Mapping recent timber harvest activity in a temperate forest using single date airborne LiDAR surveys and machine learning: lessons for conservation planning

References

• Achim, A., G. Moreau, N. C. Coops, J. N. Axelson, J. Barrette, S. Bédard, K. E. Byrne, et al. 2021. “The Changing Culture of Silviculture.” Forestry 95 (2): 143–23. https://doi.org/10.1093/forestry/cpab047.
   Web of Science ®Google Scholar
• Arundel, S. T., A. N. Bulen, K. F. Adkins, R. E. Brown, A. J. Lowe, K. S. Mantey, and L. A. Phillips. 2018. “Assimilation of the National Elevation Dataset and Launch of the 3D Elevation Program Through the USGS Spatial Data Infrastructure.” International Journal of Cartography 4 (2): 129–150. https://doi.org/10.1080/23729333.2017.1288533.
   Google Scholar
• Atkins, J. W., G. Bohrer, R. T. Fahey, B. S. Hardiman, T. H. Morin, A. E. L. Stovall, N. Zimmerman, and C. M. Gough. 2018. “Quantifying Vegetation and Canopy Structural Complexity from Terrestrial Li DAR Data Using the Forestr R Package.” Methods in Ecology and Evolution 9 (10): 2057–2066. https://doi.org/10.1111/2041-210X.13061.
   Web of Science ®Google Scholar
• Banskota, A., N. Kayastha, M. J. Falkowski, M. A. Wulder, R. E. Froese, and J. C. White. 2014. “Forest Monitoring Using Landsat Time Series Data: A Review.” Canadian Journal of Remote Sensing 40 (5): 362–384. https://doi.org/10.1080/07038992.2014.987376.
   Web of Science ®Google Scholar
• Barrett, J. W. 1962. “Regional Silviculture of the United States.” Soil Science 94 (2): 134. https://doi.org/10.1097/00010694-196208000-00016.
   Google Scholar
• Baskent, E. Z., and S. Keles. 2005. “Spatial Forest Planning: A Review.” Ecological Modelling 188 (2–4): 145–173. https://doi.org/10.1016/j.ecolmodel.2005.01.059.
   Web of Science ®Google Scholar
• Belair, E. P., and M. J. Ducey. 2018. “Patterns in Forest Harvesting in New England and New York: Using FIA Data to Evaluate Silvicultural Outcomes.” Journal of Forestry 116 (3): 273–282. https://doi.org/10.1093/jofore/fvx019.
   Web of Science ®Google Scholar
• Beland, M., G. Parker, B. Sparrow, D. Harding, L. Chasmer, S. Phinn, A. Antonarakis, and A. Strahler. 2019. “On Promoting the Use of Lidar Systems in Forest Ecosystem Research.” Forest Ecology & Management 450:117484. https://doi.org/10.1016/j.foreco.2019.117484.
   Web of Science ®Google Scholar
• Bergen, K. M., S. J. Goetz, R. O. Dubayah, G. M. Henebry, C. T. Hunsaker, M. L. Imhoff, R. F. Nelson, G. G. Parker, and V. C. Radeloff. 2009. “Remote Sensing of Vegetation 3-D Structure for Biodiversity and Habitat: Review and Implications for Lidar and Radar Spaceborne Missions.” Journal of Geophysical Research: Biogeosciences 114 (G2): 114. https://doi.org/10.1029/2008jg000883.
   Google Scholar
• Boutin, S., and D. Hebert. 2002. “Landscape Ecology and Forest Management: Developing an Effective Partnership.” Ecological Applications 12 (2): 390–397. https://doi.org/10.1890/1051-0761(2002)012[0390:LEAFMD]2.0.CO;2.
   Web of Science ®Google Scholar
• Boves, T. J., D. A. Buehler, J. Sheehan, P. B. Wood, A. D. Rodewald, J. L. Larkin, P. D. Keyser, et al. 2013. “Emulating Natural Disturbances for Declining Late-Successional Species: A Case Study of the Consequences for Cerulean Warblers (Setophaga Cerulea).” PLOS ONE 8 (1): e52107. https://doi.org/10.1371/journal.pone.0052107.
   PubMed Web of Science ®Google Scholar
• Brooks, R. T. 2003. “Abundance, Distribution, Trends, and Ownership Patterns of Early-Successional Forests in the Northeastern United States.” Forest Ecology & Management 185 (1–2): 65–74. https://doi.org/10.1016/S0378-1127(03)00246-9.
