{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T16:36:06Z","timestamp":1774715766990,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,8,23]],"date-time":"2017-08-23T00:00:00Z","timestamp":1503446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009464","name":"Ministry for Foreign Affairs of Finland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009464","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Airborne imaging spectroscopy (IS) and laser scanning (ALS) have been explored widely for tree species classification during the past decades. However, African agroforestry areas, where a few exotic tree species are dominant and many native species occur less frequently, have not yet been studied. Obtaining maps of tree species would provide useful information for the characterization of agroforestry systems and detecting invasive species. Our objective was to study tree species classification in a diverse tropical landscape using IS and ALS data at the tree crown level, with primary interest in the exotic tree species. We performed multiple analyses based on different IS and ALS feature sets, identified important features using feature selection, and evaluated the impact of combining the two data sources. Given that a high number of tree species with limited sample size (499 samples for 31 species) was expected to limit the classification accuracy, we tested different approaches to group the species based on the frequency of their occurrence and Jeffries\u2013Matusita (JM) distance. Surface reflectance at wavelengths between 400\u2013450 nm and 750\u2013800 nm, and height to crown width ratio, were identified as important features. Nonetheless, a selection of minimum noise fraction (MNF) transformed reflectance bands showed superior performance. Support vector machine classifier performed slightly better than the random forest classifier, but the improvement was not statistically significant for the best performing feature set. The highest F1-scores were achieved when each of the species was classified separately against a mixed group of all other species, which makes this approach suitable for invasive species detection. Our results are valuable for organizations working on biodiversity conservation and improving agroforestry practices, as we showed how the non-native Eucalyptus spp., Acacia mearnsii and Grevillea robusta (mean F1-scores 76%, 79% and 89%, respectively) trees can be mapped with good accuracy. We also found a group of six fruit bearing trees using JM distance, which was classified with mean F1-score of 65%. This was a useful finding, as these species could not be classified with acceptable accuracy individually, while they all share common economic and ecological importance.<\/jats:p>","DOI":"10.3390\/rs9090875","type":"journal-article","created":{"date-parts":[[2017,8,23]],"date-time":"2017-08-23T11:32:27Z","timestamp":1503487947000},"page":"875","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning"],"prefix":"10.3390","volume":"9","author":[{"given":"Rami","family":"Piiroinen","sequence":"first","affiliation":[{"name":"Earth Change Observation Laboratory, Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3899-8860","authenticated-orcid":false,"given":"Janne","family":"Heiskanen","sequence":"additional","affiliation":[{"name":"Earth Change Observation Laboratory, Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7932-1824","authenticated-orcid":false,"given":"Eduardo","family":"Maeda","sequence":"additional","affiliation":[{"name":"Fisheries and Environmental Management Group, Department of Environmental Sciences, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, Finland"}]},{"given":"Arto","family":"Viinikka","sequence":"additional","affiliation":[{"name":"Finnish Environmental Institute (SYKE), Environmental Policy Centre, P.O. Box 140, FI-00251 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5996-9268","authenticated-orcid":false,"given":"Petri","family":"Pellikka","sequence":"additional","affiliation":[{"name":"Earth Change Observation Laboratory, Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16732","DOI":"10.1073\/pnas.0910275107","article-title":"Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s","volume":"107","author":"Gibbs","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","first-page":"60","article-title":"Anthropogenic pressure in East Africa\u2014Monitoring 20 years of land cover changes by means of medium resolution satellite data","volume":"28","author":"Brink","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.cosust.2013.11.030","article-title":"Knowledge gaps and research needs concerning agroforestry\u2019s contribution to Sustainable Development Goals in Africa","volume":"6","author":"Mbow","year":"2014","journal-title":"Curr. Opin. Environ. Sustain."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cosust.2013.07.013","article-title":"Agroforestry systems in a changing climate\u2014Challenges in projecting future performance","volume":"6","author":"Luedeling","year":"2014","journal-title":"Curr. Opin. Environ. Sustain."