{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T16:36:08Z","timestamp":1774715768898,"version":"3.50.1"},"reference-count":101,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T00:00:00Z","timestamp":1533254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004955","name":"\u00d6sterreichische Forschungsf\u00f6rderungsgesellschaft","doi-asserted-by":"publisher","award":["854027"],"award-info":[{"award-number":["854027"]}],"id":[{"id":"10.13039\/501100004955","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve \u2018Wienerwald\u2019, a temperate Austrian forest. The study also assesses the automation potential for the delineation of tree crowns using a mean shift segmentation algorithm in order to permit model application over large areas. Object-based Random Forest classification was carried out on variables that were derived from 699 manually delineated as well as automatically segmented reference trees. The models were trained separately for two strata: small and\/or conifer stands and high broadleaf forests. The two strata were delineated beforehand using CHM-based tree height and NDVI. The predictor variables encompassed spectral reflectance, vegetation indices, textural metrics and principal components. After feature selection, the overall classification accuracy (OA) of the classification based on manual delineations of the 13 tree species was 91.7% (Cohen\u2019s kappa (\u03ba) = 0.909). The highest user\u2019s and producer\u2019s accuracies were most frequently obtained for Weymouth pine and Scots Pine, while European ash was most often associated with the lowest accuracies. The classification that was based on mean shift segmentation yielded similarly good results (OA = 89.4% \u03ba = 0.883). Based on the automatically segmented trees, the Random Forest models were also applied to the whole study site (1050 ha). The resulting tree map of the study area confirmed a high abundance of European beech (58%) with smaller amounts of oak (6%) and Scots pine (5%). We conclude that highly accurate tree species classifications can be obtained from hyperspectral data covering the visible and near-infrared parts of the electromagnetic spectrum. Our results also indicate a high automation potential of the method, as the results from the automatically segmented tree crowns were similar to those that were obtained for the manually delineated tree crowns.<\/jats:p>","DOI":"10.3390\/rs10081218","type":"journal-article","created":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T11:03:26Z","timestamp":1533294206000},"page":"1218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":141,"title":["Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9094-693X","authenticated-orcid":false,"given":"Julia","family":"Maschler","sequence":"first","affiliation":[{"name":"Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-8009","authenticated-orcid":false,"given":"Clement","family":"Atzberger","sequence":"additional","affiliation":[{"name":"Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6758-1207","authenticated-orcid":false,"given":"Markus","family":"Immitzer","sequence":"additional","affiliation":[{"name":"Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,3]]},"reference":[{"key":"ref_1","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_2","unstructured":"Wulder, M.A., and Franklin, S.E. (2012). Remote Sensing of Forest Environments: Concepts and Case Studies, Kluwer Academic Publishers."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree species classification with Random Forest using very high spatial resolution 8-band WorldView-2 satellite data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1111\/j.1654-109X.2012.01194.x","article-title":"Mapping invasive woody species in coastal dunes in the Netherlands: a remote sensing approach using LIDAR and high-resolution aerial photographs","volume":"15","author":"Hantson","year":"2012","journal-title":"Appl. Veg. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TGRS.2008.916480","article-title":"Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas","volume":"46","author":"Dalponte","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/S0034-4257(98)00014-5","article-title":"Biophysical and Biochemical Sources of Variability in Canopy Reflectance","volume":"64","author":"Asner","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Forest biophysical and biochemical properties from hyperspectral and LiDAR remote sensing. Land Resources Monitoring, Modeling and Mapping with Remote Sensing, CRC Press.","DOI":"10.1201\/b19322"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Rautiainen, M., Luke\u0161, P., Homolov\u00e1, L., Hovi, A., Pisek, J., and M\u00f5ttus, M. (2018). Spectral Properties of Coniferous Forests: A Review of In Situ and Laboratory Measurements. Remote Sens., 10.","DOI":"10.3390\/rs10020207"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1127\/1432-8364\/2014\/0234","article-title":"Method analysis for collecting and processing in-situ hyperspectral needle reflectance data for monitoring Norway spruce","volume":"2014","author":"Einzmann","year":"2014","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_12","first-page":"177","article-title":"Hyperspectral