Windthrow damage detection in Nordic forests by 3D reconstruction of very high-resolution stereo optical satellite imagery

References

• Aguilar, M. A., A. Nemmaoui, F. J. Aguilar, and R. Qin. 2019. “Quality Assessment of Digital Surface Models Extracted from WorldView-2 and WorldView-3 Stereo Pairs Over Different Land Covers.” GIScience & Remote Sensing 56 (1): 109–129. https://doi.org/10.1080/15481603.2018.1494408.
   Web of Science ®Google Scholar
• Aguilar, M. A., M. D. M. Saldaña, and F. J. Aguilar. 2013. “Assessing Geometric Accuracy of the Orthorectification Process from GeoEye-1 and WorldView-2 Panchromatic Images.” International Journal of Applied Earth Observation and Geoinformation 21:427–435. https://doi.org/10.1016/j.jag.2012.06.004.
   Web of Science ®Google Scholar
• Aguilar, M. A., M. D. M. Saldaña, and F. J. Aguilar. 2014. “Generation and Quality Assessment of Stereo-Extracted DSM from GeoEye-1 and WorldView-2 Imagery.” IEEE Transactions on Geoscience and Remote Sensing 52 (2): 1259–1271. https://doi.org/10.1109/TGRS.2013.2249521.
   Web of Science ®Google Scholar
• Baldi, P., S. Brunak, Y. Chauvin, C. A. F. Andersen, and H. Nielsen. 2000. “Assessing the Accuracy of Prediction Algorithms for Classification: An Overview.” Bioinformatics 16 (5): 412–424. https://doi.org/10.1093/bioinformatics/16.5.412.
   PubMed Web of Science ®Google Scholar
• Blennow, K., and Erik, P. 2013. ”Societal Impacts of Storm Damage.” in Living with Storm Damage to Forests. What Science Can Tell Us 3, edited by Barry Gardiner, Andreas Schuck, Mart-Jan Schelhaas, Christophe Orazio, Kristina Blennow and Bruce Nicoll, 70–79. Joensuu, Finland: European Forest Institute. https://efi.int/sites/default/files/files/publication-bank/2018/efi_wsctu3_2013.pdf.
   Google Scholar
• Brandt, M., C. J. Tucker, A. Kariryaa, K. Rasmussen, C. Abel, J. Small, J. Chave, et al. 2020. “An Unexpectedly Large Count of Trees in the West African Sahara and Sahel.” Nature 587 (7832): 78–82. https://doi.org/10.1038/s41586-020-2824-5.
   PubMed Web of Science ®Google Scholar
• Bresnahan, P., R. Powers, and L. H. Vazquez. 2016. “WorldView-3 Geolocation Accuracy and Band Co-Registration Analysis.” Paper presented at the 15th JACIE (Joint Agency Commercial Imagery Evaluation) Workshop, co-located with ASPRS Imaging and Geospatial Technology Forum, April 2016, Fort Worth, Texas.
   Google Scholar
• Chehata, N., C. Orny, S. Boukir, D. Guyon, and J. P. Wigneron. 2014. “Object-Based Change Detection in Wind Storm-Damaged Forest Using High-Resolution Multispectral Images.” International Journal of Remote Sensing 35 (13): 4758–4777. https://doi.org/10.1080/01431161.2014.930199.
   Web of Science ®Google Scholar
• Chicco, D., and G. Jurman. 2020. “The Advantages of the Matthews Correlation Coefficient (MCC) Over F1 Score and Accuracy in Binary Classification Evaluation.” BMC Genomics 21 (1): 6. https://doi.org/10.1186/s12864-019-6413-7.
   PubMed Web of Science ®Google Scholar
• Dalponte, M., S. Marzini, Y. Tatiana Solano-Correa, G. Tonon, L. Vescovo, and D. Gianelle. 2020. “Mapping Forest Windthrows Using High Spatial Resolution Multispectral Satellite Images.” International Journal of Applied Earth Observation and Geoinformation 93:102206. https://doi.org/10.1016/j.jag.2020.102206.
   Web of Science ®Google Scholar
• Dowman, I., K. Jacobsen, G. Konecny, and R. Sandau. 2022. High Resolution Optical Satellite Imagery. 2nd ed. Caithness, Scotland: Whittles Publishing.
