File Geodatabase Feature Class
Tags
lifeform, tree canopy
cover, Landsat 8, biology, tree size, hierarchical classification, eCognition,
and biophysical, R1-VMap, ecology, tree dominance type, Northern Rockies
This dataset was produced for use at project levels of analysis and planning (in some cases additional work would be needed for site specific or project level work).
One of the most fundamental information needs to support ecosystem assessment and land management planning is consistent, continuous, and up to date vegetation data of sufficient accuracy and precision. The Northern Region Existing Vegetation Mapping Program (VMap) database and map products help meet this information need and provides the Northern Region with a geospatial database of existing vegetation produced using consistent analytical methodology according to the Existing Vegetation Classification and Mapping Technical Guide (Brohman and Bryant, 2005) to support the Region 1 Multi-level Classification, Mapping, Inventory, and Analysis System, R1-CMIA (Berglund et. al., 2009). The Idaho Panhandle and Kootenai National Forests (IPKNF)VMap database provides four primary map products; lifeform, tree canopy cover class, tree size class, and tree dominance type to support mid and base-level analysis and planning. VMap uses the Region 1 Existing Vegetation Classification System (R1-ExVeg) (Barber, et.al. 2009) in its map unit design. The R1-ExVeg system describes the logic for grouping entities by similarities in their floristic characteristics. This has been an iterative process in Region 1 as different classification schemes have been tested and evaluated for utmost utility by end users. The system was designed to allow consistent applications between Regional inventory and map products within the R1-CMIA framework. VMap is a remote sensing derived product. As such, it uses a combination of airborne imagery and a nationally available digital elevation model (DEM). The IPKNF is located in a region that is often and persistently cloaked in clouds. In addition to being obscured by cloud cover, the area of interest was also obscured by forest fire smoke in 2015. For these reasons, high resolution NAIP imagery was not available with full coverage for the area. Thus, in order to obtain contemporary and full coverage imagery of the mapping area, Rapid Eye high resolution satellite data was sourced between July 18 and August 8 to capture exiting and relevant vegetation patterns. The imagery was delivered with 5 meter pixel resolution, and five spectral bands of radiometric resolution, including red, green, blue, and infrared components. However, even with a custom collection of image data, cloud cover was still present. To reveal could obscured areas, cloud patches in the Rapid Eye data were masked, and coded as no data. Those areas of no data were then supplemented with cloud free Landsat 8 data. In 2016, no entirely cloud free Landsat data were available either. Nonetheless, a full area composite Landsat 8 scene was assembled with image data captured between June 4 and August 16, 2016. Areas obscured by clouds in this dataset were substituted with could free data acquired June 16, 2015. Finally, a composite of could free Rapid Eye and Landsat 8 image data, wereput through a process of aggregation to derive spatially cohesive units (i.e., polygons), that ultimately resemble stand boundaries.
In the field, reference information is collected and used to make spatial predictions of the vegetation attributes contained in the database. Predicted raster surfaces of the attributes are summarized to the delineated polygons.
Draft map products are then reviewed and appropriate changes are made in the labeling algorithms. Final results are then used to populate the VMap database. An accuracy assessment was conducted to provide a validation of the data, giving an indication of reliability of the map products, so that managers are fully informed throughout the decision making process. Estimates of overall map accuracy and confidence of individual map classes can be inferred from the accuracy assessment error matrix derived from the comparison of known reference sites to mapped data. These accuracy assessment results are relevant to the entire IPKNFas a whole ranging from 60-90%, depending on the particular attribute.
There are no credits for this item.
The USDA Forest Service manages resource information and derived data as a service to USDA Forest Service users of digital geographic data. The USDA Forest Service is in no way condoning or endorsing the application of these data for any given purpose. It is the sole responsibility of the user to determine whether or not the data are suitable for the intended purpose. It is also the obligation of the user to apply those data in an appropriate and conscientious manner. The USDA Forest Service provides no warranty, nor accepts any liability occurring from any incorrect, incomplete, or misleading data, or from any incorrect, incomplete, or misleading use of these data.