discrimination of tree species with different classifications using single- and multiple-endmember","volume":"23","author":"Ghiyamat","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Immitzer, M., and Atzberger, C. (2014). Early Detection of Bark Beetle Infestation in Norway Spruce (Picea abies, L.) using WorldView-2 Data. Photogramm. Fernerkund. Geoinf., 351\u2013367.","DOI":"10.1127\/1432-8364\/2014\/0229"},{"key":"ref_14","unstructured":"Schlerf, M., Atzberger, C., and Hill, J. (2003). Tree species and age class mapping in a Central European woodland using optical remote sensing imagery and orthophoto derived stem density\u2013performance of multispectral and hyperspectral sensors. Geoinformation for European-Wide Integration, Proceedings of the 22nd Symposium of the European Association of Remote Sensing Laboratories, Prague, Czech Republic, 4\u20136 June 2002, Millpress."},{"key":"ref_15","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_16","unstructured":"Jones, H.G., and Vaughan, R.A. (2010). Remote Sensing of Vegetation: Principles, Techniques, and Applications, Oxford University Press."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/01431169008955002","article-title":"Remote sensing of temperate coniferous forest leaf area index The influence of canopy closure, understory vegetation and background reflectance","volume":"11","author":"Spanner","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.rse.2005.10.006","article-title":"Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data","volume":"100","author":"Schlerf","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_19","unstructured":"Chang, C.-I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic Publishers."},{"key":"ref_20","first-page":"16","article-title":"Plant species discrimination using emissive thermal infrared imaging spectroscopy","volume":"53","author":"Rock","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","unstructured":"Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. (2008). Remote Sensing and Image Interpretation, John Wiley & Sons."},{"key":"ref_22","unstructured":"\u00d8rka, H.O., and Hauglin, M. (2018, June 25). Use of Remote Sensing for Mapping of Non-Native Conifer Species. Available online: http:\/\/www.umb.no\/statisk\/ina\/publikasjoner\/fagrapport\/if33.pdf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2632","DOI":"10.1109\/TGRS.2012.2216272","article-title":"Tree Species Classification in Boreal Forests With Hyperspectral Data","volume":"51","author":"Dalponte","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1016\/j.envsoft.2010.03.019","article-title":"An automatic region-based image segmentation algorithm for remote sensing applications","volume":"25","author":"Wang","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4725","DOI":"10.1080\/01431161.2010.494184","article-title":"A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing","volume":"32","author":"Ke","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3259","DOI":"10.3390\/rs5073259","article-title":"Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique","volume":"5","author":"Fu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isprsjprs.2012.03.005","article-title":"Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment","volume":"69","author":"Naidoo","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","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_32","unstructured":"Kilian, W., M\u00fcller, F., and Starlinger, F. (2018, June 16). Die forstlichen Wuchsgebiete \u00d6sterreichs: Eine Naturraumgliederung Nach Wald\u00f6kologischen Gesichtspunkten. Available online: https:\/\/bfw.ac.at\/300\/pdf\/1027.pdf."},{"key":"ref_33","unstructured":"(2018, June 08). Biosph\u00e4renpark Wienerwald Management GmbH Zonation. Available online: https:\/\/www.bpww.at\/en\/node\/183."},{"key":"ref_34","unstructured":"(2018, June 08). \u00d6sterreichische Bundesforste Biosph\u00e4renpark Wienerwald. Available online: http:\/\/www.bundesforste.at\/natur-erlebnis\/biosphaerenpark-wienerwald.html."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1080\/01431169608949077","article-title":"spatially adaptive fast atmospheric correction algorithm","volume":"17","author":"Richter","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A Perfect Smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3689","DOI":"10.1080\/01431161003762405","article-title":"Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements","volume":"32","author":"Atzberger","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/17538947.2010.505664","article-title":"A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America","volume":"4","author":"Atzberger","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_40","unstructured":"Lobo, A., and Mattiuzzi, M. (2018, June 22). Modified Whittaker Smoother. Available online: https:\/\/github.com\/MatMatt\/MODIS\/blob\/master\/R\/miwhitatzb1.R."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lang, M., Alleaume, S., Luque, S., Baghdadi, N., and F\u00e9ret, J.