{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T02:56:20Z","timestamp":1764212180668,"version":"build-2065373602"},"reference-count":12,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,23]],"date-time":"2020-09-23T00:00:00Z","timestamp":1600819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC18K0336","80NNSC19M0222"],"award-info":[{"award-number":["80NSSC18K0336","80NNSC19M0222"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents a simple and efficient image processing method for estimating the number of coconut trees in the Tonga region using very high spatial resolution data (30 cm) in the blue, green, red and near infrared spectral bands acquired by the WorldView-3 sensor. The method is based on the detection of tree shadows and the further analysis to reject false detection using geometrical properties of the derived segments. The algorithm is evaluated by comparing coconut tree counts derived by an expert through photo-interpretation over 57 randomly distributed (4% sampling rate) segments of 200 m \u00d7 200 m over the Vaini region of the Tongatapu island. The number of detected trees agreed within 5% versus validation data. The proposed method was also evaluated over the whole Tonga archipelago by comparing satellite-derived estimates to the 2015 agricultural census data\u2014the total tree counts for both Tonga and Tongatapu agreed within 3%.<\/jats:p>","DOI":"10.3390\/rs12193113","type":"journal-article","created":{"date-parts":[[2020,9,23]],"date-time":"2020-09-23T09:28:08Z","timestamp":1600853288000},"page":"3113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Remote Sensing of Coconut Trees in Tonga Using Very High Spatial Resolution WorldView-3 Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4883-2765","authenticated-orcid":false,"given":"Eric F.","family":"Vermote","sequence":"first","affiliation":[{"name":"NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9039-0174","authenticated-orcid":false,"given":"Sergii","family":"Skakun","sequence":"additional","affiliation":[{"name":"NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"},{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inbal","family":"Becker-Reshef","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keiko","family":"Saito","sequence":"additional","affiliation":[{"name":"World Bank Group, Global Facility for Disaster Reduction and Recovery, Washington, DC 20006, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,23]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Teina, R., B\u00e9r\u00e9ziat, D., Stoll, B., and Chabrier, S. (2009, January 7\u201311). Toward a global Tuamotu archipelago coconut trees sensing using high resolution optical data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS 2008, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779114"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Weinstein, B.G., Marconi, S., Bohlman, S., Zare, A., and White, E. (2019). Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sens., 11.","DOI":"10.1101\/532952"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Tianyang, D., Jian, Z., Sibin, G., Ying, S., and Jing, F. (2018). Single-tree detection in high-resolution remote-sensing images based on a cascade neural network. ISPRS Int. J. Geo Inf., 7.","DOI":"10.3390\/ijgi7090367"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, W., Zheng, J., Fu, H., Li, W., and Yu, L. (2020, January 13\u201319). Cross-Regional Oil Palm Tree Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPR2020, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00036"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Vargas-Mu\u00f1oz, J.E., Zhou, P., Falc\u00e3o, A.X., and Tuia, D. (August, January 28). Interactive Coconut Tree Annotation Using Feature Space Projections. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS 2019, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899005"},{"key":"ref_9","unstructured":"(2020, September 22). WorldView-3. Available online: http:\/\/worldview3.digitalglobe.com."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_11","first-page":"22","article-title":"Efficiency assessment of using satellite data for crop area estimation in Ukraine","volume":"29","author":"Gallego","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.rse.2017.01.008","article-title":"National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey","volume":"190","author":"Song","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3113\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:12:39Z","timestamp":1760177559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,23]]},"references-count":12,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193113"],"URL":"https:\/\/doi.org\/10.3390\/rs12193113","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,9,23]]}}}