{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T11:28:32Z","timestamp":1769599712368,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2015,10,12]],"date-time":"2015-10-12T00:00:00Z","timestamp":1444608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007797","name":"University of Helsinki","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007797","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Knowledge of the floral cycle and the spatial distribution and abundance of flowering plants is important for bee health studies to understand the relationship between landscape and bee hive productivity and honey flow. The key objective of this study was to show how AISA Eagle hyperspectral data and random forest (RF) can be optimally utilized to produce flowering and spatially explicit land use\/land cover (LULC) maps for a study site in Kenya. AISA Eagle imagery was captured at the early flowering period (January 2014) and at the peak flowering season (February 2013). Data on white and yellow flowering trees as well as LULC classes in the study area were collected and used as ground-truth points. We utilized all 64 AISA Eagle bands and also used variable importance in RF to identify the most important bands in both AISA Eagle data sets. The results showed that flowering was most accurately mapped using the AISA Eagle data from the peak flowering period (85.71%\u201388.15% overall accuracy for the peak flowering season imagery versus 80.82%\u201383.67% for the early flowering season). The variable optimization (i.e., variable selection) analysis showed that less than half of the AISA bands (n = 26 for the February 2013 data and n = 21 for the January 2014 data) were important to attain relatively reliable classification accuracies. Our study is an important first step towards the development of operational flower mapping routines and for understanding the relationship between flowering and bees\u2019 foraging behavior.<\/jats:p>","DOI":"10.3390\/rs71013298","type":"journal-article","created":{"date-parts":[[2015,10,14]],"date-time":"2015-10-14T02:36:30Z","timestamp":1444790190000},"page":"13298-13318","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["The Utility of AISA Eagle Hyperspectral Data and  Random Forest Classifier for Flower Mapping"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5694-0291","authenticated-orcid":false,"given":"Elfatih","family":"Abdel-Rahman","sequence":"first","affiliation":[{"name":"International Center for Insect Physiology and Ecology (ICIPE), P.O. Box 30772,  Nairobi 00100, Kenya"},{"name":"Department of Agronomy, Faculty of Agriculture, University of Khartoum,  Khartoum North 13314, Sudan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Makori","sequence":"additional","affiliation":[{"name":"International Center for Insect Physiology and Ecology (ICIPE), P.O. Box 30772,  Nairobi 00100, Kenya"},{"name":"Department of Geosciences and Geography, University of Helsinki, Gustaf H\u00e4llstr\u00f6min katu 2b, Helsinki 00560, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tobias","family":"Landmann","sequence":"additional","affiliation":[{"name":"International Center for Insect Physiology and Ecology (ICIPE), P.O. Box 30772,  Nairobi 00100, Kenya"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rami","family":"Piiroinen","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Agricultural, Environment and Earth Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seif","family":"Gasim","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Faculty of Agriculture, University of Khartoum,  Khartoum North 13314, Sudan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5996-9268","authenticated-orcid":false,"given":"Petri","family":"Pellikka","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Agricultural, Environment and Earth Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suresh","family":"Raina","sequence":"additional","affiliation":[{"name":"International Center for Insect Physiology and Ecology (ICIPE), P.O. Box 30772,  Nairobi 00100, Kenya"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sammataro, D., and Weiss, M. (2013). Comparison of productivity of colonies of honey bees, Apis mellifera, supplemented with sucrose or high fructose corn syrup. J. Insect Sci., 13.","DOI":"10.1673\/031.013.1901"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sponsler, D.B., and Johnson, R.M. (2015). Honey bee success predicted by landscape composition in Ohio, USA. PeerJ, 3.","DOI":"10.7717\/peerj.838"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1146\/annurev-ento-120709-144805","article-title":"Forest habitat conservation in Africa using commercially important insects","volume":"56","author":"Raina","year":"2011","journal-title":"Annu. Rev. Entomol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1016\/j.ecolind.2008.09.009","article-title":"Indicator of flower status derived from in situ hyperspectral measurement in an alpine meadow on the Tibetan Plateau","volume":"9","author":"Chen","year":"2009","journal-title":"Ecol. Indic."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1051\/apido\/2010024","article-title":"Landscape enhancement of floral resources for honey bees in agro-ecosystems","volume":"41","author":"Decourtye","year":"2010","journal-title":"Apidologie"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1126\/science.1071617","article-title":"Rapid changes in flowering time in British plants","volume":"296","author":"Fitter","year":"2002","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1139\/X06-239","article-title":"Spring-flowering herbaceous plant species of the deciduous forests of eastern Canada and 20th century climate warming","volume":"37","author":"Houle","year":"2007","journal-title":"Can. J. For. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2015.06.006","article-title":"Application of hyperspectral remote sensing for flower mapping in African savannas","volume":"166","author":"Landmann","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_9","unstructured":"van der Meer, F.D., and de Jong, S.M. (2003). Image Spectrometry, Kluwer Academic Publishers."},{"key":"ref_10","unstructured":"Lillesand, T.M., and Kiefer, R.W. (2001). Remote Sensing and Image Interpretation, John Wiley & Sons Inc.. [4th ed.]."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isprsjprs.2012.03.005","article-title":"Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment","volume":"69","author":"Naidoo","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.rse.2012.07.010","article-title":"Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system","volume":"125","author":"Cho","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/BF00192415","article-title":"Colour preferences of flower-naive honeybees","volume":"177","author":"Giurfa","year":"1995","journal-title":"J. Comp. Physiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1111\/j.1439-0310.2006.01174.x","article-title":"Flower choice and learning in foraging bumblebees: Effects of variation in nectar volume and concentration","volume":"112","author":"Cnaani","year":"2006","journal-title":"Ethology"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2011). Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, CRC Press.","DOI":"10.1201\/b10599"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, J.G., and Huete, A. (2011). Hyperspectral Remote Sensing of Vegetation, CRC Press.","DOI":"10.1201\/b11222-41"},{"key":"ref_17","first-page":"1601","article-title":"Classification of tropical trees growing in a sanctuary using Hyperion (EO-1) and SAM algorithm","volume":"96","author":"Christian","year":"2009","journal-title":"Curr. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TGRS.2012.2199323","article-title":"Tree species discrimination in tropical forests using airborne imaging spectroscopy","volume":"51","author":"Asner","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8494","DOI":"10.3390\/rs6098494","article-title":"Plant species discrimination in a tropical wetland using in situ hyperspectral data","volume":"6","author":"Prospere","year":"2014","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rse.2005.10.014","article-title":"Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest)","volume":"100","author":"Lawrence","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106","DOI":"10.5589\/m09-018","article-title":"Mapping of hyperspectral AVIRIS data using machine-learning algorithms","volume":"35","author":"Waske","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2013.04.012","article-title":"Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data","volume":"82","author":"Ramoelo","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2013.11.013","article-title":"Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers","volume":"88","author":"Mutanga","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","unstructured":"Ngugi, R.K. (1999). Use of Indigenous and Contemporary Knowledge on Climate and Drought Forecasting Information in Mwingi District, Kenya, College of Agriculture and Veterinary Science, University of Nairobi. A Report to UNDP."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1079\/IJT200588","article-title":"Variations in races of the honeybee Apis mellifera (Hymenoptera: Apidae) in Kenya","volume":"25","author":"Raina","year":"2005","journal-title":"Int. J. Trop. Insect Sci."},{"key":"ref_29","unstructured":"Delaplane, K.S. (2010). Honey Bees and Beekeeping, College of Agricultural and Environmental Sciences, College of Family and Consumer Sciences, The University of Georgia. A Report of Cooperative Extension."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1002\/bies.201000075","article-title":"Colony collapse disorder in context","volume":"32","author":"Williams","year":"2010","journal-title":"Bioessays"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104","DOI":"10.3843\/Biodiv.4.2:4","article-title":"Monitoring wild silkmoth, Gonometa postica Walker, abundance, host plant diversity and distribution in Imba and Mumoni woodlands in Mwingi, Kenya","volume":"4","author":"Fening","year":"2008","journal-title":"Int. J. Biodivers. Sci. Manage."},{"key":"ref_32","unstructured":"Muya, B.I. (2014). Determinants of Adoption of Modern Technologies in Beekeeping Projects: The Case of Women Groups in Kajiado County, Kenya. [Master\u2019s Thesis, University of Nairobi]."