{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T22:25:47Z","timestamp":1776464747226,"version":"3.51.2"},"reference-count":78,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T00:00:00Z","timestamp":1572998400000},"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>Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution data for classification of five coniferous and seven broadleaved tree species in a diverse Central European Forest. To run the classification, 18 cloud-free S2 acquisitions were analyzed in a two-step approach. The available scenes were first used to stratify the study area into six broad land-cover classes. Subsequently, additional classification models were created separately for the coniferous and the broadleaved forest strata. To permit a deeper analytical insight in the benefits of multi-temporal datasets for species identification, classification models were developed taking into account all 262,143 possible permutations of the 18 S2 scenes. Each model was fine-tuned using a stepwise recursive feature reduction. The additional use of vegetation indices improved the model performances by around 5 percentage points. Individual mono-temporal tree species accuracies range from 48.1% (January 2017) to 78.6% (June 2017). Compared to the best mono-temporal results, the multi-temporal analysis approach improves the out-of-bag overall accuracy from 72.9% to 85.7% for the broadleaved and from 83.8% to 95.3% for the coniferous tree species, respectively. Remarkably, a combination of six\u2013seven scenes achieves a model quality equally high as the model based on all data; images from April until August proved most important. The classes European Beech and European Larch attain the highest user\u2019s accuracies of 96.3% and 95.9%, respectively. The most important spectral variables to distinguish between tree species are located in the Red (coniferous) and short wave infrared (SWIR) bands (broadleaved), respectively. Overall, the study highlights the high potential of multi-temporal S2 data for species-level classifications in Central European forests.<\/jats:p>","DOI":"10.3390\/rs11222599","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T06:52:36Z","timestamp":1573109556000},"page":"2599","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":192,"title":["Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6758-1207","authenticated-orcid":false,"given":"Markus","family":"Immitzer","sequence":"first","affiliation":[{"name":"University of Natural Resources and Life Sciences, Vienna (BOKU), Institute of Geomatics, Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]},{"given":"Martin","family":"Neuwirth","sequence":"additional","affiliation":[{"name":"University of Natural Resources and Life Sciences, Vienna (BOKU), Institute of Geomatics, Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]},{"given":"Sebastian","family":"B\u00f6ck","sequence":"additional","affiliation":[{"name":"University of Natural Resources and Life Sciences, Vienna (BOKU), Institute of Geomatics, Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]},{"given":"Harald","family":"Brenner","sequence":"additional","affiliation":[{"name":"Biosph\u00e4renpark Wienerwald Management GmbH, Norbertinumstra\u00dfe 9, 3013 Tullnerbach, Austria"}]},{"given":"Francesco","family":"Vuolo","sequence":"additional","affiliation":[{"name":"University of Natural Resources and Life Sciences, Vienna (BOKU), Institute of Geomatics, Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-8009","authenticated-orcid":false,"given":"Clement","family":"Atzberger","sequence":"additional","affiliation":[{"name":"University of Natural Resources and Life Sciences, Vienna (BOKU), Institute of Geomatics, Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,6]]},"reference":[{"key":"ref_1","unstructured":"D\u00edaz, S., Settele, J., Brondizio, E.S., Ngo, H.T., Gu\u00e8ze, M., Agard, J., Arneth, A., Balvanera, P., Brauman, K.A., and Butchart, S.H.M. (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, IPBES secretariat."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6599","DOI":"10.1080\/01431161.2014.964349","article-title":"Earth observation satellite sensors for biodiversity monitoring: Potentials and bottlenecks","volume":"35","author":"Kuenzer","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1080\/01431160117096","article-title":"Using remote sensing to assess biodiversity","volume":"22","author":"Nagendra","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1002\/rse2.15","article-title":"Framing the concept of satellite remote sensing essential biodiversity variables: Challenges and future directions","volume":"2","author":"Pettorelli","year":"2016","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"20130190","DOI":"10.1098\/rstb.2013.0190","article-title":"Satellite remote sensing, biodiversity research and conservation of the future","volume":"369","author":"Pettorelli","year":"2014","journal-title":"Phil. Trans. R. Soc. B"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1038\/s41559-018-0667-3","article-title":"Towards global data products of Essential Biodiversity Variables on species traits","volume":"2","author":"Kissling","year":"2018","journal-title":"Nat. Ecol. Evol."},{"key":"ref_7","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_8","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_9","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_10","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_11","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1109\/JSTARS.2014.2329390","article-title":"Comparison of Feature Reduction Algorithms for Classifying Tree Species With Hyperspectral Data on Three Central European Test Sites","volume":"7","author":"Fassnacht","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.isprsjprs.2013.01.013","article-title":"Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu\u2013Natal, South Africa","volume":"79","author":"Peerbhay","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"135","DOI":"10.14358\/PERS.70.1.135","article-title":"Exploitation of very high resolution satellite data for tree species identification","volume":"70","author":"Carleer","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_15","first-page":"613","article-title":"Estimating stand density, biomass and tree species from very high resolution stereo-imagery\u2014Towards an all-in-one sensor for forestry applications?","volume":"90","author":"Fassnacht","year":"2017","journal-title":"For. Int. J. For. Res."