Extent
| West | -117.314886 | East | -114.474215 |
| North | 49.124429 | South | 46.759160 |
| Maximum (zoomed in) | 1:5,000 |
| Minimum (zoomed out) | 1:150,000,000 |
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email preferred
Monday-Friday, 8am-4:30 pm (MST)
email preferred
The USDA Forest Service manages resource information and derived data as a service to USDA Forest Service users of digital geographic data. The USDA Forest Service is in no way condoning or endorsing the application of these data for any given purpose. It is the sole responsibility of the user to determine whether or not the data are suitable for the intended purpose. It is also the obligation of the user to apply those data in an appropriate and conscientious manner. The USDA Forest Service provides no warranty, nor accepts any liability occurring from any incorrect, incomplete, or misleading data, or from any incorrect, incomplete, or misleading use of these data.
This dataset is in the public domain, and the recipient may not assert any proprietary rights thereto nor represent it to anyone as other than a dataset produced by the USDA Forest Service, Northern Region.
The USDA Forest Service manages resource information and derived data as a service to USDA Forest Service users of digital geographic data. The USDA Forest Service is in no way condoning or endorsing the application of these data for any given purpose. It is the sole responsibility of the user to determine whether or not the data are suitable for the intended purpose. It is also the obligation of the user to apply those data in an appropriate and conscientious manner. The USDA Forest Service provides no warranty, nor accepts any liability occurring from any incorrect, incomplete, or misleading data, or from any incorrect, incomplete, or misleading use of these data.
<15 meters
Field Data Collection: Ground or other reference data is used to build the relationships between the observed phenomena and the spectral and biophysical information derived from remotely sensed and ancillary data. Collectively, ground and other reference data are known as training data because they are used to construct algorithms that relate observations to quantified variables and are used to interpret and label areas that have not been sampled within a study area. Thus, they “train” the algorithm to distinguish between and label the unknown areas. Specifically, training data was collected to distinguish Lifeform, tree canopy cover, tree size, and vegetation dominance type.
Create derivatives of Rapid Eye and Landsat 8 imagery: Image derivatives provide spectral and texture-based information that is useful for landcover mapping. All derivatives used in the mapping process are processed to a 10 meter pixel resolution to enhance processing speed and reduce variability in the dataset. Image derivatives created used in the IPKNF process were based on Rapid Eye and Landsat 8 data. As a first step in the derivative process, a principal component analysis of the five bands of Rapid Eye image data was conducted, and the first three components were retained and stacked to create a three band principal component raster with 5 meter pixel resolution, called PCARE. From this raster, a focal mean, focal standard deviation, and contrast gray level co-occurrence matrix were created, using a seven pixel by seven pixel moving window. The results of the focal computation are degraded to 10 meter pixel resolution for their final application. In a similar fashion, Landsat 8 data were also transformed into a three band principal component raster, called PCALS. Focal derivatives were not created for the Landsat 8 data because of the course resolution of the raw imagery, but the PCA raster is resmapled to 10 meter pixel resolution to facilitate integration with other datasets.
Create derivatives of biophysical data: An integrated biophysical representation of the landscape is created for segmentation and modeling purposes. A raster derivative that integrates precipitation, solar radiation, and topography is used to quantify the physical environment. This provides a physical foundation for processes that are associated with the availability of water. Because it integrates precipitation, heat load from the sun, and water routing by topographic elements, it is called PHEAT (Precipitation Heat & Elevation Adjusted Topography). PHEAT is used to help inform the delineation of polygons in the segmentation process, and the derivation of vegetation characteristics in modeling processes.
Create derivative of vegetation index data: The normalized difference vegetation index (NDVI) computes a measure of photosynthetic activity in plants, using information related to the wavelengths of light that exist in the “photosynthetically active” portion of the electromagnetic spectrum, namely the “Red” and “Near-Infrared” that are captured by image sensors. In its original format, NDVI describes photosynthetic activity at the instantaneous time of image collection, and while this is useful, it does not provide information about long term processes of trajectories over time. By summing individually collected NDVI values over each time period the data are collected, seasonal patterns of green-up to senescence can be interpreted by the magnitude of accumulated values. For examples, an area of deciduous shrubs that is very active photosynthetically will have very high individual NDVI values during the active growing season, and those values will accumulate to be higher than its evergreen counterparts that rely on lower levels of sustained photosynthesis over longer periods of time. An index that captures the accumulated values of NDVI is called Time-Integrated NDVI (TINDVI). For modeling and segmentation purposes, a 30 year period record for the growing season months (July to September) was computed from Landsat data and is referred to as the vegetation index derivative, TINDVI.
