References

• Abdullah, H., Darvishzadeh, R., Skidmore, A.K., Groen, T.A., and Heurich, M. 2018. “European spruce bark beetle (Ips typographus L.) green attack affects foliar reflectance and biochemical properties.” International Journal of Applied Earth Observation and Geoinformation, Vol. 64: pp. 199–209. doi:https://doi.org/10.1016/j.jag.2017.09.009.
   Web of Science ®Google Scholar
• Bechtold, W.A., and Patterson, P.L. 2005. “The enhanced forest inventory and analysis program–national sampling design and estimation procedures.” USDA Forest Service, Southern Research Station, Gen. Tech. Rep. SRS-80.
   Google Scholar
• Belgiu, M., and Drăguţ, L. 2016. “Random forest in remote sensing: A review of applications and future directions.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 114: pp. 24–31. doi:https://doi.org/10.1016/j.isprsjprs.2016.01.011.
   Web of Science ®Google Scholar
• Bhattarai, R., Rahimzadeh-Bajgiran, P., Weiskittel, A., and MacLean, D.A. 2020. “Sentinel-2 based prediction of spruce budworm defoliation using red-edge spectral vegetation indices.” Remote Sensing Letters, Vol. 11(No. 8): pp. 777–786. doi:https://doi.org/10.1080/2150704X.2020.1767824.
   Web of Science ®Google Scholar
• Bhattarai, R., Rahimzadeh-Bajgiran, P., Weiskittel, A., Meneghini, A., and MacLean, D.A. 2021. “Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 17: pp. 28–40. doi:https://doi.org/10.1016/j.isprsjprs.2020.11.023.
   Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning, Vol. 45(No. 1): pp. 5–32. doi:https://doi.org/10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Bright, B.C., Hudak, A.T., Meddens, A.J.H., Egan, J.M., and Jorgensen, C.L. 2020. “Mapping multiple insect outbreaks across large regions annually using landsat time series data.” Remote Sensing, Vol. 12(No. 10): pp. 1655. doi:https://doi.org/10.3390/rs12101655.
   Web of Science ®Google Scholar
• Broders, K., Munck, I., Wyka, S., Iriarte, G., and Beaudoin, E. 2015. “Characterization of fungal pathogens associated with white pine needle damage (WPND) in northeastern North America.” Forests, Vol. 6(No. 12): pp. 4088–4104. doi:https://doi.org/10.3390/f6114088.
   Google Scholar
• Cai, Z., Jönsson, P., Jin, H., and Eklundh, L. 2017. “Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS.” Remote Sensing, Vol. 9(No. 12): pp. 1271. doi:https://doi.org/10.3390/rs9121271.
   Web of Science ®Google Scholar
• Chen, J.M. 1996. “Evaluation of vegetation indices and a modified simple ratio for boreal applications.” Canadian Journal of Remote Sensing, Vol. 22(No. 3): pp. 229–242. doi:https://doi.org/10.1080/07038992.1996.10855178.
   Google Scholar
• Clevers, J.G.P.W., De Jong, S.M., Epema, G.F., Van Der Meer, F.D., Bakker, W.H., Skidmore, A.K., and Scholte, K.H. 2002. “Derivation of the red edge index using the MERIS standard band setting.” International Journal of Remote Sensing, Vol. 23(No. 16): pp. 3169–3184. doi:https://doi.org/10.1080/01431160110104647.
   Web of Science ®Google Scholar
• Costanza, K.K.L., Crandall, M.S., Rice, R.W., Livingston, W.H., Munck, I.A., and Lombard, K. 2019. “Economic implications of a native tree disease, Caliciopsis canker, on the white pine (Pinus strobus) lumber industry in the northeastern United States.” Canadian Journal of Forest Research, Vol. 49(No. 5): pp. 521–530. doi:https://doi.org/10.1139/cjfr-2018-0380.
