The purpose of the project is to collect and analyze high-resolution hyperspectral and LiDAR data to map the severity of drought effects on mature giant sequoia trees and surrounding forest, assess confidence in the results using available ground data, and evaluate next steps to improve methodology. Investigators from Carnegie Institution of Washington, NPS staff, and other partners will collaborate to accomplish the following project objectives. This agreement initially funds Phase 1 work, but may be modified to add future phases, subject to the availability of funding and satisfactory progress of project work. Please see attached work plan. Phase 1 objectives: 1. Contribute state-of -the-art, high-resolution hyperspectral and LiDAR imagery data collected during drought conditions from giant sequoia mixed conifer forests (i.e., study areas identified in work plan). 2. Analyze these remotely sensed data to produce high-resolution moisture stress indices for mature giant sequoia trees and surrounding forest. The indices calculated from the high-resolution imagery will provide information directly related to water content in the canopy, canopy greenness and canopy structure. Map these indices across the study area. 3. Determine relationships between remotely sensed canopy anomalies and ground-based observations of canopy drought stress. 4. Interpret the findings and confidence. Understanding the findings will rely on understanding the type and extent of drought impacts on the forest canopy with respect to the anomalies in the mapped data products. 5. Assess prospects for the future of mapping grove moisture stress vulnerabilities. Future phase objectives: 6. Objectives are to be determined based on project results, availability of other relevant data, and funding availability. These objectives could include testing and applying improved methodology or extending geographic or temporal scales of measurements and analyses.