Create image segmentation: Image segmentation is the process of combining pixels within digital images into spatially cohesive regions. These individual regions are called image objects and represent distinct areas within the image that generally correspond to patches of similar vegetation type/conditions. Ultimately, the raster-based image objects are converted to vector-based polygons which can have associated image statistics as attributes. The segmentation process is performed using a proprietary software package, Definiens’ eCognition, and is based on the local variance structure within imagery and User defined shape indices. These image objects effectively depict elements of vegetation and other patterns on the landscape (McDonald et al. 2002), and all VMap attributes are associated with the polygons derived from the segmentation process.
Lifeform Classification: Mapped lifeform is derived from a combined process of image object, or polygon, classification and photo/image-interpretation. Percent abundance of a given lifeform within polygons is determined using species canopy cover, with a minimum of 10% canopy cover needed to assign lifeform as defined in the R1 Existing Vegetation Classification document. Polygon classification is accomplished with the RandomForest algorithm using field collected reference information and summarized image derivative, biophysical derivative, and vegetation index derivative statistics associated with the polygons obtained from the segmentation process. Mapped lifeforms include Tree, Shrub, Herbaceous, Sparsely Vegetated, and Water with precedence order being tree, shrub, herbaceous in the lifeform key.
Canopy Cover Tree: For polygons where a tree lifeform has been assigned, tree canopy cover values are estimated. Traditionally the tree canopy cover values in the VMap database were only available in four classes: Low (10-24.9% Cover), Moderate Low (25-39.9% Cover), Moderate High (40-59.9% Cover) and High (60%+ Cover). Recent advances in the modeling capabilities, however, have enabled us to produce canopy cover estimates as continuous variables that can be parceled into the stated classes. Providing the flexibility to assess continuous canopy cover percentage values or categorical groupings of canopy cover enable increased precision for model and decision support. Canopy cover models are based reference data obtained through analyst-based image interpretation, and RandomForests regression modeling using a suite of image derivatives, a biophysical derivative, and a vegetation index derivative described in the above sections. Using a 70 meter by 70 meter grid, which resembles the dimensions of an FIA plot, an image analyst randomly selects 1000 grid cells across the mapping area and then uses high resolution NAIP or other imagery to assign a canopy cover estimate to each cell. A full range of canopy cover values, from 10% up to greater than 60%, are generated and subsequently used as reference data in the modeling process. The selection of reference sites are used in combination with the Rapid Eye image derivatives in a RandomForest regression model to estimate the full range of canopy cover values across the mapping area. In the case of the IPKNF, a second round of modeling was accomplished in a similar way, using the same reference data, where appropriate, to model continuous canopy cover values across the mapping area with Landsat 8 derivatives. The second round of modeling was implemented to fill in the holes created by the cloud mask. Thus, locations were a cloud mask was present, were filled with estimates based on Landsat 8 estimates. For all other locations, estimates are based on Rapid Eye image derivatives. Continuous canopy cover values based on either Rapid Eye or Landsat 8 derivatives were grouped into canopy cover classes based on the specifications of the Region 1 Existing Vegetation Classification System, as described above.
Tree Size: Tree size class is modeled from field collected data that quantifies basal area weighted average tree diameter at breast height (BAWDBH), as described in the Region 1 Exiting Vegetation Classification System. BAWDBH is computed with a variable radius plot to the nearest 1”. In a process similar to canopy cover modeling, data from reference sites are associated with image derivatives, a biophysical derivative, and a vegetation index derivative and used in a RandomForest regression model to estimate continuous tree size values for every pixel. For all polygons classified as the tree lifeform, individual pixel values are summarized and the mean BAWDBH is associated with those polygons. Due to the cloud mask, estimates were first generated with Rapid Eye image derivatives, and then repeated with Landsat 8 derivatives. No data values in the Rapid Eye estimates were replaced with values genereated with Landsat 8 derivatives. The resulting mean values in all tree class polygons were grouped into the Region 1 Existing Vegetation Classification System tree size classes ranging from 0-4.9, to 5.0-9.9, 10.0-14.9, 15-19.9, 20.0 – 24.9, and greater than 25 inches dbh mean values for stands.