   Google Scholar
• Fassnacht, F. E., D. Mangold, J. Schäfer, M. Immitzer, T. Kattenborn, B. Koch, and H. Latifi. 2017. “Estimating Stand Density, Biomass and Tree Species from Very High Resolution Stereo-Imagery – Towards an All-In-One Sensor for Forestry Applications?” Forestry: An International Journal of Forest Research 90 (5): 613–631. https://doi.org/10.1093/forestry/cpx014.
   Google Scholar
• Feser, F., M. Barcikowska, O. Krueger, F. Schenk, R. Weisse, and L. Xia. 2015. “Storminess Over the North Atlantic and Northwestern Europe—A Review.” Quarterly Journal of the Royal Meteorological Society 141 (687): 350–382. https://doi.org/10.1002/qj.2364.
   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
• Foody, G. M. 2010. “Assessing the Accuracy of Land Cover Change with Imperfect Ground Reference Data.” Remote Sensing of Environment 114 (10): 2271–2285. https://doi.org/10.1016/j.rse.2010.05.003.
   Web of Science ®Google Scholar
• Foody, G. M. 2011. “Impacts of Imperfect Reference Data on the Apparent Accuracy of Species Presence–Absence Models and Their Predictions.” Global Ecology and Biogeography 20 (3): 498–508. https://doi.org/10.1111/j.1466-8238.2010.00605.x.
   Web of Science ®Google Scholar
• Francini, S., R. E. McRoberts, F. Giannetti, M. Mencucci, M. Marchetti, G. S. Mugnozza, and G. Chirici. 2020. “Near-Real Time Forest Change Detection Using PlanetScope Imagery.” European Journal of Remote Sensing 53 (1): 233–244. https://doi.org/10.1080/22797254.2020.1806734.
   Web of Science ®Google Scholar
• Goldbergs, G. 2021. “Impact of Base-To-Height Ratio on Canopy Height Estimation Accuracy of Hemiboreal Forest Tree Species by Using Satellite and Airborne Stereo Imagery.” Remote Sensing 13 (15): 2941. https://doi.org/10.3390/rs13152941.
   Web of Science ®Google Scholar
• Goldbergs, G., S. W. Maier, S. R. Levick, and A. Edwards. 2019. “Limitations of High Resolution Satellite Stereo Imagery for Estimating Canopy Height in Australian Tropical Savannas.” International Journal of Applied Earth Observation and Geoinformation 75:83–95. https://doi.org/10.1016/j.jag.2018.10.021.
   Web of Science ®Google Scholar
• Gomes, M. F., and P. Maillard. 2016. “Detection of Tree Crowns in Very High Spatial Resolution Images.” In Environmental Applications of Remote Sensing, edited by M. Maged, 41–73. Rijeka, Croatia: IntechOpen. https://doi.org/10.5772/62122.
   Google Scholar
• Gregow, H., A. Laaksonen, and M. E. Alper. 2017. “Increasing Large Scale Windstorm Damage in Western, Central and Northern European Forests, 1951–2010.” Scientific Reports 7 (1): 46397. https://doi.org/10.1038/srep46397.
   PubMedGoogle Scholar
• Gregow, H., M. Rantanen, T. K. Laurila, and A. Mäkelä. 2020. Review on winds, extratropical cyclones and their impacts in Northern Europe and Finland. Report 2020:3. Helsinki, Finland: Finnish Meteorological Institute.
   Google Scholar
• Grodecki, J., and G. Dial. 2003. “Block Adjustment of High-Resolution Satellite Images Described by Rational Polynomials.” Photogrammetric Engineering & Remote Sensing 69 (1): 59–68. https://doi.org/10.14358/PERS.69.1.59.
   Web of Science ®Google Scholar
• Guilford, J. P. 1954. Psychometric Methods. 2nd ed. New York: McGraw-Hill.
