{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T09:33:38Z","timestamp":1774863218145,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,20]],"date-time":"2018-08-20T00:00:00Z","timestamp":1534723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sago palm (Metroxylon sagu) is a palm tree species originating in Indonesia. In the future, this starch-producing tree will play an important role in food security and biodiversity. Local governments have begun to emphasize the sustainable development of sago palm plantations; therefore, they require near-real-time geospatial information on palm stands. We developed a semi-automated classification scheme for mapping sago palm using machine learning within an object-based image analysis framework with Pleiades-1A imagery. In addition to spectral information, arithmetic, geometric, and textural features were employed to enhance the classification accuracy. Recursive feature elimination was applied to samples to rank the importance of 26 input features. A support vector machine (SVM) was used to perform classifications and resulted in the highest overall accuracy of 85.00% after inclusion of the eight most important features, including three spectral features, three arithmetic features, and two textural features. The SVM classifier showed normal fitting up to the eighth most important feature. According to the McNemar test results, using the top seven to 14 features provided a better classification accuracy. The significance of this research is the revelation of the most important features in recognizing sago palm among other similar tree species.<\/jats:p>","DOI":"10.3390\/rs10081319","type":"journal-article","created":{"date-parts":[[2018,8,20]],"date-time":"2018-08-20T11:23:06Z","timestamp":1534764186000},"page":"1319","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Object-Based Image Analysis for Sago Palm Classification: The Most Important Features from High-Resolution Satellite Imagery"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4023-3469","authenticated-orcid":false,"given":"Sarip","family":"Hidayat","sequence":"first","affiliation":[{"name":"Remote Sensing Technology and Data Center, Indonesian National Institute of Aeronautics and Space (Lembaga Penerbangan dan Antariksa Nasional, LAPAN), Jakarta 13710, Indonesia"},{"name":"Departement of Soil Science, Faculty of Agriculture, Hasanudin University, Makassar 90245, Indonesia"},{"name":"Faculty of Agriculture and Marine Science, Kochi University, Kochi 783-8502, Japan"}]},{"given":"Masayuki","family":"MATSUOKA","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture and Marine Science, Kochi University, Kochi 783-8502, Japan"}]},{"given":"Sumbangan","family":"Baja","sequence":"additional","affiliation":[{"name":"Departement of Soil Science, Faculty of Agriculture, Hasanudin University, Makassar 90245, Indonesia"}]},{"given":"Dorothea","family":"Rampisela","sequence":"additional","affiliation":[{"name":"Departement of Soil Science, Faculty of Agriculture, Hasanudin University, Makassar 90245, Indonesia"},{"name":"Research Institute for Humanity and Nature, Kyoto 603-8047, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ehara, H., Toyoda, Y., and Johnson, D.V. (2018). Status and Outlook of Global Food Security and the Role of Underutilized Food Resources: Sago Palm. Sago Palm: Multiple Contributions to Food Security and Sustainable Livelihoods, Springer.","DOI":"10.1007\/978-981-10-5269-9"},{"key":"ref_2","unstructured":"Heller, J., Engels, J., and Hammer, K. (1997). Sago palm. Metroxylon sagu rottb. Promoting the Conservation and Use of Underutilized and Neglected Crops, IPGRI."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1111\/j.1541-4337.2008.00042.x","article-title":"Starch from the Sago (Metroxylon sagu) Palm Tree Properties, Prospects, and Challenges as a New Industrial Source for Food and Other Uses","volume":"7","author":"Karim","year":"2008","journal-title":"Compr. Rev. Food Sci. Food Saf."},{"key":"ref_4","unstructured":"Elevitch, C.R. (2006). Metroxylon amicarum, M. paulcoxii, M. sagu, M. salomonense, M. vitiense, and M. warburgii (sago palm) ver. 2.1. Species Profiles for Pacific Island Agroforestry, Permanent Agriculture Resources (PAR). Available online: http:\/\/agroforestry.org\/free-publications\/traditional-tree-profiles."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112","DOI":"10.13057\/biodiv\/d110302","article-title":"Genetic diversity of sago palm in Indonesia based on chloroplast DNA (cpDNA) markers","volume":"11","author":"Abbas","year":"2010","journal-title":"Biodivers. J. Biol. Divers."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ehara, H., Toyoda, Y., and Johnson, D.V. (2018). Growing Area of Sago Palm and Its Environment. Sago Palm: Multiple Contributions to Food Security and Sustainable Livelihoods, Springer.","DOI":"10.1007\/978-981-10-5269-9"},{"key":"ref_7","unstructured":"Santillan, J.R., Santillan, M.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\u2014Aiming Smart Space Sensing, Pattaya, Thailand."},{"key":"ref_8","unstructured":"Santillan, J.R. (2013, January 28\u201329). Mapping the starch-rich sago palsm through Maximum likelihood classification of multi-source data. Proceedings of the 2nd Philippine Geomatics Symposium (PhilGEOS): Geomatics for a Resilient Agriculture and Forestry, University of The Philippines, Diliman, Quezon City, Philippines."},{"key":"ref_9","unstructured":"Paluga, M.J.D. (2016). Santillan & Meriam Makinano-Santillan Recent Distribution of Sago Palms in the Philippines. BANWA Monograph Series 1 Mapping Sago: Anthropological, Biophysical and Economic Aspects, University of the Philippines."