   Google Scholar
• Costanza, K.K.L., Whitney, T.D., McIntire, C.D., Livingston, W.H., and Gandhi, K.J.K. 2018. “A synthesis of emerging health issues of eastern white pine (Pinus strobus) in eastern North America.” Forest Ecology and Management, Vol. 423: pp. 3–17. doi:https://doi.org/10.1016/j.foreco.2018.02.049.
   Web of Science ®Google Scholar
• Dash, J., and Curran, P.J. 2004. “The MERIS terrestrial chlorophyll index.” International Journal of Remote Sensing, Vol. 25(No. 23): pp. 5403–5413. doi:https://doi.org/10.1080/0143116042000274015.
   Web of Science ®Google Scholar
• Daughtry, C. 2000. “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance.” Remote Sensing of Environment, Vol. 74(No. 2): pp. 229–239. doi:https://doi.org/10.1016/S0034-4257(00)00113-9.
   Web of Science ®Google Scholar
• De Beurs, K., and Townsend, P. 2008. “Estimating the effect of gypsy moth defoliation using MODIS.” Remote Sensing of Environment, Vol. 112(No. 10): pp. 3983–3990. doi:https://doi.org/10.1016/j.rse.2008.07.008.
   Web of Science ®Google Scholar
• Donovan, S.D., MacLean, D.A., Zhang, Y., Lavigne, M.B., and Kershaw, J.A. 2021. “Evaluating annual spruce budworm defoliation using change detection of vegetation indices calculated from satellite hyperspectral imagery.” Remote Sensing of Environment, Vol. 253: pp. 112204. doi:https://doi.org/10.1016/j.rse.2020.112204.
   Web of Science ®Google Scholar
• Eklundh, L., Johansson, T., and Solberg, S. 2009. “Mapping insect defoliation in Scots pine with MODIS time-series data.” Remote Sensing of Environment, Vol. 113(No. 7): pp. 1566–1573. doi:https://doi.org/10.1016/j.rse.2009.03.008.
   Web of Science ®Google Scholar
• Frampton, W.J., Dash, J., Watmough, G., and Milton, E.J. 2013. “Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 82: pp. 83–92. doi:https://doi.org/10.1016/j.isprsjprs.2013.04.007.
   Web of Science ®Google Scholar
• Gandhi, K.J.K., Livingston, W.H., and Munck, I.A. 2018. “Resilience and health of eastern white pine (Pinus strobus) forests under novel and historical factors in eastern North America.” Forest Ecology and Management, Vol. 423: pp. 1–2. doi:https://doi.org/10.1016/j.foreco.2018.04.023.
   Web of Science ®Google Scholar
• Genuer, R., Poggi, J.-M., and Tuleau-Malot, C. 2015. “VSURF: An R package for variable selection using Random Forests.” The R Journal, Vol. 7(No. 2): pp. 19. doi:https://doi.org/10.32614/RJ-2015-018.
   Google Scholar
• Genuer, R., Poggi, J.-M., and Tuleau-Malot, C. 2010. “Variable selection using random forests.” Pattern Recognition Letters, Vol. 31(No. 14): pp. 2225–2236. doi:https://doi.org/10.1016/j.patrec.2010.03.014.
   Web of Science ®Google Scholar
• Gillies, S. 2013. “Rasterio: Geospatial raster I/O for Python programmers.” Mapbox, last modified March 15, 2020, https://github.com/mapbox/rasterio.
   Google Scholar
• Gitelson, A.A. 2005. “Remote estimation of canopy chlorophyll content in crops.” Geophysical Research Letters, Vol. 32(No. 8): pp. 1–4. doi:https://doi.org/10.1029/2005GL022688.
   Web of Science ®Google Scholar
• Gitelson, A.A. 2004. “Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation.” Journal of Plant Physiology, Vol. 161(No. 2): pp. 165–173. doi:https://doi.org/10.1078/0176-1617-01176.
