic-foot volume per acre figures in this table for individual strata and converted them into green tons per acre (GT/acre) and air-dry tons per acre (DT/acre) using researched conversion data (Table 2). A list of the cover types present in the cover type GIS layer was prepared, and each cover type was associated with a stratum in the TCC inventory database. Strata and cover type associations from the Tanana forest inventory were used (Table 3); those cover type associations not defined in the Tanana inventory were subjectively assigned considering the species and stocking levels generated from the inventory stocking data. Using the polygon cover type codes, the DT/acre figures for the strata were related to polygons in the intersected GIS layer and stored in the attribute table. Acreage for each stand in the intersected layer was calculated using tools in the GIS software and stored in the attribute table. Multiplying the DT/acre figure by the acreage for each stand in the intersected layer produces an estimate of total dry tons of woody biomass for each stand. Tanana Biomass Resource Assessment, Phase 2 14 Estimating AAC and assigning rotation and growth param eters to stands In order to assess sustainability, the traditional forestry concept of Annual Allowable Cut (AAC) was applied. AAC is deemed to be the maximum level of annual harvest that is possible in perpetuity without diminishment of the level of harvest or the amount and quality of the resource. There are a variety of techniques used to calculate AAC, including the “Hanzlik formula”, which was designed to attempt to deal with areas still in an unmanaged “old-growth” state. The Hanzlik formula uses mature standing volume, rotation length, and growth (increment) as parameters required to calculate AAC: Allowable cut (AAC) = (Mature Standing Volume / Rotation ) + Growth Standing volume is determined from the inventory data as described above, but figures for rotation length and growth are more difficult to determine or estimate. “Rotation”, or “rotation length” refers to the hypothetical length of time required for a forest stand to reforest, grow, and replace itself after harvest. At first glance this appears quite simple, but there are a number of complicating factors, including: • What species the stand regenerates to – different species will grow at different rates and mature at different time intervals. • Site potential may vary over time; in fact, in interior Alaska, the act of harvesting (or other disturbances, such as fire) may change the growth potential of a site, and as a result, the anticipated rotation length. • Anything other than even-aged management may complicate the determination of rotation length, particularly if it involves multiple tree species and multiple stand entries in a rotation. • Differing economic conditions or other factors may dictate a different array of forest products requiring material to reach different sizes or ages to be marketable. Similarly, “growth” can be a concept that may be simple to visualize, but involves a number of factors that make it difficult to determine with any precision. The ability to gauge the capacity of woody biomass to grow and replace itself after harvest is a critical component of any assessment that would attempt to evaluate the sustainability of the resource. Unfortunately, this is one area where hard data to drive the analysis is in short supply. It is an exceedingly complex situation that is being modeled – growth rates of individual trees and the stands they grow in vary by site, species, tree age, stand age, stand density, reproductive capacity, di sturbance 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, th is 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 c onditions. 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 Tanana Biomass Resource Assessment, Phase 2 15 produce those volumes (~50-80 years). Based on this, a value of 0.5 tons/acre/year is assumed as an optimum mean annual growth rate. Optimal rotation length is assumed to be 50 years, based on a hypothetical rotation length for the deciduous broadleaf tree species (birch, aspen, and balsam poplar). Although white spruce has traditionally been the favored species for timber management in interior Alaska, it is assumed that managing for hardwoods is desirable from a woody biomass perspective because of faster juvenile growth rates, shorter rotations, ease in regenerating, importance in wildlife habitat, and desirability from a community wildfire protection perspective. Several key assumptions were made to facilitate adjusting the optimum growth and rotation figures based on the availability of existing information. The assumptions used in this analysis to estimate growth and rotation include: 1. Relatively mature stands will show less current increment (growth). 