   Web of Science ®Google Scholar
• Brown, C. F., S. P. Brumby, B. Guzder-Williams, T. Birch, S. B. Hyde, J. Mazzariello, W. Czerwinski, et al. 2022. “Dynamic World, Near Real-Time Global 10 M Land Use Land Cover Mapping.” Scientific Data 9 (1): 1–17. https://doi.org/10.1038/s41597-022-01307-4.
   PubMed Web of Science ®Google Scholar
• Brubaker, K. M., S. E. Johnson, J. Brinks, and L. P. Leites. 2014. “Estimating Canopy Height of Deciduous Forests at a Regional Scale with Leaf-Off, Low Point Density LiDAR.” Can J Remote Sens 40 (2): 123–134. https://doi.org/10.1080/07038992.2014.943392.
   Web of Science ®Google Scholar
• Chen, T., and C. Guestrin. 2016. “XGBoost: A Scalable Tree Boosting System.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, 785–794. New York, NY: Association for Computing Machinery.
   Google Scholar
• Ciuti, S., H. Tripke, P. Antkowiak, R. S. Gonzalez, C. F. Dormann, M. Heurich, and D. Warton. 2018. “An Efficient Method to Exploit Li DAR Data in Animal Ecology.” Methods in Ecology and Evolution 9 (4): 893–904. https://doi.org/10.1111/2041-210X.12921.
   Web of Science ®Google Scholar
• Ciuzio, E., W. L. Hohman, B. Martin, M. D. Smith, S. Stephens, A. M. Strong, and T. Vercauteren. 2013. “Opportunities and Challenges to Implementing Bird Conservation on Private Lands.” Wildlife Society Bulletin 37 (2): 267–277. https://doi.org/10.1002/wsb.266.
   Web of Science ®Google Scholar
• Cohen, W. B., T. A. Spies, and M. Fiorella. 1995. “Estimating the Age and Structure of Forests in a Multi-Ownership Landscape of Western Oregon, USA.” International Journal of Remote Sensing 16 (4): 721–746. https://doi.org/10.1080/01431169508954436.
   Web of Science ®Google Scholar
• Coops, N. C., P. Tompalski, T. R. H. Goodbody, M. Queinnec, J. E. Luther, D. K. Bolton, J. C. White, M. A. Wulder, O. R. van Lier, and T. Hermosilla. 2021. “Modelling Lidar-Derived Estimates of Forest Attributes Over Space and Time: A Review of Approaches and Future Trends.” Remote Sensing of Environment 260:112477. https://doi.org/10.1016/j.rse.2021.112477.
   Web of Science ®Google Scholar
• Coops, N. C., P. Tompaski, W. Nijland, G. J. M. Rickbeil, S. E. Nielsen, C. W. Bater, and J. J. Stadt. 2016. “A Forest Structure Habitat Index Based on Airborne Laser Scanning Data.” Ecological Indicators 67:346–357. https://doi.org/10.1016/j.ecolind.2016.02.057.
   Web of Science ®Google Scholar
• Corte, A. P. D., D. V. Souza, F. E. Rex, C. R. Sanquetta, M. Mohan, C. A. Silva, A. M. A. Zambrano, et al. 2020. “Forest Inventory with High-Density UAV-Lidar: Machine Learning Approaches for Predicting Individual Tree Attributes.” Computers and Electronics in Agriculture 179:105815. https://doi.org/10.1016/j.compag.2020.105815.
   Web of Science ®Google Scholar
• Curtze, A. C., A. B. Muth, J. L. Larkin, and L. P. Leites. 2022a. “Decision Support Tools to Inform the Rehabilitation and Management of High Graded Forests.” Journal of Forestry 120 (5): 527–542. https://doi.org/10.1093/jofore/fvab077.
   Web of Science ®Google Scholar
• Curtze, A. C., A. B. Muth, J. L. Larkin, and L. P. Leites. 2022b. “Seeing Past the Green: Structure, Composition, and Biomass Differences in High Graded and Silviculture-Managed Forests of Similar Stand Density.” Forest Ecology & Management 526:120598. https://doi.org/10.1016/j.foreco.2022.120598.
   Web of Science ®Google Scholar
• Davidson, C. B., K. W. Gottschalk, and J. E. Johnson. 1999. “Tree Mortality Following Defoliation by the European Gypsy Moth (Lymantria Dispar L.) in the United States: A Review.” Forest Science 45 (1): 74–84. https://doi.org/10.1093/forestscience/45.1.74.
   Web of Science ®Google Scholar
• Dey, D. C. 2014. “Sustaining Oak Forests in Eastern North America: Regeneration and Recruitment, the Pillars of Sustainability.” Forest Science 60 (5): 926–942. https://doi.org/10.5849/forsci.13-114.
