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GIScience & Remote Sensing Volume 61, 2024 - Issue 1
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Research Article

Ecological and climatic transferability of airborne lidar-driven aboveground biomass models in Piñon-Juniper woodlands

Jessie F. Eastburna Department of Geography, University of Utah, Salt Lake City, UT, USACorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-1923-2026View further author information
ORCID Icon,
Michael J. Campbella Department of Geography, University of Utah, Salt Lake City, UT, USAView further author information
,
Philip E. Dennisona Department of Geography, University of Utah, Salt Lake City, UT, USAView further author information
,
William Rl Andereggb School of Biological Sciences, University of Utah, Salt Lake City, UT, USA;c Wilkes Center for Climate Science and Policy, University of Utah, Salt Lake City, UT, USAView further author information
,
Kevin J. Barrettd Colorado Forest Restoration Institute, Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO, USAView further author information
,
Patrick A. Feketye Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO, USAView further author information
,
Samuel W. Flakef Department of Natural Resources and Environmental Science, University of Nevada – Reno, Reno, NV, USA;g Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USAView further author information
,
David W. Huffmanh Ecological Restoration Institute, Northern Arizona University, Flagstaff, AZ, USAView further author information
,
Steven A. Kannenbergi Department of Biology, West Virginia University, Morgantown, WV, USAView further author information
,
Kelly L. Kerrj Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, USAView further author information
,
Andrew J. Sánchez Meadorh Ecological Restoration Institute, Northern Arizona University, Flagstaff, AZ, USA;k School of Forestry, Northern Arizona University, Flagstaff, AZ, USAView further author information
&
Jody C. Vogelere Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO, USAView further author information
 show all
Article: 2363577 | Received 31 Jan 2024, Accepted 30 May 2024, Published online: 10 Jun 2024

References

  • Ahlström, A., M. R. Raupach, G. Schurgers, B. Smith, A. Arneth, M. Jung, M. Reichstein, et al. 2015. “Carbon Cycle. The Dominant Role of Semi-Arid Ecosystems in the Trend and Variability of the Land CO₂ Sink.” Science 348 (6237): 895–24. https://doi.org/10.1126/SCIENCE.AAA1668.
  • Anderegg, W. R. L., J. A. Hicke, R. A. Fisher, C. D. Allen, J. Aukema, B. Bentz, S. Hood, et al. 2015. “Tree Mortality from Drought, Insects, and Their Interactions in a Changing Climate.” New Phytologist 208 (3): 674–683. https://doi.org/10.1111/nph.13477.
  • Ayrey, E., D. J. Hayes, S. Fraver, J. A. Kershaw, and A. R. Weiskittel. 2019. “Ecologically-Based Metrics for Assessing Structure in Developing Area-Based, Enhanced Forest Inventories from LiDAR.” Canadian Journal of Remote Sensing 45 (1): 88–112. https://doi.org/10.1080/07038992.2019.1612738.
  • Blackburn, R. C., R. Buscaglia, and A. J. Sánchez Meador. 2021. “Mixtures of Airborne Lidar-Based Approaches Improve Predictions of Forest Structure.” Canadian Journal of Forest Research 51 (8): 1106–1116. https://doi.org/10.1139/cjfr-2020-0506.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
  • Breshears, D. D., N. S. Cobb, P. M. Rich, K. P. Price, C. D. Allen, R. G. Balice, W. H. Romme, et al. 2005. “Regional Vegetation Die-Off in Response to Global-Change-Type Drought.” Proceedings of the National Academy of Sciences 102 (42): 15144–15148. https://doi.org/10.1073/pnas.0505734102.
  • Campbell, M. J., P. E. Dennison, K. L. Kerr, S. C. Brewer, and W. R. L. Anderegg. 2021. “Scaled Biomass Estimation in Woodland Ecosystems: Testing the Individual and Combined Capacities of Satellite Multispectral and Lidar Data.” Remote Sensing of Environment 262 (12511): 112511. https://doi.org/10.1016/j.rse.2021.112511.
  • Campbell, M. J., P. E. Dennison, J. W. Tune, S. A. Kannenberg, K. L. Kerr, B. F. Codding, and W. R. L. Anderegg. 2020. “A Multi-Sensor, Multi-Scale Approach to Mapping Tree Mortality in Woodland Ecosystems.” Remote Sensing of Environment 245:111853. https://doi.org/10.1016/J.RSE.2020.111853.