   Google Scholar
• Hanewinkel, M., and Jean, L. P. 2013. ”The Economic Impact of Storms.” In Living with Storm Damage to Forests. What Science Can Tell Us 3, edited by Barry Gardiner, Andreas Schuck, Mart-Jan Schelhaas, Christophe Orazio, Kristina Blennow and Bruce Nicoll, 55–64. Joensuu, Finland: European Forest Institute. https://efi.int/sites/default/files/files/publication-bank/2018/efi_wsctu3_2013.pdf.
   Google Scholar
• Hirschmüller, H. 2008. “Stereo Processing by Semiglobal Matching and Mutual Information.” IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (2): 328–341. https://doi.org/10.1109/TPAMI.2007.1166.
   PubMed Web of Science ®Google Scholar
• Hobi, M. L., and C. Ginzler. 2012. “Accuracy Assessment of Digital Surface Models Based on WorldView-2 and ADS80 Stereo Remote Sensing Data.” Sensors 12 (5): 6347–6368. https://doi.org/10.3390/s120506347.
   PubMed Web of Science ®Google Scholar
• Höhle, J., and M. Höhle. 2009. “Accuracy Assessment of Digital Elevation Models by Means of Robust Statistical Methods.” ISPRS Journal of Photogrammetry and Remote Sensing 64 (4): 398–406. https://doi.org/10.1016/j.isprsjprs.2009.02.003.
   Web of Science ®Google Scholar
• Honkavaara, E., P. Litkey, and K. Nurminen. 2013. “Automatic Storm Damage Detection in Forests Using High‑Altitude Photogrammetric Imagery.” Remote Sensing 5 (3): 1405–1424. https://doi.org/10.3390/rs5031405.
   Web of Science ®Google Scholar
• Immitzer, M., C. Stepper, S. Böck, C. Straub, and C. Atzberger. 2016. “Use of WorldView-2 Stereo Imagery and National Forest Inventory Data for Wall-To-Wall Mapping of Growing Stock.” Forest Ecology and Management 359:232–246. https://doi.org/10.1016/j.foreco.2015.10.018.
   Web of Science ®Google Scholar
• Kamimura, K., K. Kitagawa, S. Saito, and H. Mizunaga. 2012. “Root Anchorage of Hinoki (Chamaecyparis Obtuse (Sieb. Et Zucc.) Endl.) Under the Combined Loading of Wind and Rapidly Supplied Water on Soil: Analyses Based on Tree-Pulling Experiments.” European Journal of Forest Research 131 (1): 219–227. https://doi.org/10.1007/s10342-011-0508-2.
   Web of Science ®Google Scholar
• Kislov, D. E., and K. A. Korznikov. 2020. “Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning.” Remote Sensing 12 (7): 1145. https://doi.org/10.3390/rs12071145.
   Web of Science ®Google Scholar
• Kislov, D. E., K. A. Korznikov, J. Altman, A. S. Vozmishcheva, P. V. Krestov, M. Disney, and A. Cord. 2021. “Extending Deep Learning Approaches for Forest Disturbance Segmentation on Very High-Resolution Satellite Images.” Remote Sensing in Ecology and Conservation 7 (3): 355–368. https://doi.org/10.1002/rse2.194.
   Web of Science ®Google Scholar
• Komonen, A., L. M. Schroeder, and J. Weslien. 2011. “Ips Typographus Population Development After a Severe Storm in a Nature Reserve in Southern Sweden.” Journal of Applied Entomology 135 (1–2): 132–141. https://doi.org/10.1111/j.1439-0418.2010.01520.x.
   Web of Science ®Google Scholar
• Kvaalen, H., S. Solberg, and J. May. 2015. Aldersuavhengig bonitering med laserskanning av enkelttrær. Report 1/67 . Ås, Norway: NIBIO .
   Google Scholar
• Laben, C. A., and B. V. Brower. 2000. Process for Enhancing the Spatial Resolution of Multispectral Imagery using Pan-Sharpening. U.S. Patent 6011875.
   Google Scholar
• Loghin, A.-M., J. Otepka-Schremmer, and N. Pfeifer. 2020. “Potential of Pléiades and WorldView-3 Tri-Stereo DSMs to Represent Heights of Small Isolated Objects.” Sensors 20 (9). https://doi.org/10.3390/s20092695.
