{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:28:14Z","timestamp":1780928894628,"version":"3.54.1"},"reference-count":106,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T00:00:00Z","timestamp":1626652800000},"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>Forests are the planet\u2019s main CO2 filtering agent as well as important economical, environmental and social assets. Climate change is exerting an increased stress, resulting in a need for improved research methodologies to study their health, composition or evolution. Traditionally, information about forests has been collected using expensive and work-intensive field inventories, but in recent years unoccupied autonomous vehicles (UAVs) have become very popular as they represent a simple and inexpensive way to gather high resolution data of large forested areas. In addition to this trend, deep learning (DL) has also been gaining much attention in the field of forestry as a way to include the knowledge of forestry experts into automatic software pipelines tackling problems such as tree detection or tree health\/species classification. Among the many sensors that UAVs can carry, RGB cameras are fast, cost-effective and allow for straightforward data interpretation. This has resulted in a large increase in the amount of UAV-acquired RGB data available for forest studies. In this review, we focus on studies that use DL and RGB images gathered by UAVs to solve practical forestry research problems. We summarize the existing studies, provide a detailed analysis of their strengths paired with a critical assessment on common methodological problems and include other information, such as available public data and code resources that we believe can be useful for researchers that want to start working in this area. We structure our discussion using three main families of forestry problems: (1) individual Tree Detection, (2) tree Species Classification, and (3) forest Anomaly Detection (forest fires and insect Infestation).<\/jats:p>","DOI":"10.3390\/rs13142837","type":"journal-article","created":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T10:07:37Z","timestamp":1626689257000},"page":"2837","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":139,"title":["Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4521-9113","authenticated-orcid":false,"given":"Yago","family":"Diez","sequence":"first","affiliation":[{"name":"Faculty of Science, Yamagata University, Yamagata 990-8560, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5693-5217","authenticated-orcid":false,"given":"Sarah","family":"Kentsch","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture, Yamagata University, Tsuruoka 997-8555, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6757-1700","authenticated-orcid":false,"given":"Motohisa","family":"Fukuda","sequence":"additional","affiliation":[{"name":"Faculty of Science, Yamagata University, Yamagata 990-8560, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9748-7120","authenticated-orcid":false,"given":"Maximo Larry Lopez","family":"Caceres","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture, Yamagata University, Tsuruoka 997-8555, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koma","family":"Moritake","sequence":"additional","affiliation":[{"name":"Faculty of Science, Yamagata University, Yamagata 990-8560, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4417-1704","authenticated-orcid":false,"given":"Mariano","family":"Cabezas","sequence":"additional","affiliation":[{"name":"Brain and Mind Centre, University of Sydney, Sydney 2050, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.3390\/f5061481","article-title":"Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas","volume":"5","author":"McCall","year":"2014","journal-title":"Forests"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.12988\/ces.2016.68130","article-title":"Forest and UAV: A bibliometric review","volume":"9","author":"Gambella","year":"2016","journal-title":"Contemp. Eng. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, N., P\u00e1dua, L., Marques, P., Silva, N., Peres, E., and Sousa, J.J. (2020). Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities. Remote Sens., 12.","DOI":"10.3390\/rs12061046"},{"key":"ref_4","first-page":"557","article-title":"The Use of Drones in Forestry","volume":"5","author":"Banu","year":"2016","journal-title":"J. Environ. