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Ecosys_TCA_Core_2025
002
-124.416305
-70.599578
28.960192
49.001087
1
-
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Geographic
GCS_North_American_1983
Angular Unit: Degree (0.017453)
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20260102
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ArcGIS Metadata
1.0
USFS Chief Information Office, Enterprise Data Warehouse
Please send an e-mail to the address below.
Washington
DC
20250
US
SM.FS.data@usda.gov
dataset
USA
Ecosys_TCA_Core_2025
USDA Forest Service
2025-04-11T07:55:29
The Terrestrial Condition Assessment (TCA) was developed by an interdisciplinary team of resource specialists and scientists to characterize the condition of landscape ecosystems across all National Forests and Grasslands in the United States. A primary goal was to identify restoration needs and opportunities at a landscape scale. The TCA interprets and synthesizes data for 12 key ecological indicators composed of 30 metrics to determine the condition of terrestrial ecosystems.The TCA uses landscape ecosystems as analysis and reporting units. Landscape ecosystems were defined using Regional maps of Landtype Associations or coarsened maps of Biophysical Settings available through the LANDFIRE program. On Forest Service lands across the conterminous United States, there are 17,901 landscape-scale ecological units that range in size from 2,000 to 35,000 acres and average 12,000 acres. For each landscape ecosystem, each metric is evaluated according to thresholds determined by the scientific literature and expert panels. Thresholds determine what constitutes very good condition and very poor condition and given scores of +1 and -1, respectively; all values in-between are scaled to form a continuous series of scores between +1 and -1. All metric scores are synthesized into an indicator score, and then, all indicator scores are synthesized to form an overall TCA score for each landscape ecosystem.
The USDA-USFS Terrestrial Condition Assessment (TCA) for the conterminous US version 2.0 interprets and synthesizes data for 12 key ecological indicators composed of 30 metrics to determine the condition of terrestrial ecosystems on National Forest System (NFS) Administrative lands at a landscape scale (1:250,000 to 1:60,000). The TCA supports the production of a key performance indicator (KPI) for the US Forest Service. The TCA KPI leverages the TCA model’s ability to assess ecological conditions to describe the effects of USFS management activities, as recorded in the Forest Service Activity Tracking System (FACTS) database. The TCA KPI is defined as the “percent area of National Forest System landscape ecosystems where management actions have occurred that are improved, maintained or recovering.” The TCA model produces baseline scores for each indicator and each landscape ecosystem. Management activities reported in FACTS are translated into their effect on the datasets underlying the TCA model.
USFS Chief Information Office, Enterprise Data Warehouse
Please send an e-mail to the address below.
Washington
DC
20250
US
SM.FS.data@usda.gov
TCA; Landscapes
environment
ISO 19115 Topic Categories
The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.
The U.S. Forest Service makes no warranty, express or implied, nor assumes any liability or responsibility for the accuracy, reliability, completeness, or utility of these geospatial data or for the improper or incorrect use of those data. The data are dynamic and may change over time. The user is responsible for verifying the limitations of the geospatial data and for using the data accordingly.
