{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T22:32:21Z","timestamp":1772663541857,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"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>Riparian habitats provide a series of ecological services vital for the balance of the environment, and are niches and resources for a wide variety of species. Monitoring riparian environments at the intra-habitat level is crucial for assessing and preserving their conservation status, although it is challenging due to their landscape complexity. Unmanned aerial vehicles (UAV) and multi-spectral optical sensors can be used for very high resolution (VHR) monitoring in terms of spectral, spatial, and temporal resolutions. In this contribution, the vegetation species of the riparian habitat (91E0*, 3240 of Natura 2000 network) of North-West Italy were mapped at individual tree (ITD) level using machine learning and a multi-temporal phenology-based approach. Three UAV flights were conducted at the phenological-relevant time of the year (epochs). The data were analyzed using a structure from motion (SfM) approach. The resulting orthomosaics were segmented and classified using a random forest (RF) algorithm. The training dataset was composed of field-collected data, and was oversampled to reduce the effects of unbalancing and size. Three-hundred features were computed considering spectral, textural, and geometric information. Finally, the RF model was cross-validated (leave-one-out). This model was applied to eight scenarios that differed in temporal resolution to assess the role of multi-temporality over the UAV\u2019s VHR optical data. Results showed better performances in multi-epoch phenology-based classification than single-epochs ones, with 0.71 overall accuracy compared to 0.61. Some classes, such as Pinus sylvestris and Betula pendula, are remarkably influenced by the phenology-based multi-temporality: the F1-score increased by 0.3 points by considering three epochs instead of two.<\/jats:p>","DOI":"10.3390\/rs13091756","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T10:53:29Z","timestamp":1619780009000},"page":"1756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Mapping Riparian Habitats of Natura 2000 Network (91E0*, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3592-9384","authenticated-orcid":false,"given":"Elena","family":"Belcore","sequence":"first","affiliation":[{"name":"DIATI, Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6748-8790","authenticated-orcid":false,"given":"Marco","family":"Pittarello","sequence":"additional","affiliation":[{"name":"DISAFA, Department of Agricultural, Forest, and Food Sciences, University of Torino, Largo Paolo Braccini 2, 10035 Grugliasco, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5930-2711","authenticated-orcid":false,"given":"Andrea Maria","family":"Lingua","sequence":"additional","affiliation":[{"name":"DIATI, Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8886-0328","authenticated-orcid":false,"given":"Michele","family":"Lonati","sequence":"additional","affiliation":[{"name":"DISAFA, Department of Agricultural, Forest, and Food Sciences, University of Torino, Largo Paolo Braccini 2, 10035 Grugliasco, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1146\/annurev.ecolsys.28.1.621","article-title":"The Ecology of Interfaces: Riparian Zones","volume":"28","author":"Naiman","year":"1997","journal-title":"Annu. Rev. Ecol. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s00267-003-2834-8","article-title":"Allocation of River Flows for Restoration of Floodplain Forest Ecosystems: A Review of Approaches and Their Applicability in Europe","volume":"32","author":"Hughes","year":"2003","journal-title":"Environ. Manag."},{"key":"ref_3","first-page":"657","article-title":"Riparia\u2014Ecology, Conservation and Management of Streamside Communities","volume":"17","author":"Naiman","year":"2007","journal-title":"Aquat. Conserv. Mar. Freshw. Ecosyst."},{"key":"ref_4","unstructured":"Angelini, P., Ministero dell\u2019Ambiente e della Tutela del Territorio e del Mare, Italy, and ISPRA (2016). Manuali per il Monitoraggio di Specie e Habitat di Interesse Comunitario (Direttiva 92\/43\/CEE) in Italia: Habitat, ISPRA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"485","DOI":"10.2307\/1934981","article-title":"Plant Species Diversity in Old-Field Successional Ecosystems in Southern Illinois","volume":"56","author":"Bazzaz","year":"1975","journal-title":"Ecology"},{"key":"ref_6","unstructured":"Biondi, E., Blasi, C., Burrascano, S., Casavecchia, S., Copiz, R., El Vico, E., Galdenzi, D., Gigante, D., Lasen, C., and Spampinato, G. (2009). Manuale Italiano di Interpretazione Degli Habitat (Direttiva 92\/43\/CEE) 2009, Direzione per la Protezione della Natura."},{"key":"ref_7","unstructured":"Frick, A., Haest, B., Buck, O., Vanden Borre, J., Foerster, M., Pernkopf, L., and Lang, S. (2011, January 7). Fostering Sustainability in European Nature Conservation NATURA 2000 Habitat Monitoring Based on Earth Observation Services. Proceedings of the 1st World Sustainability Forum, Web Conference."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.jnc.2010.07.003","article-title":"Integrating Remote Sensing in Natura 2000 Habitat Monitoring: Prospects on the Way Forward","volume":"19","author":"Paelinckx","year":"2011","journal-title":"J. Nat. Conserv."