Nãsi, R. et al. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sens. 7, 15467–15493. https://doi.org/10.3390/rs71115467 (2015).
Google Scholar
Navarro, A. et al. The application of unmanned aerial vehicles (UAVs) to estimate above-ground biomass of mangrove ecosystems. Remote Sens. Environ. 242, 111747. https://doi.org/10.1016/j.rse.2020.111747 (2020).
Google Scholar
Reis, B. P. et al. Management recommendation generation for areas under forest restoration process through images obtained by UAV and LiDAR. Remote Sens. 11, 1508. https://doi.org/10.3390/rs11131508 (2019).
Google Scholar
Saarinen, N. et al. Assessing biodiversity in boreal forests with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens. 10, 338. https://doi.org/10.3390/rs10020338 (2018).
Google Scholar
Casapia, X. T. et al. Identifying and quantifying the abundance of economically important palms in tropical moist forest using UAV imagery. Remote Sens. 12, 9. https://doi.org/10.3390/rs12010009 (2019).
Google Scholar
Li, L. et al. Quantifying understory and overstory vegetation cover using UAV-based RGB imagery in forest plantation. Remote Sens. 12, 298. https://doi.org/10.3390/rs12020298 (2020).
Google Scholar
dos Santos, A. A. et al. Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UAVs. Sensors 19, 3595. https://doi.org/10.3390/s19163595 (2019).
Google Scholar
Miyoshi, G. T., Imai, N. N., Tommaselli, A. M. G., de Moraes, M. V. A. & Honkavaara, E. Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse Atlantic forest. Remote Sens. 12, 244. https://doi.org/10.3390/rs12020244 (2020).
Google Scholar
Morales, G. et al. Automatic segmentation of Mauritia flexuosa in unmanned aerial vehicle (UAV) imagery using deep learning. Forests 9, 736. https://doi.org/10.3390/f9120736 (2018).
Google Scholar
Voss, M. & Sugumaran, R. Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object- oriented approach. Sensors 8, 3020–3036. https://doi.org/10.3390/s8053020 (2008).
Google Scholar
Andersen, H.-E., Reutebuch, S. E. & McGaughey, R. J. A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods. Can. J. Remote Sens. 32, 355–366. https://doi.org/10.5589/m06-030 (2006).
Google Scholar
Ganz, S., Käber, Y. & Adler, P. Measuring tree height with remote sensing—A comparison of photogrammetric and LiDAR data with different field measurements. Forests 10, 694. https://doi.org/10.3390/f10080694 (2019).
Google Scholar
Csillik, O., Cherbini, J., Johnson, R., Lyons, A. & Kelly, M. Identification of citrus trees from unmanned aerial vehicle imagery using convolutional neural networks. Drones 2, 39. https://doi.org/10.3390/drones2040039 (2018).
Google Scholar
Berveglieri, A., Imai, N. N., Tommaselli, A. M., Casagrande, B. & Honkavaara, E. Successional stages and their evolution in tropical forests using multi-temporal photogrammetric surface models and superpixels. ISPRS J. Photogram. Remote Sens. 146, 548–558. https://doi.org/10.1016/j.isprsjprs.2018.11.002 (2018).
Google Scholar
Cao, J. et al. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens. 10, 89. https://doi.org/10.3390/rs10010089 (2018).
Google Scholar
Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A. & White, E. Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sens. 11, 1309. https://doi.org/10.3390/rs11111309 (2019).
Google Scholar
Torres, D. L. et al. Applying fully convolutional architectures for semantic segmentation of a single tree species in urban environment on high resolution UAV optical imagery. Sensors 20, 563. https://doi.org/10.3390/s20020563 (2020).
Google Scholar
Liu, L., Song, B., Zhang, S. & Liu, X. A novel principal component analysis method for the reconstruction of leaf reflectance spectra and retrieval of leaf biochemical contents. Remote Sens. 9, 1113. https://doi.org/10.3390/rs9111113 (2017).
Google Scholar
Maschler, J., Atzberger, C. & Immitzer, M. Individual tree crown segmentation and classification of 13 tree species using airborne hyperspectral data. Remote Sens. 10, 1218. https://doi.org/10.3390/rs10081218 (2018).
Google Scholar
Hennessy, A., Clarke, K. & Lewis, M. Hyperspectral classification of plants: A review of waveband selection generalisability. Remote Sens. 12, 113. https://doi.org/10.3390/rs12010113 (2020).
