{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T22:01:13Z","timestamp":1774994473886,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T00:00:00Z","timestamp":1561680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000014","name":"Smithsonian Institution","doi-asserted-by":"publisher","award":["Scholarly Studies Grant"],"award-info":[{"award-number":["Scholarly Studies Grant"]}],"id":[{"id":"10.13039\/100000014","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000014","name":"Smithsonian Institution","doi-asserted-by":"publisher","award":["ForestGEO Global Earth Observatories"],"award-info":[{"award-number":["ForestGEO Global Earth Observatories"]}],"id":[{"id":"10.13039\/100000014","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007698","name":"University of Florida","doi-asserted-by":"publisher","award":["Biodiversity Institute graduate fellowship"],"award-info":[{"award-number":["Biodiversity Institute graduate fellowship"]}],"id":[{"id":"10.13039\/100007698","id-type":"DOI","asserted-by":"publisher"}]},{"name":"sDiv (the Synthesis Centre of iDiv; DFG FZT 118)","award":["Sabbatical fellowship"],"award-info":[{"award-number":["Sabbatical fellowship"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Tropical forests exhibit complex but poorly understood patterns of leaf phenology. Understanding species- and individual-level phenological patterns in tropical forests requires datasets covering large numbers of trees, which can be provided by Unmanned Aerial Vehicles (UAVs). In this paper, we test a workflow combining high-resolution RGB images (7 cm\/pixel) acquired from UAVs with a machine learning algorithm to monitor tree and species leaf phenology in a tropical forest in Panama. We acquired images for 34 flight dates over a 12-month period. Crown boundaries were digitized in images and linked with forest inventory data to identify species. We evaluated predictions of leaf cover from different models that included up to 14 image features extracted for each crown on each date. The models were trained and tested with visual estimates of leaf cover from 2422 images from 85 crowns belonging to eight species spanning a range of phenological patterns. The best-performing model included both standard color metrics, as well as texture metrics that quantify within-crown variation, with r2 of 0.84 and mean absolute error (MAE) of 7.8% in 10-fold cross-validation. In contrast, the model based only on the widely-used Green Chromatic Coordinate (GCC) index performed relatively poorly (r2 = 0.52, MAE = 13.6%). These results highlight the utility of texture features for image analysis of tropical forest canopies, where illumination changes may diminish the utility of color indices, such as GCC. The algorithm successfully predicted both individual-tree and species patterns, with mean r2 of 0.82 and 0.89 and mean MAE of 8.1% and 6.0% for individual- and species-level analyses, respectively. Our study is the first to develop and test methods for landscape-scale UAV monitoring of individual trees and species in diverse tropical forests. Our analyses revealed undescribed patterns of high intraspecific variation and complex leaf cover changes for some species.<\/jats:p>","DOI":"10.3390\/rs11131534","type":"journal-article","created":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T11:20:26Z","timestamp":1561720826000},"page":"1534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-0367","authenticated-orcid":false,"given":"John","family":"Park","sequence":"first","affiliation":[{"name":"Department of Biology, University of Florida, P.O. Box 118525, Gainesville, FL 32611, USA"}]},{"given":"Helene","family":"Muller-Landau","sequence":"additional","affiliation":[{"name":"Smithsonian Tropical Research Institute, Apartado 0843\u201303092 Balboa, Panama"}]},{"given":"Jeremy","family":"Lichstein","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Florida, P.O. Box 118525, Gainesville, FL 32611, USA"}]},{"given":"Sami","family":"Rifai","sequence":"additional","affiliation":[{"name":"Oxford Martin School, University of Oxford, 34 Broad Street, Oxford OX1 3BD, UK"},{"name":"School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA"}]},{"given":"Jonathan","family":"Dandois","sequence":"additional","affiliation":[{"name":"Smithsonian Tropical Research Institute, Apartado 0843\u201303092 Balboa, Panama"},{"name":"JMT Technology Group, Hunt Valley, MD 21030, USA"}]},{"given":"Stephanie","family":"Bohlman","sequence":"additional","affiliation":[{"name":"Smithsonian Tropical Research Institute, Apartado 0843\u201303092 Balboa, Panama"},{"name":"School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,28]]},"reference":[{"key":"ref_1","unstructured":"Pachauri, R.K., and Meyer, L. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.agrformet.2012.09.012","article-title":"Climate change, phenology, and phenological control of vegetation feedbacks to the climate system","volume":"169","author":"Richardson","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1111\/nph.12599","article-title":"Progress towards an interdisciplinary science of plant phenology: Building predictions across space, time and species diversity","volume":"201","author":"Wolkovich","year":"2014","journal-title":"New Phytol."