   PubMed Web of Science ®Google Scholar
• Gitelson, A.A., Merzlyak, M.N., and Chivkunova, O.B. 2001. “Optical properties and nondestructive estimation of anthocyanin content in plant leaves.” Photochemistry and Photobiology, Vol. 74(No. 1): pp. 38–45. 8655(2001).
   PubMed Web of Science ®Google Scholar
• Gitelson, A.A., Zur, Y., Chivkunova, O.B., and Merzlyak, M.N. 2007. “Assessing carotenoid content in plant leaves with reflectance spectroscopy.” Photochemistry and Photobiology, Vol. 75 (No. 3): pp. 272–281. doi:https://doi.org/10.1562/0031-8655(2002)0750272ACCIPL2.0.CO2.
   Google Scholar
• Grabska, E., Hawryło, P., and Socha, J. 2020. “Continuous detection of small-scale changes in scots pine dominated stands using dense Sentinel-2 time series.” Remote Sensing, Vol. 12(No. 8): pp. 1298. doi:https://doi.org/10.3390/rs12081298.
   Web of Science ®Google Scholar
• Hall, R.J., Castilla, G., White, J.C., Cooke, B.J., and Skakun, R.S. 2016. “Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective.” The Canadian Entomologist, Vol. 148(No. S1): pp. S296–S356. doi:https://doi.org/10.4039/tce.2016.11.
   Google Scholar
• Harris, C.R., Millman, K.J., van der Walt, S.J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., et al. 2020. “Array programming with NumPy.” Nature, Vol. 585(No. 7825): pp. 357–362. doi:https://doi.org/10.1038/s41586-020-2649-2.
   PubMed Web of Science ®Google Scholar
• Hershey, R., and Befort, W. 1995. “Aerial photo guide to New England Forest cover types (General Technical Report No. NE-195).” United States Department of Agriculture, Forest Service. Northeastern Forest Experiment Station.
   Google Scholar
• Hijmans, R. J. 2021. “raster: Geographic data analysis and modeling.” R package.
   Google Scholar
• Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., and Ferreira, L.G. 2002. “Overview of the radiometric and biophysical performance of the MODIS vegetation indices.” Remote Sensing of Environment, Vol. 83(No. 1–2): pp. 195–213. doi:https://doi.org/10.1016/S0034-4257(02)00096-2.
   Web of Science ®Google Scholar
• Hunt, E., and Rock, B. 1989. “Detection of changes in leaf water content using near- and middle-infrared reflectances.” Remote Sensing of Environment, Vol. 30(No. 1): pp. 43–54. doi:https://doi.org/10.1016/0034-4257(89)90046-1.
   Web of Science ®Google Scholar
• Iverson, L.R., Matthews, S.N., Prasad, A.M., Peters, M.P., and Yohe, G. 2012. “Development of risk matrices for evaluating climatic change responses of forested habitats.” Climatic Change, 114(No. 2): pp. 231–243. doi:https://doi.org/10.1007/s10584-012-0412-x.
   Web of Science ®Google Scholar
• Kennedy, R.E., Yang, Z., and Cohen, W.B. 2010. “Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms.” Remote Sensing of Environment, Vol. 114(No. 12): pp. 2897–2910. doi:https://doi.org/10.1016/j.rse.2010.07.008.
   Web of Science ®Google Scholar
• Kuhn, M. 2008. “Building predictive models in R using the caret package.” Journal of Statistical Software, Vol. 28(No. 5): pp. 1–26. doi:https://doi.org/10.18637/jss.v028.i05.
   PubMed Web of Science ®Google Scholar
• Leckie, D.G., Teillet, P.M., Ostaff, D.P., and Fedosejevs, G. 1988. “Sensor band selection for detecting current defoliation caused by the spruce budworm.” Remote Sensing of Environment, Vol. 26(No. 1): pp. 31–50. doi:https://doi.org/10.1016/0034-4257(88)90118-6.