   Web of Science ®Google Scholar
• Dickinson, Y., E. K. Zenner, and D. Miller. 2014. “Examining the Effect of Diverse Management Strategies on Landscape Scale Patterns of Forest Structure in Pennsylvania Using Novel Remote Sensing Techniques.” Canadian Journal of Forest Research 44 (4): 301–312. https://doi.org/10.1139/cjfr-2013-0315.
   Web of Science ®Google Scholar
• Dubayah, R. O., and J. B. Drake. 2000. “Lidar Remote Sensing for Forestry.” Journal of Forestry 98 (6): 44–46. https://doi.org/10.1093/jof/98.6.44.
   Web of Science ®Google Scholar
• Fajvan, M. A., S. T. Grushecky, and C. C. Hassler. 1998. “The Effects of Harvesting Practices on West Virginia’s Wood Supply.” Journal of Forestry 96 (5): 33–39. https://doi.org/10.1093/jof/96.5.33.
   Web of Science ®Google Scholar
• Falkowski, M. J., J. S. Evans, S. Martinuzzi, P. E. Gessler, and A. T. Hudak. 2009. “Characterizing Forest Succession with Lidar Data: An Evaluation for the Inland Northwest, USA.” Remote Sensing of Environment 113 (5): 946–956. https://doi.org/10.1016/j.rse.2009.01.003.
   Web of Science ®Google Scholar
• Farrell, S. L., B. A. Collier, K. L. Skow, A. M. Long, A. J. Campomizzi, M. L. Morrison, and K. B. Hays. 2013. “Using LiDAR‐Derived Vegetation Metrics for High‐Resolution, Species Distribution Models for Conservation Planning.” Ecosphere 4 (3): 1–18. https://doi.org/10.1890/ES12-000352.1.
   Web of Science ®Google Scholar
• Fedrigo, M., G. J. Newnham, N. C. Coops, D. S. Culvenor, D. K. Bolton, and C. R. Nitschke. 2018. “Predicting Temperate Forest Stand Types Using Only Structural Profiles from Discrete Return Airborne Lidar.” ISPRS Journal of Photogrammetry and Remote Sensing 136:106–119. https://doi.org/10.1016/j.isprsjprs.2017.11.018.
   Web of Science ®Google Scholar
• Fielding, A. H., and J. F. Bell. 1997. “A Review of Methods for the Assessment of Prediction Errors in Conservation Presence/Absence Models.” Environmental Conservation 24 (1): 38–49. https://doi.org/10.1017/S0376892997000088.
   Web of Science ®Google Scholar
• Fiss, C. J., D. J. McNeil, A. D. Rodewald, J. E. Duchamp, and J. L. Larkin. 2020. “Post-Fledging Golden-Winged Warblers Require Forests with Multiple Stand Developmental Stages.” Condor 122. https://doi.org/10.1093/condor/duaa052.
   Google Scholar
• Fiss, C., D. McNeil, A. Rodewald, D. Heggenstaller, and J. L. Larkin. 2021. “Cross-Scale Habitat Selection Reveals Within-Stand Structural Requirements for Fledgling Golden-Winged Warblers.” Avian Conservation & Ecology 16 (1). https://doi.org/10.5751/ACE-01807-160116.
   Web of Science ®Google Scholar
• Frank, B., F. Mauro, H. Temesgen, and K. R. Ford. 2019. “Analysis of Classification Methods for Identifying Stands for Commercial Thinning Using LiDAR.” Canadian Journal of Remote Sensing 45 (5): 673–690. https://doi.org/10.1080/07038992.2019.1670051.
   Web of Science ®Google Scholar
• Franklin, J. 2010. Mapping Species Distributions: Spatial Inference and Prediction. Cambridge: Cambridge University Press.
   Google Scholar
• Gao, Y., M. Skutsch, J. Paneque-Gálvez, and A. Ghilardi. 2020. “Remote Sensing of Forest Degradation: A Review.” Environmental Research Letters 15 (10): 103001. https://doi.org/10.1088/1748-9326/abaad7.
   Web of Science ®Google Scholar
• Goetz, S. J., D. Steinberg, M. G. Betts, R. T. Holmes, P. J. Doran, R. Dubayah, and M. Hofton. 2010. “Lidar Remote Sensing Variables Predict Breeding Habitat of a Neotropical Migrant Bird.” Ecology 91 (6): 1569–1576. https://doi.org/10.1890/09-1670.1.
   PubMed Web of Science ®Google Scholar
• Greenberg, C. H., B. Collins, F. R. Thompson, and W. H. McNab. 2011. “Introduction: What are Early Successional Habitats, Why are They Important, and How Can They Be Sustained?” In Sustaining Young Forest Communities: Ecology and Management of Early Successional Habitats in the Central Hardwood Region, USA, edited by C. Greenberg, B. Collins, and F. Thompson III, 1–10. Dordrecht, Netherlands: Springer.