[LANDFIRE 2.2.0] Existing Vegetation Type Layer, LANDFIRE 2.2, U.S. Department of the Interior, Geological Survey, and U.S. Department of Agriculture.http://www.landfire/viewer (2022). see also, Reeves, M. C., and Mitchell, J. E. (2011). Extent of coterminous US rangelands: quantifying implications of differing agency perspectives. Rangeland Ecology and Management, 64(6), 585-597. [USGS National Transportation Dataset] https://data.usgs.gov/datacatalog/data/USGS:ad3d631d-f51f-4b6a-91a3-e617d6a58b4e (April 25, 2024) [PRISM Dataset] http://www.prism.oregonstate.edu/ (August 8, 2024)
[EPA’s Air Quality System Dataset] https://aqs.epa.gov/aqsweb/airdata/download_files.html#Raw (August 19, 2024) [Nitrogen Deposition Dataset] (https://gaftp.epa.gov/castnet/tdep/CURRENT_grids/) (July 1, 2024) [MTBS Fire Severity Dataset] http://mtbs.gov (2024) [USFS Fire Modeling Institute Dataset] Scott, Joe H.; Dillon, Gregory K.; Jaffe, Melissa R.; Vogler, Kevin C.; Olszewski, Julia H.; Callahan, Michael N.; Karau, Eva C.; Lazarz, Mitchell T.; Short, Karen C.; Riley, Karin L.; Finney, Mark A.; Grenfell, Isaac C. 2024. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States. 2nd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2020-0016-2; https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0016-2 (September 10, 2024) [USFS FHAAST custom product] https://www.fs.usda.gov/science-technology/forest-health-protection/publications-reports/forest-health-assessment-applied-sciences-team-publications (August 16, 2024) [Insect and Diseease Hazard] https://www.fs.usda.gov/science-technology/data-tools-products/fhp-mapping-reporting/national-insect-disease-risk-and-hazard-mapping (March 5, 2019) [Vegetation Departure Metric] Swaty, R.; Blankenship, K.; Hall, K.R.; Smith, J.; Dettenmaier, M.; Hagen, S. Assessing Ecosystem Condition: Use and Customization of the Vegetation Departure Metric. Land 2022, 11, 28. https://doi.org/10.3390/land11010028 (November 29, 2022) [Grassland Indicators] https://rangelands.app/ ; https://www.mrlc.gov/data (August 2024) [LANDFIRE BioPhysical Setting 2.2.0] BioPhysical Setting, LANDFIRE 2.2, U.S. Department of the Interior, Geological Survey, and U.S. Department of Agriculture.http://www.landfire/viewer (2022) [LANDFIRE 2.3.0] Succession Class, LANDFIRE 2.3.0, U.S. Department of the Interior, Geological Survey, and U.S. Department of Agriculture.http://www.landfire/viewer (2023) [Composite Burn Index Methods] Parks, S.; Holsinger, L.; Koontz, M; Collins, L; Whitman E.; Parisien, M.; Loehman, R.; Barnes, J.; Bourdon, J.; Boucher, J; Boucher, Y.; Caprio, A.; Collingwood, A.; Hall, R.; Park, J.; Saperstein, L.; Smetanka, C.; Smith, R.; Soverel, N. Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests. Remote Sensing 2019, 11(14). https://doi.org/10.3390/rs11141735. [Drought Impact Methods] https://www.fs.usda.gov/research/treesearch/43361 (July 29, 2024)
Esri ArcGIS 13.3.4.52636
2025-04-11
1
-124.416305
-70.599578
49.001087
28.960192
Publication Date, CONUS
0
Publication Date, CONUS
For the EMDS related fields (MI/MS/MC), values maybe be null depending on the availability of input data (e.g., incomplete coverage of ozone monitoring sites) or the physiognomic type of the LTA. Some indicators are assessed for all LTAs, some are only assessed for forested LTAs, and others are only assessed for grassland LTAs. When calculating percent area (used for some EMDS input data), only terrestrial area is considered (i.e., water bodies are removed), but inholdings are included. Region 9 has halted aerial detection surveys reducing reliabiliity of Tree Mortality data starting in 2023.
Data spatial accuracy depends on the input data that is summarized to the LTAs to form the inputs of the EMDS model and the user accuracy when entering activities into the FACTS database. With regard to the input data, the different sources have varying degrees of positional accuracy associated with them depending on the processing methods used to create them, and the source data layers range in spatial resolution from as fine as 30m to as coarse as 4km. Projected for use in the contiguous United States Albers Equal Area Conic (EPSG:5070).
Data in the MI (model input) columns were produced by generating zonal statistics from inputs such as TCA indicator raster data sets, and other corporate or publicly available data from sources. Data in the MS (model score) columns are the results of running the Ecosystem Management Decision Support (EMDS) model for TCA using the MI columns as inputs. Data in the MC (model class) columns were created by classifying the MS data into their respective classes. KPI related fields are the result of using the EMDS model scores along with FACTS activities from the last 5 years to determine where and to what degree management is improving ecological integrity.
2025-04-11
withheld
withheld
withheld
File Geodatabase Feature Class
0
Feature class: A collection of geographic features with the same geometry type (such as point, line, or polygon), the same attributes, and the same spatial reference.
ESRI GIS Dictionary
Ecosys_TCA_Core_2025
Feature Class
0
OBJECTID
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
OBJECTID
OID
4
0
0
TCA_ID
A unique landscape analysis unit identifier. Comprised of a concatenation of the USFS Region code, USFS Forest code, and a unique sequential number based on number of landscape analysis units within a forest.
