I am Bryce Frank.

I am also a research scientist with the Pacific Northwest Research Station of the United States Forest Service. I conduct research across the broad domain of forest biometrics, with a particular attachment to the Forest Inventory and Analysis program .

Bryce Frank

Research Areas

Statistical methods in forestry

Statistical estimation and inference for forest inventories: sampling designs, model-based methods, and small-area estimators that keep the national inventory current at ever finer scales.

STATStatistical methods in forestry

Remote sensing of the forested environment

Turning lidar and other remote sensing data into fine-scale forest inventory products through statistical modeling.

RSRemote sensing of the forested environment

Allometrics, growth, and yield

Improving how trees are measured and modeled, from field protocols and measurement error to growth and allometric models.

AGYAllometrics, growth, and yield

Other Foci

Allometric Models 3 Biomass 1 Classification and Regression Trees 1 Fire and Fuels 4 Fire Effects 4 Forest Management 15 Forest Measurements 4 Forest Monitoring 6 Forest Productivity 2 Forest Sampling 1 Geostatistics 1 Growth and Yield 1 kNN Imputation 1 Lidar 16 Measurement Error 1 Photogrammetry 4 Quality Assurance 1 Random Forests 1 Sampling Designs 4 Site Index 2 Small Area Estimation 10 Spatial Models 1 Tree Defect 1 Tree Detection 1 Tropical Forests 1

Recent Research

All research »

  1. Estimating crown fuels in Pinus pinaster Aiton forests of interior Spain using remote sensing and national forest inventory data

    Mauro, Francisco, Alcasena, F., Domingo-Ruiz, D., Frank, B., Fekety, P., Garcia-Gomez, R., Gonzalez-Garcia, C., Gonzalez-Mesquida, J. B., Gomez-Almaraz, C., Gomez-Roux, M., Hudak, A., Martin-Fernandez, S., Manzanera, J. A., Sanchez-Lopez, N., Vogeler, J. 2026. Forest Systems.

    RSRemote sensing of the forested environment

  2. Post-stratification in the Forest Inventory and Analysis Program: The Case of the Pacific States and Affiliated Islands

    Frank, Bryce, Kuegler, O., Andersen, H.-E., Gray, A. N., Cahoon, S. M. 2026. General Technical Report.

    STATStatistical methods in forestry

  3. Combining multitemporal remote sensing data and repeated ground observations from the Spanish National Forest Inventory to inform uneven-aged management of Juniperus thurifera L. forests.

    Mauro, Francisco, González-Mesquida, J. B., Gomez-Roux, M., Domingo-Ruiz, D., Gómez, C., Caiza-Morales, L., Rodríguez-Puerta, F., Águeda, B., Frank, B., Filippelli, S., Breidenbach, J., Hudak, A., Temesgen, H., Fekety, P., Manzanera, J. A., Candel-Pérez, D. 2025. Forestry.

    STATStatistical methods in forestryRSRemote sensing of the forested environment

  4. Comparison of digital aerial photogrammetry, lidar, and Sentinel-2 for evaluating forest fire effects.

    Frank, Bryce, Strunk, J. L., Fried, J. S., Wolken, K., McKenzie, S. C. 2025. Forest Ecology and Management.

    RSRemote sensing of the forested environment

  5. Roles of Growth and Yield Models for Informed Management Decisions in the Pacific Northwest United States.

    Joo, Sukhyun, Hailemariam, T., Frank, B., Weiskittel, A. R., Reimer, D. 2025. Journal of Forestry.

    AGYAllometrics, growth, and yield