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Here, we use a deep learning algorithm called U-net and very high-resolution multispectral images (0.5 m) from GeoEye satellite to identify, segment and map canopy palms over \u223c3000 km     2     of Amazonian forest. The map was used to analyse the spatial distribution of canopy palm trees and its relation to human disturbance and edaphic conditions. The overall accuracy of the map was 95.5% and the F1-score was 0.7. Canopy palm trees covered 6.4% of the forest canopy and were distributed in more than two million patches that can represent one or more individuals. The density of canopy palms is affected by human disturbance. The post-disturbance density in secondary forests seems to be related to the type of disturbance, being higher in abandoned pasture areas and lower in forests that have been cut once and abandoned. Additionally, analysis of palm trees\u2019 distribution shows that their abundance is controlled naturally by local soil water content, avoiding both flooded and waterlogged areas near rivers and dry areas on the top of the hills. They show two preferential habitats, in the low elevation above the large rivers, and in the slope directly below the hill tops. Overall, their distribution over the region indicates a relatively pristine landscape, albeit within a forest that is critically endangered because of its location between two deforestation fronts and because of illegal cutting. New tree species distribution data, such as the map of all adult canopy palms produced in this work, are urgently needed to support Amazon species inventory and to understand their distribution and diversity.<\/jats:p>","DOI":"10.3390\/rs12142225","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T09:30:49Z","timestamp":1594719049000},"page":"2225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9623-1182","authenticated-orcid":false,"given":"Fabien H.","family":"Wagner","sequence":"first","affiliation":[{"name":"GeoProcessing Division, Foundation for Science, Technology and Space Applications\u2014FUNCATE, S\u00e3o Jos\u00e9 dos Campos SP 12210-131, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7151-8697","authenticated-orcid":false,"given":"Ricardo","family":"Dalagnol","sequence":"additional","affiliation":[{"name":"Remote Sensing Division, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos SP 12227-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4152-2051","authenticated-orcid":false,"given":"Ximena","family":"Tagle Casapia","sequence":"additional","affiliation":[{"name":"Probosques, Instituto de Investigaciones de la Amazon\u00eda Peruana\u2014IIAP, Av. A. Jos\u00e9 Qui\u00f1ones km 2.5, Iquitos AP 784, Peru"},{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 06708 PB Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annia S.","family":"Streher","sequence":"additional","affiliation":[{"name":"Remote Sensing Division, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos SP 12227-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8993-6168","authenticated-orcid":false,"given":"Oliver L.","family":"Phillips","sequence":"additional","affiliation":[{"name":"School of Geography, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuel","family":"Gloor","sequence":"additional","affiliation":[{"name":"School of Geography, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4134-6708","authenticated-orcid":false,"given":"Luiz E. O. C.","family":"Arag\u00e3o","sequence":"additional","affiliation":[{"name":"Remote Sensing Division, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos SP 12227-010, Brazil"},{"name":"Geography, College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1111\/1365-2745.12025","article-title":"Identification of 100 fundamental ecological questions","volume":"101","author":"Sutherland","year":"2013","journal-title":"J. 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