  • Campbell, M. J., and J. F. Eastburn. 2024. “Piñon-Juniper Woodland Species-Based Plot-Level Aboveground Biomass Dataset [ dataset]. https://doi.org/10.17605/OSF.IO/8Q9Y5.
  • Campbell, M. J., J. F. Eastburn, K. A. Mistick, A. M. Smith, and A. E. L. Stovall. 2023. “Mapping Individual Tree and Plot-Level Biomass Using Airborne and Mobile Lidar in piñon-Juniper Woodlands.” International Journal of Applied Earth Observation and Geoinformation 118 (103232): 103232. https://doi.org/10.1016/j.jag.2023.103232.
  • Cao, L., J. Pan, R. Li, J. Li, and Z. Li. 2018. “Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China.” Remote Sensing 10 (4): 532. https://doi.org/10.3390/rs10040532.
  • Chave, J., R. Condit, S. Aguilar, A. Hernandez, S. Lao, R. Perez, Y. Malhi, and O. L. Phillips. 2004. “Error Propagation and Sealing for Tropical Forest Biomass Estimates.” Philosophical Transactions of the Royal Society B: Biological Sciences 359 (1443): 409–420. https://doi.org/10.1098/rstb.2003.1425.
  • Chave, J., S. Davies, O. Phillips, S. Lewis, P. Sist, D. Schepaschenko, J. Armston, T. Baker, D. Coomes, and M. Disney. 2019. “Ground Data Are Essential for Biomass Remote Sensing Missions.” Surveys in Geophysics 40 (4): 863–880. https://doi.org/10.1007/s10712-019-09528-w.
  • Chen, L., C. Ren, G. Bao, B. Zhang, Z. Wang, M. Liu, W. Man, and J. Liu. 2022. “Improved Object-Based Estimation of Forest Aboveground Biomass by Integrating LiDAR Data from GEDI and ICESat-2 with Multi-Sensor Images in a Heterogeneous Mountainous Region.” Remote Sensing 14 (12): 2743. https://doi.org/10.3390/rs14122743.
  • Chojnacky, D. C., L. S. Heath, and J. C. Jenkins. 2014. “Updated Generalized Biomass Equations for North American Tree Species.” Forestry 87 (1): 129–151. https://doi.org/10.1093/forestry/cpt053.
  • Chojnacky, D. C., and P. Rogers. 1999. “Converting Tree Diameter Measured at Root Collar to Diameter at Breast Height.” Western Journal of Applied Forestry 14 (1): 14–16. https://doi.org/10.1093/wjaf/14.1.14.
  • Core Team, R. 2021. “R: A Language and Environment for Statistical Computing.” Computer Software. R Foundation for Statistical Computing, Vienna, Austria.https://www.R-project.org/.
  • Cunliffe, A. M., C. D. McIntire, F. Boschetti, K. J. Sauer, M. Litvak, K. Anderson, and R. E. Brazier. 2020. “Allometric Relationships for Predicting Aboveground Biomass and Sapwood Area of Oneseed Juniper (Juniperus Monosperma) Trees.” Frontiers in Plant Science 11:94. https://doi.org/10.3389/fpls.2020.00094.
  • Cutler, D. R., T. C. Edwards, K. H. Beard, A. Cutler, K. T. Hess, J. Gibson, and J. J. Lawler. 2007. “Random Forests for Classification in Ecology.” Ecology 88 (11): 2783–2792. https://doi.org/10.1890/07-0539.1.
  • Dubayah, R., J. B. Blair, S. Goetz, L. Fatoyinbo, M. Hansen, S. Healey, M. Hofton, et al. 2020. “The Global Ecosystem Dynamics Investigation: High-Resolution Laser Ranging of the Earth’s Forests and Topography.” Science of Remote Sensing: 1(1000022. https://doi.org/10.1016/j.srs.2020.100002.
  • Eisfelder, C., C. Kuenzer, and S. Dech. 2012. “Derivation of Biomass Information for Semi-Arid Areas Using Remote-Sensing Data.” International Journal of Remote Sensing 33 (9): 2937–2984. https://doi.org/10.1080/01431161.2011.620034.
  • Ernst-Brock, C., L. Turner, R. J. Tausch, and E. A. Leger. 2019. “Long-Term Vegetation Responses to Pinyon-Juniper Woodland Reduction Treatments in Nevada, USA.” Journal of Environmental Management 242:315–326. https://doi.org/10.1016/j.jenvman.2019.04.053.