2. Fully stocked stands will show best realization of potential increment. 3. Lower site quality will result in longer rotations and slower growth. Implementing the first assumption relies on coming up with a method of determining maturity for a forest stand. Maturity is an attribute that would be a function of stand age which, since it is lacking in the available data, can only be arrived at for a stand indirectly through interpretation of the other cover type attributes of species, size class, and density. All cover types in the project were assigned a maturity code from 0 to 4 using the above guidelines in a VERY subjective way , with 1 indicating young stands in early stages of development, ranging up to 4, assigned to stands interpreted to be mature or possibly decadent stands in a late development stage . Each of the four maturity codes were assigned a relative growth rate expressed as a proportion of optimum mean annual increment ; these proportions could be greater than 1.0 for those ages (maturity level) where growth may be greater, and less than 1.0 for those ages where growth may be less, such as early in the establishment of a stand or in an older decadent st and. The optimum growth proportions assigned to each maturity level were: Maturity Growth proportion 1 0.3 2 1.2 3 1.2 4 0.5 The second assumption of stand stocking levels influencing relative growth can be dealt with more directly using the stand density component of the cover type calls. Each of the three density codes were assigned a relative growth rate expressed as a proportion of optimum growth in the same manner as the maturity codes: Stand Density Growth Proportion 1 (0-30% crown closure) 0.3 2 (30-60% crown closure) 0.6 3 (60-100% crown closure) 1.0 Tanana Biomass Resource Assessment, Phase 2 16 Similarly, the third assumption of relative growth varying by site quality is handled by taking the site class codes as assigned to areas on the landscape and adjusting the optim al rotation of 50 years upwards for poorer site classes, as well as assigning degraded growth proportions for lower sites: Site Class Growth proportion Rotation (years) 0 0 none 1 0.3 90 2 0.6 70 3 1.0 50 Using this approach, annual allowable cut will be seriously degraded for those areas interpreted to be of poor site quality, by calculating a lower current growth and by using a longer rotation in the AAC formula. By applying a series of update queries in the database, allowable cut figures were calculated for every stand in the project area. Growth for each individual stand is determined by multiplying the optimum growth rate (0.5 tons/acre/year) by the growth proportion number assigned to the maturity code associated with that stand, multiplied again by the growth proportion number assigned to the stand density of the stand, and multiplied again by the growth proportion assigned to the site class of the stand. Rotation length for each stand is determined by applying the rotation length assigned to the site class of the stand. The resulting figures for growth and rotation are used with the stocking of each stand in the Hanzlik formula to generate an AAC for each stand. The resulting AAC figures for each stand are not meant to mean that some ca lculated portion of every stand is a portion of the volume cut in any given time frame, but refers to the contribution that the resource represented by that stand contributes to the harvestable volume of biomass over the project as a whole. Through the ot her attributes assigned to each stand by creating the intersected layer, both standing stock and AAC figures can be broken out by ownership, management option, proximity to the village, or other stand attributes. Cost modeling In addition to estimates of the amount and growth of the woody biomass resource, it is also useful to estimate the costs involved in making the biomass available to an energy facility. This estimation could include the modeling of costs associated with harvesting, transport, reforestation, stumpage, and other costs. At this stage of the project, much is unclear in terms of type of harvest and equipment to be used, the nature and extent of the transportation network to be established and other cost factors, but all of these factors can be modeled in the GIS and reported back from the database. Table 4 presents a list of cost factors used in this analysis as an example of how these costs could be modeled. Per acre costs are converted into costs per ton for each polygon. 