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.jhydrol.2010.11.027","article-title":"Influence of Eucalyptus globulus plantation growth on water table levels and low flows in a small catchment","volume":"396","author":"Soto","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0378-1127(02)00603-5","article-title":"Water use in a Grevillea robusta-maize overstorey agroforestry system in semi-arid Kenya","volume":"180","author":"Lott","year":"2003","journal-title":"For. Ecol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/BF00705236","article-title":"Effects of mulching with multipurpose-tree prunings on soil and water run-off under semi-arid conditions in Kenya","volume":"22","author":"Omoro","year":"1993","journal-title":"Agrofor. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.landurbplan.2015.07.015","article-title":"Trees in a human-modified tropical landscape: Species and trait composition and potential ecosystem services","volume":"144","author":"Thijs","year":"2015","journal-title":"Landsc. Urban Plan."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Graves, S.J., Asner, G.P., Martin, R.E., Anderson, C.B., Colgan, M.S., Kalantari, L., and Bohlman, S.A. (2016). Tree species abundance predictions in a tropical agricultural landscape with a supervised classification model and imbalanced data. Remote Sens., 8.","DOI":"10.3390\/rs8020161"},{"key":"ref_11","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":"Feret","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2682","DOI":"10.3390\/rs6042682","article-title":"Improving remote species identification through efficient training data collection","volume":"6","author":"Baldeck","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecolmodel.2012.03.007","article-title":"Evaluating effectiveness of down-sampling for stratified designs and unbalanced prevalence in Random Forest models of tree species distributions in Nevada","volume":"233","author":"Freeman","year":"2012","journal-title":"Ecol. Model."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.isprsjprs.2015.10.010","article-title":"Semi-supervised SVM for individual tree crown species classification","volume":"110","author":"Dalponte","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","unstructured":"Vapnik, V. (1998). Statistical Learning Theory, John Wiley & Sons."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","first-page":"49","article-title":"A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","first-page":"298","article-title":"A comparison of selected classification algorithms for mappingbamboo 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_20","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A transformation for ordering multispectral data in terms of image quality with implications for noise removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Piiroinen, R., Heiskanen, J., M\u00f5ttus, M., and Pellikka, P. (2015). Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data. Int. J. Appl. Earth Obs. Geoinf., 39.","DOI":"10.1016\/j.jag.2015.02.005"},{"key":"ref_22","unstructured":"Jaetzold, R., Schmidt, H., Hornetz, B., and Shisanya, C. (1983). Farm Management Handbook of Kenya: Natural Conditions and Farm Management Information, German Agency for Technical Cooperation."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s11258-010-9853-3","article-title":"Woody plant communities of isolated Afromontane cloud forests in Taita Hills, Kenya","volume":"212","author":"Aerts","year":"2011","journal-title":"Plant Ecol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Clark, B., and Pellikka, P. (2009). Landscape analysis using multi-scale segmentation and objectoriented classification. Recent Adv. Remote Sens., 323\u2013341.","DOI":"10.1201\/9780203875445.ch21"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/B978-0-444-59559-1.00013-X","article-title":"Agricultural expansion and its consequences in the Taita Hills, Kenya","volume":"16","author":"Pellikka","year":"2013","journal-title":"Dev. Earth Surf. Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.biocon.2006.08.015","article-title":"The biological importance of the Eastern Arc Mountains of Tanzania and Kenya","volume":"134","author":"Burgess","year":"2007","journal-title":"Biol. Conserv."},{"key":"ref_27","first-page":"221","article-title":"Airborne remote sensing of spatiotemporal change (1955\u20132004) in indigenous and exotic forest cover in the Taita Hills, Kenya","volume":"11","author":"Pellikka","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","unstructured":"Hohenthal, J., R\u00e4s\u00e4nen, M., Owidi, E., Andersson, B., Minoia, P., and Pellikka, P.K.E. (2015). Community and Institutional Perspectives on Water Management and Environmental Changes in the Taita Hills, Kenya, University of Helsinki."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/BF02985254","article-title":"Sources of tannin: Alternatives to wattle (Acacia mearnsii) among indigenous Kenyan species","volume":"46","author":"Mugedo","year":"1992","journal-title":"Econ. Bot."