  • Fekety, P. A., M. J. Falkowski, A. T. Hudak, T. B. Jain, and J. S. Evans. 2018. “Transferability of Lidar-Derived Basal Area and Stem Density Models within a Northern Idaho Ecoregion.” Canadian Journal of Remote Sensing 44 (2): 131–143. https://doi.org/10.1080/07038992.2018.1461557.
  • Filippelli, S. K., M. J. Falkowski, A. T. Hudak, P. A. Fekety, J. C. Vogeler, A. H. Khalyani, B. M. Rau, and E. K. Strand. 2020. “Monitoring Pinyon-Juniper Cover and Aboveground Biomass Across the Great Basin.” Environmental Research Letters 15 (2): 025004. https://doi.org/10.1088/1748-9326/AB6785.
  • Flake, S. W., and P. J. Weisberg. 2019. “Fine‐Scale Stand Structure Mediates Drought‐Induced Tree Mortality in Pinyon–Juniper Woodlands.” Ecological Applications 29 (2): e01831. https://doi.org/10.1002/eap.1831.
  • Floyd, M. L., D. D. Hanna, and W. H. Romme. 2004. “Historical and Recent Fire Regimes in Piñon-Juniper Woodlands on Mesa Verde, Colorado, USA.” Forest Ecology and Management 198 (1–3): 269–289. https://doi.org/10.1016/j.foreco.2004.04.006.
  • Gaylord, M. L., T. E. Kolb, W. T. Pockman, J. A. Plaut, E. A. Yepez, A. K. Macalady, R. E. Pangle, and N. G. Mcdowell. 2013. “Drought Predisposes piñon–Juniper Woodlands to Insect Attacks and Mortality.” New Phytologist 198 (2): 567–578. https://doi.org/10.1111/NPH.12174.
  • Genuer, R., J.-M. Poggi, and C. Tuleau-Malot. 2015. VSURF: An R Package for Variable Selection Using Random Forests. http://CRAN.R-project.org/package=VSURF.
  • Gleason, C. J., and J. Im. 2012. “Forest Biomass Estimation from Airborne LiDAR Data Using Machine Learning Approaches.” Remote Sensing of Environment 125:80–91. https://doi.org/10.1016/j.rse.2012.07.006.
  • Greenwood, D. L., and P. J. Weisberg. 2008. “Density-Dependent Tree Mortality in Pinyon-Juniper Woodlands.” Forest Ecology and Management 255 (7): 2129–2137. https://doi.org/10.1016/j.foreco.2007.12.048.
  • Grier, C. C., K. J. Elliott, and D. G. McCullough. 1992. “Biomass Distribution and Productivity ofPinus Edulis—Juniperus Monosperma Woodlands of North-Central Arizona.” Forest Ecology and Management 50 (3–4): 331–350. https://doi.org/10.1016/0378-1127(92)90346-B.
  • Grüner, E., T. Astor, and M. Wachendorf. 2021. “Prediction of Biomass and N Fixation of Legume–Grass Mixtures Using Sensor Fusion.” Frontiers in Plant Science 11:603921. https://doi.org/10.3389/fpls.2020.603921.
  • Huang, C. Y., G. P. Asner, R. E. Martin, N. N. Barger, and J. C. Neff. 2009. “Multiscale Analysis of Tree Cover and Aboveground Carbon Stocks in Pinyon–Juniper Woodlands.” Ecological Applications 19 (3): 668–681. https://doi.org/10.1890/07-2103.1.
  • Huffman, D. W., P. Z. Fulé, J. E. Crouse, and K. M. Pearson. 2009. “A Comparison of Fire Hazard Mitigation Alternatives in Pinyon–Juniper Woodlands of Arizona.” Forest Ecology and Management 257 (2): 628–635. https://doi.org/10.1016/j.foreco.2008.09.041.
  • Huffman, D. W., M. T. Stoddard, J. D. Springer, J. E. Crouse, A. J. Sánchez Meador, and S. Nepal. 2019. “Stand Dynamics of Pinyon-Juniper Woodlands After Hazardous Fuels Reduction Treatments in Arizona.” Rangeland Ecology & Management 72 (5): 757–767. https://doi.org/10.1016/j.rama.2019.05.005.
  • Isenburg, M. 2006. “LAStools—Efficient LiDAR Processing Software (No. 211112).” Computer Software.