},{"key":"ref_9","first-page":"7","article-title":"Remote Sensing for Mapping Natural Habitats and Their Conservation Status\u2014New Opportunities and Challenges","volume":"37","author":"Corbane","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","first-page":"61","article-title":"Adapting a Natura 2000 Field Guideline for a Remote Sensing-Based Assessment of Heathland Conservation Status","volume":"60","author":"Schmidt","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","first-page":"151","article-title":"UAV for Mapping Shrubland Vegetation: Does Fusion of Spectral and Vertical Information Derived from a Single Sensor Increase the Classification Accuracy?","volume":"75","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Carvajal-Ram\u00edrez, F., Serrano, J.M.P.R., Ag\u00fcera-Vega, F., and Mart\u00ednez-Carricondo, P. (2019). A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest. Remote Sens., 11.","DOI":"10.3390\/rs11212579"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"2991","DOI":"10.3390\/rs70302991","article-title":"Mapping Natura 2000 Habitat Conservation Status in a Pannonic Salt Steppe with Airborne Laser Scanning","volume":"7","author":"Zlinszky","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","first-page":"102173","article-title":"Tree Species Classification Using UAS-Based Digital Aerial Photogrammetry Point Clouds and Multispectral Imageries in Subtropical Natural Forests","volume":"92","author":"Xu","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Takahashi Miyoshi, G., Imai, N.N., Garcia Tommaselli, A.M., 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_17","doi-asserted-by":"crossref","unstructured":"Sothe, C., Dalponte, M., de Almeida, C.M., Schimalski, M.B., Lima, C.L., Liesenberg, V., Miyoshi, G.T., 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_18","first-page":"101970","article-title":"Improving LiDAR-Based Tree Species Mapping in Central European Mixed Forests Using Multi-Temporal Digital Aerial Colour-Infrared Photographs","volume":"84","author":"Shi","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5236","DOI":"10.1080\/01431161.2017.1363442","article-title":"Deciduous Tree Species Classification Using Object-Based Analysis and Machine Learning with Unmanned Aerial Vehicle Multispectral Data","volume":"39","author":"Franklin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.isprsjprs.2020.10.015","article-title":"Mapping Forest Tree Species in High Resolution UAV-Based RGB-Imagery by Means of Convolutional Neural Networks","volume":"170","author":"Schiefer","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"118397","DOI":"10.1016\/j.foreco.2020.118397","article-title":"Individual Tree Detection and Species Classification of Amazonian Palms Using UAV Images and Deep Learning","volume":"475","author":"Ferreira","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"De Luca, G., Silva, J.M.N., Cerasoli, S., Ara\u00fajo, J., Campos, J., Di Fazio, S., and Modica, G. (2019). Object-Based Land Cover Classification of Cork Oak Woodlands Using UAV Imagery and Orfeo ToolBox. Remote Sens., 11.","DOI":"10.3390\/rs11101238"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s10661-015-4996-2","article-title":"Classification of Riparian Forest Species and Health Condition Using Multi-Temporal and Hyperspatial Imagery from Unmanned Aerial System","volume":"188","author":"Michez","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.3390\/rs1041353","article-title":"Using Urban Landscape Trajectories to Develop a Multi-Temporal Land Cover Database to Support Ecological Modeling","volume":"1","author":"Coe","year":"2009","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1080\/15481603.2013.817150","article-title":"Object-Oriented Crop Classification Using Multitemporal ETM+ SLC-off Imagery and Random Forest","volume":"50","author":"Long","year":"2013","journal-title":"GIScience Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.rse.2016.01.028","article-title":"The Benefit of Synthetically Generated RapidEye and Landsat 8 Data Fusion Time Series for Riparian Forest Disturbance Monitoring","volume":"177","author":"Kleinschmit","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.06.012","article-title":"Accurate Mapping of Forest Types Using Dense Seasonal Landsat Time-Series","volume":"96","author":"Zhu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/S0034-4257(00)00159-0","article-title":"A Comparison of Multispectral and Multitemporal Information in High Spatial Resolution Imagery for Classification of Individual Tree Species in a Temperate Hardwood Forest","volume":"75","author":"Key","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_31","unstructured":"Mondino, G.P. (1963). Boschi Planiziali a Pinus Sylvestris e Alnus Incana delle Alluvioni del Torrente Bardonecchia, Regione Piemonte."},{"key":"ref_32","unstructured":"Camerano, P., Gottero, F., Terzuolo, P.G., and Varese, P. (2008). Tipi Forestali del Piemonte, IPLA S.p.A., Regione Piemonte, Blu Edizioni."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.3390\/rs4051392","article-title":"An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds","volume":"4","author":"Turner","year":"2012","journal-title":"Remote Sens."},{"key":"ref_34","unstructured":"(2021, February 12). Agisoft Metashape. Available online: https:\/\/www.agisoft.com\/."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chiabrando, F., Lingua, A., and Piras, M. (2013, January 16). Direct Photogrammetry Using UAV: Tests And First Results. Proceedings of the ISPRS\u2014International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Rostock, Germany.","DOI":"10.5194\/isprsarchives-XL-1-W2-81-2013"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A Survey of Image Classification Methods and Techniques for Improving Classification Performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6261","DOI":"10.1007\/s10661-012-3022-1","article-title":"Mapping Trees Outside Forests Using High-Resolution Aerial Imagery: A Comparison of Pixel- and Object-Based Classification Approaches","volume":"185","author":"Meneguzzo","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1109\/JSTARS.2013.2274668","article-title":"A Comparison of Pixel- and Object-Based Glacier Classification With Optical Satellite Images","volume":"7","author":"Rastner","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_41","unstructured":"(2021, February 11). ECognition|Trimble Geospatial. Available online: https:\/\/geospatial.trimble.com\/products-and-solutions\/ecognition."