USFS
Unique ID code
TCA_ID
String
20
0
0
GIS_ACRES
The GIS acres of the landscape analysis unit polygon as calculated by ArcGIS in the NAD83 projection.
USFS
Acres
GIS_Acres
Double
8
0
0
SQ_MILES
The square miles of the landscape analysis unit polygon as calculated by ArcGIS in the NAD83 projection.
USFS
Square Miles
Sq_Miles
Double
8
0
0
TCA_ACRES
The terrestrial acres of the landscape analysis unit polygon as calculated by subtracting the acres of water identified by LANDFIRE BpS 1.4.0 dataset from the polygon GIS acres. This value was used to determine the percent of the landscape analysis unit affected by each indicator since it approximates the real non-water area of a landscape.
USFS
Acres
TCA_Acres
Double
8
0
0
FS_REGION
The USFS Region code for the majority area of a landscape analysis unit polygon.
USFS
Numeric code
FS_Region
SmallInteger
2
0
0
FS_FOREST
The USFS Forest name for the majority area of a landscape analysis unit polygon.
USFS
Forest name
FS_Forest
String
80
0
0
FS_DISTRICT
The USFS District name for the majority area of a landscape analysis unit polygon.
USFS
District name
FS_District
String
80
0
0
PHYSIOGNOMICTYPE
The dominant physiognomic type as determined by Reeves et al. 2011 using LANDFIRE BpS modelled pre-European vegetation types.
USFS
Vegetation type name
physiognomictype
String
20
0
0
MS_TERRESTRIAL_CONDITION
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_terrestrial_condition
Double
8
0
0
MC_TERRESTRIAL_CONDITION
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_terrestrial_condition
String
20
0
0
MS_DISTURBANCE_AGENTS
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_disturbance_agents
Double
8
0
0
MC_DISTURBANCE_AGENTS
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_disturbance_agents
String
20
0
0
MS_BIOTIC_AGENTS
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_biotic_agents
Double
8
0
0
MC_BIOTIC_AGENTS
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_biotic_agents
String
20
0
0
MS_TREE_MORTALITY
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_tree_mortality
Double
8
0
0
MC_TREE_MORTALITY
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_tree_mortality
String
20
0
0
MI_TREE_MORTALITY_0_5YR
The percent area of a landscape analysis unit that has been identified as having tree mortality or defoliation 3 out of 5 years from USFS Forest Health Areal Detection Surveys in the most recent 5 year period available (2019-2023).
USFS
Percent
MI_tree_mortality_0_5yr
Double
8
0
0
MS_TREE_MORTALITY_0_5YR
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_tree_mortality_0_5yr
Double
8
0
0
MC_TREE_MORTALITY_0_5YR
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_tree_mortality_0_5yr
String
20
0
0
MI_TREE_MORTALITY_6_10YR
The percent area of a landscape analysis unit that has been identified as having tree mortality or defoliation 3 out of 5 years from USFS Forest Health Areal Detection Surveys in the 5 year period preceding the other Tree Mortality metric (2014-2018).
USFS
Percent (0.0)
MI_tree_mortality_6_10yr
Double
8
0
0
MS_TREE_MORTALITY_6_10YR
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_tree_mortality_6_10yr
Double
8
0
0
MC_TREE_MORTALITY_6_10YR
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_tree_mortality_6_10yr
String
20
0
0
MS_ABIOTIC_AGENTS
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_abiotic_agents
Double
8
0
0
MC_ABIOTIC_AGENTS
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_abiotic_agents
String
20
0
0
MS_ROAD_DENSITY
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_road_density
Double
8
0
0
MC_ROAD_DENSITY
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_road_density
String
20
0
0
MI_PAVED_ROADS
Linear miles of "paved" road per square mile (mi/sq. mi.) of the terrestrial landscape (excludes water) within a landscape analysis unit based on USGS Transportation Dataset.
USFS
Miles of road per sq. mile of unit area (0.0)
MI_paved_roads
Double
8
0
0
MS_PAVED_ROADS
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_paved_roads
Double
8
0
0
MC_PAVED_ROADS
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_paved_roads
String
20
0
0
MI_LIGHT_DUTY_ROADS
Linear miles of "light duty" road per square mile (mi/sq. mi.) of the terrestrial landscape (excludes water) within a landscape analysis unit based on USGS Transportation Dataset.