Google Scholar
Hamraz, H., Contreras, M. A. & Zhang, J. Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds. Sci. Rep. 7, 1–9. https://doi.org/10.1038/s41598-017-07200-0 (2017).
Google Scholar
Cho, M. A. et al. Mapping tree species composition in south African savannas using an integrated airborne spectral and LiDAR system. Remote Sens. Environ. 125, 214–226. https://doi.org/10.1016/j.rse.2012.07.010 (2012).
Google Scholar
Apostol, B. et al. Species discrimination and individual tree detection for predicting main dendrometric characteristics in mixed temperate forests by use of airborne laser scanning and ultra-high-resolution imagery. Sci. Total Environ. 698, 134074. https://doi.org/10.1016/j.scitotenv.2019.134074 (2020).
Google Scholar
Immitzer, M., Atzberger, C. & Koukal, T. Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sens. 4, 2661–2693. https://doi.org/10.3390/rs4092661 (2012).
Google Scholar
Franklin, S. E. & Ahmed, O. S. Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data. Int. J. Remote Sens. 39, 5236–5245. https://doi.org/10.1080/01431161.2017.1363442 (2017).
Google Scholar
Dalponte, M., Orka, H. O., Gobakken, T., Gianelle, D. & Naesset, E. Tree species classification in boreal forests with hyperspectral data. IEEE Trans. Geosci. Remote Sens. 51, 2632–2645. https://doi.org/10.1109/tgrs.2012.2216272 (2013).
Google Scholar
Guimarães, N. et al. Forestry remote sensing from unmanned aerial vehicles: A review focusing on the data, processing and potentialities. Remote Sens. 12, 1046. https://doi.org/10.3390/rs12061046 (2020).
Google Scholar
Kattenborn, T., Eichel, J. & Fassnacht, F. E. Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Sci. Rep. 9, 1–9. https://doi.org/10.1038/s41598-019-53797-9 (2019).
Google Scholar
Onishi, M. & Ise, T. Explainable identification and mapping of trees using UAV RGB image and deep learning. Sci. Rep. 11, 1–15. https://doi.org/10.1038/s41598-020-79653-9 (2021).
Google Scholar
Näsi, R. et al. Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban For. Urban Green. 30, 72–83. https://doi.org/10.1016/j.ufug.2018.01.010 (2018).
Google Scholar
Nezami, S., Khoramshahi, E., Nevalainen, O., Pölönen, I. & Honkavaara, E. Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks. Remote Sens. 12, 1070. https://doi.org/10.3390/rs12071070 (2020).
Google Scholar
Nevalainen, O. et al. Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens. 9, 185. https://doi.org/10.3390/rs9030185 (2017).
Google Scholar
Raczko, E. & Zagajewski, B. Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. Eur. J. Remote Sens. 50, 144–154. https://doi.org/10.1080/22797254.2017.1299557 (2017).
Google Scholar
Tuominen, S. et al. Assessment of classifiers and remote sensing features of hyperspectral imagery and stereo-photogrammetric point clouds for recognition of tree species in a forest area of high species diversity. Remote Sens. 10, 714. https://doi.org/10.3390/rs10050714 (2018).
Google Scholar
Xie, Z., Chen, Y., Lu, D., Li, G. & Chen, E. Classification of land cover, forest, and tree species classes with ZiYuan-3 multispectral and stereo data. Remote Sens. 11, 164. https://doi.org/10.3390/rs11020164 (2019).
Google Scholar
Maxwell, A. E., Warner, T. A. & Fang, F. Implementation of machine-learning classification in remote sensing: an applied review. Int. J. Remote Sens. 39, 2784–2817. https://doi.org/10.1080/01431161.2018.1433343 (2018).
Google Scholar
Osco, L. P. et al. Predicting canopy nitrogen content in citrus-trees using random forest algorithm associated to spectral vegetation indices from UAV-imagery. Remote Sens. 11, 2925. https://doi.org/10.3390/rs11242925 (2019).
Google Scholar
Marrs, J. & Ni-Meister, W. Machine learning techniques for tree species classification using co-registered LiDAR and hyperspectral data. Remote Sens. 11, 819. https://doi.org/10.3390/rs11070819 (2019).
Google Scholar
Imangholiloo, M. et al. Characterizing seedling stands using leaf-off and leaf-on photogrammetric point clouds and hyperspectral imagery acquired from unmanned aerial vehicle. Forests 10, 415. https://doi.org/10.3390/f10050415 (2019).
