Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network
1.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).ADS
Article
Google Scholar
2.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).ADS
Article
Google Scholar
3.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).ADS
Article
Google Scholar
4.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).ADS
Article
Google Scholar
5.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).ADS
Article
Google Scholar
6.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).ADS
Article
Google Scholar
7.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).ADS
Article
PubMed Central
Google Scholar
8.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).ADS
Article
Google Scholar
9.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).Article
Google Scholar
10.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).ADS
Article
PubMed
PubMed Central
Google Scholar
11.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).ADS
Article
Google Scholar
12.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).Article
Google Scholar
13.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).Article
Google Scholar
14.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).ADS
Article
Google Scholar
15.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).ADS
Article
Google Scholar
16.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).ADS
Article
Google Scholar
17.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).ADS
Article
Google Scholar
18.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).ADS
Article
Google Scholar
19.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).ADS
Article
Google Scholar
20.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).Article
Google Scholar
21.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).CAS
Article
Google Scholar
22.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).ADS
Article
Google Scholar
23.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).ADS
CAS
Article
PubMed
Google Scholar
24.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).ADS
Article
Google Scholar
25.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).Article
Google Scholar
26.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).ADS
Article
Google Scholar
27.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).ADS
Article
Google Scholar
28.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).CAS
Article
Google Scholar
29.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).CAS
Article
Google Scholar
30.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).Article
Google Scholar
31.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).ADS
Article
Google Scholar
32.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).ADS
Article
Google Scholar
33.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).Article
Google Scholar
34.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).ADS
Article
Google Scholar
35.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).ADS
Article
Google Scholar
36.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).ADS
Article
Google Scholar
37.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).ADS
Article
Google Scholar
38.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).ADS
Article
Google Scholar
39.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).Article
Google Scholar
40.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).ADS
Article
Google Scholar
41.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).ADS
Article
Google Scholar
42.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).ADS
Article
Google Scholar
43.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).Article
Google Scholar
44.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).Article
Google Scholar
45.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).Article
Google Scholar
46.Redmon, J. & Farhadi, A. Yolov3: An incremental improvement (2018). arXiv:1804.02767.47.Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Dollár, P. Focal loss for dense object detection (2018). arXiv:1708.0200248.Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks (2016). arXiv:1506.0149749.Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition (2015). arXiv:1409.155650.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).ADS
Article
Google Scholar
51.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).ADS
Article
PubMed Central
Google Scholar
52.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).ADS
Article
Google Scholar
53.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).54.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/10.21826/2446-8231201873s10155.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).Article
Google Scholar
56.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).57.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.2018.851918858.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).ADS
Article
Google Scholar
59.Goldman, E. et al. Precise detection in densely packed scenes (2019). arXiv:1904.0085360.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).Article
Google Scholar
61.Zhao, H., Shi, J., Qi, X., Wang, X. & Jia, J. Pyramid scene parsing network (2017). arXiv:1612.01105 More