  • Jacobs B. F. 2008. “Southwestern, U.S. juniper savanna and pinon-juniper woodland communities: Ecological history and natural range of variability“ In Ecology, Management, and Restoration of Pinon-Juniper and Ponderosa Pine Ecosystems: Combined Proceedings of the 2005 St. George Utah and 2006 Albuquerque, Albuquerque, NM, USA; New Mexico Workshops, St. George, UT, USA. Proceedings RMRS-P-51, edited by G. J. Gottfried, J. D. Shaw, and P. L. Ford, 11–19. Fort Collins, CO, USA: US Deparatment of Agriculture, Forest Service, Rocky Mountain Research Station.
  • Jain, A. K. 2010. “Data Clustering: 50 Years Beyond K-Means.” Pattern Recognition Letters 31 (8): 651–666. https://doi.org/10.1016/j.patrec.2009.09.011.
  • Jenkins, J. C., D. C. Chojnacky, L. S. Heath, and R. A. Birdsey. 2003. “National-Scale Biomass Estimators for United States Tree Species.” Forest Science 49 (1): 12–35. https://doi.org/10.1093/forestscience/49.1.12.
  • Jiang, J., Y. Shu, J. Wang, and M. Long. 2022. “Transferability in Deep Learning: A Survey.” https://doi.org/10.48550/ARXIV.2201.05867.
  • Kannenberg, S. A., A. W. Driscoll, D. Malesky, and W. R. L. Anderegg. 2021. “Rapid and Surprising Dieback of Utah Juniper in the Southwestern USA Due to Acute Drought Stress.” Forest Ecology and Management 480 (118639): 118639. https://doi.org/10.1016/j.foreco.2020.118639.
  • Krofcheck, D. J., M. E. Litvak, C. D. Lippitt, and A. Neuenschwander. 2016. “Woody Biomass Estimation in a Southwestern U.S. Juniper Savanna Using LiDAR-Derived Clumped Tree Segmentation and Existing Allometries.” Remote Sensing 8 (6): 453. https://doi.org/10.3390/RS8060453.
  • LANDFIRE. 2020. LANDFIRE Remap 2016 Existing Vegetation Type (EVT) CONUS [ dataset]. https://landfire.gov/evt.php.
  • Lefsky, M. A., W. B. Cohen, G. G. Parker, and D. J. Harding. 2002. “Lidar Remote Sensing for Ecosystem Studies.” BioScience 52 (1): 19–30. https://doi.org/10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2.
  • Magargal, K., K. Wilson, S. Chee, M. J. Campbell, V. Bailey, P. E. Dennison, W. R. L. Anderegg, A. Cachelin, S. Brewer, and B. F. Codding. 2023. “The Impacts of Climate Change, Energy Policy and Traditional Ecological Practices on Future Firewood Availability for Diné (Navajo) People.” Philosophical Transactions of the Royal Society B: Biological Sciences 378 (1889): 20220394. https://doi.org/10.1098/rstb.2022.0394.
  • McIntire, C. D., A. M. Cunliffe, F. Boschetti, K. J. Sauer, M. Litvak, K. Anderson, and R. E. Brazier. 2022. “Allometric Relationships for Predicting Aboveground Biomass, Sapwood, and Leaf Area of Two-Needle Piñon Pine (Pinus edulis) Amid Open-Grown Conditions in Central New Mexico.” Forest Science 68 (2): 152–161. https://doi.org/10.1093/forsci/fxac001.
  • McTague, J. P., and A. Weiskittel. 2021. “Evolution, History, and Use of Stem Taper Equations: A Review of Their Development, Application, and Implementation.” Canadian Journal of Forest Research 51 (2): 210–235. https://doi.org/10.1139/cjfr-2020-0326.
  • Miller, R. F., J. C. Chambers, L. Evers, C. J. Williams, K. A. Snyder, B. A. Roundy, and F. B. Pierson. 2019. “The Ecology, History, Ecohydrology, and Management of Pinyon and Juniper Woodlands in the Great Basin and Northern Colorado Plateau of the Western United States.” United States Department of Agriculture, Forest Service: RMRS-GTR-403. https://doi.org/10.2737/RMRS-GTR-403.
  • Miller, E. L., R. O. Meeuwig, and J. D. Budy. 1981. Biomass of Singleleaf Pinyon and Utah Juniper (INT-RP-273). U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. https://doi.org/10.2737/INT-RP-273.
  • Miller, R. F., R. J. Tausch, E. D. Mcarthur, D. D. Johnson, and S. C. Sanderson. 2008. “Age Structure and Expansion of Piñon-Juniper Woodlands: A Regional Perspective in the Intermountain West.” United States Department of Agriculture, Forest Service: RMRS-RP-69. https://doi.org/10.2737/RMRS-RP-69.
