NPS Whitebark Pine Mapping

Project ID: P25Ac00427

Federal Agency: National Park Service

Partner Institution: Portland State University

Fiscal Year: 2025

Initial Funding: $99,696

Total Funding: $99,696

Project Type: Research

Principal Investigator: Brunner, Ray

Agreement Technical Representative: Esser, Scott

Abstract: 

Performance Goals – The goal of this project is to model whitebark pine (WBP) distribution and health in seven national parks using the available empirical data to inform stewardship, recovery and compliance actions. Separate models with compatible resolution and response variables will be created by subregion to account for regional differences in WBP habitat and structure.

Project Objectives – Whitebark pine (WBP) occurs in both pure and mixed stands with other conifers, presents as both an upright tree and in krummholz form, and is found in the most remote wilderness areas of the Cascades, Olympics, and Sierra Nevada ranges.

The development of an accurate spatially explicit WBP distribution and health layer at a scale relevant to conservation action poses unique challenges. We propose that this project leverage existing vegetation mapping products from OLYM, MORA, NOCA, CRLA, LAVO, YOSE and SEKI, together with known occurrence data and remotely sensed structural and spectral imagery products, to develop detailed distribution maps of WBP for the participating parks. Specific objectives include 1) obtaining occurrence data and remotely sensed data products, 2) alignment of explanatory data for distribution mapping, selecting variables and evaluating models, development of distribution and health maps for WBP in each park, review and assessment of maps, and finalize map accuracy, model performance, and provide reports. Products include High accuracy maps and spatial data products of WBP presence and heath for all seven parks. This will include confidence levels for location and health status with explanations of categorical thresholds of confidence that are highly likely to occur, medium likelihood, etc., a full report on project including finalized maps and data and specifics on model performance, and analytical methods used with modelling code.