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.soilbio.2013.12.030","article-title":"Soil Bradyrhizobium population response to invasion of a natural Quercus suber forest by the introduced nitrogen-fixing tree Acacia mearnsii in El Kala National Park, Algeria","volume":"70","author":"Boudiaf","year":"2014","journal-title":"Soil Biol. Biochem."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.2307\/1939636","article-title":"A comparison of plotless density estimators using Monte Carlo simulation","volume":"75","author":"Engeman","year":"1994","journal-title":"Ecology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"863","DOI":"10.14358\/PERS.80.9.863","article-title":"Generating pit-free canopy height models from airborne lidar","volume":"80","author":"Khosravipour","year":"2014","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/978-94-017-8663-8_4","article-title":"Integrating airborne laser scanning with data from global navigation satellite systems and optical sensors","volume":"Volume 27","author":"Maltamo","year":"2014","journal-title":"Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Baldeck, C.A., Asner, G.P., Martin, R.E., Anderson, C.B., Knapp, E., Kellner, J.R., and Wright, S.J. (2015). Operational tree species mapping in a diverse tropical forest with airborne imaging spectroscopy. PLoS ONE.","DOI":"10.1371\/journal.pone.0118403"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1111\/2041-210X.12575","article-title":"Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data","volume":"7","author":"Dalponte","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Roussel, J.-R., and Auty, D. (2017, June 13). lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications, Version 1.2.0. Available online: https:\/\/rdrr.io\/cran\/lidR\/.","DOI":"10.32614\/CRAN.package.lidR"},{"key":"ref_38","unstructured":"R Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Version 3.4.0."},{"key":"ref_39","unstructured":"RSI (2004). ENVI User\u2019s Guide, Research Systems, Inc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2012.05.015","article-title":"Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery","volume":"124","author":"Zhang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, P.S., and Huete, J.G. (2011). Hyperspectral vegetation indices. Hyperspectral Remote Sensing of Vegetation, CRC press.","DOI":"10.1201\/b11222-3"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"19","DOI":"10.32614\/RJ-2015-018","article-title":"VSURF: An R package for variable selection using random forests","volume":"7","author":"Genuer","year":"2015","journal-title":"R J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v011.i09","article-title":"kernlab\u2014An S4 Package for Kernel Methods in R","volume":"11","author":"Karatzoglou","year":"2004","journal-title":"J. Stat. Softw."},{"key":"ref_46","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_48","unstructured":"Dalponte, M., and \u00d8rka, H.O. (2016, November 05). varSel: Sequential Forward Floating Selection Using Jeffries-Matusita Distance. Available online: https:\/\/rdrr.io\/cran\/varSel\/."},{"key":"ref_49","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_50","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_51","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate statistical tests for comparing supervised classi cation learning algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1016\/j.rse.2010.07.002","article-title":"Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada","volume":"114","author":"Jones","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.rse.2015.08.019","article-title":"LiDAR waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters","volume":"173","author":"Hovi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.sajb.2012.08.008","article-title":"Die-off of giant Euphorbia trees in South Africa: Symptoms and relationships to climate","volume":"83","author":"Roux","year":"2012","journal-title":"S. Afr. J. Bot."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.3390\/rs4113462","article-title":"Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data","volume":"4","author":"Colgan","year":"2012","journal-title":"Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.3390\/rs4061820","article-title":"Species-Level Differences in Hyperspectral Metrics among Tropical Rainforest Trees as Determined by a Tree-Based","volume":"4","author":"Clark","year":"2012","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/875\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:43:08Z","timestamp":1760208188000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/875"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,23]]},"references-count":57,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["rs9090875"],"URL":"https:\/\/doi.org\/10.3390\/rs9090875","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,23]]}}}