Location: Range Management ResearchTitle: High quality data: An evaluation of AIM data quality and data quality procedures Author
|Burnett, Sarah - Bureau Of Land Management|
|Cappuccio, Nicole - Bureau Of Land Management|
|Courtwright, Jennifer - Utah State University|
Submitted to: Society for Range Management Meeting Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 10/23/2017
Publication Date: 1/28/2018
Citation: Mccord, S.E., Burnett, S., Cappuccio, N., Courtwright, J. 2018. High quality data: An evaluation of AIM data quality and data quality procedures [abstract]. 2018 Conference of The Society for Range Management. January 28-February 2, 2018. Sparks, Nevada.
Technical Abstract: The goal of every monitoring program is to collect high-quality data which can then be used to provide information to decision makers. The Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) program is one such data set which provides rangeland status, condition, and trend information across BLM rangelands. These points represent aquatic and terrestrial resources across the western United States and Alaska. To date there are over 18,000 terrestrial points and 1400 aquatic points. While these data were collected for specific objectives, they are available for use by other BLM resource managers, other agencies and academic institutions to meet multiple resource questions and objectives. The broad utility of these data is due to the core methods and protocols employed during data collection as well as the quality assurance and quality control protocols employed by the AIM program. Here we evaluate the steps AIM takes to ensure quality, including project planning, core method implementation, observer training and calibration, electronic data capture, automated and manual data checks, and database structures. We describe the terrestrial database, TerrADat, and aquatic database, AquADat, and how these databases can be accessed by non-BLM users. Known quality procedures and evaluation measures improve the AIM data and can help users understand how and when the AIM data or data procedures may be useful to current and future applications.