LandCart Expansion for Global Range Attributes

The summary for the LandCart Expansion for Global Range Attributes grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the Engineer Research and Development Center, which is the U.S. government agency offering this grant.

LandCart Expansion for Global Range Attributes: ERDC seeks applications for an opportunity to enter into a cooperative agreement for: Global Rangeland Attribute Model. A highly functional, highly accurate rangeland attribute model, LandCart, has been developed for the western United States using a significant and standardized rangeland data set. The overall goal of this agreement is to expand a LandCart type of predictive algorithm to global rangelands. As global rangelands lack the level and quality of data that were available for LandCart development, the first step is determining if the data that exists can be utilized in a manner that is conducive to development of rangeland attribute algorithms. If it is, the next step is utilizing this and supplemental data to develop a global rangeland attribute model. Once a model is completed, validation and model improvements will be required. Finally, model delivery and reporting will ensure technology transfer. Rangeland landscapes cover approximately 50% of Earth's land surface. Knowledge of rangeland attributes at a global scale is important for ecosystem sustainment and sustainable use. The ability to remotely predict these attributes is paramount to planning and management of these critical environments. Spatial and temporal patterns of biomass and cover have been estimated for decades using satellite imagery, which allows for the examination of larger areas with limited field work and the ability to reproduce outputs. Biomass, cover, and height can be estimated or directly measured in the field, but it is impractical for managers to carry out field sampling across extensive rangeland systems, often covering tens of thousands of hectares or more, with adequate temporal frequency and spatial resolution to capture the spatiotemporal heterogeneity that commonly exists across rangeland landscapes. Remote sensing-based methods to produce estimates of rangeland properties at varying scales would be highly desirable to facilitate a more comprehensive understanding of the spatial and temporal trends in rangeland condition. However, global satellite-based predictions applicable to rangeland vegetation properties remain sparse. This new project seeks to expand existing capabilities in the western United States to predict rangeland attributes at a global scale. A highly functional, highly accurate rangeland attribute model, LandCart, has been developed for the western United States using a significant and standardized rangeland data set. The overall goal of this agreement is to expand a LandCart type of predictive algorithm to global rangelands. As global rangelands lack the level and quality of data that were available for LandCart development, the first step is determining if the data that exists can be utilized in a manner that is conducive to development of rangeland attribute algorithms. Funding agency has created a global database of field-based rangeland vegetation measurements. However, it is unknown if this database still possesses the breadth and depth of information required to develop a functional predictive algorithm. Expertise is sought to review the data in the database and make a determination of whether it is adequate, what is required to make it adequate if it is not, or if it cannot be utilized in this capacity in the first year. If it is, the next step is utilizing this and other potential data sources to develop a predictive global rangeland attribute model, with a primary focus on vegetative cover and height prediction in year 2. Once a model is completed, validation and model improvements will be required through a collaborative effort to acquire, analyze, and incorporate field data during the third and fourth years. Finally, model delivery and reporting will ensure technology transfer.