   Google Scholar
• Gunn, J. S., M. J. Ducey, and E. Belair. 2019. “Evaluating Degradation in a North American Temperate Forest.” Forest Ecology & Management 432:415–426. https://doi.org/10.1016/j.foreco.2018.09.046.
   Web of Science ®Google Scholar
• Hansen, A. J., L. B. Phillips, R. Dubayah, S. Goetz, and M. Hofton. 2014. “Regional-Scale Application of Lidar: Variation in Forest Canopy Structure Across the Southeastern US.” Forest Ecology & Management 329:214–226. https://doi.org/10.1016/j.foreco.2014.06.009.
   Web of Science ®Google Scholar
• Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (6160): 850–853. https://doi.org/10.1126/science.1244693.
   PubMed Web of Science ®Google Scholar
• Hardiman, B. S., E. A. LaRue, J. W. Atkins, R. T. Fahey, F. W. Wagner, and C. M. Gough. 2018. “Spatial Variation in Canopy Structure Across Forest Landscapes.” Forest Trees Livelihoods 9 (8): 474. https://doi.org/10.3390/f9080474.
   Google Scholar
• Hermosilla, T., M. A. Wulder, J. C. White, N. C. Coops, and G. W. Hobart. 2018. “Disturbance-Informed Annual Land Cover Classification Maps of Canada’s Forested Ecosystems for a 29-Year Landsat Time Series.” Canadian Journal of Remote Sensing 44 (1): 67–87. https://doi.org/10.1080/07038992.2018.1437719.
   Web of Science ®Google Scholar
• Jarron, L. R., T. Hermosilla, N. C. Coops, M. A. Wulder, J. C. White, G. W. Hobart, and D. G. Leckie. 2016. “Differentiation of Alternate Harvesting Practices Using Annual Time Series of Landsat Data.” Forest Trees Livelihoods 8 (1): 15. https://doi.org/10.3390/f8010015.
   Google Scholar
• Kangas, A., T. Gobakken, S. Puliti, M. Hauglin, and E. Naesset. 2018. “Value of Airborne Laser Scanning and Digital Aerial Photogrammetry Data in Forest Decision Making.” Silva Fennica 52 (1). https://doi.org/10.14214/sf.9923.
   Web of Science ®Google Scholar
• King, D. I., R. M. Degraaf, M.-L. Smith, and J. P. Buonaccorsi. 2006. “Habitat Selection and Habitat-Specific Survival of Fledgling Ovenbirds (Seiurus Aurocapilla).” Journal of Zoology 269 (4): 414–421. https://doi.org/10.1111/j.1469-7998.2006.00158.x.
   Web of Science ®Google Scholar
• King, D. I., and S. Schlossberg. 2014. “Synthesis of the Conservation Value of the Early-Successional Stage in Forests of Eastern North America.” Forest Ecology & Management 324:186–195. https://doi.org/10.1016/j.foreco.2013.12.001.
   Web of Science ®Google Scholar
• Klooster, W. S., D. A. Herms, K. S. Knight, C. P. Herms, D. G. McCullough, A. Smith, K. J. K. Gandhi, and J. Cardina. 2014. “Ash (Fraxinus Spp.) Mortality, Regeneration, and Seed Bank Dynamics in Mixed Hardwood Forests Following Invasion by Emerald Ash Borer (Agrilus Planipennis).” Biological Invasions 16 (4): 859–873. https://doi.org/10.1007/s10530-013-0543-7.
   Web of Science ®Google Scholar
• Lanham, J. D., P. D. Keyser, P. H. Brose, and D. H. Van Lear. 2002. “Oak Regeneration Using the Shelterwood-Burn Technique: Management Options and Implications for Songbird Conservation in the Southeastern United States.” Forest Ecology & Management 155 (1–3): 143–152. https://doi.org/10.1016/S0378-1127(01)00554-0.
   Web of Science ®Google Scholar
• LaRue, E. A., R. Fahey, T. L. Fuson, J. R. Foster, J. H. Matthes, K. Krause, and B. S. Hardiman. 2022. “Evaluating the Sensitivity of Forest Structural Diversity Characterization to LiDAR Point Density.” Ecosphere 13 (9): e4209. https://doi.org/10.1002/ecs2.4209.
   Web of Science ®Google Scholar
• Litvaitis, J. A., J. L. Larkin, D. J. McNeil, D. Keirstead, and B. Costanzo. 2021. “Addressing the Early-Successional Habitat Needs of At-Risk Species on Privately Owned Lands in the Eastern United States.” The Land 10 (11): 1116. https://doi.org/10.3390/land10111116.