   Web of Science ®Google Scholar
• Matthews, B. W. 1975. “Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme.” Biochimica et Biophysica Acta (BBA) - Protein Structure 405 (2): 442–451. https://doi.org/10.1016/0005-2795(75)90109-9.
   PubMed Web of Science ®Google Scholar
• Maxar Technologies. 2020a WorldView-3. Data Sheet. https://resources.maxar.com/data-sheets/worldview-3.
   Google Scholar
• Maxar Technologies. 2020b. System-Ready (1B) Imagery. Data Sheet. https://resources.maxar.com/data-sheets/system-ready-imagery-data-sheet.
   Google Scholar
• Maxar Technologies. 2020c. View-Ready (2A) Imagery. Data Sheet. https://resources.maxar.com/data-sheets/view-ready-imagery-data-sheet.
   Google Scholar
• Montesano, P. M., C. Neigh, G. Sun, L. Duncanson, J. Van Den Hoek, and K. Jon Ranson. 2017. “The Use of Sun Elevation Angle for Stereogrammetric Boreal Forest Height in Open Canopies.” Remote Sensing of Environment 196:76–88. https://doi.org/10.1016/j.rse.2017.04.024.
   PubMed Web of Science ®Google Scholar
• Mugabowindekwe, M., M. Brandt, J. Chave, F. Reiner, D. L. Skole, A. Kariryaa, C. Igel, et al. 2023. “Nation-Wide Mapping of Tree-Level Aboveground Carbon Stocks in Rwanda.” Nature Climate Change 13 (1): 91–97. https://doi.org/10.1038/s41558-022-01544-w.
   PubMed Web of Science ®Google Scholar
• Neigh, C. S. R., J. G. Masek, P. Bourget, B. Cook, C. Huang, K. Rishmawi, and F. Zhao. 2014. “Deciphering the Precision of Stereo IKONOS Canopy Height Models for US Forests with G-Liht Airborne LiDar.” Remote Sensing 6 (3): 1762–1782. https://doi.org/10.3390/rs6031762.
   Web of Science ®Google Scholar
• NIBIO. 2022. “SR16 - the Norwegian Forest Resource Map.” Accessed October 10, 2022. https://kilden.nibio.no.
   Google Scholar
• Norwegian Mapping Authority. 2022. “Høydedata.” Accessed April 10, 2022. https://hoydedata.no/LaserInnsyn2/.
   Google Scholar
• Norwegian Mapping Authority and Geovekst. 2022. “Norge i bilder: Østlandet 2016, Valdres 2019.” Accessed April 10, 2022. https://norgeibilder.no/.
   Google Scholar
• Nowak Da Costa, J. K. 2010. Geometric Quality Testing of the WorldView-2 Image Data Acquired over the JRC Maussane Test Site using ERDAS LPS, PCI Geomatics and Keystone digital photogrammetry software packages – Initial Findings. Luxembourg, Luxembourg: Publications Office of the European Union.
   Google Scholar
• Økland, B., and A. Berryman. 2004. “Resource Dynamic Plays a Key Role in Regional Fluctuations of the Spruce Bark Beetles Ips Typographus.” Agricultural and Forest Entomology 6 (2): 141–146. https://doi.org/10.1111/j.1461-9555.2004.00214.x.
   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
• Patacca, M., M. Lindner, M. Esteban Lucas‐Borja, T. Cordonnier, G. Fidej, B. Gardiner, Y. Hauf, et al. 2023. “Significant Increase in Natural Disturbance Impacts on European Forests Since 1950.” Global Change Biology 29 (5): 1359–1376. https://doi.org/10.1111/gcb.16531.
   PubMed Web of Science ®Google Scholar
• Pearse, G. D., J. P. Dash, H. J. Persson, and M. S. Watt. 2018. “Comparison of High-Density LiDar and Satellite Photogrammetry for Forest Inventory.” ISPRS Journal of Photogrammetry and Remote Sensing 142:257–267. https://doi.org/10.1016/j.isprsjprs.2018.06.006.
   Web of Science ®Google Scholar
• Perko, R., H. Raggam, K. Gutjahr, and M. Schardt. 2014. “Assessment of the Mapping Potential of Pléiades Stereo and Triplet Data.” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2 (3): 103. https://doi.org/10.5194/isprsannals-II-3-103-2014.
