{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:40:32Z","timestamp":1760146832338,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation (NSF)","doi-asserted-by":"publisher","award":["2047940","0837531"],"award-info":[{"award-number":["2047940","0837531"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Arecaceae (palms) play a crucial role for native communities and wildlife in the Amazon region. This study presents a first-of-its-kind regional-scale spatial cataloging of palms using remotely sensed data for the country of Guyana. Using very high-resolution satellite images from the GeoEye-1 and WorldView-2 sensor platforms, which collectively cover an area of 985 km2, a total of 472,753 individual palm crowns are detected with F1 scores of 0.76 and 0.79, respectively, using a convolutional neural network (CNN) instance segmentation model. An example of CNN model transference between images is presented, emphasizing the limitation and practical application of this approach. A method is presented to optimize precision and recall using the confidence of the detection features; this results in a decrease of 45% and 31% in false positive detections, with a moderate increase in false negative detections. The sensitivity of the CNN model to the size of the training set is evaluated, showing that comparable metrics could be achieved with approximately 50% of the samples used in this study. Finally, the diameter of the palm crown is calculated based on the polygon identified by mask detection, resulting in an average of 7.83 m, a standard deviation of 1.05 m, and a range of {4.62, 13.90} m for the GeoEye-1 image. Similarly, for the WorldView-2 image, the average diameter is 8.08 m, with a standard deviation of 0.70 m and a range of {4.82, 15.80} m.<\/jats:p>","DOI":"10.3390\/rs16244642","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T12:50:08Z","timestamp":1733921408000},"page":"4642","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Regional-Scale Detection of Palms Using VHR Satellite Imagery and Deep Learning in the Guyanese Rainforest"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1781-7887","authenticated-orcid":false,"given":"Matthew J.","family":"Drouillard","sequence":"first","affiliation":[{"name":"Geospatial Information Sciences, School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0902-6883","authenticated-orcid":false,"given":"Anthony R.","family":"Cummings","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Sciences, Wesleyan University, Middletown, CT 06459, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1093\/aob\/mcr146","article-title":"Geographical ecology of the palms (Arecaceae): Determinants of diversity and distributions across spatial scales","volume":"108","author":"Eiserhardt","year":"2011","journal-title":"Ann. Bot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1111\/geb.13123","article-title":"The global abundance of tree palms","volume":"29","author":"Muscarella","year":"2020","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"578","DOI":"10.2307\/1221101","article-title":"Phytogeographical Characteristics of the Guianan Forests","volume":"37","author":"Granville","year":"1988","journal-title":"Taxon"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s12229-011-9084-x","article-title":"Species Diversity and Growth Forms in Tropical American Palm Communities","volume":"77","author":"Balslev","year":"2011","journal-title":"Bot. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e20180537","DOI":"10.1590\/0001-3765201920180537","article-title":"Consumption of Euterpe edulis fruit by wildlife: Implications for conservation and management of the Southern Brazilian Atlantic Forest","volume":"91","author":"Silva","year":"2019","journal-title":"An. Acad. Bras. Ci\u00eancias"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ba\u00f1os-Villalba, A., Blanco, G., D\u00edaz-Luque, J.A., D\u00e9nes, F.V., Hiraldo, F., and Tella, J.L. (2017). Seed dispersal by macaws shapes the landscape of an Amazonian ecosystem. Sci. Rep., 7.","DOI":"10.1038\/s41598-017-07697-5"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2425","DOI":"10.1111\/brv.12761","article-title":"Sowing forests: A synthesis of seed dispersal and predation by agoutis and their influence on plant communities","volume":"96","author":"Mittelman","year":"2021","journal-title":"Biol. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1111\/brv.12809","article-title":"The mutualism\u2013antagonism continuum in Neotropical palm\u2013frugivore interactions: From interaction outcomes to ecosystem dynamics","volume":"97","author":"Kissling","year":"2022","journal-title":"Biol. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ozanne, C.M.P., Cabral, C., and Shaw, P.J. (2014). Variation in Indigenous Forest Resource Use in Central Guyana. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0102952"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/21513732.2015.1136841","article-title":"Drawing on traditional knowledge to identify and describe ecosystem services associated with Northern Amazon\u2019s multiple-use plants","volume":"12","author":"Cummings","year":"2016","journal-title":"Int. J. Biodivers. Sci. Ecosyst. Serv. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1007\/s12229-011-9086-8","article-title":"Palm Uses in Northwestern South America: A Quantitative Review","volume":"77","author":"Armesilla","year":"2011","journal-title":"Bot. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.foreco.2014.08.019","article-title":"Ecological community traits and traditional knowledge shape palm ecosystem services in northwestern South America","volume":"334","author":"Balslev","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1590\/1809-4392202004682","article-title":"Brazilian Amazonian palm-stem types and uses: A review","volume":"51","author":"Kikuchi","year":"2021","journal-title":"Acta Amaz."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1111\/j.1365-2745.2011.01834.x","article-title":"Local and regional palm (Arecaceae) species richness patterns and their cross-scale determinants in the western Amazon","volume":"99","author":"Kristiansen","year":"2011","journal-title":"J. Ecol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1017\/S0266467414000431","article-title":"Influences of forest structure and landscape features on spatial variation in species composition in a palm community in central Amazonia","volume":"30","author":"Rodrigues","year":"2014","journal-title":"J. Trop. Ecol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1590\/1809-4392201401533","article-title":"Palm community transitions along a topographic gradient from floodplain to terra firme in the eastern Amazon","volume":"45","author":"Salm","year":"2015","journal-title":"Acta Amaz."},{"key":"ref_17","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_18","unstructured":"Santillan, J., Makinano-Santillan, M., and Francisco, R. (2012, January 26\u201330). Using remote sensing to map the distribution of sago palms in Northeastern Mindanao, Philippines: Results based on landsat ETM+ image analysis. Proceedings of the 33rd Asian Conference on Remote Sensing, Pattaya, Thailand."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.3390\/rs70201206","article-title":"Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kahn, F., and Granville, J.J. (1992). Palms in Forest Ecosystems of Amazonia, Springer. [1st ed.].","DOI":"10.1007\/978-3-642-76852-1"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wagner, F.H., Dalagnol, R., Tagle Casapia, X., Streher, A.S., Phillips, O.L., Gloor, E., and Arag\u00e3o, L.E.O.C. (2020). Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images. Remote Sens., 12.","DOI":"10.3390\/rs12142225"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/S0034-4257(98)00014-5","article-title":"Biophysical and Biochemical Sources of Variability in Canopy Reflectance","volume":"64","author":"Asner","year":"1998","journal-title":"Remote. Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2016.03.021","article-title":"Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data","volume":"179","author":"Ferreira","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, W., Fu, H., Yu, L., and Cracknell, A. (2016). Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images. Remote Sens., 9.","DOI":"10.3390\/rs9010022"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7500","DOI":"10.1080\/01431161.2019.1569282","article-title":"Young and mature oil palm tree detection and counting using convolutional neural network deep learning method","volume":"40","author":"Mubin","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zheng, J., Li, W., Xia, M., Dong, R., Fu, H., and Yuan, S. (2019, January 2). Large-Scale Oil Palm Tree Detection from High-Resolution Remote Sensing Images Using Faster-RCNN. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898360"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Freudenberg, M., N\u00f6lke, N., Agostini, A., Urban, K., W\u00f6rg\u00f6tter, F., and Kleinn, C. (2019). Large Scale Palm Tree Detection in High Resolution Satellite Images Using U-Net. Remote Sens., 11.","DOI":"10.3390\/rs11030312"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Alburshaid, E., and Mangoud, M. (November, January 31). Palm Trees Detection Using the Integration between GIS and Deep Learning. Proceedings of the 2021 International Symposium on Networks, Computers and Communications (ISNCC), Dubai, United Arab Emirates.","DOI":"10.1109\/ISNCC52172.2021.9615721"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"533","DOI":"10.3406\/bifea.1992.1072","article-title":"Life forms and growth strategies of Guianan palms as related to their ecology","volume":"21","author":"Granville","year":"1992","journal-title":"Bull. L\u2019Institut Fran\u00e7ais D\u2019\u00e9tudes Andin."},{"key":"ref_31","first-page":"57","article-title":"Palms and Palm Communities in the Upper Ucayali River Valley-a Little-Known Region in the Amazon Basin","volume":"54","author":"Balslev","year":"2010","journal-title":"Palms"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1139\/cjb-2019-0090","article-title":"Flood disturbance and shade stress shape the population structure of a\u00e7a\u00ed palm Euterpe precatoria, the most abundant Amazon species","volume":"98","author":"Brum","year":"2020","journal-title":"Botany"},{"key":"ref_33","unstructured":"Braswell, B., Hagen, S., Salas, W., Palace, M., Brown, S.A., Casarim, F., and Harris, N. (2013). Assessing Forest Degradation in Guyana with GeoEye, Quickbird and Landsat, Quickbird and Landsat."},{"key":"ref_34","first-page":"315","article-title":"National Scale Monitoring Reporting and Verification of Deforestation and Forest Degradation in Guyana. The International Archives of the Photogrammetry","volume":"XL-7-W3","author":"Bholanath","year":"2015","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Brown, S., Mahmood, A.R.J., Goslee, K.M., Pearson, T.R.H., Sukhdeo, H., Donoghue, D.N.M., and Watt, P. (2020). Accounting for Greenhouse Gas Emissions from Forest Edge Degradation: Gold Mining in Guyana as a Case Study. Forests, 11.","DOI":"10.3390\/f11121307"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"29","DOI":"10.4236\/ars.2023.121002","article-title":"Remote Sensing for Analyzing Forested Landscape Structure and Land-Use Histories in Guyana\u2019s Bauxite Mining Landscapes","volume":"12","author":"Lewis","year":"2023","journal-title":"Adv. Remote Sens."