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This dataset was created to monitor the status, condition and trend of national BLM resources in accordance with BLM policies. The methodology used for the collection of these data can be found on landscapetoolbox.org and the Monitoring Manual, 2nd Edition. These data should not be used for statistical or spatial inferences without knowledge of how the sample design was drawn or without calculating spatial weights for the points based on the sample design. These include monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic...
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This interactive web application viewer shows locations where the BLM collects information about vegetation and soils to help us understand and monitor the health of terrestrial ecosystems such as sagebrush-steppe.
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This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic sampling design, standard core indicators and methods, electronic data capture and management, and integration with remote sensing. Attributes include the BLM aquatic core indicators: pH, conductivity, temperature, pool depth, length, frequency, streambed particles sizes, bank stability and cover, floodplain connectivity, large woody debris, macroinvertebrate biological integrity, ocular estimates of vegetative type, cover, and structure...
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This web tool generates monitoring locations that are spatially balanced across a landscape. First, it will display your study area in the Plot tab. From there, you can request the number of spatially balanced points you wish to have created, by strata. Oversampled points can also be requested. After selecting 'generate points', the tool will create the points and display them for you, both in the plot, and in table format. Point coordinates in the table are in the same projection as your input dataset.
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This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the Interpreting Indicators for Rangeland Health (IIRH) method. The IIRH method is a qualitative assessment of rangeland health, defined as "the degree to which the integrity of the soil, vegetation, water, and air, as well as the ecological processes of the rangeland ecosystem are balanced and sustained." Attributes include the final rating for IIRH indicators: hydrologic function, biotic integrity, and soil/site stability (see Entity/Attribute Section for exact details on attributes). Data were collected and managed by BLM Field Offices,...
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This tool enables calculation of monitoring sample sufficiency based on observed and desired minimum detectable difference (MDD or effect size) from a set of sample data. This tool can also be used to estimate sample sizes necessary to meet monitoring objectives from pilot data.
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This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic sampling design, standard core indicators and methods, electronic data capture and management, and integration with remote sensing. Attributes include indicator summaries (all first hit percentages) needed for remote sensing purposes (see Entity/Attribute Section for exact details on attributes). Data were collected and managed by BLM Field Offices, BLM Districts, and/or affiliated field crews with support from the BLM National Operations...
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The Horwitz-Thompson Estimator tool can be used to produce indicator estimates from monitoring data. Estimates can be for 1) a study area without strata or 2) within a stratum. Sample points can be unweighted (most typical) or individually weighted (i.e., unequal probability sampling).
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This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic sampling design, standard core indicators and methods, electronic data capture and management, and integration with remote sensing. Attributes include the BLM terrestrial core indicators: bare ground, vegetation composition, plant species of management concern, non-native invasive species, and percent canopy gaps (see Entity/Attribute Section for exact details on attributes). Data were collected and managed by BLM Field Offices, BLM Districts,...
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U.S. Geological Survey (USGS) scientists conducted field data collection efforts during the weeks of September 9-13 and November 18-22, 2019, using a combination of technologies to map and validate topography, vegetation, and features in two areas of interest (AOI's) in north central Colorado. The western AOI included land managed by the Bureau of Land Management and the U.S. Forest Service. The eastern AOI included agricultural and urban areas. The work was initiated as an effort to test and evaluate the Leica Geosystems CountryMapper sensor. The CountryMapper is a hybrid sensor that collects imagery and light detection and ranging (lidar) data simultaneously. The CountryMapper has the potential to collect data...
Tags: 3D Elevation Program, 3DEP, AIM, Arapahoe National Forest, Assessment, Inventory, and Monitoring Plot, All tags...
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This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic sampling design, standard core indicators and methods, electronic data capture and management, and integration with remote sensing. Attributes include the BLM aquatic core indicators: pH, conductivity, temperature, pool depth, length, frequency, streambed particles sizes, bank stability and cover, floodplain connectivity, large woody debris, macroinvertebrate biological integrity, ocular estimates of vegetative type, cover, and structure...
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The Escalante Watershed case study describes the application and integration of multi-scale geospatial data from the Colorado Plateau and Utah Landscape Assessment 2014 (Rapid Ecoregional Assessment or REA), Assessment, Inventory, and Monitoring (AIM), and Riparian Condition Assessment Tool (R-CAT) to help answer management questions. While REA provides broad-scale data and AIM provides field-scale point data, R-CAT, a geospatial tool designed at Utah State University, provides the middle ground reach-scale data. The Escalante watershed is a complex area with land managed by BLM, USFS, and NPS as well as privately owned land.


    map background search result map search result map BLM NOC Integrating Multiscale Assessment Data to Inform Resource Management: The Escalante Watershed Case Study Story Map BLM AIM Assessment, Inventory, and Monitoring Efforts to Date Interactive Map (Web Application Viewer) BLM AIM TerrADat TerrestrialAIM point BLM AIM TerrADat RemoteSensing point BLM AIM TerrADat RangelandHealth point BLM AIM AquADat Point BLM AIM Terrestrial Data (TerrADat) Map Service BLM AIM Aquatic Data (AquADat) Map Service CountryMapper Sensor Validation Survey Data CountryMapper Sensor Validation Survey Data BLM NOC Integrating Multiscale Assessment Data to Inform Resource Management: The Escalante Watershed Case Study Story Map BLM AIM TerrADat RangelandHealth point BLM AIM TerrADat TerrestrialAIM point BLM AIM TerrADat RemoteSensing point BLM AIM Terrestrial Data (TerrADat) Map Service BLM AIM AquADat Point BLM AIM Aquatic Data (AquADat) Map Service BLM AIM Assessment, Inventory, and Monitoring Efforts to Date Interactive Map (Web Application Viewer)