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chadwick, A.J., Goodbody, T.R.H., Coops, N.C., Hervieux, A., Bater, C.W., Martens, L.A., White, B., and R\u00f6eser, D. (2020). Automatic Delineation and Height Measurement of Regenerating Conifer Crowns under Leaf-Off Conditions Using UAV Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12244104"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1080\/2150704X.2020.1784491","article-title":"Tree extraction from multi-scale UAV images using Mask R-CNN with FPN","volume":"11","author":"Ocer","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Fujimoto, A., Haga, C., Matsui, T., Machimura, T., Hayashi, K., Sugita, S., and Takagi, H. (2019). An End to End Process Development for UAV-SfM Based Forest Monitoring: Individual Tree Detection, Species Classification and Carbon Dynamics Simulation. Forests, 10.","DOI":"10.3390\/f10080680"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kentsch, S., Lopez Caceres, M.L., Serrano, D., Roure, F., and Diez, Y. (2020). Computer Vision and Deep Learning Techniques for the Analysis of Drone-Acquired Forest Images, a Transfer Learning Study. Remote Sens., 12.","DOI":"10.3390\/rs12081287"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Egli, S., and H\u00f6pke, M. (2020). CNN-Based Tree Species Classification Using High Resolution RGB Image Data from Automated UAV Observations. Remote Sens., 12.","DOI":"10.3390\/rs12233892"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Tran, D.Q., Park, M., Jung, D., and Park, S. (2020). Damage-Map Estimation Using UAV Images and Deep Learning Algorithms for Disaster Management System. Remote Sens., 12.","DOI":"10.3390\/rs12244169"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Safonova, A., Tabik, S., Alcaraz-Segura, D., Rubtsov, A., Maglinets, Y., and Herrera, F. (2019). Detection of fir trees (Abies sibirica) damaged by the bark beetle in unmanned aerial vehicle images with deep learning. Remote Sens., 11.","DOI":"10.3390\/rs11060643"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1080\/22797254.2018.1474722","article-title":"Single-tree detection in high-density LiDAR data from UAV-based survey","volume":"51","author":"Balsi","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qin, J., Wang, B., Wu, Y., Lu, Q., and Zhu, H. (2021). Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote Sens., 13.","DOI":"10.3390\/rs13020162"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101694","DOI":"10.1016\/j.media.2020.101694","article-title":"Convolutional neural networks for classification of Alzheimer\u2019s disease: Overview and reproducible evaluation","volume":"63","author":"Wen","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The PASCALVisual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_20","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. 25th International Conference on Neural Information Processing Systems\u2014Volume 1, Curran Associates Inc."},{"key":"ref_21","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_23","unstructured":"Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50 \u00d7  fewer parameters and <1 MB model size. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_25","unstructured":"Richard, C., Wilson, E.R.H., and Smith, W.A.P. (2016). Wide Residual Networks. Proceedings of the British Machine Vision Conference (BMVC), BMVA Press."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019, January 27\u201328). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). SSD: Single Shot MultiBox Detector. Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46454-1"},{"key":"ref_33","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Computer Vision\u2014ECCV 2018, Springer International Publishing."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kirillov, A., He, K., Girshick, R., Rother, C., and Doll\u00e1r, P. (2019, January 15\u201320). Panoptic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00963"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_37","unstructured":"Jung, A.B. (2020, July 01). Imgaug. Available online: https:\/\/github.com\/aleju\/imgaug."},{"key":"ref_38","unstructured":"Agisoft LLC (2021, June 12). Agisoft Metashape, Professional Edition. Available online: http:\/\/agisoft.com\/."},{"key":"ref_39","unstructured":"QGIS Geographic Information System (2021, June 12). Open Source Geospatial Foundation Project. Available online: http:\/\/qgis.org\/."},{"key":"ref_40","unstructured":"ESRI (2021, June 12). ArcGIS Desktop v10.4 Software. Available online: https:\/\/www.esri.com\/."},{"key":"ref_41","unstructured":"Toffain, P., Benjamin, D., Riba, E., Mather, S., Fitzsimmons, S., Gelder, F., Bargen, D., Cesar de Menezes, J., and Joseph, D. (2021, April 14). OpendroneMap\/ODM: 1.0.1. Available online: https:\/\/github.com\/OpenDroneMap\/ODM."},{"key":"ref_42","unstructured":"(2021, April 14). Drone & UAV Mapping Platform DroneDeploy. Available online: http:\/\/www.dronedeploy.com\/."