   Web of Science ®Google Scholar
• Linnakoski, R., Kasanen, R., Dounavi, A., and Forbes, K.M. 2019. “Editorial: Forest health under climate change: Effects on tree resilience, and pest and pathogen dynamics.” Frontiers of Plant Science, Vol. 10: pp. 1157. doi:https://doi.org/10.3389/fpls.2019.01157.
   PubMed Web of Science ®Google Scholar
• Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., and Gascon, F. 2017. “Sen2Cor for Sentinel-2.” In Image and Signal Processing for Remote Sensing XXIII, Vol. 10427, p. 1042704. International Society for Optics and Photonics. doi:https://doi.org/10.1117/12.2278218.
   Google Scholar
• McIntire, C.D., Huggett, B.A., Dunn, E., Munck, I.A., Vadeboncoeur, M.A., and Asbjornsen, H. 2021. “Pathogen-induced defoliation impacts on transpiration, leaf gas exchange, and non-structural carbohydrate allocation in eastern white pine (Pinus strobus).” Trees, Vol. 35(No. 2): pp. 357–373. doi:https://doi.org/10.1007/s00468-020-02037-z.
   Web of Science ®Google Scholar
• McIntire, C.D., Munck, I.A., Ducey, M.J., and Asbjornsen, H. 2018a. “Thinning treatments reduce severity of foliar pathogens in eastern white pine.” Forest Ecology and Management, Vol. 423: pp. 106–113. doi:https://doi.org/10.1016/j.foreco.2018.03.032.
   Web of Science ®Google Scholar
• McIntire, C.D., Munck, I.A., Vadeboncoeur, M.A., Livingston, W.H., and Asbjornsen, H. 2018b. “Impacts of white pine needle damage on seasonal litterfall dynamics and wood growth of eastern white pine (Pinus strobus) in northern New England.” Forest Ecology and Management, Vol. 423: pp. 27–36. doi:https://doi.org/10.1016/j.foreco.2018.02.034.
   Web of Science ®Google Scholar
• Meigs, G.W., Kennedy, R.E., and Cohen, W.B. 2011. “A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests.” Remote Sensing of Environment, Vol. 115(No. 12): pp. 3707–3718. doi:https://doi.org/10.1016/j.rse.2011.09.009.
   Web of Science ®Google Scholar
• Munck, I., Ostrofsky, W., and Burns, B. 2012. “Eastern white pine needle damage survey.” USDA Forest Service Northeastern Area State and Private Forestry, NA-PR-01-11.
   Google Scholar
• Olsson, P.-O., Jönsson, A.M., and Eklundh, L. 2012. “A new invasive insect in Sweden – Physokermes inopinatus: Tracing forest damage with satellite based remote sensing.” Forest Ecology and Management, Vol. 285: pp. 29–37. doi:https://doi.org/10.1016/j.foreco.2012.08.003.
   Web of Science ®Google Scholar
• Pérez-Romero, N.-C., Palacios-Rodriguez, A., and Mesas-Carrascosa,   2019. “Improvement of remote sensing-based assessment of defoliation of Pinus spp. caused by Thaumetopoea Pityocampa Denis and Schiffermüller and related environmental drivers in southeastern Spain.” Remote Sensing, Vol. 11: pp. 1736. doi:https://doi.org/10.3390/rs11141736.
   Google Scholar
• Rahimzadeh-Bajgiran, P., Weiskittel, A., Kneeshaw, D., and MacLean, D. 2018. “Detection of annual spruce budworm defoliation and severity classification using Landsat imagery.” Forests, Vol. 9(No. 6): pp. 357–374. doi:https://doi.org/10.3390/f9060357.
   Web of Science ®Google Scholar
• Rahimzadeh-Bajgiran, P., Hennigar, C., Weiskittel, A., and Lamb, S. 2020. “Forest potential productivity mapping by linking remote-sensing-derived metrics to site variables.” Remote Sensing, Vol. 12(No. 12):pp. 2056. doi:https://doi.org/10.3390/rs1212.