   Web of Science ®Google Scholar
• Loftis, D. L. 1990. “A Shelterwood Method for Regenerating Red Oak in the Southern Appalachians.” Forest Science 36 (4): 917–929. https://doi.org/10.1093/forestscience/36.4.917.
   Web of Science ®Google Scholar
• Lundberg, S. M., G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee. 2020. “From Local Explanations to Global Understanding with Explainable AI for Trees.” Nature Machine Intelligence 2 (1): 56–67. https://doi.org/10.1038/s42256-019-0138-9.
   PubMedGoogle Scholar
• Lutter, S. H., A. A. Dayer, A. D. Rodewald, D. J. McNeil, and J. L. Larkin. 2019. “Early Successional Forest Management on Private Lands As a Coupled Human and Natural System.” Forest Trees Livelihoods 10 (6): 499. https://doi.org/10.3390/f10060499.
   Google Scholar
• Maltamo, M., H. Kinnunen, A. Kangas, and L. Korhonen. 2020. “Predicting Stand Age in Managed Forests Using National Forest Inventory Field Data and Airborne Laser Scanning.” Forest Ecosystems 7 (1): 44. https://doi.org/10.1186/s40663-020-00254-z.
   Web of Science ®Google Scholar
• Manel, S., H. C. Williams, and S. J. Ormerod. 2001. “Evaluating Presence–Absence Models in Ecology: The Need to Account for Prevalence.” The Journal of Applied Ecology 38 (5): 921–931. https://doi.org/10.1046/j.1365-2664.2001.00647.x.
   Web of Science ®Google Scholar
• Martinuzzi, S., L. A. Vierling, W. A. Gould, M. J. Falkowski, J. S. Evans, A. T. Hudak, and K. T. Vierling. 2009. “Mapping Snags and Understory Shrubs for a LiDAR-Based Assessment of Wildlife Habitat Suitability.” Remote Sensing of Environment 113 (12): 2533–2546. https://doi.org/10.1016/j.rse.2009.07.002.
   Web of Science ®Google Scholar
• Matasci, G., T. Hermosilla, M. A. Wulder, J. C. White, N. C. Coops, G. W. Hobart, and H. S. J. Zald. 2018. “Large-Area Mapping of Canadian Boreal Forest Cover, Height, Biomass and Other Structural Attributes Using Landsat Composites and Lidar Plots.” Remote Sensing of Environment 209:90–106. https://doi.org/10.1016/j.rse.2017.12.020.
   Web of Science ®Google Scholar
• McAuley, D. G., J. R. Longcore, G. F. Sepik, and G. W. Pendleton. 1996. “Habitat Characteristics of American Woodcock Nest Sites on a Managed Area in Maine.” The Journal of Wildlife Management 60 (1): 138–148. https://doi.org/10.2307/3802048.
   Web of Science ®Google Scholar
• McNeil, D. J., K. R. Aldinger, M. H. Bakermans, J. A. Lehman, A. C. Tisdale, J. A. Jones, P. B. Wood, et al. 2017. “An Evaluation and Comparison of Conservation Guidelines for an At-Risk Migratory Songbird.” Global Ecology and Conservation 9:90–103. https://doi.org/10.1016/j.gecco.2016.12.006.
   Web of Science ®Google Scholar
• McNeil, D. J., G. Fisher, C. J. Fiss, A. J. Elmore, M. C. Fitzpatrick, J. W. Atkins, J. Cohen, and J. L. Larkin. 2023. “Using Aerial LiDAR to Assess Regional Availability of Potential Habitat for a Conservation Dependent Forest Bird.” Forest Ecology & Management 540:121002. https://doi.org/10.1016/j.foreco.2023.121002.
   Web of Science ®Google Scholar
• McNeil, D. J., C. J. Fiss, E. M. Wood, J. E. Duchamp, M. Bakermans, and J. L. Larkin. 2018. “Using a Natural Reference System to Evaluate Songbird Habitat Restoration.” Avian Conservation & Ecology Oiseaux 13. https://doi.org/10.5751/ace-01193-130122.
   Web of Science ®Google Scholar
• McNeil, D. J., A. D. Rodewald, V. Ruiz‐Gutierrez, K. E. Johnson, M. Strimas‐Mackey, S. Petzinger, O. J. Robinson, G. E. Soto, A. A. Dhondt, and J. L. Larkin. 2020. “Multiscale Drivers of Restoration Outcomes for an Imperiled Songbird.” Restoration Ecology 28 (4): 880–891. https://doi.org/10.1111/rec.13147.