-B. (2018). Monitoring and Characterizing Heterogeneous Mediterranean Landscapes with Continuous Textural Indices Based on VHSR Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10060868"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_43","unstructured":"(2018, June 12). OTB Development Team the ORFEO Tool Box Software Guide Updated for OTB-6.4.0. Available online: https:\/\/www.orfeo-toolbox.org\/SoftwareGuide\/."},{"key":"ref_44","unstructured":"Maaten, V.D.L., Postma, E., and Van Den Herik, J. (2009). Dimensionality Reduction: A Comparative Review, Tilburg Centre for Creative Computing, Tilburg University."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Salkind, N.S. (2010). Normalizing data. Encyclopedia of Research Design, Sage Publications.","DOI":"10.4135\/9781412961288"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean shift: A robust approach toward feature space analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","article-title":"The estimation of the gradient of a density function, with applications in pattern recognition","volume":"21","author":"Fukunaga","year":"1975","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1109\/TGRS.2014.2330857","article-title":"Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images","volume":"53","author":"Michel","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4758","DOI":"10.1080\/01431161.2014.930199","article-title":"Object-based change detection in wind storm-damaged forest using high-resolution multispectral images","volume":"35","author":"Chehata","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Einzmann, K., Immitzer, M., B\u00f6ck, S., Bauer, O., Schmitt, A., and Atzberger, C. (2017). Windthrow Detection in European Forests with Very High-Resolution Optical Data. Forests, 8.","DOI":"10.3390\/f8010021"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_53","unstructured":"Breiman, L. (2012, May 03). Manual on Setting up, Using, and Understanding Random Forests V3.1. Available online: http:\/\/oz.berkeley.edu\/users\/breiman\/Using_random_forests_V3.1.pdf."},{"key":"ref_54","unstructured":"Tso, B., and Mather, P.M. (2009). Classification Methods for Remotely Sensed Data, CRC Press."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1127\/1432-8364\/2013\/0162","article-title":"Texturanalyse mittels diskreter Wavelet Transformation f\u00fcr die objektbasierte Klassifikation von Orthophotos","volume":"2","author":"Toscani","year":"2013","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_56","first-page":"122","article-title":"How much does multi-temporal Sentinel-2 data improve crop type classification?","volume":"72","author":"Vuolo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_57","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2\/3","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_58","unstructured":"Team, R.C. (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_59","unstructured":"Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., and Benesty, M. (2018, June 18). Caret: Classification and Regression Training, R Package. Available online: http:\/\/CRAN.R-project.org\/package=caret."},{"key":"ref_60","unstructured":"Hijmans, R.J. (2018, January 10). Raster: Geographic Data Analysis and Modeling, R Package Version 2.6-7. Available online: http:\/\/CRAN.R-project.org\/package=raster."},{"key":"ref_61","first-page":"223","article-title":"An improved Random Forests approach with application to the performance prediction challenge datasets","volume":"Volume 1","author":"Guyon","year":"2011","journal-title":"Hands-on Pattern Recognition, Challenges in Machine Learning"},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.chemolab.2006.01.007","article-title":"Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products","volume":"83","author":"Granitto","year":"2006","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.rse.2017.09.031","article-title":"Fractional cover mapping of spruce and pine at 1ha resolution combining very high and medium spatial resolution satellite imagery","volume":"204","author":"Immitzer","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"14482","DOI":"10.3390\/rs71114482","article-title":"Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil","volume":"7","author":"Schultz","year":"2015","journal-title":"Remote Sens."},{"key":"ref_66","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_67","doi-asserted-by":"crossref","unstructured":"Fassnacht, F., Neumann, C., Forster, M., Buddenbaum, H., Ghosh, A., Clasen, A., Joshi, P., and Koch, B. (2014). Comparison of Feature Reduction Algorithms for Classifying Tree Species with Hyperspectral Data on Three Central European Test Sites. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7.","DOI":"10.1109\/JSTARS.2014.2329390"},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1093\/forestry\/cpx014","article-title":"Estimating stand density, biomass and tree species from very high resolution stereo-imagery-towards an all-in-one sensor for forestry applications?","volume":"90","author":"Fassnacht","year":"2017","journal-title":"Forestry"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhen, Z., Quackenbush, L.J., and Zhang, L. (2016). Trends in Automatic Individual Tree Crown Detection and Delineation\u2014Evolution of LiDAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8040333"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"9975","DOI":"10.3390\/rs70809975","article-title":"aTrunk\u2014An ALS-Based Trunk Detection Algorithm","volume":"7","author":"Lamprecht","year":"2015","journal-title":"Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Vaughn, N.R., Asner, G.P., Brodrick, P.G., Martin, R.E., Heckler, J.W., Knapp, D.E., and Hughes, R.F. (2018). An Approach for High-Resolution Mapping of Hawaiian Metrosideros Forest Mortality Using Laser-Guided Imaging Spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10040502"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"B\u00f6ck, S., Immitzer, M., and Atzberger, C. (2017). On the Objectivity of the Objective Function\u2014Problems with Unsupervised Segmentation Evaluation Based on Global Score and a Possible Remedy. Remote Sens., 9.","DOI":"10.3390\/rs9080769"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Weinmann, M., Weinmann, M., Mallet, C., and Br\u00e9dif, M. (2017). A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas. Remote Sens., 9.","DOI":"10.3390\/rs9030277"},{"key":"ref_75","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_76","unstructured":"Ellenberg, H., and Leuschner, C. (2010). Vegetation Mitteleuropas Mit Den Alpen: in \u00d6kologischer, Dynamischer und Historischer Sicht, UTB. [6th ed.]."},{"key":"ref_77","unstructured":"Chen, C., Liaw, A., and Breiman, L. (2004). Using Random Forest to Learn Imbalanced Data, University of California, Berkeley."},{"key":"ref_78","first-page":"4133","article-title":"Improving discrimination of savanna tree species through a multiple-endmember spectral angle mapper approach: Canopy-level analysis","volume":"48","author":"Cho","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_79","first-page":"51","article-title":"Scale and texture in digital image classification","volume":"68","author":"Fern","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.compag.2010.05.006","article-title":"Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat","volume":"73","author":"Atzberger","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_83","unstructured":"Rouse, J., Haas, R., Schell, J., and Deering, D. (2018, June 25). Monitoring Vegetation Systems in the Great Plains with ERTS, Available online: https:\/\/ntrs.nasa.gov\/archive\/nasa\/casi.ntrs.nasa.gov\/19740022614.pdf."},{"key":"ref_84","unstructured":"Chamard, P., Courel, M.F., Guenegou, M., Lerhun, J., Levasseur, J., and Togola, M. (1991). Utilisation des bandes spectrales du vert et du rouge pour une meilleure \u00e9valuation des formations v\u00e9g\u00e9tales actives. T\u00e9l\u00e9d\u00e9tection et Cartographie, AUPELF-UREF."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/0034-4257(83)90039-1","article-title":"Discrimination of Growth and Water Stress in Wheat by Various Vegetation Indices through Clear and Turbid Atmospheres","volume":"13","author":"Jackson","year":"1983","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/S0176-1617(96)80284-7","article-title":"Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll","volume":"148","author":"Gitelson","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback based modification of the NDVI to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1007\/s004420050337","article-title":"The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels","volume":"112","author":"Gamon","year":"1997","journal-title":"Oecologia"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(92)90059-S","article-title":"A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency","volume":"41","author":"Gamon","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_95","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_96","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0034-4257(90)90085-Z","article-title":"Calculating the vegetation index faster","volume":"34","author":"Crippen","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_97","unstructured":"Barnes, E.M., Clarke, T.R., Richards, S.E., Colaizzi, P.D., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T. (2000, January 16\u201319). Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1034\/j.1399-3054.1999.106119.x","article-title":"Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening","volume":"106","author":"Merzlyak","year":"1999","journal-title":"Physiol. Plant."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0034-4257(89)90076-X","article-title":"Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture","volume":"29","author":"Clevers","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0034-4257(88)90041-7","article-title":"The derivation of a simplified reflectance model for the estimation of leaf area index","volume":"25","author":"Clevers","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_101","first-page":"1541","article-title":"Distinguishing Vegetation from Soil Background Information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1218\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:16:25Z","timestamp":1760195785000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,3]]},"references-count":101,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["rs10081218"],"URL":"https:\/\/doi.org\/10.3390\/rs10081218","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,3]]}}}