},{"key":"ref_16","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_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.1080\/01431160500104152","article-title":"Model development and comparison to predict softwood and hardwood per cent cover using high and medium spatial resolution imagery","volume":"26","author":"Metzler","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","unstructured":"(2017, February 08). EEA Forests\u2014Copernicus Land Monitoring Service. Available online: http:\/\/land.copernicus.eu\/pan-european\/high-resolution-layers\/forests."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"172","DOI":"10.25518\/1780-4507.16524","article-title":"Forest mapping and species composition using supervised per pixel classification of Sentinel-2 imagery","volume":"22","author":"Bolyn","year":"2018","journal-title":"Biotechnol. Agron. Soc. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K. (2019). Forest Stand Species Mapping Using the Sentinel-2 Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11101197"},{"key":"ref_24","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_25","unstructured":"Immitzer, M., Vuolo, F., Einzmann, K., Ng, W.-T., B\u00f6ck, S., and Atzberger, C. (2016, January 7\u20139). Verwendung von multispektralen Sentinel-2 Daten f\u00fcr die Baumartenklassifikation und Vergleich mit anderen Satellitensensoren. Proceedings of the Beitr\u00e4ge zur 36. Wissenschaftlich-Technischen Jahrestagung der DGPF, Bern, Switzerland."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ng, W.-T., Rima, P., Einzmann, K., Immitzer, M., Atzberger, C., and Eckert, S. (2017). Assessing the Potential of Sentinel-2 and Pl\u00e9iades Data for the Detection of Prosopis and Vachellia spp. in Kenya. Remote Sens., 9.","DOI":"10.3390\/rs9010074"},{"key":"ref_27","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_28","first-page":"32","article-title":"Use of Sentinel-2 for forest classification in Mediterranean environments","volume":"42","author":"Puletti","year":"2018","journal-title":"Ann. Silvic. Res."},{"key":"ref_29","unstructured":"Wulf, H., and Stuhler, S. (2015, January 29\u201330). Sentinel-2: Land Cover, Preliminary User Feedback on Sentinel-2A Data. Proceedings of the Sentinel-2A Expert Users Technical Meeting, Frascati, Italy."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sheeren, D., Fauvel, M., Josipovi\u0107, V., Lopes, M., Planque, C., Willm, J., and Dejoux, J.-F. (2016). Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8090734"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","unstructured":"Nelson, M. (2017). Evaluating Multitemporal Sentinel-2 Data for Forest Mapping Using Random Forest. [Master\u2019s Thesis, Stockholm University]."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wessel, M., Brandmeier, M., and Tiede, D. (2018). Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10091419"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ho\u015bci\u0142o, A., and Lewandowska, A. (2019). Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data. Remote Sens., 11.","DOI":"10.3390\/rs11080929"},{"key":"ref_35","first-page":"249","article-title":"Artenreichtum, Artenverteilung und r\u00e4umliche Aspekte der Biodiversit\u00e4t der Gef\u00e4\u00dfpflanzen in W\u00e4ldern des Biosph\u00e4renpark Wienerwald","volume":"25","author":"Staudinger","year":"2014","journal-title":"Wiss. Mitteilungen Nieder\u00f6sterreichischen Landesmus."},{"key":"ref_36","first-page":"41","article-title":"Kernzonen im Biosph\u00e4renpark Wienerwald\u2014Urw\u00e4lder von morgen","volume":"25","author":"Mrkvicka","year":"2014","journal-title":"Wiss. Mitteilungen Nieder\u00f6sterreichischen Landesmus."},{"key":"ref_37","first-page":"9","article-title":"Der Wienerwald ist UNESCO-Biosph\u00e4renpark\u2014Eine Modellregion f\u00fcr Nachhaltigkeit","volume":"25","author":"Drozdowski","year":"2014","journal-title":"Wiss. Mitteilungen Nieder\u00f6sterreichischen Landesmus."},{"key":"ref_38","unstructured":"Pflug, B., Bieniarz, J., Debaecker, V., Louis, J., and M\u00fcller-Wilms, U. (2016, January 17\u201322). Some experience using sen2cor. Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Vuolo, F., \u017b\u00f3\u0142tak, M., Pipitone, C., Zappa, L., Wenng, H., Immitzer, M., Weiss, M., Baret, F., and Atzberger, C. (2016). Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples. Remote Sens., 8.","DOI":"10.3390\/rs8110938"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mathieu, P.-P., and Aubrecht, C. (2018). Development of an Earth Observation Cloud Platform in Support to Water Resources Monitoring. Earth Observation Open Science and Innovation, Springer International Publishing.","DOI":"10.1007\/978-3-319-65633-5"},{"key":"ref_41","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_42","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_43","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_44","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_45","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","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_48","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_49","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1080\/10549811.2019.1598443","article-title":"Evaluating the potential of sentinel-2, landsat-8, and irs satellite images in tree species classification of hyrcanian forest of iran using random forest","volume":"38","author":"Soleimannejad","year":"2019","journal-title":"J. Sustain. For."