USFS
Miles of road per sq. mile of unit area (0.0)
MI_light_duty_roads
Double
8
0
0
MS_LIGHT_DUTY_ROADS
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_light_duty_roads
Double
8
0
0
MC_LIGHT_DUTY_ROADS
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_light_duty_roads
String
20
0
0
MI_UNIMPROVED_ROADS
Linear miles of "unimproved" road per square mile (mi/sq. mi.) of the terrestrial landscape (excludes water) within a landscape analysis unit based on USGS Transportation Dataset.
USFS
Miles of road per sq. mile of unit area (0.0)
MI_unimproved_roads
Double
8
0
0
MS_UNIMPROVED_ROADS
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_unimproved_roads
Double
8
0
0
MC_UNIMPROVED_ROADS
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_unimproved_roads
String
20
0
0
MS_CLIMATE_EXPOSURE
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_climate_exposure
Double
8
0
0
MC_CLIMATE_EXPOSURE
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_climate_exposure
String
20
0
0
MI_DROUGHT_IMPACT
The average moisture deficit as a z-score calculated from the most recent 3 year period (2021-2023) relative to the reference time period (1900-2020). Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.
USFS
Z-score mean change (0.0)
MI_drought_impact
Double
8
0
0
MS_DROUGHT_IMPACT
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_drought_impact
Double
8
0
0
MC_DROUGHT_IMPACT
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_drought_impact
String
20
0
0
MS_TEMPERATURE_EXPOSURE
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_temperature_exposure
Double
8
0
0
MC_TEMPERATURE_EXPOSURE
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_temperature_exposure
String
20
0
0
MI_TEMP_EXPOSURE_SPRING
The difference in Spring temperature (F) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Spring months include March, April, May. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Degrees Fahrenheit
MI_temp_exposure_spring
Double
8
0
0
MS_TEMP_EXPOSURE_SPRING
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_temp_exposure_spring
Double
8
0
0
MC_TEMP_EXPOSURE_SPRING
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_temp_exposure_spring
String
20
0
0
MI_TEMP_EXPOSURE_SUMMER
The difference in Summer temperature (F) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Summer months include June, July, August. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Degrees Fahrenheit
MI_temp_exposure_summer
Double
8
0
0
MS_TEMP_EXPOSURE_SUMMER
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_temp_exposure_summer
Double
8
0
0
MC_TEMP_EXPOSURE_SUMMER
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_temp_exposure_summer
String
20
0
0
MI_TEMP_EXPOSURE_FALL
The difference in Fall temperature (F) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Fall months include September, October, and November. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Degrees Fahrenheit
MI_temp_exposure_fall
Double
8
0
0
MS_TEMP_EXPOSURE_FALL
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_temp_exposure_fall
Double
8
0
0
MC_TEMP_EXPOSURE_FALL
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_temp_exposure_fall
String
20
0
0
MI_TEMP_EXPOSURE_WINTER
The difference in Winter temperature (F) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Winter months include December, January, and February. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Degrees Fahrenheit
MI_temp_exposure_winter
Double
8
0
0
MS_TEMP_EXPOSURE_WINTER
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_temp_exposure_winter
Double
8
0
0
MC_TEMP_EXPOSURE_WINTER
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_temp_exposure_winter
String
20
0
0
MS_PRECIPITATION_EXPOSURE
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_precipitation_exposure
Double
8
0
0
MC_PRECIPITATION_EXPOSURE
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_precipitation_exposure
String
20
0
0
MI_PRECIP_EXPOSURE_SPRING
The difference in Spring precipitation (in inches) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Spring months include March, April, May. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Inches
MI_precip_exposure_spring
Double
8
0
0
MS_PRECIP_EXPOSURE_SPRING
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_precip_exposure_spring
Double
8
0
0
MC_PRECIP_EXPOSURE_SPRING
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_precip_exposure_spring
String
20
0
0
MI_PRECIP_EXPOSURE_SPRING_PCT
The percent difference in Spring precipitation (as a percent change) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Spring months include March, April, May. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Percent
MI_precip_exposure_spring_pct
Double
8
0
0
MS_PRECIP_EXPOSURE_SPRING_PCT
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_precip_exposure_spring_pct
Double
8
0
0
MC_PRECIP_EXPOSURE_SPRING_PCT
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_precip_exposure_spring_pct
String
20
0
0
MI_PRECIP_EXPOSURE_SUMMER
The difference in Summer precipitation (in inches) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Summer months include June, July, August. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University .