   Web of Science ®Google Scholar
• Moore, J., and Y. Lin. 2019. “Determining the Extent and Drivers of Attrition Losses from Wind Using Long-Term Datasets and Machine Learning Techniques.” Forestry: An International Journal of Forest Research 92 (4): 425–435. https://doi.org/10.1093/forestry/cpy047.
   Google Scholar
• North American Bird Conservation Initiative, U.S. Committee. 2013. The State of The Birds 2013 Report on Private Lands. Washington, DC: U.S. Department of Interior.
   Google Scholar
• Nyland, R. D. 1992. “Exploitation and Greed in Eastern Hardwood Forests.” Journal of Forestry 90 (1): 33–37. https://doi.org/10.1093/jof/90.1.33.
   Web of Science ®Google Scholar
• Olofsson, P., G. M. Foody, M. Herold, S. V. Stehman, C. E. Woodcock, and M. A. Wulder. 2014. “Good Practices for Estimating Area and Assessing Accuracy of Land Change.” Remote Sensing of Environment 148:42–57. https://doi.org/10.1016/j.rse.2014.02.015.
   Web of Science ®Google Scholar
• O’Neil-Dunne, J., S. MacFaden, and A. Royar. 2014. “A Versatile, Production-Oriented Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes Using GEOBIA and Data Fusion.” Remote Sensing 6 (12): 12837–12865. https://doi.org/10.3390/rs61212837.
   Web of Science ®Google Scholar
• PA-DCNR. 2020. Pennsylvania Forest Action Plan. Harrisburg, PA: Pennsylvania Department of Conservation and Natural Resources.
   Google Scholar
• Parent, C. J., and F. Hernández. 2021, “The Landscape Perspective in WIldlife and Habitat Management.” Wildlife Management and Landscapes: Principles and Applications, edited by W. F. Porter, C. J. Parent, R. A. Stewart, andf D. M. Williams, 3–18. Baltimore: Johns Hopins University Press. https://doi.org/10.1353/book.83118.
   Google Scholar
• Pasquarella, V. J., C. E. Holden, L. Kaufman, C. E. Woodcock, H. Nagendra, and K. He. 2016. “From Imagery to Ecology: Leveraging Time Series of All Available Landsat Observations to Map and Monitor Ecosystem State and Dynamics.” Remote Sensing in Ecology and Conservation 2 (3): 152–170. https://doi.org/10.1002/rse2.24.
   Web of Science ®Google Scholar
• Phillips, M. J., and C. R. Blinn. 2007. “Practices Evaluated and Approaches Used to Select Sites for Monitoring the Application of Best Management Practices: A Regional Summary.” Journal of Forestry 105 (4): 179–183. https://doi.org/10.1093/jof/105.4.179.
   Web of Science ®Google Scholar
• Pierzchała, M., P. Giguère, and R. Astrup. 2018. “Mapping Forests Using an Unmanned Ground Vehicle with 3D LiDAR and Graph-SLAM.” Computers and Electronics in Agriculture 145:217–225. https://doi.org/10.1016/j.compag.2017.12.034.
   Web of Science ®Google Scholar
• Pressey, R. L., M. Cabeza, M. E. Watts, R. M. Cowling, and K. A. Wilson. 2007. “Conservation Planning in a Changing World.” Trends in Ecology & Evolution 22 (11): 583–592. https://doi.org/10.1016/j.tree.2007.10.001.
   PubMed Web of Science ®Google Scholar
• Racine, E. B., N. C. Coops, B. St-Onge, and J. Bégin. 2013. “Estimating Forest Stand Age from LiDAR-Derived Predictors and Nearest Neighbor Imputation.” Forest Science 60 (1): 128–136. https://doi.org/10.5849/forsci.12-088.
   Web of Science ®Google Scholar
• Raybuck, D. W., J. L. Larkin, S. H. Stoleson, and T. J. Boves. 2019. “Radio-Tracking Reveals Insight into Survival and Dynamic Habitat Selection of Fledgling Cerulean Warblers.” The Condor 122 (1): duz063. https://doi.org/10.1093/condor/duz063.
   Web of Science ®Google Scholar
• Riitters, K. H., J. W. Coulston, and J. D. Wickham. 2012. “Fragmentation of Forest Communities in the Eastern United States.” Forest Ecology & Management 263:85–93. https://doi.org/10.1016/j.foreco.2011.09.022.
   Web of Science ®Google Scholar
• Roussel, J.-R., D. Auty, N. C. Coops, P. Tompalski, T. R. H. Goodbody, A. S. Meador, J.-F. Bourdon, F. de Boissieu, and A. Achim. 2020. “LidR: An R Package for Analysis of Airborne Laser Scanning (ALS) Data.” Remote Sensing of Environment 251:112061. https://doi.org/10.1016/j.rse.2020.112061.