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2018.02.064","article-title":"Improved mapping of forest type using spectral-temporal Landsat features","volume":"210","author":"Pasquarella","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.rse.2018.12.001","article-title":"Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey","volume":"221","author":"Pflugmacher","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_52","first-page":"202","article-title":"Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data","volume":"57","author":"Vuolo","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","unstructured":"Elatawneh, A., Rappl, A., Rehush, N., Schneider, T., and Knoke, T. (2013, January 20\u201321). Forest tree species identification using phenological stages and RapidEye data: A case study in the forest of Freising. Proceedings of the 5th RESA Workshop, From the Basics to the Service, DLR e.V., Neustrelitz, Germany."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/j.1654-109X.2009.01053.x","article-title":"Mapping tree species in temperate deciduous woodland using time-series multi-spectral data","volume":"13","author":"Hill","year":"2010","journal-title":"Appl. Veg. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"922","DOI":"10.3390\/f4040922","article-title":"A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery","volume":"4","author":"Lisein","year":"2013","journal-title":"Forests"},{"key":"ref_56","first-page":"321","article-title":"Evaluating seasonal variability as an aid to cover-type mapping from Landsat Thematic Mapper data in the Northeast","volume":"61","author":"Schriever","year":"1995","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.3390\/f6061982","article-title":"Satellite-Based Derivation of High-Resolution Forest Information Layers for Operational Forest Management","volume":"6","author":"Stoffels","year":"2015","journal-title":"Forests"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"16917","DOI":"10.3390\/rs71215861","article-title":"Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Mascaro, J., Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Martin, R.E., Anderson, C., Higgins, M., and Chadwick, K.D. (2014). A Tale of Two \u201cForests\u201d: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0085993"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1080\/01431169208904233","article-title":"Crop-system characterization by multitemporal SPOT data in the South-East of France","volume":"13","author":"Guerif","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","first-page":"27","article-title":"A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery","volume":"18","author":"Shahi","year":"2015","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/S0176-1617(99)80314-9","article-title":"A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests using Eucalyptus Leaves","volume":"154","author":"Datt","year":"1999","journal-title":"J. Plant Physiol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/BF00031911","article-title":"GEMI: A non-linear index to monitor global vegetation from satellites","volume":"101","author":"Pinty","year":"1992","journal-title":"Vegetatio"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2003.09.004","article-title":"Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements","volume":"89","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_65","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_66","unstructured":"Vogelmann, J.E., and Rock, B.N. (1985, January 8\u201310). Spectral Characterization of Suspected Acid Deposition Damage in Red Spruce (picea Rubens) Stands from Vermont. Proceedings of the Airborne Imaging Spectrometer Data Anal. Workshop, Pasadena, CA, USA."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Radoux, J., Chom\u00e9, G., Jacques, D.C., Waldner, F., Bellemans, N., Matton, N., Lamarche, C., d\u2019Andrimont, R., and Defourny, P. (2016). Sentinel-2\u2032s Potential for Sub-Pixel Landscape Feature Detection. Remote Sens., 8.","DOI":"10.3390\/rs8060488"},{"key":"ref_68","first-page":"87","article-title":"Using thematic mapper data to identify contrasting soil plains and tillage practices","volume":"63","author":"Ward","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_69","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_70","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2006.07.012","article-title":"Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal","volume":"106","author":"Lacaux","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1080\/01431169408954177","article-title":"The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status","volume":"15","author":"Filella","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.rse.2014.07.027","article-title":"Monitoring dry vegetation masses in semi-arid areas with MODIS SWIR bands","volume":"153","author":"Jacques","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_74","first-page":"512","article-title":"New index for crop canopy fresh biomass estimation","volume":"30","author":"Chen","year":"2010","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/S0176-1617(96)80081-2","article-title":"Detection of Vegetation Stress Via a New High Resolution Fluorescence Imaging System","volume":"148","author":"Lichtenthaler","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_77","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_78","unstructured":"Domenech, E., and Mallet, C. (2014). Change Detection in High resolution land use\/land cover geodatabases (at object level). EuroSDR Off. Publ., 64."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2599\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:32:12Z","timestamp":1760189532000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2599"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,6]]},"references-count":78,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11222599"],"URL":"https:\/\/doi.org\/10.3390\/rs11222599","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,6]]}}}