USFS
Inches
MI_precip_exposure_summer
Double
8
0
0
MS_PRECIP_EXPOSURE_SUMMER
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_precip_exposure_summer
Double
8
0
0
MC_PRECIP_EXPOSURE_SUMMER
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_precip_exposure_summer
String
20
0
0
MI_PRECIP_EXPOSURE_SUMMER_PCT
The percent difference in Summer precipitation (as a percent change) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Summer months include June, July, August. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Percent
MI_precip_exposure_summer_pct
Double
8
0
0
MS_PRECIP_EXPOSURE_SUMMER_PCT
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_precip_exposure_summer_pct
Double
8
0
0
MC_PRECIP_EXPOSURE_SUMMER_PCT
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_precip_exposure_summer_pct
String
20
0
0
MI_PRECIP_EXPOSURE_FALL
The difference in Fall precipitation (in inches) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Fall months include September, October, and November. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Inches
MI_precip_exposure_fall
Double
8
0
0
MS_PRECIP_EXPOSURE_FALL
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_precip_exposure_fall
Double
8
0
0
MC_PRECIP_EXPOSURE_FALL
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_precip_exposure_fall
String
20
0
0
MI_PRECIP_EXPOSURE_FALL_PCT
The percent difference in Fall precipitation (as a percent change) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Fall months include September, October, and November. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Percent
MI_precip_exposure_fall_pct
Double
8
0
0
MS_PRECIP_EXPOSURE_FALL_PCT
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_precip_exposure_fall_pct
Double
8
0
0
MC_PRECIP_EXPOSURE_FALL_PCT
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_precip_exposure_fall_pct
String
20
0
0
MI_PRECIP_EXPOSURE_WINTER
The difference in Winter precipitation (in inches) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Winter months include December, January, and February. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Inches
MI_precip_exposure_winter
Double
8
0
0
MS_PRECIP_EXPOSURE_WINTER
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_precip_exposure_winter
Double
8
0
0
MC_PRECIP_EXPOSURE_WINTER
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_precip_exposure_winter
String
20
0
0
MI_PRECIP_EXPOSURE_WINTER_PCT
The percent difference in Winter precipitation (as a percent change) between the current time period (2019-2023, the most recent 5 years) and reference time period (1900-2018). Winter months include December, January, and February. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA. Data used are sourced from PRISM, Oregon State University.
USFS
Percent
MI_precip_exposure_winter_pct
Double
8
0
0
MS_PRECIP_EXPOSURE_WINTER_PCT
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_precip_exposure_winter_pct
Double
8
0
0
MC_PRECIP_EXPOSURE_WINTER_PCT
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_precip_exposure_winter_pct
String
20
0
0
MS_AIR_POLLUTION
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_air_pollution
Double
8
0
0
MC_AIR_POLLUTION
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_air_pollution
String
20
0
0
MI_NITROGEN_DEPOSITION
The average annual atmospheric deposition of nitrogen within a landscape analysis unit. Deposition data is the three year average output from the Total Deposition (TDep) modeling system (https://nadp.slh.wisc.edu/committees/tdep/) for most recent period available (2020-2022). Resolution is 400m, resampled from 4km.