  • Muldavin, E., and F. J. Triepke. 2020. “North American Pinyon-Juniper Woodlands: Ecological Composition, Dynamics, and Future Trends.” InEncyclopedia of the World’s Biomes, edited by M. I. Goldstein and D. A. DellaSella, 516–531. https://doi.org/10.1016/B978-0-12-409548-9.12113-X.
  • Neeti, N., and R. Kennedy. 2016. “Comparison of National Level Biomass Maps for Conterminous US: Understanding Pattern and Causes of Differences.” Carbon Balance and Management 11 (1): 19. https://doi.org/10.1186/s13021-016-0060-y.
  • PRISM Climate Group at Oregon State University. 2022). [ dataset] https://prism.oregonstate.edu.
  • Probst, P., M. N. Wright, and A.-L. Boulesteix. 2018. “Hyperparameters and Tuning Strategies for Random Forest.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9 (3): e1301. https://doi.org/10.1002/widm.1301.
  • Randin, C. F., T. Dirnböck, S. Dullinger, N. E. Zimmermann, M. Zappa, and A. Guisan. 2006. “Are Niche-Based Species Distribution Models Transferable in Space?” Journal of Biogeography 33 (10): 1689–1703. https://doi.org/10.1111/j.1365-2699.2006.01466.x.
  • Redmond, M. D., N. S. Cobb, M. E. Miller, and N. N. Barger. 2013. “Long-Term Effects of Chaining Treatments on Vegetation Structure in piñon-Juniper Woodlands of the Colorado Plateau.” Forest Ecology and Management 305:120–128. https://doi.org/10.1016/j.foreco.2013.05.020.
  • Riley, K. L., I. C. Grenfell, M. A. Finney, and J. M. Wiener. 2021. “TreeMap, a Tree-Level Model of Conterminous US Forests Circa 2014 Produced by Imputation of FIA Plot Data.” Scientific Data 8 (1): 11. https://doi.org/10.1038/s41597-020-00782-x.
  • Romme, W. H., C. D. Allen, J. D. Bailey, W. L. Baker, B. T. Bestelmeyer, P. M. Brown, K. S. Eisenhart, et al. 2009. “Historical and Modern Disturbance Regimes, Stand Structures, and Landscape Dynamics in piñon-Juniper Vegetation of the Western United States.” Rangeland Ecology and Management 62 (3): 203–222. https://doi.org/10.2111/08-188R1.1.
  • Roussel, J. R., and D. Auty. 2023. Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. R package version 4.0.3. https://cran.r-project.org/package=lidR.
  • Roussel, J. R., D. Auty, N. C. Coops, P. Tompalski, T. R. H. Goodbody, A. S. Meador, J. F. Bourdon, F. D. Boissieu, and A. Achim. 2020. “LidR: An R Package for Analysis of Airborne Laser Scanning (ALS) Data.” Remote Sensing of Environment 251 (112061): 112061. https://doi.org/10.1016/j.rse.2020.112061.
  • Saarela, S., A. Wästlund, E. Holmström, A. A. Mensah, S. Holm, M. Nilsson, J. Fridman, and G. Ståhl. 2020. “Mapping Aboveground Biomass and Its Prediction Uncertainty Using LiDAR and Field Data, Accounting for Tree-Level Allometric and LiDAR Model Errors.” Forest Ecosystems 7 (1): 1–17. https://doi.org/10.1186/s40663-020-00245-0.
  • Sankey, T., R. Shrestha, J. B. Sankey, S. Hardegree, and E. Strand. 2013. “Lidar-Derived Estimate and Uncertainty of Carbon Sink in Successional Phases of Woody Encroachment.” Journal of Geophysical Research: Biogeosciences 118 (3): 1144–1155. https://doi.org/10.1002/JGRG.20088.
  • Sequeira, A. M. M., P. J. Bouchet, K. L. Yates, K. Mengersen, M. J. Caley, and J. McPherson. 2018. “Transferring Biodiversity Models for Conservation: Opportunities and Challenges.” Methods in Ecology and Evolution 9 (5): 1250–1264. https://doi.org/10.1111/2041-210X.12998.
  • Shaw, J. D., B. E. Steed, and L. T. Deblander. 2005. “Forest Inventory and Analysis (FIA) Annual Inventory Answers the Question: What Is Happening to Pinyon-Juniper Woodlands?” Journal of Forestry 103 (6): 280–285. https://doi.org/10.1093/jof/103.6.280.
