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Supplemental Data

These data provided here are for the western Mogollon Plateau area only. Please contact us to receive a copy of the available data products on CD or DVD for the White Mountains region in eastern Arizona (GIS Data List) or the North-central New Mexico area (GIS Data List). Additional descriptions and maps of the available GIS data for these study areas can be found in their respective Data Atlas. These are downloadable from our Documents web page under the "Major Reports" section.

These data layers are the property of the Forest Ecosystem Restoration Analysis (ForestERA) project, the Ecological Restoration Institute (ERI), and Northern Arizona University (NAU). They are provided free of charge to the public. However, we request that users carefully review the metadata provided with the layers and consult with the ForestERA project in advance of using these data in publications and/or presentations to ensure that the strengths and limitations of the data are considered.

Publications and presentations that include these data should acknowledge the ForestERA project. We suggest citing the data using the following references:

Hampton, H. M., Y. Xu, J. W. Prather, E. N. Aumack, B. G. Dickson, M. M. Howe, and T. D. Sisk. 2003. Spatial tools for guiding forest restoration and fuel reduction efforts. Proceedings of the 2003 ESRI Users Conference, San Diego, CA. Retrieved [month day, year], from http://gis.esri.com/library/userconf/proc03/p0679.pdf.

Forest Ecosystem Restoration Analysis Project. 2004. ForestERA supplemental data layer. Northern Arizona University. Retrieved [month day, year], from http://forestera.nau.edu/data_supplemental.htm

Human Communities and Infrastructure
Additional Community Names
Land Ownership
Specially Designated Areas
Insect and Drought Related Tree Mortality
Fuel Models
Stand Height and Crown Base Height
Crown Bulk Density
Perennial Streams
New Perennial Water Bodies
Aquatic Organisms
Quadratic Mean Diameter
Weather Data
TNC Ecoregional Biodiversity Areas
Invasive Plant Species Locations
Fire Regime Condition Class (FRCC)
USDA Forest Service Stand Data
Geographic Names Information System (GNIS)
Fire Perimeters
Grazing Allotments
Wildlife Corridors
Coconino County Wildlife Data

Human Communities and Infrastructure Data

General description

We have created several layers by combining data from a number of different datasets relating to human communities and infrastructure across the assessment area. These layers include urban areas, roads, power lines and cell/radio towers, and others. The original layers were modified in various ways for use in the planning process. For example we have created a number of layers representing different definitions of wildland-urban interface (WUI) areas. These layers were created using combinations of the layers mentioned above along with different buffers around the features in those layers.

Layer creation

The urban areas layer was created by identifying incorporated communities within the assessment area and adding any additional communities identified as “communities at risk from wildfire” from the national register of communities. Urban areas were considered to be all areas within the incorporated boundary or community center and any additional continuous areas of private property outside these boundaries. Private property boundaries were derived from a land ownership map (see below). The layers describing roads and power lines were obtained from the Arizona Land Department, Arizona Land Resource Information System (ALRIS). They were created in 2000 by the United States Geological Survey and as part of their TIGER / Lines data system. The locations of cell and radio towers were extracted from the Geographic Names Information System (GNIS, see below).

Layer accuracy

We have not undertaken an accuracy assessment on these layers. The layers are all assumed to meet accuracy standards of the agency or group that provided them.

Additional Community Names

Upon the recommendation of several workshop participants, we improved the base community layer used in scenarios by adding additional communities defined as "At Risk" within the Federal Register, and adding developed areas beyond the Register identified on map by participants (see map below). We are currently working with Coconino County to develop a higher resolution community layer by assessing parcel maps and improvement values (as an indicator of structures or homes on the property).

Land Ownership Data

General description

This layer was derived from a statewide layer documenting the owners and/or management agencies for all lands within Arizona. The original dataset was created in 1988 by the United States Geological Survey.

Layer creation

We obtained this layer from the Arizona Land Resource Information System (ALRIS). We clipped the features that lie within the assessment area from the original statewide layer.

Layer accuracy

We have not undertaken an accuracy assessment on this layer. It is assumed to meet accuracy standards of the agency that provided it.