},{"key":"ref_43","unstructured":"Trimble (2021, June 12). eCognition Developer v9.0.0 Software. Available online: https:\/\/www.trimble.com\/."},{"key":"ref_44","unstructured":"Team, T.G. (2019, August 19). GNU Image Manipulation Program. Available online: http:\/\/gimp.org."},{"key":"ref_45","unstructured":"(2021, April 14). RectLabel. Available online: https:\/\/rectlabel.com\/."},{"key":"ref_46","unstructured":"LabelImg (2021, April 14). T.GitCode. Available online: http:\/\/github.com\/tzutalin\/labelImg."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.ecoinf.2019.05.005","article-title":"Columnar cactus recognition in aerial images using a deep learning approach","volume":"52","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Fromm, M., Schubert, M., Castilla, G., Linke, J., and McDermid, G. (2019). Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11212585"},{"key":"ref_49","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_50","doi-asserted-by":"crossref","unstructured":"Morales, G., Kemper, G., Sevillano, G., Arteaga, D., Ortega, I., and Telles, J. (2018). Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning. Forests, 9.","DOI":"10.3390\/f9120736"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s12524-020-01231-3","article-title":"Deep Learning Based Supervised Image Classification Using UAV Images for Forest Areas Classification","volume":"49","author":"Haq","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"17656","DOI":"10.1038\/s41598-019-53797-9","article-title":"Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery","volume":"9","author":"Kattenborn","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1002\/rse2.146","article-title":"Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery","volume":"6","author":"Kattenborn","year":"2020","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Nezami, S., Khoramshahi, E., Nevalainen, O., P\u00f6l\u00f6nen, I., and Honkavaara, E. (2020). Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks. Remote Sens., 12.","DOI":"10.20944\/preprints202002.0334.v1"},{"key":"ref_55","unstructured":"Onishi, M., and Ise, T. (2018). Automatic classification of trees using a UAV onboard camera and deep learning. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1038\/s41598-020-79653-9","article-title":"Explainable identification and mapping of trees using UAV RGB image and deep learning","volume":"11","author":"Onishi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"17736","DOI":"10.1109\/ACCESS.2019.2895243","article-title":"Fourier Dense Network to Conduct Plant Classification Using UAV-Based Optical Images","volume":"7","author":"Lin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_58","first-page":"475","article-title":"Resnet-based tree species classification using UAV images","volume":"XLII-2\/W13","author":"Natesan","year":"2019","journal-title":"ISPRS Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1139\/juvs-2020-0014","article-title":"Individual tree species identification using Dense Convolutional Network (DenseNet) on multitemporal RGB images from UAV","volume":"8","author":"Natesan","year":"2020","journal-title":"J. Unmanned Veh. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Barmpoutis, P., Kamperidou, V., and Stathaki, T. (2019, January 16\u201318). Estimation of extent of trees and biomass infestation of the suburban forest of Thessaloniki (Seich Sou) using UAV imagery and combining R-CNNs and multichannel texture analysis. Proceedings of the Twelfth International Conference on Machine Vision (ICMV 2019), Amsterdam, The Netherlands.","DOI":"10.1117\/12.2556378"},{"key":"ref_61","unstructured":"Humer, C. (2020). Early Detection of Spruce Bark Beetles Using Semantic Segmentation and Image Classification. [Ph.D. Thesis, Universitat Linz]."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3390\/agriengineering2020019","article-title":"Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing","volume":"2","author":"Deng","year":"2020","journal-title":"AgriEngineering"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Nguyen, H.T., Lopez Caceres, M.L., Moritake, K., Kentsch, S., Shu, H., and Diez, Y. (2021). Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13020260"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Kim, S., Lee, W., Park, Y.s., Lee, H.W., and Lee, Y.T. (2016, January 13\u201315). Forest fire monitoring system based on aerial image. Proceedings of the 2016 3rd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), Vienna, Austria.","DOI":"10.1109\/ICT-DM.2016.7857214"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1139\/juvs-2020-0009","article-title":"Forest fire flame and smoke detection from UAV-captured images using fire-specific color features and multi-color space local binary pattern","volume":"8","author":"Hossain","year":"2020","journal-title":"J. Unmanned Veh. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ma, J., Li, X., and Zhang, J. (2018). Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery. Sensors, 18.","DOI":"10.3390\/s18030712"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, Y., Jing, X., Wang, G., Mu, L., Yi, Y., Liu, H., and Liu, D. (2019, January 19\u201321). UAV Image-based Forest Fire Detection Approach Using Convolutional Neural Network. Proceedings of the 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi\u2019an, China.","DOI":"10.1109\/ICIEA.2019.8833958"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.jhydrol.2007.02.039","article-title":"Interannual environmental-soil thawing rate variation and its control on transpiration from Larix cajanderi, Central Yakutia, Eastern Siberia","volume":"338","author":"Saito","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1016\/j.agrformet.2008.09.013","article-title":"Comparison of carbon and water vapor exchange of forest and grassland in permafrost regions, Central Yakutia, Russia","volume":"148","author":"Gerasimov","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Diez, Y., Kentsch, S., Lopez-Caceres, M.L., Nguyen, H.T., Serrano, D., and Roure, F. (2020). Comparison of Algorithms for Tree-top Detection in Drone Image Mosaics of Japanese Mixed Forests. ICPRAM 2020, INSTICC, SciTePress.","DOI":"10.5220\/0009165800750087"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_72","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016). R-fcn: Object detection via region-based fully convolutional networks. arXiv."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Microsoft COCO: Common Objects in Context. Computer Vision\u2014ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10602-1"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Weinstein, B.G., Marconi, S., Bohlman, S., Zare, A., and White, E. (2019). Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sens., 11.","DOI":"10.1101\/532952"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"101061","DOI":"10.1016\/j.ecoinf.2020.101061","article-title":"Cross-site learning in deep learning RGB tree crown detection","volume":"56","author":"Weinstein","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"518","DOI":"10.5424\/fs\/2014233-06256","article-title":"European mixed forests: Definition and research perspectives","volume":"23","author":"Pretzsch","year":"2014","journal-title":"For. Syst."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Huuskonen, S., Domisch, T., Fin\u00e9r, L., Hantula, J., Hynynen, J., Matala, J., Miina, J., Neuvonen, S., Nevalainen, S., and Niemist\u00f6, P. (2021). What is the potential for replacing monocultures with mixed-species stands to enhance ecosystem services in boreal forests in Fennoscandia?. For. Ecol. Manag., 479.","DOI":"10.1016\/j.foreco.2020.118558"},{"key":"ref_79","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_80","doi-asserted-by":"crossref","unstructured":"Micha\u0142owska, M., and Rapi\u0144ski, J. (2021). A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers. Remote Sens., 13.","DOI":"10.3390\/rs13030353"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Kentsch, S., Cabezas, M., Tomhave, L., Gro\u00df, J., Burkhard, B., Lopez Caceres, M.L., Waki, K., and Diez, Y. (2021). Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning. Sensors, 21.","DOI":"10.3390\/s21020471"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_83","unstructured":"McGaughey, R.J. (2009). FUSION\/LDV: Software for LIDAR Data Analysis and Visualization."},{"key":"ref_84","unstructured":"De Marsico, M., Sanniti di Baja, G., and Fred, A. (2020). A Preliminary Study on Tree-Top Detection and Deep Learning Classification Using Drone Image Mosaics of Japanese Mixed Forests. Pattern Recognition Applications and Methods, Springer International Publishing."},{"key":"ref_85","first-page":"433","article-title":"The Morphological Approach to Segmentation: The Watershed Transformation","volume":"34","author":"Beucher","year":"1993","journal-title":"Math. Morphol. Image Process."},{"key":"ref_86","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 9). Automatic Differentiation in PyTorch. Proceedings of the NIPS Autodiff Workshop, Long Beach, CA, USA."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1090\/S0025-5718-1965-0178586-1","article-title":"An Algorithm for the Machine Calculation of Complex Fourier Series","volume":"19","author":"Cooley","year":"1965","journal-title":"Math. Comput."