  • Smith, W. K., M. P. Dannenberg, D. Yan, S. Herrmann, M. L. Barnes, G. A. Barron-Gafford, J. A. Biederman, et al. 2019. “Remote Sensing of Dryland Ecosystem Structure and Function: Progress, Challenges, and Opportunities.” Remote Sensing of Environment 233 (111401): 111401. https://doi.org/10.1016/j.rse.2019.111401.
  • Sprinkle, W., and J. Klepac. 2015. “Models of Utah Juniper and Two-Needle Pinyon Biomass for One Site in Southwest Utah.” Forest Science 61 (1): 162–168. https://doi.org/10.5849/forsci.12-541.
  • Sugarbaker, L. J., E. W. Constance, H. K. Heidemann, A. L. Jason, V. Lukas, D. L. Saghy, and J. M. Stoker. 2014. “The 3D Elevation Program Initiative—A Call for Action: Geological Survey Circular 1399.” https://doi.org/10.3133/cir1399.
  • Tenneson, K., M. Patterson, T. Mellin, M. Nigrelli, P. Joria, and B. Mitchell. 2018. “Development of a Regional Lidar-Derived Above-Ground Biomass Model with Bayesian Model Averaging for Use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA.” Remote Sensing 10 (3): 442. https://doi.org/10.3390/rs10030442.
  • Torre-Tojal, L., A. Bastarrika, A. Boyano, J. M. Lopez-Guede, and M. Graña. 2022. “Above-Ground Biomass Estimation from LiDAR Data Using Random Forest Algorithms.” Journal of Computational Science 58:101517. https://doi.org/10.1016/J.JOCS.2021.101517.
  • Wager, S., T. Hastie, and B. Efron. 2014. “Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife.” Journal of Machine Learning Research 15 (1): 1625–1651.
  • Wallach, D., and M. Genard. 1998. “Effect of Uncertainty in Input and Parameter Values on Model Prediction Error.” Ecological Modelling 105 (2–3): 337–345. https://doi.org/10.1016/S0304-3800(97)00180-4.
  • Wenger, S. J., and J. D. Olden. 2012. “Assessing Transferability of Ecological Models: An Underappreciated Aspect of Statistical Validation.” Methods in Ecology and Evolution 3 (2): 260–267. https://doi.org/10.1111/J.2041-210X.2011.00170.X.
  • Westfall, J. A., J. W. Coulston, A. N. Gray, J. D. Shaw, P. J. Radtke, D. M. Walker, A. R. Weiskittel, et al. 2023. A National-Scale Tree Volume, Biomass, and Carbon Modeling System for the United States (WO-GTR-104). U.S. Department of Agriculture, Forest Service. https://doi.org/10.2737/WO-GTR-104.
  • Westfall, J. A., and C. W. Woodall. 2007. “Measurement Repeatability of a Large-Scale Inventory of Forest Fuels.” Forest Ecology and Management 253 (1–3): 171–176. https://doi.org/10.1016/j.foreco.2007.07.014.
  • Williams, A. P., B. I. Cook, and J. E. Smerdon. 2022. “Rapid Intensification of the Emerging Southwestern North American Megadrought in 2020–2021.” Nature Climate Change 12 (3): 232–234. https://doi.org/10.1038/s41558-022-01290-z.
  • Wright, M. N., and A. Ziegler. 2017. “Ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software 77 (1): 1–17. https://doi.org/10.18637/jss.v077.i01.
  • Wu, Z., D. Dye, J. Vogel, and B. Middleton. 2016. “Estimating Forest and Woodland Aboveground Biomass Using Active and Passive Remote Sensing.” Photogrammetric Engineering and Remote Sensing 82 (4): 271–281. https://doi.org/10.14358/PERS.82.4.271.
  • Yates, K. L., P. J. Bouchet, M. J. Caley, K. Mengersen, C. F. Randin, S. Parnell, A. H. Fielding, et al. 2018. “Outstanding Challenges in the Transferability of Ecological Models.” Trends in Ecology and Evolution 33 (10): 790–802. https://doi.org/10.1016/J.TREE.2018.08.001.
  • Zhang, G., and Y. Lu. 2012. “Bias-Corrected Random Forests in Regression.” Journal of Applied Statistics 39 (1): 151–160. https://doi.org/10.1080/02664763.2011.578621.

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