Specially Designated Areas Data

General description

This layer was derived from two different data sources. The first was a statewide dataset that includes data from the Bureau of Land Management, U.S. Forest Service, National Park Service and Fish & Wildlife Service. Features identified in this layer include riparian natural conservation areas, wilderness study areas and wilderness or primitive areas. The second dataset identified all National Forest Inventoried Roadless Areas (IRAs) and Special Designated Areas (SDAs) for the lower 48 states, including Puerto Rico.

Layer creation

The wilderness area data layer was obtained from the Arizona Land Department, Arizona Land Resource Information System (ALRIS). The designated roadless area data layer was obtained from the USDA Forest Service, Geospatial Service and Technology Center (GSTC). We clipped all features from the original datasets that fell within the assessment area.

Layer accuracy

We have not undertaken an accuracy assessment on these layers. They are assumed to meet accuracy standards of the agencies that provided them.

Insect and Drought Related Tree Mortality Data

General description

This layer represents tree mortality across the assessment area over the past 5 years due to insect outbreaks and/or drought. In areas with dying trees, a visual assessment of the rate of death (damaged trees / acre) was taken. Due to uncertainty in the layer we categorized the death rate into 3 levels; low (1 - 10 dying trees / acre), medium (11-100 dying trees /acre), and high (more than 100 dying trees /acre).

Layer creation

The tree mortality layers were derived from aerial surveys conducted by the US Forest Service Forest Health Protection office in Flagstaff, AZ. Aerial surveys were conducted by flying the survey area in a monoplane at elevations of 1000-3000 feet above the ground. Area of forest damage were recorded either in a digital sketchmapping device (after 2001), or visually determined by the pilot via the use of hardcopy maps, and drawn directly onto paper maps, then digitized back in the laboratory (2001 and prior). The layer used in this assessment was processed and cleaned by Jesse Anderson of the Merriam-Powell Center for Environmental Research at Northern Arizona University.

Layer accuracy

There has been no accuracy assessment of this layer taken using ground data. Both positional accuracy and the rate of death estimates may vary widely in the layer depending on the mapping methodology and the individual in charge of the mapping. Estimates of the positional errors in the layer range up to 500 feet and have not been thoroughly documented.

Fuel Models

General description

This is a layer representing the ground fuel models across the region. Fuel models are developed for the purpose of fire behavior modeling. They represent the ground fuels available to a burning fire and allow prediction of fire behavior through mathematical models that link the fuels to the behavioral attributes of a fire.

Layer creation

This layer was created using the dominant overstory vegetation layers developed by the ForestERA team using Enhanced Thematic Mapper (ETM) imagery. Ground fuel models cannot be directly measured using readily available types of remote-sensing imagery and sufficient field data do not exist. Therefore, we categorized the fuel models across our region based on our dominant overstory vegetation model.

Layer accuracy

No accuracy assessment is possible for this layer as we do not have comprehensive measurements of ground fuels across this region. Use of vegetation characteristics is a standard methodology for creating maps of ground fuels. The accuracy of our dominant overstory vegetation layer is described in the metadata distributed with that layer.

Stand Height and Crown Base Height

General description

These are layers representing various attributes of forest canopy across the study region. Stand height (hereafter SH) is the mean height of the canopy trees in a given area. Crown base height (hereafter CBH) is a measure of the distance from the ground to the lowest live branches at the bottom of a tree. A mean or other percentile of individual tree crown base heights is usually used to define CBH for all trees in a given area

Layer creation

These layers were created using the quadratic mean diameter (QMD) layer developed by the ForestERA team. Attributes of the tree canopy cannot be directly measured using readily available and inexpensive types of remote-sensing imagery. Because of this, it was necessary to use allometric equations to develop these layers. Mean values for crown base height and tree height for individual trees are strongly correlated to the mean diameter at breast height (DBH) of those trees. Since DBH and QMD are related measures, we were able to use our QMD layer and linear regression equations to estimate CBH and SH across the region.

Layer accuracy

No accuracy assessment is possible for these layers as we do not have comprehensive measurements of tree heights or crown base heights across this region. The use of this methodology resulted in reasonable values for SH, mean CBH, and minimum CBH. However, we note that there are accuracy issues with the QMD layer (see the metadata for that layer), and that all of these attributes may vary widely across the region depending on actual tree size class distributions, management history, and other factors. Therefore, we do not believe this layer is highly accurate.