   Google Scholar
• Perko, R., H. Raggam, and P. M. Roth. 2019. “Mapping with Pléiades—End-to-end Workflow.” Remote Sensing 11 (17): 2052. https://doi.org/10.3390/rs11172052.
   Web of Science ®Google Scholar
• Persson, H. J. 2016. “Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data.” Remote Sensing 8 (9): 736. https://doi.org/10.3390/rs8090736.
   Web of Science ®Google Scholar
• Persson, H. J., and R. Perko. 2016. “Assessment of Boreal Forest Height from WorldView-2 Satellite Stereo Images.” Remote Sensing Letters 7 (12): 1150–1159. https://doi.org/10.1080/2150704X.2016.1219424.
   Web of Science ®Google Scholar
• Piermattei, L., M. Marty, C. Ginzler, M. Pöchtrager, W. Karel, C. Ressl, N. Pfeifer, and M. Hollaus. 2019. “Pléiades Satellite Images for Deriving Forest Metrics in the Alpine Region.” International Journal of Applied Earth Observation and Geoinformation 80:240–256. https://doi.org/10.1016/j.jag.2019.04.008.
   Web of Science ®Google Scholar
• Piermattei, L., M. Marty, W. Karel, C. Ressl, M. Hollaus, C. Ginzler, and N. Pfeifer. 2018. “Impact of the Acquisition Geometry of Very High-Resolution Pléiades Imagery on the Accuracy of Canopy Height Models Over Forested Alpine Regions.” Remote Sensing 10 (10): 1542. https://doi.org/10.3390/rs10101542.
   Web of Science ®Google Scholar
• Poli, D., F. Remondino, E. Angiuli, and G. Agugiaro. 2015. “Radiometric and Geometric Evaluation of GeoEye-1, WorldView-2 and Pléiades-1A Stereo Images for 3D Information Extraction.” ISPRS Journal of Photogrammetry and Remote Sensing 100:35–47. https://doi.org/10.1016/j.isprsjprs.2014.04.007.
   Web of Science ®Google Scholar
• Qin, R. 2019. “A Critical Analysis of Satellite Stereo Pairs for Digital Surface Model Generation and a Matching Quality Prediction Model.” ISPRS Journal of Photogrammetry and Remote Sensing 154:139–150. https://doi.org/10.1016/j.isprsjprs.2019.06.005.
   Web of Science ®Google Scholar
• R Core Team. 2022. R: A Language and Environment for Statistical Computing. Version 4.2.2 R Foundation for Statistical Computing. https://www.R-project.org.
   Google Scholar
• Schelhaas, M.-J., G.-J. Nabuurs, and A. Schuck. 2003. “Natural Disturbances in the European Forests in the 19th and 20th Centuries.” Global Change Biology 9 (11): 1620–1633. https://doi.org/10.1046/j.1365-2486.2003.00684.x.
   Web of Science ®Google Scholar
• Schwarzbauer, P., and Peter, R. 2013. ”Impact on Industry and Markets - Roundwood Prices and Procurement Risks.” In Living with Storm Damage to Forests. What Science Can Tell Us 3, edited by Barry Gardiner, Andreas Schuck, Mart-Jan Schelhaas, Christophe Orazio, Kristina Blennow and Bruce Nicoll, 64–70. Joensuu, Finland: European Forest Institute. https://efi.int/sites/default/files/files/publication-bank/2018/efi_wsctu3_2013.pdf.
   Google Scholar
• Schwarz, M., C. Steinmeier, F. Holecz, O. Stebler, and H. Wagner. 2003. “Detection of Windthrow in Mountainous Regions with Different Remote Sensing Data and Classification Methods.” Scandinavian Journal of Forest Research 18 (6): 525–536. https://doi.org/10.1080/02827580310018023.
   Web of Science ®Google Scholar
• Shamsoddini, A., J. C. Trinder, and R. Turner. 2013. “Pine Plantation Structure Mapping Using WorldView-2 Multispectral Image.” International Journal of Remote Sensing 34 (11): 3986–4007. https://doi.org/10.1080/01431161.2013.772308.