Google Scholar
Pham, T., Yokoya, N., Bui, D., Yoshino, K. & Friess, D. Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges. Remote Sens. 11, 230. https://doi.org/10.3390/rs11030230 (2019).
Google Scholar
Ma, L. et al. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS J. Photogram. Remote Sens. 152, 166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015 (2019).
Google Scholar
Safonova, A. et al. Detection of fir trees (Abies sibirica) damaged by the bark beetle in unmanned aerial vehicle images with deep learning. Remote Sens. 11, 643. https://doi.org/10.3390/rs11060643 (2019).
Google Scholar
Kamilaris, A. & Prenafeta-Boldú, F. X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016 (2018).
Google Scholar
Khamparia, A. & Singh, K. M. A systematic review on deep learning architectures and applications. Exp. Syst. 36, e12400. https://doi.org/10.1111/exsy.12400 (2019).
Google Scholar
Sothe, C. et al. Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data. GISci. Remote Sens. 57, 369–394. https://doi.org/10.1080/15481603.2020.1712102 (2020).
Google Scholar
Redmon, J. & Farhadi, A. Yolov3: An incremental improvement (2018). arXiv:1804.02767.
Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Dollár, P. Focal loss for dense object detection (2018). arXiv:1708.02002
Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks (2016). arXiv:1506.01497
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition (2015). arXiv:1409.1556
Sylvain, J.-D., Drolet, G. & Brown, N. Mapping dead forest cover using a deep convolutional neural network and digital aerial photography. ISPRS J. Photogram. Remote Sens. 156, 14–26. https://doi.org/10.1016/j.isprsjprs.2019.07.010 (2019).
Google Scholar
Hartling, S., Sagan, V., Sidike, P., Maimaitijiang, M. & Carron, J. Urban tree species classification using a WorldView-2/3 and LiDAR data fusion approach and deep learning. Sensors 19, 1284. https://doi.org/10.3390/s19061284 (2019).
Google Scholar
Culman, M., Delalieux, S. & Tricht, K. V. Individual palm tree detection using deep learning on RGB imagery to support tree inventory. Remote Sens. 12, 3476. https://doi.org/10.3390/rs12213476 (2020).
Google Scholar
Aburasain, R. Y., Edirisinghe, E. A. & Albatay, A. Palm tree detection in drone images using deep convolutional neural networks: Investigating the effective use of YOLO v3. In Digital Interaction and Machine Intelligence, 21–36, https://doi.org/10.1007/978-3-030-74728-2_3 (Springer International Publishing, 2021).
Bortolotto, I. M., Damasceno-Junior, G. A. & Pott, A. Preliminary list of native food plants from mato grosso do sul, brazil. Iheringia, Série Botânica 73, 101–116 (2018). https://doi.org/10.21826/2446-8231201873s101
van der Hoek, Y., Solas, S. Á. & Peñuela, M. C. The palm Mauritia flexuosa, a keystone plant resource on multiple fronts. Biodiver. Conserv. 28, 539–551. https://doi.org/10.1007/s10531-018-01686-4 (2019).
Google Scholar
Agostini-Costa, T. d. S., Faria, J. P., Naves, R. V. & Vieira, R. F. Espécies Nativas da Flora Brasileira de Valor Econômico Atual ou Potencial Plantas para o Futuro – Região Centro-Oeste (Ministério do Meio Ambiente – MMA, 2016).
Djerriri, K., Ghabi, M., Karoui, M. S. & Adjoudj, R. Palm trees counting in remote sensing imagery using regression convolutional neural network. In IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 2627–2630 (2018). https://doi.org/10.1109/IGARSS.2018.8519188
Osco, L. P. et al. A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery. ISPRS J. Photogram. Remote Sens. 160, 97–106. https://doi.org/10.1016/j.isprsjprs.2019.12.010 (2020).
Google Scholar
Goldman, E. et al. Precise detection in densely packed scenes (2019). arXiv:1904.00853
Holm, J. A., Miller, C. J. & Cropper, W. P. Population dynamics of the dioecious amazonian palm Mauritia flexuosa: Simulation analysis of sustainable harvesting. Biotropica 40, 550–558. https://doi.org/10.1111/j.1744-7429.2008.00412.x (2008).
Google Scholar
Zhao, H., Shi, J., Qi, X., Wang, X. & Jia, J. Pyramid scene parsing network (2017). arXiv:1612.01105
Source: Ecology - nature.com