Stand height
Crown base height

Crown Bulk Density

General description

Crown Bulk Density is a measure of canopy fuels used in fire behavior modeling applications. Typically it is the weight of fine canopy fuels (leaves, needles, smaller branches, etc.) divided by the total canopy volume. Crown bulk density values range from zero to approximately 0.5 kg / m3 (0 - 0.03 lb / ft3) across the region.

Layer creation

This layer was created using the basal area, tree density, and dominant overstory vegetation layers developed by the ForestERA team. Crown bulk density cannot be directly measured using readily available types of remote-sensing imagery. Because of this, it was necessary to us basal area and tree density values, along with allometric equations obtained from the published literature, to predict crown bulk density.

Layer accuracy

For accuracy assessment we obtained crown bulk density estimates from 43 locations spread across the assessment area. We used linear regression to determine if there were statistically significant relationships between the values of crown bulk density as estimated from the ground data and the values for crown bulk density as estimated from the layers produced using ETM imagery. The results of this regression analysis indicated that a highly significant relationship exists between the two measures (R2 = 0.601, P < 0.0001). The slope of the line from this relationship is 1.01 indicating that the CBD estimates from the ETM derived layers are unbiased with respect to the estimates from the ground layers. Analysis of the differences between the estimated values for CBD indicate that over 50% of the estimated CBD values based on the ETM derived layers lie within 0.03 kg / m3 of the estimated values based on the ground data and over 80% of the estimated CBD values based on the ETM derived layers lie within 0.05 kg / m3 of the estimated values based on the ground data.

Perennial water bodies

Perennial streams are important water sources for communities, provide habitat for many important organisms, and support riparian communities. We created a perennial water bodies layer with the help of the Arizona Game and Fish Department and the U.S. Fish and Wildlife Service. The new layer includes perennially flowing streams and perennially full reservoirs in the area as well as aqueducts, and those reservoirs or streams that dry up but contain permanent pools. We are continuing to work with the U.S. Fish and Wildlife Service to create a list of rare or endangered aquatic organisms in the perennially flowing streams and rare species that are dependant on riparian areas around the water bodies.

Aquatic Organisms

We are working with the Shaula Hedwall of the US Fish and Wildlife Service to provide a data layer that includes information about rare, threatened, and endangered aquatic organisms in perennial water bodies across the study area. Due to limitations in the available data, information in this layer is primarily limited to fish. Data on other aquatic organisms, such as mollusks, amphibians, and aquatic invertebrates is mostly lacking.

Quadratic Mean Diameter

General description

Quadratic mean diameter (hereafter QMD) is a measure used by foresters as an index of the size of trees in a stand. It is directly related to diameter at breast height (DBH). When all the trees in a stand are the same size QMD = DBH.

Layer creation

This layer was created using the basal area (BA) and tree density (TD) layers created by the ForestERA team. To produce the QMD layer from those two layers, the following equation was used: QMD = 2 * ((BA * 10,000) / TD) / ?)-2.

Layer accuracy

For accuracy assessment we obtained QMD for all trees at 567 ground plots from 63 locations spread across the assessment area. We used linear regression to determine the relationships between QMD values obtained from the ground data and the QMD estimates from our data layers and the equation above. The results of this regression analysis indicated that a significant relationship exists between the two measures (Fig. 1; r² = 0.259, P < 0.001). The slope of the line from this relationship is 1.09 indicating that the ETM derived QMD estimate is nearly unbiased with relation to the ground estimate. However, the relatively low r² value indicates that there is not a particularly strong relationship between the predicted QMD and the actual QMD.

Daymet Weather Data

General description

This layer is a subset of climate data produced for the continental United States by the National Center for Atmospheric Research. It is a 1km (100 ha or 240 acre) resolution map of predicted average annual precipitation across the assessment area. Predicted annual precipitation values range from 15” (38 cm) to 39” (98 cm) over the assessment area.