   Web of Science ®Google Scholar
• Skattør, H. B., J. Mamen, H. Mjelstad, A. C. Berger, and T. A. Walløe. 2021. Svært kraftige vindkast i Agder og på Østlandet fredag 19. november 2021. Hendelsesrapport 25/2021. Oslo: Meteorologisk institutt. https://www.met.no/publikasjoner/met-info/met-info-2021/_/attachment/download/31cf2504-1255-4fba-a408-4c85d448d7dc:e99e45a5a2d0a0bbc682ad3549d146991809d124/MET-info-25-2021.pdf.
   Google Scholar
• Skogbrand Forsikringsselskap Gjensidig. 2022a. “Forsikringsvilkår av 1. mars 2022.” Skogbrand Forsikringsselskap Gjensidig. Accessed March 15, 2022. https://www.skogbrand.no/wp-content/uploads/2022/01/Skogforsikring_Forsikringsvilkar_01_03_2022.pdf.
   Google Scholar
• Skogbrand Forsikringsselskap Gjensidig. 2022b. “Skadet volum etter stormen 19.11.2021 og stormene i januar-februar 2022.” Skogbrand Forsikringsselskap Gjensidig. Accessed February 2, 2023. https://www.skogbrand.no/volum-skadet-etter-stormen-19-11-21-og-stormene-i-januar-februar-22/.
   Google Scholar
• Skurikhin, A., N. McDowell, and R. Middleton. 2016. “Unsupervised Individual Tree Crown Detection in High-Resolution Satellite Imagery.” Journal of Applied Remote Sensing 10 (1): 010501. https://doi.org/10.1117/1.JRS.10.010501.
   Google Scholar
• St‐Onge, B., Y. Hu, and C. Vega. 2008. “Mapping the Height and Above‐Ground Biomass of a Mixed Forest Using Lidar and Stereo Ikonos Images.” International Journal of Remote Sensing 29 (5): 1277–1294. https://doi.org/10.1080/01431160701736505.
   Web of Science ®Google Scholar
• Staquet, M., M. Rozencweig, Y. J. Lee, and F. M. Muggia. 1981. “Methodology for the Assessment of New Dichotomous Diagnostic Tests.” Journal of Chronic Diseases 34 (12): 599–610. https://doi.org/10.1016/0021-9681(81)90059-X.
   PubMedGoogle Scholar
• St-Onge, B., and S. Grandin. 2019. “Estimating the Height and Basal Area at Individual Tree and Plot Levels in Canadian Subarctic Lichen Woodlands Using Stereo WorldView-3 Images.” Remote Sensing 11 (3): 248. https://doi.org/10.3390/rs11030248.
   Web of Science ®Google Scholar
• Straub, C., J. Tian, R. Seitz, and P. Reinartz. 2013. “Assessment of Cartosat-1 and WorldView-2 Stereo Imagery in Combination with a LiDAR-DTM for Timber Volume Estimation in a Highly Structured Forest in Germany.” Forestry: An International Journal of Forest Research 86 (4): 463–473. https://doi.org/10.1093/forestry/cpt017.
   Google Scholar
• Sun, W., B. Chen, and D. Messinger. 2014. “Nearest-Neighbor Diffusion-Based Pan-Sharpening Algorithm for Spectral Images.” Optical Engineering 53 (1): 013107. https://doi.org/10.1117/1.OE.53.1.013107.
   Web of Science ®Google Scholar
• Thiele, C., and G. Hirschfeld. 2021. “Cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R.” Journal of Statistical Software 98 (11): 1–27. https://doi.org/10.18637/jss.v098.i11.
   Web of Science ®Google Scholar
• Thompson, I. D., S. C. Maher, D. P. Rouillard, J. M. Fryxell, and J. A. Baker. 2007. “Accuracy of Forest Inventory Mapping: Some Implications for Boreal Forest Management.” Forest Ecology and Management 252 (1): 208–221. https://doi.org/10.1016/j.foreco.2007.06.033.
   Web of Science ®Google Scholar
• Tian, J., T. Schneider, C. Straub, F. Kugler, and P. Reinartz. 2017. “Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection.” Remote Sensing 9 (3): 287. https://doi.org/10.3390/rs9030287.
