{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T05:13:38Z","timestamp":1725254018433},"reference-count":57,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:00:00Z","timestamp":1664928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Government","award":["DP180103460"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Information on tree species and changes in forest composition is necessary to understand species-specific responses to change, and to develop conservation strategies. Remote sensing methods have been increasingly used for tree detection and species classification. In mixed species forests, conventional tree detection methods developed with assumptions about uniform tree canopy structure often fail. The main aim of this study is to identify effective methods for tree delineation and species classification in an Australian native forest. Tree canopies were delineated at three different spatial scales of analysis: (i) superpixels representing small elements in the tree canopy, (ii) tree canopy objects generated using a conventional segmentation technique, multiresolution segmentation (MRS), and (iii) individual tree bounding boxes detected using deep learning based on the DeepForest open-source algorithm. Combinations of spectral, texture, and structural measures were tested to assess features relevant for species classification using RandomForest. The highest overall classification accuracies were achieved at the superpixel scale (0.84 with all classes and 0.93 with Eucalyptus classes grouped). The highest accuracies at the individual tree bounding box and object scales were similar (0.77 with Eucalyptus classes grouped), highlighting the potential of tree detection using DeepForest, which uses only RGB, compared to site-specific tuning with MRS using additional layers. This study demonstrates the broad applicability of DeepForest and superpixel approaches for tree delineation and species classification. These methods have the potential to offer transferable solutions that can be applied in other forests.<\/jats:p>","DOI":"10.3390\/rs14194963","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T07:07:28Z","timestamp":1665385648000},"page":"4963","source":"Crossref","is-referenced-by-count":12,"title":["Tree Detection and Species Classification in a Mixed Species Forest Using Unoccupied Aircraft System (UAS) RGB and Multispectral Imagery"],"prefix":"10.3390","volume":"14","author":[{"given":"Poornima","family":"Sivanandam","sequence":"first","affiliation":[{"name":"School of Geography, Planning, and Spatial Sciences, University of Tasmania, Sandy Bay, TAS 7005, Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9468-4516","authenticated-orcid":false,"given":"Arko","family":"Lucieer","sequence":"additional","affiliation":[{"name":"School of Geography, Planning, and Spatial Sciences, University of Tasmania, Sandy Bay, TAS 7005, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1111\/jvs.12378","article-title":"Eucalyptus forest shows low structural resistance and resilience to climate change-type drought","volume":"27","author":"Matusick","year":"2016","journal-title":"J. Veg. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jiao, T., Williams, C.A., Rogan, J., De Kauwe, M.G., and Medlyn, B.E. (2020). Drought Impacts on Australian Vegetation During the Millennium Drought Measured with Multisource Spaceborne Remote Sensing. J. Geophys. Res. Biogeosci., 125.","DOI":"10.1029\/2019JG005145"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1126\/science.aat7631","article-title":"Hanging by a thread? Forests and drought","volume":"368","author":"Brodribb","year":"2020","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.rse.2005.12.015","article-title":"The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data","volume":"101","author":"Bunting","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_5","unstructured":"Williams, J., and Woinarski, J.C.Z. (1997). Eucalypt Ecology: Individuals to Ecosystems, Cambridge University Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"956","DOI":"10.2134\/jeq2004.0956","article-title":"Assessment of Crown Condition in Eucalypt Vegetation by Remotely Sensed Optical Indices","volume":"33","author":"Coops","year":"2004","journal-title":"J. Environ. Qual."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1016\/j.rse.2007.10.011","article-title":"Classification of Australian forest communities using aerial photography, CASI and HyMap data","volume":"112","author":"Lucas","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1071\/BT04085","article-title":"Classifying Eucalyptus forests with high spatial and spectral resolution imagery: An investigation of individual species and vegetation communities","volume":"53","author":"Goodwin","year":"2005","journal-title":"Aust. J. Bot."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1016\/j.rse.2010.12.012","article-title":"Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data","volume":"115","author":"Youngentob","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/JSTARS.2013.2282166","article-title":"Classification of Australian Native Forest Species Using Hyperspectral Remote Sensing and Machine-Learning Classification Algorithms","volume":"7","author":"Shang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cavender-Bares, J., Gamon, J.A., and Townsend, P.A. (2020). Remote Sensing of Plant Biodiversity. [Electronic Resource], Springer International Publishing.","DOI":"10.1007\/978-3-030-33157-3"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.rse.2016.10.014","article-title":"Mapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a floodplain eucalypt forest","volume":"187","author":"Shendryk","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4725","DOI":"10.1080\/01431161.2010.494184","article-title":"A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing","volume":"32","author":"Ke","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/S0034-4257(00)00101-2","article-title":"Local Maximum Filtering for the Extraction of Tree Locations and Basal Area from High Spatial Resolution Imagery","volume":"73","author":"Wulder","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/S0034-4257(02)00050-0","article-title":"Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration","volume":"82","author":"Pouliot","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1002\/rse2.44","article-title":"UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring","volume":"4","author":"Sankey","year":"2018","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Marques, P., P\u00e1dua, L., Ad\u00e3o, T., Hru\u0161ka, J., Peres, E., Sousa, A., and Sousa, J.J. (2019). UAV-Based Automatic Detection and Monitoring of Chestnut Trees. Remote Sens., 11.","DOI":"10.3390\/rs11070855"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","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_21","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_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/07038992.1995.10874590","article-title":"Comparison of Possible Multispectral Classification Schemes for Tree Crowns Individually Delineated on High Spatial Resolution MEIS Images","volume":"21","author":"Gougeon","year":"1995","journal-title":"Can. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mishra, N.B., Mainali, K.P., Shrestha, B.B., Radenz, J., and Karki, D. (2018). Species-Level Vegetation Mapping in a Himalayan Treeline Ecotone Using Unmanned Aerial System (UAS) Imagery. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7110445"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"134074","DOI":"10.1016\/j.scitotenv.2019.134074","article-title":"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","volume":"698","author":"Apostol","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Martins, J., Junior, J.M., Menezes, G., Pistori, H., SantaAna, D., and Goncalves, W. (August, January 28). Image Segmentation and Classification with SLIC Superpixel and Convolutional Neural Network in Forest Context. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898969"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s12524-020-01240-2","article-title":"An Integrated Object and Machine Learning Approach for Tree Canopy Extraction from UAV Datasets","volume":"49","author":"Adhikari","year":"2020","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Csillik, O. (2017). Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels. Remote Sens., 9.","DOI":"10.3390\/rs9030243"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Abdollahnejad, A., and Panagiotidis, D. (2020). Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging. Remote Sens., 12.","DOI":"10.3390\/rs12223722"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Miyoshi, G.T., Imai, N.N., Tommaselli, A.M.G., de Moraes, M.V.A., 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_32","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_33","doi-asserted-by":"crossref","unstructured":"Miyoshi, G.T., Arruda, M.d.S., Osco, L.P., Marcato Junior, J., Gon\u00e7alves, D.N., Imai, N.N., Tommaselli, A.M.G., Honkavaara, E., and Gon\u00e7alves, W.N. (2020). A Novel Deep Learning Method to Identify Single Tree Species in UAV-Based Hyperspectral Images. Remote Sens., 12.","DOI":"10.3390\/rs12081294"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dos Santos, A.A., Marcato Junior, J., Ara\u00fajo, M.S., Di Martini, D.R., Tetila, E.C., Siqueira, H.L., Aoki, C., Eltner, A., Matsubara, E.T., and Pistori, H. (2019). Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs. Sensors, 19.","DOI":"10.3390\/s19163595"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1007\/s11676-020-01245-0","article-title":"Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle","volume":"32","author":"Zhang","year":"2020","journal-title":"J. For. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Csillik, O., Cherbini, J., Johnson, R., Lyons, A., and Kelly, M. (2018). Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks. Drones, 2.","DOI":"10.3390\/drones2040039"},{"key":"ref_38","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_39","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_40","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., and Zhang, Y. (2018). Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071119"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1111\/2041-210X.13472","article-title":"DeepForest: A Python package for RGB deep learning tree crown delineation","volume":"11","author":"Weinstein","year":"2020","journal-title":"Methods Ecol. Evol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1111\/nph.14652","article-title":"Gas exchange recovery following natural drought is rapid unless limited by loss of leaf hydraulic conductance: Evidence from an evergreen woodland","volume":"215","author":"Skelton","year":"2017","journal-title":"New Phytol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bell, R.-A., and Callow, J.N. (2020). Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques. Remote Sens., 12.","DOI":"10.3390\/rs12040669"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ren, X., and Malik, J. (2003, January 3\u201316). Learning a classification model for segmentation. Proceedings of the IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238308"},{"key":"ref_45","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_46","unstructured":"(2021). Anaconda, Anaconda Inc.. Available online: https:\/\/anaconda.com."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approaches to texture","volume":"67","author":"Haralick","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_48","unstructured":"(2022, September 17). Trimble eCognition 2021, Trimble eCognition Developer Reference Book, Trimble Inc.. Available online: https:\/\/docs.ecognition.com\/v10.0.2\/Default.htm."},{"key":"ref_49","unstructured":"(2021, June 08). LAStools, Version 200304, Academic; Efficient LiDAR Processing Software. Available online: http:\/\/rapidlasso.com\/LAStools."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1111\/j.1442-9993.1983.tb01337.x","article-title":"The occurrence of mixed stands of the Eucalyptus subgenera Monocalyptus and Symphyomyrtus in south-eastern Tasmania","volume":"8","author":"Duff","year":"1983","journal-title":"Austral Ecol."},{"key":"ref_51","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref_52","unstructured":"McNicoll, G., Burrough, P.A., and Frank, A.U. Geographic Objects with Indeterminate Boundaries, Taylor and Francis."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Camarretta, N., A. Harrison, P., Lucieer, A., Potts, B.M., Davidson, N., and Hunt, M. (2020). From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure. Remote Sens., 12.","DOI":"10.3390\/rs12193184"},{"key":"ref_54","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_55","doi-asserted-by":"crossref","unstructured":"Braga, J.R.G., Peripato, V., Dalagnol, R., Ferreira, M.P., Tarabalka, Y., Arag\u00e3o, L.E.O.C., Velho, H.F.D.C., Shiguemori, E.H., and Wagner, F.H. (2020). Tree Crown Delineation Algorithm Based on a Convolutional Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12081288"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chadwick, A., Goodbody, T., Coops, N., Hervieux, A., Bater, C., Martens, L., 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_57","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.isprsjprs.2021.06.003","article-title":"Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN)","volume":"178","author":"Hao","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4963\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T01:12:43Z","timestamp":1722993163000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4963"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,5]]},"references-count":57,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194963"],"URL":"https:\/\/doi.org\/10.3390\/rs14194963","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,10,5]]}}}