},{"key":"ref_10","unstructured":"Santillan, M.M., Japitana, M.V., Apdohan, A.G., and Amora, A.M. (2012, January 26\u201330). Discrimination of Sago Palm from Other Palm Species Based on in-Situ Spectral Response Measurements. Proceedings of the 33rd Asian Conference on Remote Sensing\u2014Aiming Smart Space Sensing, Ambasador City Jomtien Hotel, Pattaya, Thailand."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.jenvman.2017.02.004","article-title":"Estimating babassu palm density using automatic palm tree detection with very high spatial resolution satellite images","volume":"193","author":"Mitja","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_12","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. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.isprsjprs.2014.07.013","article-title":"Determination of the age of oil palm from crown projection area detected from WorldView-2 multispectral remote sensing data: The case of Ejisu-Juaben district, Ghana","volume":"100","author":"Chemura","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","first-page":"235","article-title":"Object-oriented mapping of urban trees using Random Forest classifiers","volume":"26","author":"Puissant","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., and Wu, X. (2018). Artificial Mangrove Species Mapping Using Pl\u00e9iades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms. Remote Sens., 10.","DOI":"10.3390\/rs10020294"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object-based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","unstructured":"Gmbh, T.G. (2014). Trimble eCognition Developer 9.0 User Guide, Trimble Germany GmbH. ISBN in Part on Third-Party Software Components: eCognition Developer \u00a9 2014 Trimble Germany GmbH."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.rse.2011.01.009","article-title":"Object-based crop identification using multiple vegetation indices, textural features and crop phenology","volume":"115","author":"Ngugi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ma, L., Fu, T., Blaschke, T., Li, M., Tiede, D., Zhou, Z., Ma, X., and Chen, D. (2017). Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6020051"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cui, J., Wang, W., and Lin, C. (2017). A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm. Sensor, 17.","DOI":"10.3390\/s17071474"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s40808-016-0108-8","article-title":"A support vector machine object-based image analysis approach on urban green space extraction using Pleiades-1A imagery","volume":"2","author":"Zylshal","year":"2016","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_23","first-page":"298","article-title":"A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high-resolution WorldView 2 imagery","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/014311600210993","article-title":"Incorporating texture into classification of forest species composition from airbone multispectral images","volume":"21","author":"Franklin","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","unstructured":"Phadkay, V., and Singh, A. (2017). Machine Learning Algorithms: A Reference Guide to Popular Algorithms for Data Science and Machine Learning, Packt Publishing Ltd.. Fisrt Publ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A Tutorial on Support Vector Machines for Pattern Recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"189","DOI":"10.5194\/isprs-archives-XLI-B7-189-2016","article-title":"The application of support vector machine (SVM) using cielab color model, color intensity and color constancy as features for ortho image classification of Benthic Habitats in Hinatuan, Surigao del sur, Philippines","volume":"41","author":"Cubillas","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.12.002","article-title":"Automatic tree species recognition with quantitative structure models","volume":"191","author":"Raumonen","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_31","unstructured":"Stefan, L., and Thomas Blaschke, E.S. A Support Vector Machine Approach for Object Based Image Analysis. Proceedings of 1st International Conference on Object-Based Image Analysis (OBIA 2006), Salzburg University."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/rs70100153","article-title":"Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery","volume":"7","author":"Qian","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","unstructured":"Aggarwal, C.C. (2014). Feature Selection for Classification: A Review. Data Classification: Algorithms and Applications, Chapman and Hall\/CRC Press."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cai, J., Luo, J., Wang, S., and Yang, S. (2018). Feature selection in machine learning: A new perspective. Neurocomputing, 70\u201379.","DOI":"10.1016\/j.neucom.2017.11.077"},{"key":"ref_35","unstructured":"(2018, June 03). Airbus Defence and Space Geo-Intelligence Pl\u00e9iades Spot the Detail. Available online: http:\/\/www.intelligence-airbusds.com\/files\/pmedia\/public\/r61_9_geo_011_pleiades_en_low.pdf."},{"key":"ref_36","unstructured":"Coeurdevey, L., and Gabriel-Robez, C. (2012). Pl\u00e9iades Imagery User Guide, Astrium GEO-Information Services. v 2.0.; ISBN in Part on Third-Party Software Components: Pl\u00e9iades Direct Receiving Station."},{"key":"ref_37","unstructured":"Geomatics, P. (2018, July 02). PANSHARP. Available online: http:\/\/www.pcigeomatics.com\/geomatica-help\/references\/pciFunction_r\/modeler\/M_pansharp.html."