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"S17","DOI":"10.1016\/j.rse.2007.12.015","article-title":"Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean","volume":"113","author":"Gao","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1080\/01431160110115834","article-title":"Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric\/topographic correction","volume":"23","author":"Richter","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1080\/01431160802438555","article-title":"On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing","volume":"30","author":"Guanter","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.rse.2014.07.020","article-title":"Uncertainties of LAI estimation from satellite imaging due to atmospheric correction","volume":"153","author":"Mannschatz","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_38","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-9-319","article-title":"A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification","volume":"9","author":"Statnikov","year":"2008","journal-title":"BMC Bioinf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","article-title":"Newer classification and regression tree techniques: Bagging and random forests for ecological prediction","volume":"9","author":"Prasad","year":"2006","journal-title":"Ecosystems"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2010.08.007","article-title":"Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests","volume":"66","author":"Guo","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Ma, Y. (2012). Ensemble Machine Learning: Methods and Applications, Springer.","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"ref_43","unstructured":"R Development Core Team R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: http:\/\/www.R-project.org."},{"key":"ref_44","unstructured":"Diaz-Uriarte, R. Package \u201cvarSelRF\u201d. Available online: http:\/\/ligarto.org\/rdiaz\/Software\/Software.html."},{"key":"ref_45","first-page":"548","article-title":"Improvements on cross-validation: The .632+ bootstrap method","volume":"92","author":"Efron","year":"1997","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_46","first-page":"1","article-title":"Gene selection and classification of microarray data using random forest","volume":"7","year":"2006","journal-title":"BMC Bioinf."},{"key":"ref_47","unstructured":"Deng, H., and Runger, G. (2012, January 10\u201315). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, QLD, Australia."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1924","DOI":"10.1080\/13658816.2013.772183","article-title":"Determining the susceptibility of Eucalyptus nitens forests to Coryphodema tristis (cossid moth) occurrence in Mpumalanga, South Africa","volume":"27","author":"Adam","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"Coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Lewis Publishers.","DOI":"10.1201\/9781420048568"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1080\/01431161.2012.713142","article-title":"Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data","volume":"34","author":"Ahmed","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s10661-005-9052-1","article-title":"Hyperspectral characteristics of canopy components and structure for phenological assessment of an invasive weed","volume":"120","author":"Ge","year":"2006","journal-title":"Environ. Monit. Assess."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1080\/01431169408954177","article-title":"The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status","volume":"15","author":"Filella","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Anderson, J.R., Hardy, E.E., Roach, J.T., and Witmer, R.E. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data, US Government Printing Office.","DOI":"10.3133\/pp964"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2012.10.031","article-title":"Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation","volume":"129","author":"Olofsson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2014). Scale Issues in Remote Sensing, John Wiley & Sons Inc.","DOI":"10.1002\/9781118801628"},{"key":"ref_59","first-page":"176","article-title":"An integrated spatial and spectral approach to the classification of Mediterranean land cover types: The SSC method","volume":"3","author":"Hornstra","year":"2001","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ecoinf.2011.01.002","article-title":"A comparison of pixel-based and object-oriented approaches to VHR imagery for mapping saltmarsh plants","volume":"6","author":"Ouyang","year":"2011","journal-title":"Ecol. Inform."},{"key":"ref_61","first-page":"1","article-title":"Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data","volume":"39","author":"Piiroinen","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1111\/j.1600-0587.2012.07348.x","article-title":"Collinearity: A review of methods to deal with it and a simulation study evaluating their performance","volume":"36","author":"Dormann","year":"2013","journal-title":"Ecography"},{"key":"ref_63","first-page":"443","article-title":"Predominant melliferous plants of the western Sudano Guinean zone of Cameroon","volume":"5","author":"Dongock","year":"2011","journal-title":"AJEST"},{"key":"ref_64","first-page":"149","article-title":"Continuous management of varroa mite in honey bee, Apis. mellifera, colonies","volume":"22","year":"2014","journal-title":"Acarina"},{"key":"ref_65","first-page":"228","article-title":"The Melliferous plants of the Bulgarian flora\u2014Conservation importance","volume":"17","author":"Tashev","year":"2011","journal-title":"For. Ideas"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/10\/13298\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:49:56Z","timestamp":1760215796000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/10\/13298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,10,12]]},"references-count":65,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2015,10]]}},"alternative-id":["rs71013298"],"URL":"https:\/\/doi.org\/10.3390\/rs71013298","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,10,12]]}}}