},{"key":"ref_88","unstructured":"Code, P.W. (2021, April 08). CIFAR10 Classification Results. Available online: https:\/\/paperswithcode.com\/sota\/image-classification-on-cifar-10."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1038\/s41467-021-21399-7","article-title":"Emergent vulnerability to climate-driven disturbances in European forests","volume":"12","author":"Forzieri","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Artes, T., Oom, D., de Rigo, D., Durrant, T., Maianti, P., Libert\u00e0, G., and San-Miguel-Ayanz, J. (2019). A global wildfire dataset for the analysis of fire regimes and fire behaviour. Sci. Data, 6.","DOI":"10.1038\/s41597-019-0312-2"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s42408-019-0062-8","article-title":"Changing wildfire, changing forests: The effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA","volume":"16","author":"Halofsky","year":"2020","journal-title":"Fire Ecol."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., and Grammalidis, N. (2020). A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing. Sensors, 20.","DOI":"10.3390\/s20226442"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1139\/cjfr-2014-0347","article-title":"A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques","volume":"45","author":"Yuan","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Axel, A.C. (2018). Burned Area Mapping of an Escaped Fire into Tropical Dry Forest in Western Madagascar Using Multi-Season Landsat OLI Data. Remote Sens., 10.","DOI":"10.3390\/rs10030371"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Stoyanov, D., Taylor, Z., Carneiro, G., Syeda-Mahmood, T., Martel, A., Maier-Hein, L., Tavares, J.M.R., Bradley, A., Papa, J.P., and Belagiannis, V. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer International Publishing.","DOI":"10.1007\/978-3-030-00889-5"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.foreco.2017.11.004","article-title":"Interactions of predominant insects and diseases with climate change in Douglas-fir forests of western Oregon and Washington, U.S.A","volume":"409","author":"Agne","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.cois.2019.07.010","article-title":"Responses of forest insect pests to climate change: Not so simple","volume":"35","author":"Jactel","year":"2019","journal-title":"Curr. Opin. Insect Sci."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"118280","DOI":"10.1016\/j.foreco.2020.118280","article-title":"Bark beetle infestation spots as biodiversity hotspots: Canopy gaps resulting from insect outbreaks enhance the species richness, diversity and abundance of birds breeding in coniferous forests","volume":"473","author":"Loch","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.foreco.2015.06.010","article-title":"Global forest area disturbance from fire, insect pests, diseases and severe weather events","volume":"352","author":"Lindquist","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_100","unstructured":"Thompson, I., Mackey, B., Mcnulty, S., and Mosseler, A. (2009). Forest Resilience, Biodiversity, and Climate Change. A Synthesis of the Biodiversity\/Resilience\/Stability Relationship in Forest Ecosystems, Secretariat of the Convention on Biological Diversity."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Cabezas, M., Kentsch, S., Tomhave, L., Gross, J., Caceres, M.L.L., and Diez, Y. (2020). Detection of Invasive Species in Wetlands: Practical DL with Heavily Imbalanced Data. Remote Sens., 12.","DOI":"10.3390\/rs12203431"},{"key":"ref_102","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_103","unstructured":"Van Rossum, G., and Drake, F.L. (1995). Python Tutorial, Centrum voor Wiskunde en Informatica."},{"key":"ref_104","unstructured":"Bradski, G. (2019, August 15). The OpenCV Library. Dr. Dobb\u2019s Journal of Software Tools. Available online: https:\/\/opencv.org\/."},{"key":"ref_105","unstructured":"Chollet, F. (2021, June 12). Keras. Available online: https:\/\/keras.io."},{"key":"ref_106","unstructured":"Howard, J., Thomas, R., and Gugger, S. (2021, June 12). Fastai. Available online: https:\/\/github.com\/fastai\/fastai."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2837\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:31:53Z","timestamp":1760164313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2837"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,19]]},"references-count":106,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142837"],"URL":"https:\/\/doi.org\/10.3390\/rs13142837","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,19]]}}}