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/19479832.2013.848475","article-title":"From UNB PanSharp to Fuze Go\u2014The success behind the pan-sharpening algorithm","volume":"5","author":"Zhang","year":"2014","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_39","unstructured":"Cheng, P. (2018, July 25). Geometric Correction, Pan-sharpening and DTM Extraction: Pleiades Satellite. Available online: http:\/\/www.pcigeomatics.com\/pdf\/Geomatica-Pleiades-Processing.pdf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2005.03.009","article-title":"Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales","volume":"96","author":"Clark","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene Selection for Cancer Classification using Support Vector Machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_46","unstructured":"Witten, I.H., and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers is an Imprint of Elsevier. [2nd ed.]."},{"key":"ref_47","unstructured":"Frank, E., Hall, M., and Holland, K. (2018, November 05). SVMAttributeEval3. Available online: http:\/\/weka.sourceforge.net\/doc.packages\/SVMAttributeEval\/weka\/attributeSelection\/SVMAttributeEval.html."},{"key":"ref_48","unstructured":"Bouckaert, R.R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., and Scuse, D. (2017). WEKA Manual for Version 3-8-2, The University of Waikato. Available online: http:\/\/sourceforge.mirrorservice.org\/w\/we\/weka\/documentation\/3.8.x\/WekaManual-3-8-0.pdf."},{"key":"ref_49","first-page":"1396","article-title":"A Practical Guide to Support Vector Classification","volume":"101","author":"Hsu","year":"2008","journal-title":"BJU Int."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.neucom.2010.02.016","article-title":"Rule extraction from support vector machines: A review","volume":"74","author":"Barakat","year":"2010","journal-title":"Neurocomputing"},{"key":"ref_51","first-page":"419","article-title":"Accuracy Assessment of Satellite Derived Land-Cover Data: A Review","volume":"60","author":"Janssen","year":"1994","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_52","unstructured":"Banko, G. (1998). A Review of Assessing the Accuracy of and of Methods Including Remote Sensing Data in Forest Inventory, IASA. Available online: http:\/\/pure.iiasa.ac.at\/5570\/1\/IR-98-081.pdf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic Map Comparison-Evaluating the Statistical Significance of Differences in Classification Accuracy","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Agresti, A. (2007). An Introduction to Categorical Data Analysis, Wiley-Interscience. [2nd ed.].","DOI":"10.1002\/0470114754"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ballanti, L., Blesius, L., Hines, E., and Kruse, B. (2016). Tree species classification using hyperspectral imagery: A comparison of two classifiers. Remote Sens., 8.","DOI":"10.3390\/rs8060445"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1080\/0143116021000050538","article-title":"Texture classification of logged forests in tropical Africa using machine-learning algorithms","volume":"24","author":"Chan","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","unstructured":"Raschka, S., and Mirjalili, V. (2017). Python Machine Learning\u2014Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow, Packt Publishing Ltd.. [2nd ed.]."},{"key":"ref_59","unstructured":"Brownlee, J. (2018, May 23). Overfitting and Underfitting with Machine Learning Algorithms. Available online: https:\/\/machinelearningmastery.com\/overfitting-and-underfitting-with-machine-learning-algorithms\/."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2015.01.009","article-title":"Segmentation quality evaluation using region-based precision and recall measures for remote sensing images","volume":"102","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"289","DOI":"10.14358\/PERS.76.3.289","article-title":"Accuracy Assessment Measures for Object-based Image Segmentation Goodness","volume":"76","author":"Clinton","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1016\/0031-3203(95)00169-7","article-title":"A survey on evaluation methods for image segmentation","volume":"29","author":"Zhang","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.ecoleng.2016.06.117","article-title":"Scale parameter optimization through high-resolution imagery to support mine rehabilitated vegetation classification","volume":"97","author":"Bao","year":"2016","journal-title":"Ecol. Eng."},{"key":"ref_64","unstructured":"Hamlyn, G.J., and Robin, A.V. (2010). Remote Sensing of Vegetation\u2014Principles, Techniques, and Applications, Oxford University Press. [1st ed.]."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1080\/01431160701442120","article-title":"Harshness in image classification accuracy assessment","volume":"29","author":"Foody","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Anderson, J.R., Hardy, E.E., Roach, J.T., and Withmer, R.E. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data, Volume 964 of Geological Survey Professional Paper.","DOI":"10.3133\/pp964"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press Taylor & Francis Group LLC. [2nd ed.].","DOI":"10.1201\/9781420055139"},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2014.12.026","article-title":"Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery","volume":"102","author":"Ma","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","unstructured":"Chai, R.R. (2014). Use of Gis and Remote Sensing Techniques To Estimate Coconut Cultivation Area: Case Study of Kaloleni Subcounty, University of Nairobi."},{"key":"ref_71","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1319\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:19:51Z","timestamp":1760195991000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,20]]},"references-count":71,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["rs10081319"],"URL":"https:\/\/doi.org\/10.3390\/rs10081319","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,20]]}}}