},{"key":"ref_37","unstructured":"Roberts, T., Nunan, K., and Raffel, N. (2024, January 10\u201312). Innovation and Collaboration in ESIAs: Combining Remote Sensing with Community Validation to Map Guyana\u2019s Shoreline. Proceedings of the SPE International Conference and Exhibition on Health, Safety, Environment, and Sustainability, Abu Dhabi, United Arab Emirates."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s41748-021-00277-8","article-title":"Evaluation of Spatio-Temporal Dynamics of Guyana\u2019s Mangroves Using SAR and GEE","volume":"7","author":"Nedd","year":"2023","journal-title":"Earth Syst. Environ."},{"key":"ref_39","first-page":"30","article-title":"Identification of Laterite Bauxite Deposits with Application of Remote Sensing Techniques","volume":"1","author":"Gyorgy","year":"2012","journal-title":"RS & GIS."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"332","DOI":"10.4236\/ars.2013.24036","article-title":"A Comprehensive Evaluation of PAN-Sharpening Algorithms Coupled with Resampling Methods for Image Synthesis of Very High Resolution Remotely Sensed Satellite Data","volume":"2","author":"Jawak","year":"2013","journal-title":"Adv. Remote Sens."},{"key":"ref_41","first-page":"198","article-title":"Comparison of different pan-sharpening methods applied to ikonos imagery","volume":"16","author":"Alcaras","year":"2021","journal-title":"Geogr. Tech."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11676-020-01155-1","article-title":"A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing","volume":"32","author":"Huang","year":"2021","journal-title":"J. For. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2018). Mask R-CNN. arXiv.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201312). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_46","unstructured":"Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Lin, H., Zhang, Z., Sun, Y., He, T., Mueller, J., and Manmatha, R. (2020, January 13\u201317). ResNeSt: Split-Attention Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017). Aggregated Residual Transformations for Deep Neural Networks. arXiv.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_48","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hao, Z., Post, C.J., Mikhailova, E.A., Lin, L., Liu, J., and Yu, K. (2022). How Does Sample Labeling and Distribution Affect the Accuracy and Efficiency of a Deep Learning Model for Individual Tree-Crown Detection and Delineation. Remote Sens., 14.","DOI":"10.3390\/rs14071561"},{"key":"ref_50","unstructured":"Settles, B. (2009). Active Learning Literature Survey, University of Wisconsin-Madison Department of Computer Sciences."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2018). Neural Networks and Deep Learning: A Textbook, Springer International Publishing.","DOI":"10.1007\/978-3-319-94463-0"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Machefer, M., Lemarchand, F., Bonnefond, V., Hitchins, A., and Sidiropoulos, P. (2020). Mask R-CNN Refitting Strategy for Plant Counting and Sizing in UAV Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12183015"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"943","DOI":"10.22214\/ijraset.2022.47789","article-title":"An Introduction to Convolutional Neural Networks","volume":"10","author":"Saxena","year":"2022","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. arXiv.","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"ref_56","unstructured":"Smith, L.N. (2018). A disciplined approach to neural network hyper-parameters: Part 1 \u2014Learning rate, batch size, momentum, and weight decay. arXiv."},{"key":"ref_57","first-page":"821","article-title":"A review of non-maximum suppression algorithms for deep learning target detection","volume":"Volume 11763","author":"Gong","year":"2021","journal-title":"Proceedings of the Seventh Symposium on Novel Photoelectronic Detection Technology and Applications"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"111322","DOI":"10.1016\/j.rse.2019.111322","article-title":"Land-cover classification with high-resolution remote sensing images using transferable deep models","volume":"237","author":"Tong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Culman, M., Delalieux, S., and Van Tricht, K. (2020). Individual Palm Tree Detection Using Deep Learning on RGB Imagery to Support Tree Inventory. Remote Sens., 12.","DOI":"10.3390\/rs12213476"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2019.01.019","article-title":"Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis","volume":"149","author":"Ferreira","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Fisher, P.F. (2005). Shape-Aware Line Generalisation with Weighted Effective Area. Developments in Spatial Data Handling, Springer.","DOI":"10.1007\/b138045"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Blostein, D., and Kwon, Y.B. (2002). Smoothing and Compression of Lines Obtained by Raster-to-Vector Conversion. Graphics Recognition Algorithms and Applications, Springer.","DOI":"10.1007\/3-540-45868-9"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4642\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:52:36Z","timestamp":1760115156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4642"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,11]]},"references-count":63,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244642"],"URL":"https:\/\/doi.org\/10.3390\/rs16244642","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,12,11]]}}}