Tree Dominance: Similar to tree size, tree dominance type was modeled using a RandomForest regression based on individal tree species abundance information collected at the field plot level, and Rapid Eye image derivatives, a biophysical derivative, and a vegetation index derivative. A separate raster surface was built for each species, where a continuous range of percent abundance values represent the potential abundance of a given species in any given pixel. This process was repeated using Landsat 8 image derivatives, the same biophysical derivative, and the same vegetation index derivative. In locations where cloud masks were present in the Rapid Eye based raster surfaces, no data values were filled in with Landsat 8 based estimates. Thus a combination raster surface was created for each species of interest. The species abundance rasters were then summarized to the VMap polygons to determine percent composition and a dominance type label was then assigned based on R1 Existing Vegetation Classification System tree dominance type rules.
Accuracy Assessment: An independent accuracy assessment of the VMap products was conducted across the entire IPKNF map area. This accuracy assessment provides a validation of the data, giving an indication of reliability of the map products, so that managers are fully informed throughout the decision making process. Too often vegetation and other maps are used without a clear understanding of their reliability. A false sense of security about the accuracy of the map may result in an inappropriate use of the map and important decisions may be made based on data with unknown and/or unreliable accuracy. Estimates of overall map accuracy and confidence of individual map classes can be inferred from an error matrix derived from the comparison of known reference sites to mapped data. Overall the resulting IPKNF map products show exceptional accuracies, ranging from 60-90+% depending on attribute. Please refer to IPKNF VMap Accuracy Assessment; Version 17 (Brown, 2017) for complete details.
Tree Mortality: Tree Mortality is based off of reference data obtained through analyst-based image interpretation, and RandomForests regression modeling using a suite of Landsat image derivatives related to long-term (30 year 1985-2105) NDVI and a 2016 normalized burn ratio derivative. Using a 70 meter by 70 meter grid, which resembles the dimensions of an FIA plot, an image analyst randomly selects 1000 grid cells across the mapping area and then uses high resolution NAIP or other imagery to assign a canopy cover estimate to each cell. A full range of mortality, from 10% up to 90%, are generated and subsequently used as reference data in the modeling process. The selection of reference sites are used in combination with the Landsat image derivatives in a RandomForest regression model to estimate the full range of mortality values across the mapping area.
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A file geodatabase Feature Class
ESRI
Internal ESRI number
ESRI
Sequential unique whole numbers that are automatically generated.
Internal ESRI number
ESRI
Coordinates defining the features.
Polygon unique identifi
Area of the polygon in acres
R1 Geospatial Group
Enumerated_Domain 3100 HERB - Herbaceous Enumerated_Domain 3300 SHRUB - Shrubland Enumerated_Domain 4000 TREE - Tree Enumerated_Domain 5000 WATER - Water Enumerated_Domain 7000 SPVEG - Sparsely Vegetated Enumerated_Domain 7100 URBAN - Urban areas Enumerated_Domain 8600 DECIDTREE - Decidous Tree
R1 Geospatial Group