   Web of Science ®Google Scholar
• Rouse, J. W., Haes, R. H., Schell, J. A., and Deering, D. W. 1973. “Monitoring Vegetation Systems in the Great Plains with ERTS.” In Third Earth Resources Technology Satellite-1 Symposium. Washington, DC: Goddard Space Center.
   Google Scholar
• Strobl, C., Boulesteix, A.-L., Zeileis, A., and Hothorn, T. 2007. “Bias in random forest variable importance measures: Illustrations, sources and a solution.” BMC Bioinformatics, Vol. 8: pp. 25. doi:https://doi.org/10.1186/1471-2105-8-25.
   PubMed Web of Science ®Google Scholar
• Tane, Z., Roberts, D., Koltunov, A., Sweeney, S., and Ramirez, C. 2018. “A framework for detecting conifer mortality across an ecoregion using high spatial resolution spaceborne imaging spectroscopy.” Remote Sensing of Environment, Vol. 209: pp. 195–210. doi:https://doi.org/10.1016/j.rse.2018.02.073.
   Web of Science ®Google Scholar
• U.S. Department of Agriculture. 2020. “Individual tree species parameter maps.” Mapping and Reporting, last modified March 15, 2020, https://www.fs.fed.us/foresthealth/applied-sciences/mapping-reporting/indiv-tree-parameter-maps.shtml.
   Google Scholar
• Vose, R.S., Applequist, S., Squires, M., Durre, I., Menne, M.J., Williams, C.N., Fenimore, C., Gleason, K., and Arndt, D. 2014. “Improved historical temperature and precipitation time series for U.S. climate divisions.” Journal of Applied Meteorology and Climatology, Vol. 53(No. 5): pp. 1232–1251. doi:https://doi.org/10.1175/JAMC-D-13-0248.1.
   Web of Science ®Google Scholar
• Widmann, R., McWilliams, W., Bennett, K., and Desmarais, K. 2003. “Pine resource in New England using forest inventory and analyses data.” In Managing White Pine in a New Millennium University of New Hampshire Cooperative Extension. An Overview of the White. Hillsborough, NH, USA, pp. 1–8.
   Google Scholar
• Wong, C.Y.S., D'Odorico, P., Arain, M.A., and Ensminger, I. 2020. “Tracking the phenology of photosynthesis using carotenoid-sensitive and near-infrared reflectance vegetation indices in a temperate evergreen and mixed deciduous forest.” New Phytologist, Vol. 226(No. 6): pp. 1682–1695. doi:https://doi.org/10.1111/nph.16479.
   PubMed Web of Science ®Google Scholar
• Wong, C.Y.S., D'Odorico, P., Bhathena, Y., Arain, M.A., and Ensminger, I. 2019. “Carotenoid based vegetation indices for accurate monitoring of the phenology of photosynthesis at the leaf-scale in deciduous and evergreen trees.” Remote Sensing of Environment, Vol. 233: pp. 111407. doi:https://doi.org/10.1016/j.rse.2019.111407.
   Web of Science ®Google Scholar
• Wyka, S.A., McIntire, C.D., Smith, C., Munck, I.A., Rock, B.N., Asbjornsen, H., and Broders, K.D. 2018a. “Effect of climatic variables on abundance and dispersal of Lecanosticta acicola spores and their impact on defoliation on eastern white pine.” Phytopathology, Vol. 108(No. 3): pp. 374–383. doi:https://doi.org/10.1094/PHYTO-02-17-0065-R.
   PubMed Web of Science ®Google Scholar
• Wyka, S.A., Munck, I.A., Brazee, N.J., and Broders, K.D. 2018b. “Response of eastern white pine and associated foliar, blister rust, canker and root rot pathogens to climate change.” Forest Ecology and Management, Vol. 423: pp. 18–26. doi:https://doi.org/10.1016/j.foreco.2018.03.011.
   Web of Science ®Google Scholar