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 Fire and Fuels

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 National Forest Inventory

3. Tree growth, mortality, and climatic water deficit in west-coast states, USA

   Groom, Jeremiah, Frank, B. 2026. SSRN.

   STATStatistical methods in forestry Forest Management

4. 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. doi:10.1093/forestry/cpaf080

   STATStatistical methods in forestryRSRemote sensing of the forested environment Lidar Multispectral

5. 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. doi:10.1016/j.foreco.2025.123002

   RSRemote sensing of the forested environment Fire and Fuels Lidar Photogrammetry Multispectral

6. 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. doi:10.1007/s44392-025-00041-0

   AGYAllometrics, growth, and yield Growth and Yield Forest Management

7. A method for updating variable radius plot surveys.

   Frank, Bryce, Mauro, F., Harrington, C. A., Ford, K. R. 2024. Canadian Journal of Forest Research. doi:10.1139/cjfr-2024-0050

   STATStatistical methods in forestry Sampling Designs

8. Visual survey protocol framework for western North American freshwater mussels.

   Blevins, Emilie, Maine, A., Frank, B., Erhardt, J., Nemeth, D., Smith, A., Miller, S., Fetters, J., Seilo, Z., Newlon, C., Moss, J., Adams, B. 2024. Xerces Society for Invertebrate Conservation. doi:10.13140/RG.2.2.18433.83042

   Sampling Designs

9. Sptotal: an R package for predicting totals and weighted sums from spatial data

   Higham, Matt, Ver Hoef, J., Frank, B., Dumelle, M. 2023. The Journal of Open Source Software. doi:10.21105/joss.05363

   STATStatistical methods in forestry Spatial Models

10. Comparison of variance estimators for systematic environmental sample surveys: considerations for post-stratified estimation.

    Frank, Bryce, Monleon, V. J. 2021. Forests. doi:10.3390/f12060772

    STATStatistical methods in forestry Sampling Designs

11. Imputation to predict height to crown base for trees with predicted heights.

    Allensworth, Elijah, Temesgen, H., Frank, B., Gray, A. 2021. Forest Ecology and Management. doi:10.1016/j.foreco.2021.119574

    AGYAllometrics, growth, and yield Allometric Models

12. Regional modeling of forest fuels and structural attributes using airborne laser scanning data in Oregon

    Mauro, Francisco, Hudak, A., Fekety, P., Frank, B., Temesgen, H., Bell, D., Gregory, M., McCarley, R. 2021. Remote Sensing. doi:10.3390/rs13020261

    RSRemote sensing of the forested environment Lidar Fire and Fuels Small Area Estimation

13. Using Fay-Herriot models and variable radius plot data to develop a stand-level inventory and update a prior inventory in the Western Cascades, OR, United States

    Hailemariam, Temesgen, Mauro, F., Hudak, A. T., Frank, B., Monleon, V. J., Fekety, P., Palmer, M., Bryant, T. 2021. Frontiers in Forests and Global Change. doi:10.3389/ffgc.2021.745916

    STATStatistical methods in forestryRSRemote sensing of the forested environment Lidar Small Area Estimation Sampling Designs

14. Model-based estimation of forest inventory attributes using lidar: a comparison of the area-based and semi-individual tree crown approaches.

    Frank, Bryce, Mauro, F., Temesgen, H. 2020. Remote Sensing. doi:10.3390/rs12162525

    STATStatistical methods in forestryRSRemote sensing of the forested environment Small Area Estimation Lidar

15. Analysis of classification methods for identifying stands for commercial thinning using LiDAR.

    Frank, Bryce, Mauro, F., Temesgen, H., Ford, K. 2019. Canadian Journal of Remote Sensing. doi:10.1080/07038992.2019.1670051

    RSRemote sensing of the forested environment Forest Management Lidar

16. Estimation of changes of forest structural attributes at three different spatial aggregation levels in Northern California using multitemporal LiDAR.

    Mauro, Francisco, Ritchie, M., Wing, B., Frank, B., Monleon, V. J., Temesgen, H., Hudak, A. 2019. Remote Sensing. doi:10.3390/rs11080923

    RSRemote sensing of the forested environment Lidar Small Area Estimation

17. Prediction of diameter distributions and tree-lists in southwestern Oregon using LiDAR and stand-level auxiliary information.

    Mauro, Francisco, Frank, B., Monleon, V. J., Temesgen, H., Ford, K. 2019. Canadian Journal of Forest Research. doi:10.1139/cjfr-2018-0332

    RSRemote sensing of the forested environment Lidar Forest Management

18. Taxon-specific modeling systems for improving reliability of tree aboveground biomass and its components estimates in tropical dry dipterocarp forests.

    Huy, Bao, Tinh, N. T., Poudel, K., Frank, B., Temesgen, H. 2019. Forest Ecology and Management. doi:10.1016/j.foreco.2019.01.038

    AGYAllometrics, growth, and yield Biomass Allometric Models

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