Enumerated_Domain 3100 HERB - Herbaceous Enumerated_Domain 3160 GRASS-DRY - Dry grass or herbaceous types Enumerated_Domain 3170 GRASS-BUNCH - Bunchgrass Enumerated_Domain 3180 GRASS-SINGLESTEM - Single-stem grass Enumerated_Domain 3190 GRASS-WET - Wet grass or herbaceous types Enumerated_Domain 3300 SHRUB - Shrubland Enumerated_Domain 3320 SHRUB-XERIC - Xeric shrub type Enumerated_Domain 3330 SHRUB-MESIC - Mesic shrub type Enumerated_Domain 4000 TREE - Tree Enumerated_Domain 5000 WATER - Water Enumerated_Domain 7000 SPVEG - Sparsely vegetated Enumerated_Domain 7100 URBAN - Urban areas Enumerated_Domain 8015 PIPO - Ponderosa pine dominated (≥60% relative cover) Enumerated_Domain 8025 PSME - Douglas fir dominated (≥60% relative cover) Enumerated_Domain 8035 ABGR - Grand Fir dominated (≥60% relative cover) Enumerated_Domain 8045 LAOC - Western Larch dominated (≥60% relative cover) Enumerated_Domain 8055 PICO - Lodgepole pine dominated (≥60% relative cover) Enumerated_Domain 8065 ABLA - Subalpine fir dominated (≥60% relative cover) Enumerated_Domain 8075 PIEN - Englemann spruce dominated (≥60% relative cover) Enumerated_Domain 8085 PIMO3 – Western white pine dominated (≥60% relative cover) Enumerated_Domain 8095 THPL - Cedar dominated (≥60% relative cover) Enumerated_Domain 8105 TSHE – Western hemlock dominated Enumerated_Domain 8115 TSME - Mountain Hemlock dominated (≥60% relative cover) Enumerated_Domain 8125 PIAL - Whitebark pine dominated (≥60% relative cover) Enumerated_Domain 8135 LALY - Sub alpine fir dominated (≥60% relative cover) Enumerated_Domain 8145 PIFL2 - Paper birch dominated (≥60% relative cover) Enumerated_Domain 8155 PIFL2 - Limber pine dominated (≥60% relative cover) Enumerated_Domain 8165 POPUL - Cottonwood dominated (≥60% relative cover) Enumerated_Domain 8175 POTR5 - Aspen dominated (≥60% relative cover) Enumerated_Domain 8185 JUNIP - Juniper dominated (≥60% relative cover) Enumerated_Domain 8195 FRPE - Green ash dominated (≥60% relative cover) Enumerated_Domain 8205 CELE3 - Curl-leaf mountain mahogany dominated (≥60% relative cover) Enumerated_Domain 8215 TABR2 - Pacific yew dominated (≥60% relative cover) Enumerated_Domain 8400 IMIX - Shade-intolerant conifer mix (no single species ≥60% relative cover) Enumerated_Domain 8500 TMIX - Shade-tolerant conifer mix (no single species ≥60% relative cover) Enumerated_Domain 8600 HMIX - Hardwood mix (no single species ≥60% relative cover) Enumerated_Domain 8900 TRANSITIONAL FOREST - Forested areas in GRASS or SHRUB due to disturbance
R1 Geospatial Group
Enumerated_Domain 3100 HERB - Herbaceous Enumerated_Domain 3160 GRASS-DRY - Dry grass or herbaceous types Enumerated_Domain 3170 GRASS-BUNCH - Bunchgrass Enumerated_Domain 3180 GRASS-SINGLESTEM - Single-stem grass Enumerated_Domain 3190 GRASS-WET - Wet grass or herbaceous types Enumerated_Domain 3300 SHRUB - Shrubland Enumerated_Domain 3320 SHRUB-XERIC - Xeric shrub type Enumerated_Domain 3330 SHRUB-MESIC - Mesic shrub type Enumerated_Domain 4000 TREE - Tree Enumerated_Domain 5000 WATER - Water Enumerated_Domain 7000 SPVEG - Sparsely vegetated Enumerated_Domain 7100 URBAN - Urban areas Enumerated_Domain 8010 PIPO - Ponderosa pine dominated (≥60% relative cover) Enumerated_Domain 8020 PSME - Douglas fir dominated (≥60% relative cover) Enumerated_Domain 8030 ABGR - Grand Fir dominated (≥60% relative cover) Enumerated_Domain 8040 LAOC - Western Larch dominated (≥60% relative cover) Enumerated_Domain 8050 PICO - Lodgepole pine dominated (≥60% relative cover) Enumerated_Domain 8060 ABLA - Subalpine fir dominated (≥60% relative cover) Enumerated_Domain 8070 PIEN - Englemann spruce dominated (≥60% relative cover) Enumerated_Domain 8080 PIMO3 – Western white pine dominated (≥60% relative cover) Enumerated_Domain 8090 THPL - Cedar dominated (≥60% relative cover) Enumerated_Domain 8100 TSHE – Western hemlock dominated Enumerated_Domain 8110 TSME - Mountain Hemlock dominated (≥60% relative cover) Enumerated_Domain 8120 