{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T20:32:22Z","timestamp":1784752342165,"version":"3.55.0"},"reference-count":106,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T00:00:00Z","timestamp":1597276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455\u20132500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers\u2014support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724\u2013727 nm) and shortwave infrared (1520\u20131564 nm and 1684\u20131706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests.<\/jats:p>","DOI":"10.3390\/rs12162610","type":"journal-article","created":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T09:23:44Z","timestamp":1597310624000},"page":"2610","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7905-0446","authenticated-orcid":false,"given":"Arto","family":"Viinikka","sequence":"first","affiliation":[{"name":"Finnish Environment Institute, Latokartanonkaari 11, 00790 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1039-3357","authenticated-orcid":false,"given":"Pekka","family":"Hurskainen","sequence":"additional","affiliation":[{"name":"Finnish Environment Institute, Latokartanonkaari 11, 00790 Helsinki, Finland"},{"name":"Earth Change Observation Laboratory, Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1967-7428","authenticated-orcid":false,"given":"Sarita","family":"Keski-Saari","sequence":"additional","affiliation":[{"name":"Department of Geographical and Historical Studies, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland"},{"name":"Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonja","family":"Kivinen","sequence":"additional","affiliation":[{"name":"Finnish Environment Institute, Latokartanonkaari 11, 00790 Helsinki, Finland"},{"name":"Department of Geographical and Historical Studies, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5509-6922","authenticated-orcid":false,"given":"Topi","family":"Tanhuanp\u00e4\u00e4","sequence":"additional","affiliation":[{"name":"Department of Geographical and Historical Studies, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland"},{"name":"Department of Forest Sciences, University of Helsinki, FI-00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7622-9512","authenticated-orcid":false,"given":"Janne","family":"M\u00e4yr\u00e4","sequence":"additional","affiliation":[{"name":"Finnish Environment Institute, Latokartanonkaari 11, 00790 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Poikolainen","sequence":"additional","affiliation":[{"name":"Department of Geographical and Historical Studies, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petteri","family":"Vihervaara","sequence":"additional","affiliation":[{"name":"Finnish Environment Institute, Latokartanonkaari 11, 00790 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timo","family":"Kumpula","sequence":"additional","affiliation":[{"name":"Department of Geographical and Historical Studies, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,13]]},"reference":[{"key":"ref_1","first-page":"16","article-title":"Boreal forests","volume":"46","author":"Esseen","year":"1997","journal-title":"Ecol. Bull."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.14214\/sf.552","article-title":"Natural variability of forests as a reference for restoring and managing biological diversity in boreal Fennoscandia","volume":"36","author":"Kuuluvainen","year":"2002","journal-title":"Silva Fenn."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1007\/s10531-017-1453-2","article-title":"Forest biodiversity, ecosystem functioning and the provision of ecosystem services","volume":"26","author":"Brockerhoff","year":"2017","journal-title":"Biodivers. Conserv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1046\/j.1523-1739.2000.98533.x","article-title":"Indicators of biodiversity for ecologically sustainable forest management","volume":"14","author":"Lindenmayer","year":"2000","journal-title":"Conserv. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.jnc.2003.08.002","article-title":"Long-term persistence of aspen\u2014A key host for many threatened species\u2013is endangered in old-growth conservation areas in Finland","volume":"12","author":"Kouki","year":"2004","journal-title":"J. Nat. Conserv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"118008","DOI":"10.1016\/j.foreco.2020.118008","article-title":"A keystone species, European aspen (Populus tremula L.), in boreal forests: Ecological role, knowledge needs and mapping using remote sensing","volume":"462","author":"Kivinen","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1023\/A:1008888319031","article-title":"Substrate requirements of red-listed saproxylic invertebrates in Sweden","volume":"7","author":"Jonsell","year":"1998","journal-title":"Biodivers. Conserv."},{"key":"ref_8","first-page":"373","article-title":"Red-listed boreal forest species of Finland: Associations with forest structure, tree species, and decaying wood","volume":"43","author":"Tikkanen","year":"2006","journal-title":"Ann. Zool. Fenn."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1139\/X06-289","article-title":"The demographic structure of European aspen (Populus tremula) populations in managed and old-growth boreal forests in eastern Finland","volume":"37","author":"Siitonen","year":"2007","journal-title":"Can. J. For. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/978-94-017-8663-8_12","article-title":"Species specific management inventory in Finland","volume":"Volume 7","author":"Maltamo","year":"2014","journal-title":"Forestry Applications of Airborne Laser Scanning\u2014Concepts and Case Studies. Managing Forest Ecosystems"},{"key":"ref_11","first-page":"135","article-title":"Long-term spatio-temporal dynamics and historical continuity of European aspen (Populus tremula L.) stands in the Koli National Park, eastern Finland","volume":"82","author":"Vehmas","year":"2008","journal-title":"For. Int. J. For. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"118009","DOI":"10.1016\/j.foreco.2020.118009","article-title":"A key tree species for forest biodiversity, European aspen (Populus tremula), is rapidly declining in boreal old-growth forest reserves","volume":"462","author":"Hardenbol","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote Sensing Technologies for Enhancing Forest Inventories: A Review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_14","first-page":"215","article-title":"Area-based inventory in Norway\u2014From innovation to an operational reality","volume":"Volume 7","author":"Maltamo","year":"2014","journal-title":"Forestry Applications of Airborne Laser Scanning. Managing Forest Ecosystems"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2016.10.022","article-title":"A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory","volume":"194","author":"Nilsson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1080\/02827581.2017.1416666","article-title":"Remote sensing and forest inventories in Nordic countries\u2013roadmap for the future","volume":"33","author":"Kangas","year":"2018","journal-title":"Scand. J. For. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1641\/0006-3568(2004)054[0511:HSRRSD]2.0.CO;2","article-title":"High spatial resolution remotely sensed data for ecosystem characterization","volume":"54","author":"Wulder","year":"2004","journal-title":"BioScience"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1007\/s10021-007-9041-z","article-title":"Hyperspectral Remote Sensing of Canopy Biodiversity in Hawaiian Lowland Rainforests","volume":"10","author":"Carlson","year":"2007","journal-title":"Ecosystems"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1016\/j.foreco.2011.08.044","article-title":"Contribution of large-scale forest inventories to biodiversity assessment and monitoring","volume":"262","author":"Corona","year":"2011","journal-title":"For. Ecol. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111218","DOI":"10.1016\/j.rse.2019.111218","article-title":"Remote sensing of terrestrial plant biodiversity","volume":"231","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.rse.2007.01.005","article-title":"The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs","volume":"109","author":"Maltamo","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1080\/01431160701736471","article-title":"Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images","volume":"29","author":"Holmgren","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"319","DOI":"10.14214\/sf.156","article-title":"Tree species classification using airborne LiDAR\u2014Effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type","volume":"44","author":"Korpela","year":"2010","journal-title":"Silva Fenn."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1016\/j.rse.2010.01.024","article-title":"Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning","volume":"114","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyypp\u00e4, J., Saari, H., P\u00f6l\u00f6nen, I., and Imai, N.N. (2017). Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens., 9.","DOI":"10.3390\/rs9030185"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Saarinen, N., Vastaranta, M., N\u00e4si, R., Rosnell, T., Hakala, T., Honkavaara, E., Wulder, M.A., Luoma, V., Tommaselli, A.M.G., and Imai, N.N. (2018). Assessing biodiversity in boreal forests with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens., 10.","DOI":"10.3390\/rs10020338"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tuominen, S., N\u00e4si, R., Honkavaara, E., Balazs, A., Hakala, T., Viljanen, N., P\u00f6l\u00f6nen, I., Saari, H., and Ojanen, H. (2018). Assessment of classifiers and remote sensing features of hyperspectral imagery and stereo-photogrammetric point clouds for recognition of tree species in a forest area of high species diversity. Remote Sens., 10.","DOI":"10.3390\/rs10050714"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sothe, C., Dalponte, M., Almeida, C.M.D., Schimalski, M.B., Lima, C.L., Liesenberg, V., Takahashi Miyoshi, G., and Tommaselli, A.M.G. (2019). Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data. Remote Sens., 11.","DOI":"10.3390\/rs11111338"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Takahashi Miyoshi, G., Imai, N.N., Tommaselli, A.M.G., Antunes de Moraes, M.V., and Honkavaara, E. (2020). Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse Atlantic forest. Remote Sens., 12.","DOI":"10.3390\/rs12020244"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2005.03.009","article-title":"Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales","volume":"96","author":"Clark","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1016\/j.rse.2010.07.002","article-title":"Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada","volume":"114","author":"Jones","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.rse.2013.09.006","article-title":"Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data","volume":"140","author":"Dalponte","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_34","first-page":"464","article-title":"The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area","volume":"52","author":"Richter","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Maschler, J., Atzberger, C., and Immitzer, M. (2018). Individual tree crown segmentation and classification of 13 tree species using airborne hyperspectral data. Remote Sens., 10.","DOI":"10.3390\/rs10081218"},{"key":"ref_36","first-page":"101960","article-title":"Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data","volume":"84","author":"Modzelewska","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wu, Y., and Zhang, X. (2020). Object-Based tree species classification using airborne hyperspectral images and LiDAR data. Forests, 11.","DOI":"10.3390\/f11010032"},{"key":"ref_38","first-page":"253","article-title":"Deciduous-coniferous tree classification using difference between first and last pulse laser signatures","volume":"36","author":"Liang","year":"2007","journal-title":"IAPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"950","DOI":"10.3390\/rs4040950","article-title":"An international comparison of individual tree detection and extraction using airborne laser scanning","volume":"4","author":"Kaartinen","year":"2012","journal-title":"Remote Sens."