Location: Southwest Watershed Research Center
Project Number: 2022-13610-012-46-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 1, 2020
End Date: Sep 30, 2021
The primary Long-Term Agroecosystem Research (LTAR) goal is “sustainable intensification”. The Walnut Gulch Experimental Watershed (WGEW) LTAR site is an excellent research platform to study rangeland sustainability (especially related to soil and water), but not intensification of production. WGEW contains parts of 5 active ranches, each of which also contain grazed areas that are outside the watershed, and the ranchers do not share animal numbers, weight gains, or grazing regimes. In contrast, 80 kilometers to the west, the Santa Rita Experimental Range (SRER) has similar ecological conditions as WGEW, but is on land owned by Arizona and managed for research purposes by the University. The lone rancher on the SRER shares the details of animal numbers and weight gains, with grazing timing and intensity mutually agreed upon. The SRER is a National Ecological Observatory Network (NEON) Core site, so it will benefit from annual, high resolution NEON Airborne Observation Platform (AOP) aerial photography with coincident hyperspectral and lidar imagery. The overall objective of this project is to develop a common set of field, drone, airplane, and satellite imagery processing protocols to better understand ecological and rangeland management processes, especially related to vegetation production and lifeform, on both the SRER and WGEW sites.
In Phase 1 of this project (2015-2020) we developed methods to better understand vegetation across research sites including: scale lidar data on rangeland vegetation, quantify forage utilization with drone collected imagery, collect and process georegistered drone imagery across large areas, map common ecological sites and states, and classify vegetation and bare soil. We anticipate that both the Santa Rita Experimental Range and the Walnut Gulch Experimental Watershed will have an unprecedented opportunity to characterize the vegetation community and production across the landscape using data from the NEON AOP. However, there are a number of tasks to accomplish prior to applying the NEON AOP data: 1) Continue SRER and WGEW vegetation monitoring and supplement with additional georegistration of individual plants and mapping of vegetation communities of interest; 2) Develop and apply algorithms to analyze remotely sensed data using machine learning at various temporal and spatial scales using Google Earth Engine, the ARS SCINet platform, the University of High Performance Computing Platform, and other Southwest Watershed Research Center and University of Arizona information technology resources. With this information in hand, using both NEON AOP data and UAV and other imagery, we should be able to quantify vegetation production and consumption by ecological sites and states within pastures across both study areas.