PIAL - Whitebark pine dominated (≥60% relative cover) Enumerated_Domain 8130 LALY - Sub alpine fir dominated (≥60% relative cover) Enumerated_Domain 8140 PIFL2 - Paper birch dominated (≥60% relative cover) Enumerated_Domain 8150 PIFL2 - Limber pine dominated (≥60% relative cover) Enumerated_Domain 8160 POPUL - Cottonwood dominated (≥60% relative cover) Enumerated_Domain 8170 POTR5 - Aspen dominated (≥60% relative cover) Enumerated_Domain 8180 JUNIP - Juniper dominated (≥60% relative cover) Enumerated_Domain 8190 FRPE - Green ash dominated (≥60% relative cover) Enumerated_Domain 8200 CELE3 - Curl-leaf mountain mahogany dominated (≥60% relative cover) Enumerated_Domain 8210 TABR2 - Pacific yew dominated (≥60% relative cover) Enumerated_Domain 8400 IMIX - Shade-intolerant conifer mix (no single species ≥60% relative cover) Enumerated_Domain 8500 TMIX - Shade-tolerant conifer mix (no single species ≥60% relative cover) Enumerated_Domain 8600 HMIX - Hardwood mix (no single species ≥60% relative cover) Enumerated_Domain 8900 TRANSITIONAL FOREST - Forested areas in GRASS or SHRUB due to disturbance
R1 Geospatial Group
Enumerated_Domain 3100 HERB - Herbaceous Enumerated_Domain 3160 GRASS-DRY - Dry grass or herbaceous types Enumerated_Domain 3170 GRASS-BUNCH - Bunchgrass Enumerated_Domain 3180 GRASS-SINGLESTEM - Single-stem grass Enumerated_Domain 3190 GRASS-WET - Wet grass or herbaceous types Enumerated_Domain 3300 SHRUB - Shrubland Enumerated_Domain 3320 SHRUB-XERIC - Xeric shrub type Enumerated_Domain 3330 SHRUB-MESIC - Mesic shrub type Enumerated_Domain 4000 TREE - Tree Enumerated_Domain 5000 WATER - Water Enumerated_Domain 7000 SPVEG - Sparsely vegetated Enumerated_Domain 7100 URBAN - Urban areas Enumerated_Domain 8010 PIPO - Ponderosa pine dominated (≥60% relative cover) Enumerated_Domain 8013 PIPO-IMIX - Ponderosa pine intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8014 PIPO-TMIX - Ponderosa pine tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8016 PIPO-HMIX - Ponderosa pine hardwood mix (≥40% relative cover) Enumerated_Domain 8020 PSME - Douglas fir dominated (≥60% relative cover) Enumerated_Domain 8023 PSME-IMIX - Douglas fir intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8024 PSME-IMIX - Douglas fir tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8026 PSME-HMIX - Douglas fir hardwood mix (≥40% relative cover) Enumerated_Domain 8030 ABGR - Grand fir dominated (≥60% relative cover) Enumerated_Domain 8033 ABGR-IMIX - Grand fir intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8034 ABGR-IMIX - Grand fir tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8036 ABGR-HMIX - Grand fir hardwood mix (≥40% relative cover) Enumerated_Domain 8040 LAOC - Western larch dominated (≥60% relative cover) Enumerated_Domain 8043 LAOC-IMIX - Western larch intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8044 LAOC-IMIX - Western larch tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8046 LAOC-HMIX – Western larch hardwood mix (≥40% relative cover) Enumerated_Domain 8050 PICO - Lodgepole pine dominated (≥60% relative cover) Enumerated_Domain 8053 PICO-IMIX - Lodgepole pine intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8054 PICO-TMIX - Lodgepole pine tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8056 PICO-HMIX – Lodgepole pine hardwood mix (≥40% relative cover) Enumerated_Domain 8060 ABLA - Subalpine fir dominated (≥60% relative cover) Enumerated_Domain 8063 ABLA-TMIX - Subalpine fir intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8064 ABLA-TMIX - Subalpine fir tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8066 ABLA-HMIX - Subapline fir hardwood mix (≥40% relative cover) Enumerated_Domain 8070 PIEN - Englemann spruce dominated (≥60% relative