},{"key":"ref_40","first-page":"49","article-title":"A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2015.10.004","article-title":"The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data","volume":"171","author":"Roth","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"365","DOI":"10.5721\/EuJRS20154821","article-title":"Delineation of Individual Tree Crowns from ALS and Hyperspectral data: A comparison among four methods","volume":"48","author":"Dalponte","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Piiroinen, R., Heiskanen, J., Maeda, E.E., Viinikka, A., and Pellikka, P. (2017). Classification of tree species in a diverse African agroforestry landscape using imaging spectroscopy and laser scanning. Remote Sens., 9.","DOI":"10.3390\/rs9090875"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.rse.2007.01.012","article-title":"Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels","volume":"109","author":"Kalacska","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.3390\/rs4061820","article-title":"Species level differences in hyperspectral metrics among tropical rainforest trees as determined by a tree-based classifier","volume":"4","author":"Clark","year":"2012","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7753","DOI":"10.14214\/sf.7753","article-title":"A spectral analysis of 25 boreal tree species","volume":"51","author":"Hovi","year":"2017","journal-title":"Silva Fenn."},{"key":"ref_47","first-page":"169","article-title":"Vegetation zones and their sections in northwestern Europe","volume":"5","author":"Ahti","year":"1968","journal-title":"Ann. Bot. Fenn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2609","DOI":"10.1080\/01431160110115834","article-title":"Geo-atmospheric Processing of Airborne Imaging Spectrometry Data Part 1: Parametric Orthorectification","volume":"23","author":"Richter","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1080\/01431160110115834","article-title":"Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric\/Topographic Correction","volume":"23","author":"Richter","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","unstructured":"Dalponte, M. (2019, August 05). itcSegment: Individual Tree Crowns Segmentation. Available online: https:\/\/CRAN.R-project.org\/package=itcSegment."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_52","first-page":"115","article-title":"Principal Component Analysis for Hyperspectral Image Classification","volume":"62","author":"Rodarmel","year":"2002","journal-title":"Surv. Land Inf. Syst."},{"key":"ref_53","unstructured":"R Core Team (2019). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2812","DOI":"10.1039\/C3AY41907J","article-title":"Principal component analysis. Tutorial review","volume":"6","author":"Bro","year":"2014","journal-title":"Anal. Methods"},{"key":"ref_55","unstructured":"Kuhn, M. (2020, April 04). Caret: Classification and Regression Training. Available online: http:\/\/CRAN.R-project.org\/package=caret."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"111354","DOI":"10.1016\/j.rse.2019.111354","article-title":"Auxiliary datasets improve accuracy of object-based land use\/land cover classification in heterogeneous savanna landscapes","volume":"233","author":"Hurskainen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wang, K., Wang, T., and Liu, X. (2019). A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment. Forests, 10.","DOI":"10.3390\/f10010001"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1007\/s10661-017-6025-0","article-title":"Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology","volume":"189","author":"Fox","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_64","unstructured":"Kuhn, M. (2020, April 27). The Caret Package Documentation, 2019-03-27. Available online: http:\/\/topepo.github.io\/caret\/index.html."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2012.03.013","article-title":"Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral\/hyperspectral images and LiDAR data","volume":"123","author":"Dalponte","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1080\/014311699212560","article-title":"Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost-based approach","volume":"20","author":"Smits","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","unstructured":"Agresti, A. (1996). An Introduction to Categorical Data Analysis, Wiley."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Momeni, R., Aplin, P., and Boyd, D.S. (2016). Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach. Remote Sens., 8.","DOI":"10.3390\/rs8020088"},{"key":"ref_71","first-page":"1","article-title":"All models are wrong, but many are useful: Learning a variable\u2019s importance by studying an entire class of prediction models simultaneously","volume":"20","author":"Fisher","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.L., Zeileis, A., and Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform., 8.","DOI":"10.1186\/1471-2105-8-25"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"786","DOI":"10.21105\/joss.00786","article-title":"iml: An R package for interpretable machine learning","volume":"3","author":"Molnar","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_76","unstructured":"Molnar, C. (2020, March 05). Interpretable Machine Learning. Available online: https:\/\/christophm.github.io\/interpretable-ml-book\/."