USFS
kilograms/hectare/year
MI_nitrogen_deposition
Double
8
0
0
MS_NITROGEN_DEPOSITION
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_nitrogen_deposition
Double
8
0
0
MC_NITROGEN_DEPOSITION
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_nitrogen_deposition
String
20
0
0
MS_OZONE_EXPOSURE
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_ozone_exposure
Double
8
0
0
MC_OZONE_EXPOSURE
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_ozone_exposure
String
20
0
0
MI_OZONE_ACUTE_EXPOSURE
The 3 year average N100 value which is the sum of hours over 100 ppb in a given month and captures peak events of high exposure or acute exposure during the growing season (April-Sept) from the most recent 3 year period available (2021-2023). Data used are sourced from the EPA’s Air Quality System Data Mart: https://aqs.epa.gov/aqsweb/airdata/download_files.html#Raw
USFS
Hours
MI_ozone_acute_exposure
Double
8
0
0
MS_OZONE_ACUTE_EXPOSURE
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_ozone_acute_exposure
Double
8
0
0
MC_OZONE_ACUTE_EXPOSURE
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_ozone_acute_exposure
String
20
0
0
MI_OZONE_CHRONIC_EXPOSURE
The 3 year average W126 value which is a weighted measure of chronic (lower level) ozone exposure within a landscape analysis unit during the growing season (April to September)from the most recent 3 year period available (2021-2023). W126 is expressed in parts per million of O3-hours or ppm-hours. Data used are sourced from the EPA’s Air Quality System Data Mart: https://aqs.epa.gov/aqsweb/airdata/download_files.html#Raw
USFS
ppm-hours
MI_ozone_chronic_exposure
Double
8
0
0
MS_OZONE_CHRONIC_EXPOSURE
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_ozone_chronic_exposure
Double
8
0
0
MC_OZONE_CHRONIC_EXPOSURE
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_ozone_chronic_exposure
String
20
0
0
MS_UNCHARACTERISTIC_DISTURBANC
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_uncharacteristic_disturbance
Double
8
0
0
MC_UNCHARACTERISTIC_DISTURBANC
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_uncharacteristic_disturbance
String
20
0
0
MS_UNCHARACTERISTIC_WILDFIRE
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_uncharacteristic_wildfire
Double
8
0
0
MC_UNCHARACTERISTIC_WILDFIRE
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_uncharacteristic_wildfire
String
20
0
0
MI_WILDFIRE_SEVERITY_MOD
The percent area of a landscape analysis unit where the LANDFIRE Percent Mixed Severity (PMS) dataset predicted moderate severity fires and Monitoring Trends and Burn Severity mapped high severity fires occurring 1984-2022 with an additional year of data (2023) being filled in with Composite Burn Index data from Sean Parks. Parks, S.A.; Holsinger, L.M.; Voss, M.A.; Loehman, R.A.; Robinson, N.P. Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential. Remote Sens. 2018, 10, 879. https://doi.org/10.3390/rs10060879. [Composite Burn Index fire perimiters] https://data-nifc.opendata.arcgis.com/
USFS
Percent
MI_wildfire_severity_mod
Double
8
0
0
MS_WILDFIRE_SEVERITY_MOD
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_wildfire_severity_mod
Double
8
0
0
MC_WILDFIRE_SEVERITY_MOD
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_wildfire_severity_mod
String
20
0
0
MI_WILDFIRE_SEVERITY_LOW
The percent area of a landscape analysis unit where the LANDFIRE Percent Mixed Severity (PMS) dataset predicted moderate severity fires and Monitoring Trends and Burn Severity mapped high severity fires occurring 1984-2022 with an additional year of data (2023) being filled in with Composite Burn Index data from Sean Parks. Parks, S.A.; Holsinger, L.M.; Voss, M.A.; Loehman, R.A.; Robinson, N.P. Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential. Remote Sens. 2018, 10, 879. https://doi.org/10.3390/rs10060879. [Composite Burn Index fire perimiters] https://data-nifc.opendata.arcgis.com/
USFS
Percent
MI_wildfire_severity_low
Double
8
0
0
MS_WILDFIRE_SEVERITY_LOW
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_wildfire_severity_low
Double
8
0
0
MC_WILDFIRE_SEVERITY_LOW
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_wildfire_severity_low
String
20
0
0
MS_VEGETATION_CONDITION
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_vegetation_condition
Double
8
0
0
MC_VEGETATION_CONDITION
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_vegetation_condition
String
20
0
0
MS_WILDFIRE_HAZARD
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_wildfire_hazard
Double
8
0
0
MC_WILDFIRE_HAZARD
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_wildfire_hazard
String
20
0
0
MI_WILDFIRE_HAZARD_HIGH
The percent area of a landscape analysis unit where the Wildland Fire Hazard class is High or Very High.
USFS
Percent
MI_wildfire_hazard_high
Double
8
0
0
MS_WILDFIRE_HAZARD_HIGH
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_wildfire_hazard_high
Double
8
0
0
MC_WILDFIRE_HAZARD_HIGH
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_wildfire_hazard_high
String
20
0
0
MI_WILDFIRE_HAZARD_MOD
The percent area of a landscape analysis unit where the Wildland Fire Hazard class is Moderate and the LANDFIRE 2.2.0 Fire Regime is 1 or 2. Source: https://www.firelab.org/project/wildfire-hazard-potential.