cover) Enumerated_Domain 8073 PIEN-TMIX - Englemann spruce intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8074 PIEN-TMIX - Englemann spruce tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8076 PIEN-HMIX - Englemann spruce hardwood mix (≥40% relative cover) Enumerated_Domain 8080 PIMO3 - White pine dominated (≥60% relative cover) Enumerated_Domain 8083 PIMO3-TMIX - White pine spruce intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8084 PIMO3-TMIX - White pine tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8086 PIMO3-HMIX - White pine hardwood mix (≥40% relative cover) Enumerated_Domain 8090 THPL - Cedar dominated (≥60% relative cover) Enumerated_Domain 8093 THPL-TMIX - Cedar intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8094 THPL-TMIX - Cedar tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8096 ABGR-HMIX - Cedar hardwood mix (≥40% relative cover) Enumerated_Domain 8100 TSME - Western hemlock dominated (≥60% relative cover) Enumerated_Domain 8103 TSHE-TMIX - Western hemlock intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8104 TSHE-TMIX - Western hemlock tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8106 TSHE-HMIX - Western hemlock hardwood mix (≥40% relative cover) Enumerated_Domain 8110 TSME - Mountain hemlock dominated (≥60% relative cover) Enumerated_Domain 8113 TSME-TMIX - Mountain hemlock intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8114 TSME-TMIX - Mountain hemlock tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8116 TSME-HMIX - Mountain hemlock hardwood mix (≥40% relative cover) Enumerated_Domain 8120 PIAL - Whitebark pine dominated (≥60% relative cover) Enumerated_Domain 8123 PIAL-IMIX - Whitebark pine intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8124 PIAL-TMIX - Whitebark tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8126 PIAL-HMIX - Whitebark hardwood mix (≥40% relative cover) Enumerated_Domain 8130 LALY - Sub alpine larch dominated (≥60% relative cover) Enumerated_Domain 8133 LALY-TMIX - Sub apline larch intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8134 LALY-TMIX - Sub alpine larch tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8136 LALY-HMIX - Sub alpine larch hardwood mix (≥40% relative cover) Enumerated_Domain 8140 PIFL2 - Paper birch dominated (≥60% relative cover) Enumerated_Domain 8143 PIFL2-IMIX - Paper birch intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8144 PIFL2-TMIX - Paper birch tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8146 PIFL2-HMIX - Paper birch hardwood mix (≥40% relative cover) Enumerated_Domain 8150 PIFL2 - Limber pine dominated (≥60% relative cover) Enumerated_Domain 8153 PIFL2-IMIX - Limber pine intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8154 PIFL2-TMIX - Limber pine tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8156 PIFL2-HMIX - Limber pine hardwood mix (≥40% relative cover) Enumerated_Domain 8160 POPUL - Cottonwood dominated (≥60% relative cover) Enumerated_Domain 8163 POPUL-IMIX - Cottonwood intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8164 POPUL-TMIX - Cottonwood tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8166 POPUL-HMIX - Cottonwood hardwood mix (≥40% relative cover) Enumerated_Domain 8170 POTR5 - Aspen dominated (≥60% relative cover) Enumerated_Domain 8173 POPUL-IMIX - Aspen intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8174 POPUL-TMIX - Aspen tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8176 POPUL-HMIX - Aspen hardwood mix (≥40% relative cover) Enumerated_Domain 8180 JUNIP - Juniper dominated (≥60% relative cover) Enumerated_Domain 8183 JUNIP-IMIX - Juniper intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8184 JUNIP-TMIX - Aspen tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8186 