   Web of Science ®Google Scholar
• Tong, F., H. Tong, R. Mishra, and Y. Zhang. 2021. “Delineation of Individual Tree Crowns Using High Spatial Resolution Multispectral WorldView-3 Satellite Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:7751–7761. https://doi.org/10.1109/JSTARS.2021.3100748.
   Web of Science ®Google Scholar
• Tucker, C. J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment 8 (2): 127–150. https://doi.org/10.1016/0034-4257(79)90013-0.
   Web of Science ®Google Scholar
• Ullah, S., M. Dees, P. Datta, P. Adler, T. Saeed, M. Sadiq Khan, and B. Koch. 2020. “Comparing the Potential of Stereo Aerial Photographs, Stereo Very High-Resolution Satellite Images, and TanDEM-X for Estimating Forest Height.” International Journal of Remote Sensing 41 (18): 6976–6992. https://doi.org/10.1080/01431161.2020.1752414.
   Web of Science ®Google Scholar
• Vaglio Laurin, G., F. Saverio, T. Luti, G. Chirici, F. Pirotti, and D. Papale. 2020. “Satellite Open Data to Monitor Forest Damage Caused by Extreme Climate-Induced Events: A Case Study of the Vaia Storm in Northern Italy.” Forestry: An International Journal of Forest Research 94 (3): 407–416. https://doi.org/10.1093/forestry/cpaa043.
   Google Scholar
• Vajsova, B., A. Walczynska, P. J. Åstrand, S. Bärisch, and S. Hain. 2015. , New sensors benchmark report on WorldView-3. Geometric benchmarking over Maussane test site for CAP purposes. Luxembourg, Luxembourg: Publications Office of the European Union.
   Google Scholar
• Valinger, E., G. Kempe, and J. Fridman. 2014. “Forest Management and Forest State in Southern Sweden Before and After the Impact of Storm Gudrun in the Winter of 2005.” Scandinavian Journal of Forest Research 29 (5): 466–472. https://doi.org/10.1080/02827581.2014.927528.
   Web of Science ®Google Scholar
• Wagner, F. H., M. P. Ferreira, A. Sanchez, M. C. M. Hirye, M. Zortea, E. Gloor, O. L. Phillips, C. R. de Souza Filho, Y. E. Shimabukuro, and L. E. O. C. Aragão. 2018. “Individual Tree Crown Delineation in a Highly Diverse Tropical Forest Using Very High Resolution Satellite Images.” ISPRS Journal of Photogrammetry and Remote Sensing 145:362–377. https://doi.org/10.1016/j.isprsjprs.2018.09.013.
   Web of Science ®Google Scholar
• Wagner, F. H., A. Sanchez, Y. Tarabalka, R. G. Lotte, M. P. Ferreira, M. P. M. Aidar, E. Gloor, et al. 2019. “Using the U-Net Convolutional Network to Map Forest Types and Disturbance in the Atlantic Rainforest with Very High Resolution Images.” Remote Sensing in Ecology and Conservation 5 (4): 360–375. https://doi.org/10.1002/rse2.111.
   Web of Science ®Google Scholar
• Wang, S., Z. Ren, W. Chuanyong, Q. Lei, W. Gong, Q. Ou, H. Zhang, G. Ren, and L. Chuanyou. 2019. “DEM Generation from Worldview-2 Stereo Imagery and Vertical Accuracy Assessment for Its Application in Active Tectonics.” Geomorphology 336:107–118. https://doi.org/10.1016/j.geomorph.2019.03.016.
   Web of Science ®Google Scholar
• Yerushalmy, J. 1947. “Statistical Problems in Assessing Methods of Medical Diagnosis, with Special Reference to X-Ray Techniques.” Public Health Reports (1896-1970) 62 (40): 1432–1449. https://doi.org/10.2307/4586294.
   PubMed Web of Science ®Google Scholar
• Yu, X., H. Juha, K. Mika, N. Kimmo, K. Kirsi, V. Mikko, K. Ville, et al. 2015. “Comparison of Laser and Stereo Optical, SAR and InSar Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes.” Remote Sensing 7 (12): 15933–15954. https://doi.org/10.3390/rs71215809.
   Web of Science ®Google Scholar