Assessment, Inventory, and Monitoring Strategy
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The objective of the Assessment, Inventory, and Monitoring (AIM) Strategy is to provide a standardized monitoring strategy for assessing natural resource condition and trend on BLM public lands. The AIM Strategy provides quantitative data and tools to guide and justify policy actions, land uses, and adaptive management decisions.
- Structured implementation to guide monitoring program development, implementation, and data use for decision makers
- Standardized field measurements to allow data comparisons through space and time in support of multiple management decisions
- Appropriate sample designs to minimize bias and maximize inference of collected data
- Data management and stewardship to ensure data quality, accessibility, and use
- Integration with remote sensing to optimize sampling and calibrate continuous map products
- Standard workflows and analysis frameworks for using data.
Example management questions addressed by AIM:
- Are management areas attaining BLM Land health Standards?
- What is the distribution of invasive species and where can prioritization of treatment areas occur?
- What is the effectiveness of reclamation or restoration treatments?
- Are we maintaining or improving habitat conditions for species of management concern (e.g., greater sage-grouse, native fishes, and mule deer habitat)?
- What is the effectiveness of land use plans?
- What is the existing condition and trend of resources that may be affected by a proposed action? Is BLM meeting performance measures outlined in the Department of the Interior Strategic Plan?
- Structured Implementation
AIM monitoring starts with identifying clear management questions to inform when, where, and how often to collect data. This and all other steps of AIM implementation are supported by a network of subject matter experts including State Leads, Monitoring Coordinators, and the BLM National Operations Center. Collectively, the AIM Team provides practitioner support with:
- Contracting support for field crew hiring and other services
- Monitoring plan development
- Identification and implementation of appropriate sample designs
- Field methods training
- Data collection, storage, and access solutions
- Data quality assurance and control procedures
- Analysis and reporting tools and support
- Standardized Methods
AIM field methods were developed by a network of BLM experts and partners. The objectives were to ensure usable and defensible data for the BLM, while also standardizing monitoring efforts across agencies and jurisdictions. These dual objectives were achieved by adopting field methods used and tested by multiple agencies and partners throughout the western U.S. and Alaska.
Terrestrial – Designed for upland habitats, the terrestrial methods provide comprehensive information on rangeland vegetative and soil conditions.
- Example indicators derived from field methods: bare ground, vegetation composition, vegetation height, canopy gap, distribution of nonnative/invasive plant species
- Citation: Monitoring Manual for Grassland, Shrubland and Savanna Ecosystems Volume 1
Lotic – Designed to provide quantitative data for wadeable streams and rivers across all BLM lands.
- Example indicators derived from field methods: conductivity, temperature, pool frequency, % fine sediment in streambed substrates, bank stability and cover, floodplain connectivity, macroinvertebrate biological condition
- Citation: BLM AIM National Aquatic Monitoring Framework Protocol for Wadeable Lotic Systems
Riparian and Wetland – Designed for riparian areas, floodplains, and wetlands, the Riparian and Wetland data bridge the information gap between terrestrial and lotic areas. The newest addition to the program, the Riparian and Wetland data collection protocol was piloted in 2019 with expansion in 2020-21.
- Example indicators derived from field methods: Bare ground, vegetation composition, soil characterization, vegetation height, water source, pH, conductivity.
- Appropriate Sample Designs
BLM is actively using the AIM Strategy to inform management decisions at multiple spatial scales from individual restoration projects on up to national level reporting. The AIM Team at the BLM’s National Operations Center provides technical support for the development of appropriate sample designs to match monitoring objectives from targeted sampling to spatially balanced, random sampling.
- Data Management and Stewardship
AIM data are collected using mobile applications, stored in a centralized BLM repository and available to users via web portals and spatial data services. Mobile applications allow for greater integration of QA & QC practices while also making data available quickly. Centralized data storage gives users the ability to analyze AIM data independently or with developed tools supplied by the program.
View and access AIM data
- Integration with Remote Sensing
Available remote sensing datasets are another way that AIM data informs land-management decision-making. For example, LANDFIRE products map vegetation types and many fire-related landscape attributes (https://landfire.gov/data_overviews.php). Fractional vegetation cover maps are increasingly available as well, including RAP (https://rangelands.app/), LandCART (https://landcart.org ), RCMAP Viewer (https://www.mrlc.gov/rangeland-viewer/), Near-Real-Time Cheatgrass (https://www.usgs.gov/centers/eros/science/near-real-time-cheatgrass-monitoring), and more. Practical advice for applying these products in decision-making is available from Allred et al. 2020 and BLM TN456.
Rapid advancement of remote sensing technology combined with on the ground AIM data provides land managers with tools related to:
- Bird’s eye view of vegetation cover
- Landscape trend analyses and monitoring
- Sagebrush availability for sage grouse habitat mapping
- Treatment effectiveness modeling
- Remapping efforts of nationwide landscape datasets
Looking to learn more about the Assessment, Inventory and Monitoring program announcements and highlights? Learn more here:
AIM Announcements and Highlights
Featured Video: Introduction to AIM