JUNIP-HMIX - Aspen hardwood mix (≥40% relative cover) Enumerated_Domain 8190 FRPE - Green ash dominated (≥60% relative cover) Enumerated_Domain 8193 FRPE-IMIX - Green ash intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8194 FRPE-TMIX - Green ash tolerant conifer mix (≥40% relative cover) Enumerated_Domain 8196 FRPE-HMIX - Green ash hardwood mix (≥40% relative cover) Enumerated_Domain 8200 CELE3 - Curl-leaf mountain mahogany dominated (≥60% relative cover) Enumerated_Domain 8203 CELE3-IMIX - Curl-leaf mountain mahogany intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8204 CELE3-TMIX - Curl-leaf mountain mahogany tolerant mix (≥40% relative cover) Enumerated_Domain 8206 CELE3-HMIX - Curl-leaf mountain mahogany hardwood mix (≥40% relative cover) Enumerated_Domain 8210 CELE3 - Pacific yew dominated (≥60% relative cover) Enumerated_Domain 8213 CELE3-IMIX - Pacific yew intolerant conifer mix (≥40% relative cover) Enumerated_Domain 8214 CELE3-TMIX - Pacific yew tolerant mix (≥40% relative cover) Enumerated_Domain 8216 CELE3-HMIX - Pacific yew hardwood mix (≥40% relative cover) Enumerated_Domain 8400 IMIX - Shade-intolerant conifer mix (no single species ≥60% relative cover) Enumerated_Domain 8500 TMIX - Shade-tolerant conifer mix (no single species ≥60% relative cover) Enumerated_Domain 8600 HMIX - Hardwood mix (no single species ≥60% relative cover) Enumerated_Domain 8900 TRANSITIONAL FOREST - Forested areas in GRASS or SHRUB due to disturbance
R1 Geospatial Group
Enumerated_Domain 4001 CTR 10-24.9% - CTR 10-24.9% Enumerated_Domain 4002 CTR 25-39.9% - CTR 25-39.9% Enumerated_Domain 4003 CTR 40-59.9% - CTR 40-59.9% Enumerated_Domain 4004 CTR ≥ 60% - CTR ≥ 60% Enumerated_Domain 3100 HERB - Herbaceous Enumerated_Domain 3300 SHRUB - Shrub Enumerated_Domain 5000 WATER - Water Enumerated_Domain 7000 SPVEG - Sparsely vegetated Enumerated_Domain 7100 URBAN - Urban areas Enumerated_Domain 8600 TREE-DECID - Deciduous Tree Enumerated_Domain 8900 TRANSITIONAL FOREST - Forested areas in GRASS or SHRUB due to disturbance
R1 Geospatial Group
Percent Tree Canopy for polygons where Lifeform = 4000
R1 Geospatial Group
Enumerated_Domain 4100 DBH 0-4.9" - Basal area weighted average diameter 0-4.9" Enumerated_Domain 4200 DBH 5-9.9" - Basal area weighted average diameter 5-9.9" Enumerated_Domain 4300 DBH 10-14.9" - Basal area weighted average diameter 10-14.9" Enumerated_Domain 4400 DBH 15-19.9" - Basal area weighted average diameter 15-19.9" Enumerated_Domain 4500 DBH ≥= 20" - Basal area weighted average diameter ≥= 20" Enumerated_Domain 3100 HERB - Herbaceous Enumerated_Domain 3300 SHRUB - Shrub Enumerated_Domain 5000 WATER - Water Enumerated_Domain 7000 SPVEG - Sparsely vegetated Enumerated_Domain 7100 URBAN - Urban areas Enumerated_Domain 8600 TREE-DECID - Deciduous Tree Enumerated_Domain 8900 TRANSITIONAL FOREST - Forested areas in GRASS or SHRUB due to disturbance
R1 Geospatial Group
Average Diameter Breast Height for polygons where Lifeform = 4000
R1 Geospatial Group
Average elevation of the polygon in feet.
R1 Geospatial Group
Enumerated_Domain 0 - Flat (slope < 10%) Enumerated_Domain 1 - North (338-360 & 0-22 degrees) Enumerated_Domain 2 - Northeast (23-68 degrees) Enumerated_Domain 3 - East (68-112 degrees) Enumerated_Domain 4 - Southeast (113-157 degrees) Enumerated_Domain 5 - South (158-202 degrees) Enumerated_Domain 6 - Southwest (203-247 degrees) Enumerated_Domain 7 - West (248-292 degrees) Enumerated_Domain 8 - Northwest (293-337 degrees)
R1 Geospatial Group
Average percent slope of the polygon
R1 Geospatial Group
Percent of tree mortality for polygons where Lifeform = 4000
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
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