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1109\/TGRS.2009.2029570","article-title":"A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Images","volume":"48","author":"Persello","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.measurement.2019.05.092","article-title":"Determination and Accuracy Analysis of Individual Tree Crown Parameters Using UAV Based Imagery and OBIA Techniques","volume":"145","author":"Yurtseven","year":"2019","journal-title":"Measurement"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Belcore, E., Wawrzaszek, A., Wozniak, E., Grasso, N., and Piras, M. (2020). Individual Tree Detection from UAV Imagery Using H\u00f6lder Exponent. Remote Sens., 12.","DOI":"10.3390\/rs12152407"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s11831-017-9239-y","article-title":"Soft Computing Techniques for Land Use and Land Cover Monitoring with Multispectral Remote Sensing Images: A Review","volume":"26","author":"Thyagharajan","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"8703","DOI":"10.1080\/01431161.2018.1490976","article-title":"Land-Cover Mapping Using Random Forest Classification and Incorporating NDVI Time-Series and Texture: A Case Study of Central Shandong","volume":"39","author":"Jin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"547","DOI":"10.2478\/s11600-014-0250-5","article-title":"Testing Texture of VHR Panchromatic Data as a Feature of Land Cover Classification","volume":"63","author":"Aleksandrowicz","year":"2015","journal-title":"Acta Geophys."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring Vegetation Phenology Using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Drzewiecki, W., Wawrzaszek, A., Aleksandrowicz, S., Krupi\u0144ski, M., and Bernat, K. (2013, January 21\u201326). Comparison of Selected Textural Features as Global Content-Based Descriptors of VHR Satellite Image. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium\u2014IGARSS, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723801"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"McKinney, W. (July, January 28). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference (SCIPY 2010), Austin, TX, USA.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array Programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"ref_53","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Maimon, O., and Rokach, L. (2010). Data Mining for Imbalanced Datasets: An Overview. Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/978-0-387-09823-4"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-Sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_56","first-page":"878","article-title":"Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning","volume":"Volume 3644","author":"Han","year":"2005","journal-title":"International Conference on Intelligent Computing"},{"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":"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_59","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). Random Forests. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_60","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Press. [5th ed.]."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A Review of Supervised Object-Based Land-Cover Image Classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","first-page":"1137","article-title":"A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection","volume":"14","author":"Kohavi","year":"1995","journal-title":"IJCAI"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"291","DOI":"10.2307\/1403680","article-title":"Submodel Selection and Evaluation in Regression. The X-Random Case","volume":"60","author":"Breiman","year":"1992","journal-title":"Int. Stat. Rev. Rev. Int. Stat."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2018.2890023","article-title":"Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art","volume":"7","author":"Ghamisi","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mohan, M., Silva, C.A., Klauberg, C., Jat, P., Catts, G., Cardil, A., Hudak, A.T., and Dia, M. (2017). Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest. Forests, 8.","DOI":"10.3390\/f8090340"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Vieira, G.d.S., Rocha, B.M., Soares, F., Lima, J.C., Pedrini, H., Costa, R., and Ferreira, J. (2019, January 4\u20136). Extending the Aerial Image Analysis from the Detection of Tree Crowns. Proceedings of the 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA.","DOI":"10.1109\/ICTAI.2019.00247"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.isprsjprs.2018.06.006","article-title":"Comparison of High-Density LiDAR and Satellite Photogrammetry for Forest Inventory","volume":"142","author":"Pearse","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2011.10.006","article-title":"Combination of Individual Tree Detection and Area-Based Approach in Imputation of Forest Variables Using Airborne Laser Data","volume":"67","author":"Vastaranta","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1756\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:56:19Z","timestamp":1760162179000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1756"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,30]]},"references-count":68,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13091756"],"URL":"https:\/\/doi.org\/10.3390\/rs13091756","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,30]]}}}