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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Research Project #438062

Research Project: Streamlining the Use of Monitoring Data in Multiple Resource Decision Making

Location: Range Management Research

Project Number: 3050-11210-009-18-I
Project Type: Interagency Reimbursable Agreement

Start Date: Jul 1, 2020
End Date: Jun 30, 2025

Objective:
The objectives of this project are to support the continued implementation and refinement of the BLM’s AIM Strategy through: 1. Supporting field implementation of AIM monitoring projects – This objective includes providing scientific and technical support to the BLM on the design and implementation of AIM monitoring programs, supporting field data collection through continued development and support of desktop and mobile monitoring databases and tools, developing quality assurance and quality control (QA/QC) analytical tools, and supporting BLM staff in the analysis of AIM monitoring project data and reporting. 2. Developing and supporting consistent monitoring protocols – This objective includes documentation of the core monitoring methods through a revised edition of the Monitoring Manual for Shrubland, Grassland, and Savanna Ecosystems to support consistent monitoring within BLM and across other agencies; supporting BLM’s implementation of remote sensing technologies and models to support field-based monitoring and for monitoring at larger scales; working with the AIM program to increase consistency of monitoring protocols across BLM programs; and working jointly with BLM and other U.S. land management agencies to promote consistency in monitoring across all U.S. rangelands. Develop workflows integrating ESD’s and EDIT and Rangeland Inventory & Monitoring App based on LandPKS into BLM’s decision-making process. 3. Training BLM staff and partners in monitoring protocols, project implementation, and data analysis – This objective includes the development of training programs and materials for a) correctly applying AIM core field protocols to collect high-quality monitoring data, b) design and implementation of AIM monitoring projects, and c) data analysis and application for decision making; and conducting AIM trainings jointly with AIM program staff. 4. Developing and implementing workflows and tools for monitoring data analysis and reporting – This objective includes providing scientific and technical support to the BLM on the development and documentation of approaches and tools for analyzing and reporting on monitoring data for multiple objectives to support management decision making within BLM. 5. Integrating emerging science on land health monitoring at multiple scales into BLM decision making – This objective includes conducting research into: novel approaches for monitoring land health, analytical techniques for land health monitoring at multiple scales, compatibility and combination of monitoring techniques, development of tools that integrate monitoring indicators to value-add information about land health, and approaches for using monitoring data to support BLM decision making.

Approach:
ARS will support the BLM by completing the following tasks: 1. Support field implementation of AIM monitoring projects. a. Support monitoring project design and implementation for AIM-related projects as needed. b. Continue to develop and support software packages for monitoring data indicator calculations and to facilitate integration of monitoring data into BLM’s national monitoring database. c. Support the BLM on the development, support, and implementation of tools for designing monitoring projects and tracking their implementation in consistent and well-documented ways. d. Support and maintain the LandscapeToolbox.org family of websites for organizing and disseminating rangeland monitoring content and information related to AIM monitoring. 2. Developing and supporting consistent monitoring protocols a. Coordinate and promote adoption of consistent, core monitoring protocols within BLM and across U.S. land management agencies and other organizations. b. Edit, publish (paper and electronic), and maintain (electronic) the second edition of the Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems which documents the AIM terrestrial core methods. c. Continue work in evaluating more cost-effective and reliable methods for increasing the accuracy and precision of AIM core and supplemental indicators and improve the documentation and implementation of these methods. d. Develop and implement tools for quantifying and improving quality of AIM core field data. e. Support the integration of AIM monitoring with other datasets and indicators by: i. Providing necessary scientific and technical support to the BLM and work with the BLM to develop a remote sensing strategy for AIM to identify remote-sensing monitoring indicators and opportunities to integrate field and remote sensing monitoring at different scales. ii. Develop analytical frameworks and workflows for leveraging multi-temporal data to monitor changes in large landscapes and aid in interpretation of AIM core field data. iii. Develop workflows to integrate physically and empirically modelled indicators (e.g., aeolian dust flux, soil erosion by water) with existing core indicators. iv. Provide collaboration platforms for integrating AIM and AIM-compatible data. 3. Train BLM staff and partners in monitoring protocols and project implementation a. ARS will continue to support the BLM in the development of training materials (e.g., websites, manuals, videos) for field trainings in the BLM Core indicators and methods and workshops on AIM project design and implementation. b. ARS will contribute staff and scientific and technical expertise to support BLM trainings for BLM and partner personnel that will be collecting AIM data and BLM personnel responsible for developing and supporting AIM monitoring projects. Trainings will focus on: i. Design and implementation of AIM projects. ii. Implementation of methods following approved protocols. iii. Identification ecological sites and documentation of site characteristics. iv. Data QA/QC procedures. v. Support and feedback on data collection technologies. vi. Analysis and interpretation of AIM data.