},{"key":"ref_4","unstructured":"Croat, T.B. (1978). Flora of Barro Colorado Island, Stanford University Press."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Leigh, E.G. (1999). Tropical Forest Ecology: A View from Barro Colorado Island, Oxford University Press.","DOI":"10.1093\/oso\/9780195096026.001.0001"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"649","DOI":"10.2307\/3236572","article-title":"Quantifying the deciduousness of tropical forest canopies under varying climates","volume":"11","author":"Condit","year":"2000","journal-title":"J. Veg. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1890\/02-4047","article-title":"Leaf demography and phenology in Amazonian rain forest: A census of 40,000 leaves of 23 tree species","volume":"74","author":"Reich","year":"2004","journal-title":"Ecol. Monogr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1111\/j.1466-8238.2006.00213.x","article-title":"Leaf flushing during the dry season: The paradox of Asian monsoon forests","volume":"15","author":"Elliott","year":"2006","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1007\/s00442-007-0938-1","article-title":"Deciduousness in a seasonal tropical forest in western Thailand: Interannual and intraspecific variation in timing, duration and environmental cues","volume":"155","author":"Williams","year":"2008","journal-title":"Oecologia"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.2307\/1937384","article-title":"Seasonal drought and leaf fall in a tropical forest","volume":"71","author":"Wright","year":"1990","journal-title":"Ecology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1038\/s41467-018-03306-9","article-title":"Resource acquisition and reproductive strategies of tropical forest in response to the El Ni\u00f1o\u2013Southern Oscillation","volume":"9","author":"Detto","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/2012EI440.1","article-title":"Why is remote sensing of Amazon forest greenness so challenging?","volume":"16","author":"Samanta","year":"2012","journal-title":"Earth Interact."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/nature13006","article-title":"Amazon forests maintain consistent canopy structure and greenness during the dry season","volume":"506","author":"Morton","year":"2014","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.aad5068","article-title":"Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests","volume":"351","author":"Wu","year":"2016","journal-title":"Science"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2016.05.009","article-title":"Leaf flush drives dry season green-up of the Central Amazon","volume":"182","author":"Lopes","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"E4","DOI":"10.1038\/nature16457","article-title":"Dry-season greening of Amazon forests","volume":"531","author":"Saleska","year":"2016","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.3390\/s110403831","article-title":"Estimation of the distribution of Tabebuia guayacan (Bignoniaceae) using high-resolution remote sensing imagery","volume":"11","author":"Rivard","year":"2011","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1002\/ecy.1847","article-title":"Adult mortality in a low-density tree population using high-resolution remote sensing","volume":"98","author":"Kellner","year":"2017","journal-title":"Ecology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.ecoinf.2013.12.011","article-title":"Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation","volume":"19","author":"Alberton","year":"2014","journal-title":"Ecol. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Klosterman, S., and Richardson, A.D. (2017). Observing spring and fall phenology in a deciduous forest with aerial drone imagery. Sensors, 17.","DOI":"10.3390\/s17122852"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.agrformet.2017.10.015","article-title":"Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography","volume":"248","author":"Klosterman","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_22","first-page":"1264","article-title":"Statistical texture analysis","volume":"36","author":"Srinivasan","year":"2008","journal-title":"Proc. World Acad. Sci. Eng. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2011.11.004","article-title":"Modeling broad-scale patterns of avian species richness across the Midwestern United States with measures of satellite image texture","volume":"118","author":"Culbert","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1111\/geb.12365","article-title":"A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling","volume":"24","author":"Tuanmu","year":"2015","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hofmann, S., Everaars, J., Schweiger, O., Frenzel, M., Bannehr, L., and Cord, A.F. (2017). Modelling patterns of pollinator species richness and diversity using satellite image texture. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0185591"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1139\/cjfr-2014-0562","article-title":"Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance","volume":"46","author":"Freeman","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.isprsjprs.2014.11.007","article-title":"Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm","volume":"101","author":"Ahmed","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1126\/science.283.5401.554","article-title":"Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest","volume":"283","author":"Hubbell","year":"1999","journal-title":"Science"},{"key":"ref_29","unstructured":"Paton, S. (2017). Meteorological and Hydrological Summary for Barro Colorado Island, Smithsonian Tropical Research Institute."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13895","DOI":"10.3390\/rs71013895","article-title":"Optimal Altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure","volume":"7","author":"Dandois","year":"2015","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.biocon.2015.03.031","article-title":"Using lightweight unmanned aerial vehicles to monitor tropical forest recovery","volume":"186","author":"Zahawi","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6769","DOI":"10.5194\/bg-10-6769-2013","article-title":"Effects of topography, soil type and forest age on the frequency and size distribution of canopy gap disturbances in a tropical forest","volume":"10","author":"Lobo","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1111\/j.1365-2745.2011.01935.x","article-title":"A forest structure model that determines crown layers and partitions growth and mortality rates for landscape-scale applications of tropical forests","volume":"100","author":"Bohlman","year":"2012","journal-title":"J. Ecol."},{"key":"ref_34","first-page":"e27182v1","article-title":"A digital mapping method for linking high-resolution remote sensing images to individual tree crowns","volume":"6","author":"Graves","year":"2018","journal-title":"PeerJ Prepr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1890\/08-2022.1","article-title":"Near-surface remote sensing of spatial and temporal variation in canopy phenology","volume":"19","author":"Richardson","year":"2009","journal-title":"Ecol. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0034-4257(94)00098-8","article-title":"Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon","volume":"52","author":"Adams","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.]. Springer Series in Statistics.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"881","DOI":"10.2307\/2258961","article-title":"Comparative phenological studies of trees in tropical wet and dry forests in the lowlands of Costa Rica","volume":"62","author":"Frankie","year":"1974","journal-title":"J. Ecol."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1111\/nph.14051","article-title":"Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests","volume":"214","author":"Wu","year":"2017","journal-title":"New Phytol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.2307\/1940727","article-title":"Tropical forest litter dynamics and dry season irrigation on Barro Colorado Island, Panama","volume":"76","author":"Wieder","year":"1995","journal-title":"Ecology"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1111\/gcb.12712","article-title":"CTFS-ForestGEO: A worldwide network monitoring forests in an era of global change","volume":"21","author":"Davies","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1111\/nph.14009","article-title":"Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests","volume":"212","author":"Xu","year":"2016","journal-title":"New Phytol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/S0034-4257(02)00029-9","article-title":"Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection","volume":"82","author":"Du","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Adams, J.B., and Gillespie, A.R. (2006). Remote Sensing of Landscapes with Spectral Images, Cambridge University Press.","DOI":"10.1017\/CBO9780511617195"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40965-017-0031-6","article-title":"Orfeo ToolBox: Open source processing of remote sensing images","volume":"2","author":"Grizonnet","year":"2017","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_49","unstructured":"Jordahl, K. (2019, May 15). GeoPandas: Python Tools for Geographic Data. Available online: https:\/\/github. com\/geopandas\/geopandas."},{"key":"ref_50","first-page":"55","article-title":"GBM: Generalized boosted regression models","volume":"1","author":"Ridgeway","year":"2006","journal-title":"R Package Version"},{"key":"ref_51","first-page":"2007","article-title":"Generalized Boosted Models: A guide to the gbm package","volume":"1","author":"Ridgeway","year":"2007","journal-title":"Update"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1534\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:02:02Z","timestamp":1760187722000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,28]]},"references-count":51,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11131534"],"URL":"https:\/\/doi.org\/10.3390\/rs11131534","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,28]]}}}