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 stands. Tanana Biomass Resource Assessment, Phase 2 17 Table 4. Cost parameters used in the analysis. Cost Type Cost Stumpage (payments to owner), cost per ton $ 5 Harvest Costs Costs per acre $200 Costs per ton of woody biomass $ 10 Transportation costs Cost/ton/mile al l-season road $ 2 Cost/ton/mile off -road $ 4 Cost/ton ice bridging costs $ 5 Reforestation – cost per acre $100 Estimated transportation costs were driven by distances from the village, distances from the nearest road, and road distances, which were calculated and stored for each stand in the intersection layer. Each type of distance had a cost/ton/mile assigned to it (Table 4). A series of queries is executed in the database to calculate a total cost/ton for each stand in the inters ection layer: • Stands with a distance to the village (village proximity) less than a distance to the nearest road (road proximity) use the village proximity distance to calculate costs: • Stands that are closer to the nearest road than they are to the village use the road proximity and road distance values to calculate costs: In addition, those areas requiring winter road access across rivers and ice bridges are assigned an additional fixed amount per ton to accommodate that cost (Table 4). 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 remain relatively fixed per ton, while other costs associated with mobilization, equipment movement, etc . may remain relatively fixed per unit area. Other costs associated with biomass supply could include reforestation costs and other management costs, and stumpage payments made to a landowner. The reforestation costs initially used in this analysis are based on a lowering of known planting costs, assuming that some level of natural regeneration or other techniques may be used. This cost modeling can be modified in the future with changes to the cost parameters, modification of the modeling used to assi gn costs, etc. to create updated cost scenarios. Since the cost per ton is determined by stand, as is the annual allowable cut, one interesting ramification of this is that it is possible to evaluate AAC based on different cost thresholds. Tanana Biomass Resource Assessment, Phase 2 18 ANALYSIS AND RESULTS Woody biomass air-dry tonnage and Annual Allowable Cut (AAC) figures were summarized from the database by cover type class, species, ownership, management unit , distance from the village, and cost threshol d (Tables 5 through 10). It must be considered that any combination of these attributes can be queried from the database, and can also be displayed in the GIS and on printed maps to evaluate graphically the location of the resource. What is shown here is merely a sample of how the data may be summarized and displayed. Estimated total standing stock of woody biomass for the extent of the project is 3,274,112 dry tons (Table 5). Since the geographic extent of a large part the analysis was driven by the extent of the Tozitna inventory, the bulk of the acreage and biomass tonnage resides on Tozitna lands (3,060,687 green tons, or 9 3%), although 149,927 tons , or 5% of the total, are on Native allotments. Relatively small amounts are found on Doyon, BLM, and State lands, and they tend to be relatively distant from the village. The bulk of the standing stock is found in mixed species poletimber and sawtimber cover types (45%), hardwood poletimber types (27%), and white spruce poletimber and sawtimber types (24%) (Table 5). When broken out by species, 62% of the standing stock is white spruce and 30% is birch (Table 6). Currently, a small proportion of the project area is designated as restricted from biomass harvesting in the database, including the developed areas around the village itself and the riparian buffer areas that would fall under restrictions imposed by the Alaska Forest Resources Practices Act. These lands only occupy 1% of the total land project area, with approximately the same proportion of the estimated woody biomass (Table 7). When looking at the Management Unit designations, the 50% of the biomass stocking is in the South Unit, across the rivers from the community (Table 8). Another 21% is in the East Unit, also across the river. The North Unit, contiguous to the community, also has 21% of the stocking. Long Island, of inter est because it has been