Layer creation

The layer was created using the Daymet climate-modeling program. Daymet was developed at the University of Montana, Numerical Terradynamic Simulation Group (NTSG), to fulfill the need for fine resolution, daily meteorological and climatological data necessary for plant growth model inputs. The Daymet program generates daily surfaces of temperature, precipitation, humidity, and radiation over large regions of complex terrain using a digital elevation model coupled with daily observations of minimum and maximum temperatures and precipitation from ground-based meteorological stations over an 18 year period (1980 - 1997).

Layer accuracy

According to a published overview of the methods used in producing these data, the mean average error in observed annual precipitation totals was 19.3% across the United States. Based on this value, mean errors in total annual precipitation values could be off by 3” (7.5 cm) to 8” (20 cm) over the assessment area.

TNC Ecoregional Biodiversity Areas

This is a coarse layer identifying specific areas of the landscape identified by The Nature Conservancy as being biologically important. Its primary function is to identify areas of the landscape with similar biological characteristics and which may also contain important features for maintaining biodiversity in the region.

Invasive Plant Species Locations

We have obtained location and patch size data for a number of invasive plant species such as Yellow Starthistle (Centaurea solstitialis), Leafy Spurge (Euphorbia esula), and Spotted Knapweed (Centaurea biebersteinii). We are working with Laura Moser of the USDA Forest Service to create a tool that will model spread of invasive plant species from known locations over time. Location and patch size data came from the Southwestern Exotic Plant (SWEMP) database maintained by the USGS Colorado Plateau Research Station.

Fire Regime Condition Class (FRCC)

We have obtained a 1 km (100 ha or 240 acre) resolution Fire Regime Condition Class layer from the USDA Forest Service Fire Sciences Laboratory. However, this layer is primarily meant for national level planning and assessment of vegetation and fuels, and was not designed for regional prioritization efforts or identification of specific locations in need of treatment. A finer scale (30m resolution) FRCC data layer that is designed for use at project and regional scales is being created by the LandFire project, but has not yet been developed for this region. LandFire is a national vegetation and fire hazard mapping effort being undertaken by the USDA Forest Service and the Department of the Interior.

USDA Forest Service Stand Data

We have collected the Forest Service stand boundaries for the entire assessment area as well as data contained within the RMRIS summary tables for each stand. Data contained in this database includes primarily forest structural and composition data (Vegetation Type, Basal Area, Tree Density, and Canopy Cover). The type, amount, and completeness of the available data vary from forest to forest, and data are generally not available for lands not managed by the Forest Service. For each stand polygon, we have derived values for Canopy Cover, Basal Area, Tree Stem Density, and Dominant Overstory Vegetation based on ForestERA data layers, giving the opportunity to compare data values from both datasets over the stand polygons.

Please contact the ForestERA team to have a CD-ROM of this data mailed to you.

Geographic Names Information System (GNIS)

This dataset contains name and location information about almost 2 million physical and cultural features located throughout the United States and its territories. GNIS was developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN) to promote the standardization of feature names in the United States. Examples of features in the database include springs, wells, towers, and campgrounds.

Fire Perimeters

In order to identify where vegetation characteristics may have changed since the satellite imagery with which we build our vegetation layers was taken, we have obtained files that identify the perimeters of large fires that have occurred in the assessment area since 1997. These data were provided by the USDA Forest Service.

Grazing Allotments

We have obtained GIS layers that identify grazing allotments from the USDA Forest Service.

Wildlife Corridors

We have obtained a layer that identifies potential wildlife corridors and important habitat areas for a number of wildlife taxa across the study area. This layer was developed for Coconino County by a wildlife council consisting of local wildlife experts. A total of 16 species are covered by the layer, including Mountain Lion (Puma concolor), Wild Turkeys (Meleagris gallipavo), Black Bear (Ursus americanus), Elk (Cervus elaphus), White-tailed Deer (Odocolius virginianus), and Pronghorn (Antelocapra Americana). These data are not comprehensive, having only been completed for 7 planning areas within Coconino County.

Coconino County Wildlife Data

Wildlife data layers have been compiled within the Coconino County Comprehensive Planning process, and were acquired from the Grand Canyon Trust. Please contact us for more information on this layer (the map below is meant only to indicate the spatial extent of data acquired from the Grand Canyon Trust).

Page last updated January 10, 2007