{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T13:40:41Z","timestamp":1736170841979,"version":"3.32.0"},"reference-count":67,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T00:00:00Z","timestamp":1698969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Doctoral Program in Sustainable Use of Renewable Natural Resources (AGFOREE) at the University of Helsinki","award":["VN\/3482\/2021"]},{"DOI":"10.13039\/100007797","name":"University of Helsinki","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007797","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Academy of Finland Flagship Forest-Human-Machine Interplay\u2014Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE)","award":["337127"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Tree species information is important for forest management, especially in seedling stands. To mitigate the spectral admixture of understory reflectance with small and lesser foliaged seedling canopies, we proposed an image pre-processing step based on the canopy threshold (Cth) applied on drone-based multispectral images prior to feeding classifiers. This study focused on (1) improving the classification of seedlings by applying the introduced technique; (2) comparing the classification accuracies of the convolutional neural network (CNN) and random forest (RF) methods; and (3) improving classification accuracy by fusing vegetation indices to multispectral data. A classification of 5417 field-located seedlings from 75 sample plots showed that applying the Cth technique improved the overall accuracy (OA) of species classification from 75.7% to 78.5% on the Cth-affected subset of the test dataset in CNN method (1). The OA was more accurate in CNN (79.9%) compared to RF (68.3%) (2). Moreover, fusing vegetation indices with multispectral data improved the OA from 75.1% to 79.3% in CNN (3). Further analysis revealed that shorter seedlings and tensors with a higher proportion of Cth-affected pixels have negative impacts on the OA in seedling forests. Based on the obtained results, the proposed method could be used to improve species classification of single-tree detected seedlings in operational forest inventory.<\/jats:p>","DOI":"10.3390\/rs15215233","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T14:59:54Z","timestamp":1699023594000},"page":"5233","source":"Crossref","is-referenced-by-count":0,"title":["A New Approach for Feeding Multispectral Imagery into Convolutional Neural Networks Improved Classification of Seedlings"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6240-077X","authenticated-orcid":false,"given":"Mohammad","family":"Imangholiloo","sequence":"first","affiliation":[{"name":"Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9036-8591","authenticated-orcid":false,"given":"Ville","family":"Luoma","sequence":"additional","affiliation":[{"name":"Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland"}]},{"given":"Markus","family":"Holopainen","sequence":"additional","affiliation":[{"name":"Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland"},{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS), Geodeetinrinne 2, 02430 Masala, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6552-9122","authenticated-orcid":false,"given":"Mikko","family":"Vastaranta","sequence":"additional","affiliation":[{"name":"School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland"}]},{"given":"Antti","family":"M\u00e4kel\u00e4inen","sequence":"additional","affiliation":[{"name":"MosaicMill Oy, Presently AFRY Management Consulting, Jaakonkatu 3, 01620 Vantaa, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6307-1637","authenticated-orcid":false,"given":"Niko","family":"Koivum\u00e4ki","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS), Geodeetinrinne 2, 02430 Masala, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7236-2145","authenticated-orcid":false,"given":"Eija","family":"Honkavaara","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS), Geodeetinrinne 2, 02430 Masala, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7352-9138","authenticated-orcid":false,"given":"Ehsan","family":"Khoramshahi","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS), Geodeetinrinne 2, 02430 Masala, Finland"},{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,3]]},"reference":[{"key":"ref_1","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. 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