   Web of Science ®Google Scholar
• Sabol, D. E., A. R. Gillespie, J. B. Adams, M. O. Smith, and C. J. Tucker. 2002. “Structural Stage in Pacific Northwest Forests Estimated Using Simple Mixing Models of Multispectral Images.” Remote Sensing of Environment 80 (1): 1–16. https://doi.org/10.1016/S0034-4257(01)00245-0.
   Web of Science ®Google Scholar
• Sallabanks, R., and E. B. Arnett. 2005. Accommodating Birds in Managed Forests of North America: A Review of Bird–Forestry Relationships. Bird Conservation Implementation and Integration in the Americas. US Forest Service General Technical Report PSW-191. Albany, California, USA 345–372.
   Google Scholar
• Sampson, N., and L. DeCoster. 2000. “Forest Fragmentation: Implications for Sustainable Private Forests.” Journal of Forestry 98 (3): 4–8. https://doi.org/10.1093/jof/98.3.4.
   Web of Science ®Google Scholar
• Sanchez-Lopez, N., L. Boschetti, and A. T. Hudak. 2019. “Reconstruction of the Disturbance History of a Temperate Coniferous Forest Through Stand-Level Analysis of Airborne LiDAR Data.” Forestry: An International Journal of Forest Research 93:38–55. https://doi.org/10.1093/forestry/cpz048.
   Google Scholar
• Sauer, J. R., W. A. Link, J. E. Fallon, K. L. Pardieck, and D. J. Ziolkowski. 2013. “The North American Breeding Bird Survey 1966–2011: Summary Analysis and Species Accounts.” North American Fauna 79:1–32. https://doi.org/10.3996/nafa.79.0001.
   Google Scholar
• Schwenk, W. S., and T. M. Donovan. 2011. “A Multispecies Framework for Landscape Conservation Planning.” Conservation Biology 25 (5): 1010–1021. https://doi.org/10.1111/j.1523-1739.2011.01723.x.
   PubMed Web of Science ®Google Scholar
• Senf, C., D. Pflugmacher, P. Hostert, and R. Seidl. 2017. “Using Landsat Time Series for Characterizing Forest Disturbance Dynamics in the Coupled Human and Natural Systems of Central Europe.” ISPRS Journal of Photogrammetry & Remote Sensing 130:453–463. https://doi.org/10.1016/j.isprsjprs.2017.07.004.
   PubMed Web of Science ®Google Scholar
• Shake, C. S., C. E. Moorman, J. D. Riddle, and M. R. Burchell. 2012. “Influence of Patch Size and Shape on Occupancy by Shrubland Birds.” The Condor 114 (2): 268–278. https://doi.org/10.1525/cond.2012.110107.
   Web of Science ®Google Scholar
• Shifley, S. R., W. K. Moser, D. J. Nowak, P. D. Miles, B. J. Butler, F. X. Aguilar, R. D. DeSantis, and E. J. Greenfield. 2014. “Five Anthropogenic Factors That Will Radically Alter Forest Conditions and Management Needs in the Northern United States.” Forest Science 60 (5): 914–925. https://doi.org/10.5849/forsci.13-153.
   Web of Science ®Google Scholar
• Simonson, W. D., H. D. Allen, D. A. Coomes, and A. Tatem. 2014. “Applications of Airborne Lidar for the Assessment of Animal Species Diversity.” Methods in Ecology and Evolution 5 (8): 719–729. https://doi.org/10.1111/2041-210X.12219.
   Web of Science ®Google Scholar
• Stahl, A. T., R. Andrus, J. A. Hicke, A. T. Hudak, B. C. Bright, and A. J. H. Meddens. 2023. “Automated Attribution of Forest Disturbance Types from Remote Sensing Data: A Synthesis.” Remote Sensing of Environment 285:113416. https://doi.org/10.1016/j.rse.2022.113416.
   Web of Science ®Google Scholar
• Thom, D., and W. S. Keeton. 2020. “Disturbance-Based Silviculture for Habitat Diversification: Effects on Forest Structure, Dynamics, and Carbon Storage.” Forest Ecology & Management 469:118132. https://doi.org/10.1016/j.foreco.2020.118132.
   Web of Science ®Google Scholar
• Tobin, P. C., B. B. Bai, D. A. Eggen, and D. S. Leonard. 2012. “The Ecology, Geopolitics, and Economics of Managing Lymantria Dispar in the United States.” International Journal of Pest Management 58 (3): 195–210. https://doi.org/10.1080/09670874.2011.647836.