USFS
Percent
MI_wildfire_hazard_mod
Double
8
0
0
MS_WILDFIRE_HAZARD_MOD
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_wildfire_hazard_mod
Double
8
0
0
MC_WILDFIRE_HAZARD_MOD
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_wildfire_hazard_mod
String
20
0
0
MI_FIRE_DEFICIT
The percent area of a landscape analysis unit identified as having a fire deficit by comparing modern fire occurrence (MTBS: 1984-2022; CBI: 2023) with historical fire return intervals (LANDFIRE 2.2, Mean Fire Return Intervals).
USFS
Percent
MI_fire_deficit
Double
8
0
0
MS_FIRE_DEFICIT
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_fire_deficit
Double
8
0
0
MC_FIRE_DEFICIT
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_fire_deficit
String
20
0
0
MI_INSECT_PATHOGEN_HAZARD
The percent area of a landscape analysis unit where the USFS FHAAST Insect and Disease Risk Map (NIDRM 2018) is at risk. https://www.fs.usda.gov/foresthealth/publications/fhaast/index.shtml
USFS
Percent
MI_insect_pathogen_hazard
Double
8
0
0
MS_INSECT_PATHOGEN_HAZARD
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_insect_pathogen_hazard
Double
8
0
0
MC_INSECT_PATHOGEN_HAZARD
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_insect_pathogen_hazard
String
20
0
0
MI_VEGETATION_DEPARTURE
Vegetation departure within a TCA landscape ecosystem describes the degree to which the observed proportion (percent, 0-100%) of successional classes (i.e. A through E) derived from Landfire Succession Classes (SClass) 2.2.0 dataset, is departed from the proportions one would expect of the reference condition contained in the Landfire Biophysical Settings (BpS) 2.2.0 dataset at each cell location. Departure is calculated as:
Departure = 100 – Similarity
where Similarity = ∑ (min(Ai,Bi)) over all classes i,…n
The vegetation departure value represents the weighted average (areal extent) of the departure from each Landfire Biophysical Settings (BpS) within each landscape ecosystem. (generated Sept 2022) [citation] Swaty, R.; Blankenship, K.; Hall, K.R.; Smith, J.; Dettenmaier, M.; Hagen, S. Assessing Ecosystem Condition: Use and Customization of the Vegetation Departure Metric. Land 2022, 11, 28. https://doi.org/10.3390/land11010028
USFS
Percent
MI_vegetation_departure
Double
8
0
0
MS_VEGETATION_DEPARTURE
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_vegetation_departure
Double
8
0
0
MC_VEGETATION_DEPARTURE
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_vegetation_departure
String
20
0
0
MI_GRASSLAND_ENCROACHMENT
The percent area of a landscape analysis unit identified as historically being in a grassland condition (LANDFIRE BpS, US_140BPS_20180618) and currently having a conifer vegetation type (USGS NLCD 2021).
USFS
Percent
MI_grassland_encroachment
Double
8
0
0
MS_GRASSLAND_ENCROACHMENT
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_grassland_encroachment
Double
8
0
0
MC_GRASSLAND_ENCROACHMENT
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_grassland_encroachment
String
20
0
0
MI_GRASSLAND_PRODUCTIVITY
The average relative difference of the mean inter-annual variability of vegetation production between a reference time period (1984-2018) and the current time period (most recent 5 years available, 2019-2023).
USFS
Percent
MI_grassland_productivity
Double
8
0
0
MS_GRASSLAND_PRODUCTIVITY
EMDS model continuous score. Values range -1 (very poor condition) to +1 (very good condition).
USFS
Range (-1.0 to 1.0)
MS_grassland_productivity
Double
8
0
0
MC_GRASSLAND_PRODUCTIVITY
EMDS model classified score. Each class equals 0.4 continuous score values. Classes are "Very Good", "Good", "Moderate", "Poor", "Very Poor"
USFS
Condition Class (very good, good, moderate, poor, very poor)
MC_grassland_productivity
String
20
0
0
KPI_CAY_ACTACRES
Total activity acres or acres of selected FACTS activities (non-duplicates, or total extent) that effect individual Terrestrial Condition Assessment (TCA) metrics or indicators in the current assessment year (CAY=2024).