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"4515","DOI":"10.3390\/rs6054515","article-title":"Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality","volume":"6","author":"Waser","year":"2014","journal-title":"Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2015.05.007","article-title":"Differentiating plant species within and across diverse ecosystems with imaging spectroscopy","volume":"167","author":"Roth","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_79","first-page":"237","article-title":"Spectroscopic determination of leaf traits using infrared spectra","volume":"69","author":"Buitrago","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ballanti, L., Blesius, L., Hines, E., and Kruse, B. (2016). Tree species classification using hyperspectral imagery: A comparison of two classifiers. Remote Sens., 8.","DOI":"10.3390\/rs8060445"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1080\/22797254.2017.1299557","article-title":"Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images","volume":"50","author":"Raczko","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1111\/evo.13835","article-title":"Be careful with your principal components","volume":"73","year":"2019","journal-title":"Evolution"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1111\/j.1469-8137.2010.03536.x","article-title":"Sources of variability in canopy reflectance and the convergent properties of plants","volume":"189","author":"Ollinger","year":"2011","journal-title":"New Phytol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(98)00059-5","article-title":"Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches","volume":"66","author":"Blackburn","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1080\/01431169308953986","article-title":"Red edge spectral measurements from sugar maple leaves","volume":"14","author":"Vogelmann","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2003.09.004","article-title":"Towards universal deciduous broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements","volume":"89","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1139\/X09-015","article-title":"Development of a standardized methodology for quantifying total chlorophyll and carotenoids from foliage of hardwood and conifer tree species","volume":"39","author":"Minocha","year":"2009","journal-title":"Can. J. For. Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.ecocom.2013.11.005","article-title":"The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures","volume":"17","author":"Croft","year":"2014","journal-title":"Ecol. Complex."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"125","DOI":"10.2134\/agronj2001.931125x","article-title":"Discriminating crop residues from soil by shortwave infrared reflectance","volume":"93","author":"Daughtry","year":"2001","journal-title":"Agronomy"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1080\/22797254.2018.1434424","article-title":"Tree species classification in Norway from airborne hyperspectral and airborne laser scanning data","volume":"51","author":"Trier","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1080\/01431169408954109","article-title":"Ratios of leaf reflectances in narrow wavebands as indicators of plant stress","volume":"15","author":"Carter","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.rse.2009.02.016","article-title":"Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection","volume":"113","author":"Berni","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W., and Frangi, A. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1109\/TGRS.2003.813555","article-title":"Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index","volume":"41","author":"Pu","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2018.03.013","article-title":"Connecting infrared spectra with plant traits to identify species","volume":"139","author":"Buitrago","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.carbpol.2014.10.012","article-title":"Augmented digestion of lignocellulose by steam explosion, acid and alkaline pretreatment methods: A review","volume":"117","author":"Singh","year":"2015","journal-title":"Carbohydr. Polym."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1111\/2041-210X.12596","article-title":"Spectroscopic determination of ecologically relevant plant secondary metabolites","volume":"7","author":"Couture","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_99","first-page":"1333","article-title":"Rapid phytochemical analysis of birch (Betula) and poplar (Populus) foliage by near-infrared reflectance spectroscopy","volume":"405","author":"Holeski","year":"2012","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_100","first-page":"55","article-title":"Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 \u03bcm","volume":"43","author":"Kokaly","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1080\/02827580802249126","article-title":"Current growth differences of Norway spruce (Picea abies), Scots pine (Pinus sylvestris) and birch (Betula pendula and Betula pubescens) in different regions in Sweden","volume":"23","author":"Johansson","year":"2008","journal-title":"Scand. J. For. Res."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1016\/j.foreco.2009.08.026","article-title":"Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation","volume":"258","author":"Tomppo","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"10143","DOI":"10.14214\/sf.10143","article-title":"An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data","volume":"54","author":"Grigorieva","year":"2020","journal-title":"Silva Fenn."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Persson, M., Lindberg, E., and Reese, H. (2018). Tree species classification with multi-temporal Sentinel-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10111794"},{"key":"ref_105","unstructured":"Guanter, L., Kaufmann, H., Foerster, S., Brosinsky, A., Wulf, H., Bochow, M., Boesche, N., Brell, M., Buddenbaum, H., and Chabrillat, S. (2016). EnMAP Science Plan, GFZ Data Services. EnMAP Technical Report 2016."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.rse.2015.06.012","article-title":"An Introduction to the Nasa Hyperspectral Infrared Imager (Hyspiri) Mission and Preparatory Activities","volume":"167","author":"Lee","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/16\/2610\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:00:00Z","timestamp":1760176800000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/16\/2610"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,13]]},"references-count":106,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["rs12162610"],"URL":"https:\/\/doi.org\/10.3390\/rs12162610","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,13]]}}}