   Web of Science ®Google Scholar
• Tortini, R., A. L. Mayer, T. Hermosilla, N. C. Coops, and M. A. Wulder. 2019. “Using Annual Landsat Imagery to Identify Harvesting Over a Range of Intensities for Non-Industrial Family Forests.” Landscape and Urban Planning 188:143–150. https://doi.org/10.1016/j.landurbplan.2018.04.012.
   Web of Science ®Google Scholar
• Twedt, D. J. 2020. “Influence of Forest Harvest Severity and Time Since Perturbation on Conservation of North American Birds.” Forest Ecology & Management 458:117742. https://doi.org/10.1016/j.foreco.2019.117742.
   Web of Science ®Google Scholar
• US Fish & Wildlife Service. 2006. Strategic Habitat Conservation: A Report from the National Ecological Assessment Team. Washington, DC: US Fish and Wildlife Service.
   Google Scholar
• White, J. C., N. C. Coops, M. A. Wulder, M. Vastaranta, T. Hilker, and P. Tompalski. 2016. “Remote Sensing Technologies for Enhancing Forest Inventories: A Review.” Canadian Journal of Remote Sensing 42 (5): 619–641. https://doi.org/10.1080/07038992.2016.1207484.
   Web of Science ®Google Scholar
• White, J. C., M. A. Wulder, T. Hermosilla, N. C. Coops, and G. W. Hobart. 2017. “A Nationwide Annual Characterization of 25 Years of Forest Disturbance and Recovery for Canada Using Landsat Time Series.” Remote Sensing of Environment 194:303–321. https://doi.org/10.1016/j.rse.2017.03.035.
   Web of Science ®Google Scholar
• White, J. C., M. A. Wulder, A. Varhola, M. Vastaranta, N. C. Coops, B. D. Cook, D. Pitt, and M. Woods. 2013. “A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using an Area-Based Approach.” The Forestry Chronicle 89 (6): 722–723. https://doi.org/10.5558/tfc2013-132.
   Web of Science ®Google Scholar
• Wicker, G. 2002. Motivation for Private Forest Landowners. Southern Forest Resource Assessment, Wear, DN and G. John. Gen. Tech. Rep. SRS-53. Ashville, NC: US Department.
   Google Scholar
• Wilson, E. H., and S. A. Sader. 2002. “Detection of Forest Harvest Type Using Multiple Dates of Landsat TM Imagery.” Remote Sensing of Environment 80 (3): 385–396. https://doi.org/10.1016/S0034-4257(01)00318-2.
   Web of Science ®Google Scholar
• Wood, P., J. Sheehan, P. D. Keyser, D. A. Buehler, J. Larkin, A. D. Rodewald, S. H. Stoleson, et al. 2013. “Management Guidelines for Enhancing Cerulean Warbler Breeding Habitat in Appalachian Hardwood Forests.” 28. The Plains, Virginia: American Bird Conservancy.
   Google Scholar
• Wulder, M. A., C. W. Bater, N. C. Coops, T. Hilker, and J. C. White. 2008. “The Role of LiDAR in Sustainable Forest Management.” The Forestry Chronicle 84 (6): 807–826. https://doi.org/10.5558/tfc84807-6.
   Web of Science ®Google Scholar
• Wulder, M. A., T. Hermosilla, J. C. White, and N. C. Coops. 2020. “Biomass Status and Dynamics Over Canada’s Forests: Disentangling Disturbed Area from Associated Aboveground Biomass Consequences.” Environmental Research Letters 15 (9): 094093. https://doi.org/10.1088/1748-9326/ab8b11.
   Web of Science ®Google Scholar
• Wulder, M. A., R. S. Skakun, W. A. Kurz, and J. C. White. 2004. “Estimating Time Since Forest Harvest Using Segmented Landsat ETM+ Imagery.” Remote Sensing of Environment 93 (1–2): 179–187. https://doi.org/10.1016/j.rse.2004.07.009.
   Web of Science ®Google Scholar
• Yu, J.-W., Y.-W. Yoon, W.-K. Baek, and H.-S. Jung. 2021. “Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform Through Random Forest, XGBoost, and Support Vector Machine Approaches.” Remote Sensing 13 (21): 4282. https://doi.org/10.3390/rs13214282.
   Web of Science ®Google Scholar
• Zhu, Z., J. Zhang, Z. Yang, A. H. Aljaddani, W. B. Cohen, S. Qiu, and C. Zhou. 2020. “Continuous Monitoring of Land Disturbance Based on Landsat Time Series.” Remote Sensing of Environment 238:111116. https://doi.org/10.1016/j.rse.2019.03.009.
   Web of Science ®Google Scholar