USFS
Acres
KPI_CAY_ActAcres
Double
8
0
0
KPI_CAY_SCORE
Key Performance Indicator Score for the current assessment year (CAY) which is 2024.
Range (0.0 to 2.0)
KPI_CAY_Score
Double
8
0
0
KPI_CAY_CLASS
Classified Key Performance Indicator Score for the current assessment year (CAY=2024).
USFS
KPI Class (improved, maintained, recovering, no work, not enough work, not enough change)
KPI_CAY_Class
String
20
0
0
KPI_CAY_MINUS1_ACTACRES
Total activity acres or acres of selected FACTS activities (non-duplicates, or total extent) that effect individual Terrestrial Condition Assessment (TCA) metrics or indicators one year prior to the current assessment year (CAY=2024, and one year prior=2023).
Acres
KPI_CAY_Minus1_ActAcres
Double
8
0
0
KPI_CAY_MINUS1_SCORE
Key Performance Indicator Score for one year prior to the current assessment year (CAY=2024, and one year prior=2023).
Range (0.0 to 2.0)
KPI_CAY_Minus1_Score
Double
8
0
0
KPI_CAY_MINUS1_CLASS
Classified Key Performance Indicator Score for one year prior to current assessment year (CAY=2024, and one year prior=2023).
KPI Class (improved, maintained, recovering, no work, not enough work, not enough change)
KPI_CAY_Minus1_Class
String
20
0
0
KPI_CAY_MINUS2_ACTACRES
Total activity acres or acres of selected FACTS activities (non-duplicates, or total extent) that effect individual Terrestrial Condition Assessment (TCA) metrics or indicators two years prior to the current assessment year (CAY=2024, and two years prior=2022).
Acres
KPI_CAY_Minus2_ActAcres
Double
8
0
0
KPI_CAY_MINUS2_SCORE
Key Performance Indicator Score for two years prior to the current assessment year (CAY=2024, and two years prior=2022).
Range (0.0 to 2.0)
KPI_CAY_Minus2_Score
Double
8
0
0
KPI_CAY_MINUS2_CLASS
Classified Key Performance Indicator Score for two years prior to current assessment year (CAY=2024, and two years prior=2022).
KPI Class (improved, maintained, recovering, no work, not enough work, not enough change)
KPI_CAY_Minus2_Class
String
20
0
0
KPI_CAY_MINUS3_ACTACRES
Total activity acres or acres of selected FACTS activities (non-duplicates, or total extent) that effect individual Terrestrial Condition Assessment (TCA) metrics or indicators three years prior to the current assessment year (CAY=2024, and three years prior=2021).
Acres
KPI_CAY_Minus3_ActAcres
Double
8
0
0
KPI_CAY_MINUS3_SCORE
Key Performance Indicator Score for three years prior to the current assessment year (CAY=2024, and three years prior=2021).
Range (0.0 to 2.0)
KPI_CAY_Minus3_Score
Double
8
0
0
KPI_CAY_MINUS3_CLASS
Classified Key Performance Indicator Score for three years prior to current assessment year (CAY=2024, and three years prior=2021).
KPI Class (improved, maintained, recovering, no work, not enough work, not enough change)
KPI_CAY_Minus3_Class
String
20
0
0
KPI_CAY_MINUS4_ACTACRES
Total activity acres or acres of selected FACTS activities (non-duplicates, or total extent) that effect individual Terrestrial Condition Assessment (TCA) metrics or indicators four years prior to the current assessment year (CAY=2024, and four years prior=2020).
Acres
KPI_CAY_Minus4_ActAcres
Double
8
0
0
KPI_CAY_MINUS4_SCORE
Key Performance Indicator Score for four years prior to the current assessment year (CAY=2024, and four years prior=2020).
Range (0.0 to 2.0)
KPI_CAY_Minus4_Score
Double
8
0
0
KPI_CAY_MINUS4_CLASS
Classified Key Performance Indicator Score for four years prior to current assessment year (CAY=2024, and four years prior=2020).
KPI Class (improved, maintained, recovering, no work, not enough work, not enough change)
KPI_CAY_Minus4_Class
String
20
0
0
SHAPE
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
EPSG
6.5(3.0.1)
FGDC Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998
Simple
FALSE
0
TRUE
FALSE