designated by Tozitna, Ltd. as a woodcutting area, has 20,785 tons on about 1,000 acres . Seventy-four percent of the standing stock is greater than six miles from the village (Table 9). Under the cost modeling parameters used, the amount of woody biomass available at various cost thresholds is presented in Table 10. Only 38% of the biomass is available at less than $70/ton. Looking at how t he cost/ton values are distributed spatially, it is apparent that distance from the village has a profound effect on the cost of available biomass (Figure 9). Tanana Biomass Resource Assessment, Phase 2 19 Figure 8. Tanana project area woody biomass tons/acre. Tanana Biomass Resource Assessment, Phase 2 20 Table 5. Woody biomass tonnage and Annual Allowable Cut (AAC) by cover type class. Cover Class Acres Green Tons Dry Tons AAC (dry tons) Black spruce poletimber 9,901 84,876 72,973 2,352 Black spruce repro 3,811 0 0 138 Black spruce woodland 50,020 0 0 688 Cottonwood poletimber 972 20,986 14,746 524 Cottonwood repro 50 0 0 4 Cottonwood sawtimber 1,842 39,796 27,963 755 Hardwood poletimber 32,141 1,148,925 891,723 20,963 Hardwood repro 4,769 0 0 228 Hardwood sawtimber 5 204 157 2 Mixed poletimber 20,288 782,148 641,593 14,298 Mixed repro 6,954 17,150 13,181 582 Mixed sawtimber 21,505 964,264 822,169 15,307 White spruce poletim ber 8,728 339,264 288,138 6,437 White spruce repro 285 0 0 11 White spruce sawtimber 11,586 587,862 501,469 10,048 Barren 963 0 0 0 Cultural 328 0 0 0 Shrubland 13,837 0 0 0 Water 35,826 0 0 0 Wetland 12,277 0 0 0 Totals: 236,089 3,985,476 3,274,112 72,336 Table 6. Woody biomass tonnage by species. Species Total Tons Board Foot Sawtimber Tons White Spruce 2,033,756 561,987 Black Spruce 67,973 Birch 973,003 Aspen 137,121 Balsam Poplar 62,259 Totals: 3,274,112 561,987 Tanana Biomass Resource Assessment, Phase 2 21 Table 7. Woody biomass dry tonnage and Annual Allowable Cut (AAC) by ownership and management option. Ownership Management Acres Dry Tons AAC Tozitna, Ltd. Available 212,670 3,033,314 66,283 Riparian buffer 1,862 27,373 698 Village 338 0 0 Tozitna Totals: 214,870 3,060,687 66,982 Doyon, Ltd. Available 4,674 32,455 899 Doyon Totals: 4,674 32,455 899 Native allotments Available 8,354 147,749 3,532 Riparian buffer 100 2,178 60 Village 149 0 0 Native allotment Totals: 8,603 149,927 3,593 Bureau of Land Available 7,805 30,278 840 Management BLM Totals: 7,80 5 30,278 840 State Available 137 765 23 State Totals: 137 765 23 Totals: 236,089 3,274,112 72,336 Table 8. Woody biomass dry tonnage by Management Unit and ownership. Management Unit Ownership Acres Dry Tons East Unit Tozitna, Ltd. 41,090 642,946 Native allotment s 2,766 46,356 East Unit Totals: 43,856 689,302 Islands Tozitna, Ltd. 7,043 107,561 Native allotment s 121 4,224 Islands Totals: 7,164 111,785 Long Island Tozitna, Ltd. 998 20,785 Long Island Totals: 998 20,785 North Unit Doyon, Ltd. 4,674 32,455 Tozitna, Ltd. 50,770 552,273 Bureau of Land Management 7,805 30,278 Native allotment s 4,273 67,496 North Unit Totals: 67,522 682,50 3 River River Totals: 29,260 0 South Unit Tozitna, Ltd. 77,147 1,600,888 Native allotment 1,213 28,009 State 137 765 South Unit Totals: 78,497 1,629,662 West Unit Tozitna, Ltd. 8,645 136,234 Native allotment s 147 3,841 West Unit Totals: 8,792 140,075 Totals: 236,089 3,274,112 Tanana Biomass Resource Assessment, Phase 2 22 Table 9. Woody biomass tonnage and Annual Allowable Cut (AAC) and by ownership and distance from village. Distance from Village Acres Dry Tons AAC Tozitna, Ltd. 0-2 miles 1,993 49,239 1,259 2-4 miles 7,629 205,656 4,883 4-6 miles 15,119 449,985 9,808 6-8 miles 9,813 272,039 5,922 8-10 miles 21,889 782,059 15,029 10-12 miles 19,528 615,468 13,434 12-14 miles 12,256 358,464 7,893 14-16 miles 7,342 217,876 4,806 16-18 miles 4,329 109,684 2,958 18-20 miles 5 218 5 Tozitna Totals: 99,903 3,060,687 65,997 Native allotments 0-2 miles 200 5,711 130 2-4 miles 980 26,044 569 4-6 miles 1,059 27,080 646 6-8 miles 796 24,283 537 8-10 miles 614 18,843 443 10-12 miles 517 15,823 367 12-14 miles 536 14,862 413 14-16 miles 524 17,281 452 Native allotment Totals: 5,225 149,927 3,557 Bureau of Land Management 2-4 miles 56 1,394 33 4-6 miles 985 28,884 646 BLM Totals: 1,041 30,278 678 Doyon, Ltd. 4-6 miles 511 13,820 323 6-8 miles 711 18,635 430 Doyon Totals: 1,222 32,455 752 State of Alaska 4-6 miles 26 765 18 State Totals: 26 765 18 Totals: 107,418 3,274,112 71,003 Tanana Biomass Resource Assessment, Phase 2 23 Figure 9. Tanana project area woody biomass biomass cost per ton . Table 10. Woody biomass dry tonnage by cost per ton threshold. Total Cost per Ton Dry Tons < $30 13,387 < $40 164,031 < $50 579,109 < $60 995,736 < $70 1,240,764 < $80 2,099,423 < $90 2,478,170 < $100 2,874,154 < $125 3,240,465 < $150 3,263,504 < $175 3,274,112 Tanana Biomass Resource Assessment, Phase 2 24 Long Island Long Island, a 1,000 acre island in the Yukon River near the village itself, has been designated by the landowner, Tozitna, Ltd., as an available woodcutting area. The assessment data gathered and analyzed for the land on the island available for harvesting (not including the riparian buffer areas) up to this point indicates a total of 19,743 tons available for harvest, with an Annual Allowable Cut of 567 tons. Biomass stocking estimates are associated with 821 acres, with an average stocking rate of 24 tons/acre. Because of the potential importance of this particular land area in the short-term management of woody biomass resources for energy projects in Tanana, additional field data was gathered on Long Island in an attempt to assess the woody biomass resources on the island with greater precision. Two TCC forestry personnel traveled to Tanana in August, 2011, and with the assistance of two foresters from the State of Alaska DOF Northern Region Office, established a grid of 339 forest inventory plots across the island. The plot grid was established on those parts of the island deemed to have relatively productive forest cover as viewed on the satellite imagery, corresponding to 630 acr es outside the riparian buffers in the geodatabase. These 630 acres had an average biomass stocking estimate of 26.4 dry tons per acre as calculated from the old inventory -based data. The plots were located in a grid with plots 85 meters apart, resulting in a density of about 1.7 acres/plot (Figure 10). The summarized results for the compiled plot data are shown in Table 11. Total biomass dry ton estimates are about 23.1 tons/acre, compared to 26.4 tons/acre estimated from the previous inventory data for the 630 stand acres that the plots were located in, for a total of 14,572 tons, a reduction of 12% in the biomass stocking with the new estimates. By species, the biggest differences were that white spruce, birch, and aspen were estimated lower, and black spruce and balsam poplar were estimated higher. Differences of this nature are to be expected given the sampling errors and uncertainties inherent in the two samples being compared, but the 2011 data collection effort has substantially more ground data behind it for the area in question (Long Island), and should yield the more confident estimate by far. The data was also processed by the State of Alaska with collected radial growth and age data to calculate growth rates in the sampled stands. Growth was calculated at 2.4%, or about 0.55 tons/acre/year. Annual growth as modeled in the previous analysis calculated as being about 1.1% of standing volume, or about 0.29 tons/acre/year, indicating that the modeling applied may be underestimating the growth of existing stands. Assuming an annual consumption in Tanana of 500 cords, or about 700 tons of woody biomass, and using the Annual Allowable Cut figures as modeled previously, it would appear that there is enough biomass resources on Long Island to support the community for up to 20 years, but that level of harvest would not be sustainable from that land base in the long term. Tanana Biomass Resource Assessment, Phase 2 25 Figure 10. Location of Long Island inventory plots. Table 11. Cubic-foot volume and woody biomass dry ton estimates calculated from Long Island 2011 plot data. Species Net CF/ac Dry tons/acre Total Dry tons Previous Inventory Dry Tons White spruce 738 11.4 7,207 8898 Black spruce 48 0.7 423 16 Paper birch 345 6.6 4,130 4748 Balsam Poplar 372 4.5 2,812 2052 Aspen 0 0 0 906 Totals: 1503 23.1 14,572 16,620 Tanana Biomass Resource Assessment, Phase 2 26 ACKNOWLEDGEMENTS Many thanks are owed to the staff at the Northern Region Office of the State of Alaska Division of Forestry, especially Doug Hanson for repeated conversations and consultations. Paul Keach of the DOF Northern Region Office worked on the satellite image typing and, along with Doug, field work on the Long Island field project. TCC staff foresters Fabian Keirn and Jeremy Douse also participated in the Long Island field work, helped with the data processing, and provided project consultation and review. Thanks are also due to the staff and representatives of the City of Tanana for pursuing biomass energy projects for the benefit of their community.