{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:36:16Z","timestamp":1760232976171,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDB31030000","41901060","U1901219"],"award-info":[{"award-number":["XDB31030000","41901060","U1901219"]}]},{"DOI":"10.13039\/501100014881","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDB31030000","41901060","U1901219"],"award-info":[{"award-number":["XDB31030000","41901060","U1901219"]}],"id":[{"id":"10.13039\/501100014881","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014881","name":"NSFC-Guangdong Joint Foundation Key Project","doi-asserted-by":"publisher","award":["XDB31030000","41901060","U1901219"],"award-info":[{"award-number":["XDB31030000","41901060","U1901219"]}],"id":[{"id":"10.13039\/501100014881","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Plant functional traits are rarely used in tree species classification, and the impact of vertical canopy positions on collecting samples for classification also remains unclear. We aim to explore the feasibility and effectiveness of leaf traits in classification, as well as to detect the effect of vertical position on classification accuracy. This work will deepen our understanding of the ecological mechanism of natural forest structure and succession from new perspectives. In this study, we collected foliar samples from three canopy layers (upper, middle and lower) and measured their spectra, as well as eight well-known leaf traits. We used a leaf hyperspectral reflectance (LHR) dataset, leaf functional traits (LFT) dataset and LFT + LHR dataset to classify six dominant tree species in a subtropical evergreen broad-leaved forest. Our results showed that the LFT + LHR dataset achieved the highest classification results (overall accuracy (OA) = 77.65% and Kappa = 0.73), followed by the LFT dataset (OA = 74.26% and Kappa = 0.69) and the LHR dataset (OA = 69.06% and Kappa = 0.63). Along the vertical canopy, the OA and Kappa increased from the lower to the upper layers, and the combination data of the three canopy layers achieved the highest accuracy. For the individual tree species, the shade-tolerant species (including Machilus chinensis, Cryptocarya chinensis and Cryptocarya concinna) produced higher accuracies than the light-demanding species (including Schima superba and Castanopsis chinensis). Our results provide an approach for enhancing tree species recognition from the plant physiology and biochemistry perspective and emphasize the importance of vertical direction in forest community research.<\/jats:p>","DOI":"10.3390\/rs14246227","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T03:23:49Z","timestamp":1670556229000},"page":"6227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Tree Species Classification Using Plant Functional Traits and Leaf Spectral Properties along the Vertical Canopy Position"],"prefix":"10.3390","volume":"14","author":[{"given":"Yicen","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geographical Sciences and Remote Sensing, Guangzhou University, Guangzhou 510006, China"},{"name":"State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The Chinese Academy of Sciences, Beijing 100093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4839-7724","authenticated-orcid":false,"given":"Junjie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifeng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juyu","family":"Lian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanhui","family":"Ye","sequence":"additional","affiliation":[{"name":"Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangyuan","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1080\/02827581.2013.793386","article-title":"Characterizing forest species composition using multiple remote sensing data sources and inventory approaches","volume":"28","author":"Orka","year":"2013","journal-title":"Scand. J. Forest Res."},{"key":"ref_2","first-page":"101960","article-title":"Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data","volume":"84","author":"Modzelewska","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1016\/j.rse.2009.02.002","article-title":"Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data","volume":"113","author":"Orka","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"101","article-title":"Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation","volume":"18","author":"Heinzel","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1016\/j.rse.2007.10.011","article-title":"Classification of Australian forest communities using aerial photography, CASI and HyMap data","volume":"112","author":"Lucas","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.agrformet.2012.05.019","article-title":"Allometric equation choice impacts lidar-based forest biomass estimates: A case study from the Sierra National Forest, CA","volume":"165","author":"Zhao","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"E1","DOI":"10.3832\/ifor0901-006","article-title":"GlobAllomeTree: International platform for tree allometric equations to support volume, biomass and carbon assessment","volume":"6","author":"Henry","year":"2013","journal-title":"IForest"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1080\/10106049.2016.1240717","article-title":"Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor","volume":"33","author":"Dube","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.rse.2012.03.027","article-title":"Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data","volume":"123","author":"Yao","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5604","DOI":"10.1073\/pnas.1401181111","article-title":"Amazonian functional diversity from forest canopy chemical assembly","volume":"111","author":"Asner","year":"2014","journal-title":"PNAS"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Z.H., Wang, T.J., Darvishzadeh, R., Skidmore, A.K., Jones, S., Suarez, L., Woodgate, W., Heiden, U., Heurich, M., and Hearne, J. (2016). Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest. Remote Sens., 8.","DOI":"10.3390\/rs8060491"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1111\/avsc.12122","article-title":"Mapping continuous forest type variation by means of correlating remotely sensed metrics to canopy N:P ratio in a boreal mixedwood forest","volume":"18","author":"Gokkaya","year":"2015","journal-title":"Appl. Veg. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4521","DOI":"10.1080\/01431161.2016.1214302","article-title":"How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: A review","volume":"37","author":"Yin","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2017.08.010","article-title":"Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data","volume":"200","author":"Liu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/JSTARS.2013.2282166","article-title":"Classification of Australian Native Forest Species Using Hyperspectral Remote Sensing and Machine-Learning Classification Algorithms","volume":"7","author":"Shang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","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_18","first-page":"207","article-title":"Tree species classification using plant functional traits from LiDAR and hyperspectral data","volume":"73","author":"Shi","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2011.11.005","article-title":"The effect of seasonal spectral variation on species classification in the Panamanian tropical forest","volume":"118","author":"Hesketh","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.014","article-title":"Discrimination of liana and tree leaves from a Neotropical Dry Forest using visible-near infrared and longwave infrared reflectance spectra","volume":"219","author":"Guzman","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"68716","DOI":"10.1109\/ACCESS.2018.2880083","article-title":"Deep Learning for Fusion of APEX Hyperspectral and Full-Waveform LiDAR Remote Sensing Data for Tree Species Mapping","volume":"6","author":"Liao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1016\/j.foreco.2010.08.031","article-title":"Tree species classification from fused active hyperspectral reflectance and LIDAR measurements","volume":"260","author":"Puttonen","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_24","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 Classifier","volume":"4","author":"Clark","year":"2012","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4804","DOI":"10.1080\/01431161.2017.1320445","article-title":"Improving the classification of six evergreen subtropical tree species with multi-season data from leaf spectra simulated to WorldView-2 and RapidEye","volume":"38","author":"Cho","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","first-page":"4133","article-title":"Improving Discrimination of Savanna Tree Species Through a Multiple-Endmember Spectral Angle Mapper Approach: Canopy-Level Analysis","volume":"48","author":"Cho","year":"2010","journal-title":"IEEE Trans. Geosci. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1890\/070152","article-title":"Airborne spectranomics: Mapping canopy chemical and taxonomic diversity in tropical forests","volume":"7","author":"Asner","year":"2009","journal-title":"Front. Ecol. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1017\/S0376892914000307","article-title":"Choosing and using multiple traits in functional diversity research","volume":"42","author":"Lefcheck","year":"2015","journal-title":"Environ. Conserv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1111\/j.0030-1299.2007.15559.x","article-title":"Let the concept of trait be functional!","volume":"116","author":"Violle","year":"2007","journal-title":"Oikos"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1111\/1365-2745.12211","article-title":"The world-wide \u2018fast-slow\u2019 plant economics spectrum: A traits manifesto","volume":"102","author":"Reich","year":"2014","journal-title":"J. Ecol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1111\/j.1365-2435.2010.01727.x","article-title":"A multi-trait approach reveals the structure and the relative importance of intra- vs. interspecific variability in plant traits","volume":"24","author":"Albert","year":"2010","journal-title":"Funct. Ecol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e02719","DOI":"10.1002\/ecs2.2719","article-title":"Trait-based community assembly pattern along a forest succession gradient in a seasonally dry tropical forest","volume":"10","author":"Subedi","year":"2019","journal-title":"Ecosphere"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1111\/j.1469-8137.2010.03549.x","article-title":"Canopy phylogenetic, chemical and spectral assembly in a lowland Amazonian forest","volume":"189","author":"Asner","year":"2011","journal-title":"New Phytol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2018.03.038","article-title":"LiDAR derived forest structure data improves predictions of canopy N and P concentrations from imaging spectroscopy","volume":"211","author":"Ewald","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1890\/09-1999.1","article-title":"Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests","volume":"21","author":"Asner","year":"2011","journal-title":"Ecol. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3172","DOI":"10.1109\/JSTARS.2015.2422734","article-title":"Leaf Nitrogen Content Indirectly Estimated by Leaf Traits Derived from the PROSPECT Model","volume":"8","author":"Wang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","first-page":"128","article-title":"Detecting leaf-water content in Mediterranean trees using high-resolution spectrometry","volume":"27","author":"Addink","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1111\/j.1654-1103.2012.01473.x","article-title":"Inter-specific and intra-specific trait variation along short environmental gradients in an old-growth temperate forest","volume":"24","author":"Auger","year":"2013","journal-title":"J. Veg. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2016.09.015","article-title":"Retrieval of forest leaf functional traits from HySpex imagery using radiative transfer models and continuous wavelet analysis","volume":"122","author":"Ali","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113024","DOI":"10.1016\/j.rse.2022.113024","article-title":"Assessing biodiversity from space: Impact of spatial and spectral resolution on trait-based functional diversity","volume":"275","author":"Helfenstein","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yu, F.Y., Gara, T.W., Lian, J.Y., Ye, W.H., Shen, J., Wang, T.J., Wu, Z.F., and Wang, J.J. (2021). Understanding the Impact of Vertical Canopy Position on Leaf Spectra and Traits in an Evergreen Broadleaved Forest. Remote Sens., 13.","DOI":"10.3390\/rs13245057"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.isprsjprs.2018.02.002","article-title":"Important LiDAR metrics for discriminating forest tree species in Central Europe","volume":"137","author":"Shi","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2017.04.007","article-title":"UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA","volume":"195","author":"Sankey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2012.03.013","article-title":"Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral\/hyperspectral images and LiDAR data","volume":"123","author":"Dalponte","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.018","article-title":"Urban tree species mapping using hyperspectral and lidar data fusion","volume":"148","author":"Alonzo","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.17520\/biods.2021205","article-title":"Relationship between variation of plant functional traits and individual growth at different vertical layers in a subtropical evergreen broad-leaved forest of Dinghushan","volume":"29","author":"Li","year":"2021","journal-title":"Biodivers. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"619","DOI":"10.17520\/biods.2019107","article-title":"Vertical structure and its biodiversity in a subtropical evergreen broadleaved forest at Dinghushan in Guangdong Province, China","volume":"27","author":"Gui","year":"2019","journal-title":"Biodivers. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1071\/BT02124","article-title":"A handbook of protocols for standardised and easy measurement of plant functional traits worldwide","volume":"51","author":"Cornelissen","year":"2003","journal-title":"Aust. J. Bot."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random forests for classification in ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_50","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_51","first-page":"93","article-title":"Classification of tree species based on longwave hyperspectral data from leaves, a case study for a tropical dry forest","volume":"66","author":"Harrison","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1109\/JSTARS.2014.2329390","article-title":"Comparison of Feature Reduction Algorithms for Classifying Tree Species with Hyperspectral Data on Three Central European Test Sites","volume":"7","author":"Fassnacht","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"111232","DOI":"10.1016\/j.rse.2019.111232","article-title":"The capability of species-related forest stand characteristics determination with the use of hyperspectral data","volume":"231","author":"Wietecha","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Fricker, G.A., Ventura, J.D., Wolf, J.A., North, M.P., Davis, F.W., and Franklin, J. (2019). A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11192326"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1016\/j.rse.2011.05.004","article-title":"Spectroscopic classification of tropical forest species using radiative transfer modeling","volume":"115","author":"Feret","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"517","DOI":"10.3732\/ajb.93.4.517","article-title":"Variability in leaf optical properties of Mesoamerican trees and the potential for species classification","volume":"93","author":"Rivard","year":"2006","journal-title":"Am. J. Bot."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhao, Y.J., Zeng, Y., Zhao, D., Wu, B.F., and Zhao, Q.J. (2016). The Optimal Leaf Biochemical Selection for Mapping Species Diversity Based on Imaging Spectroscopy. Remote Sens., 8.","DOI":"10.3390\/rs8030216"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1080\/15481603.2018.1540170","article-title":"Leaf to canopy upscaling approach affects the estimation of canopy traits","volume":"56","author":"Gara","year":"2019","journal-title":"GIsci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"E185","DOI":"10.1073\/pnas.1210196109","article-title":"Hyperspectral remote sensing of foliar nitrogen content","volume":"110","author":"Knyazikhin","year":"2013","journal-title":"PNAS"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.agrformet.2018.02.010","article-title":"Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery","volume":"253","author":"Wang","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s10712-019-09534-y","article-title":"Variability and Uncertainty Challenges in Scaling Imaging Spectroscopy Retrievals and Validations from Leaves Up to Vegetation Canopies","volume":"40","author":"Malenovsky","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1016\/j.rse.2009.05.019","article-title":"Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling","volume":"113","author":"Morsdorf","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e03019","DOI":"10.1002\/ecy.3019","article-title":"Counting niches: Abundance-by-trait patterns reveal niche partitioning in a Neotropical forest","volume":"101","author":"Guittar","year":"2020","journal-title":"Ecology"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1093\/treephys\/tpu016","article-title":"Canopy position affects the relationships between leaf respiration and associated traits in a tropical rainforest in Far North Queensland","volume":"34","author":"Weerasinghe","year":"2014","journal-title":"Tree Physiol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1890\/08-1449.1","article-title":"Functional traits and environmental filtering drive community assembly in a species-rich tropical system","volume":"91","author":"Meave","year":"2010","journal-title":"Ecology"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1966","DOI":"10.1890\/0012-9658(1997)078[1966:TIOFAC]2.0.CO;2","article-title":"the interplay of facilitation and competition in plant communities","volume":"78","author":"Holmgren","year":"1997","journal-title":"Ecology"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"391","DOI":"10.3832\/ifor2045-009","article-title":"Successional leaf traits of monsoon evergreen broad-leaved forest, Southwest China","volume":"10","author":"Liu","year":"2017","journal-title":"IForest"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"442","DOI":"10.17521\/cjpe.2017.0171","article-title":"Long-term (1992\u20132015) dynamics of community composition and structure in a monsoon evergreen broad-leaved forest in Dinghushan Biosphere Reserve","volume":"42","author":"Zou","year":"2018","journal-title":"Chin. J. Plant Ecol."},{"key":"ref_69","first-page":"413","article-title":"Response of Photosynthesis to Growth Light Intensity in Some South Subtropical Woody Plants","volume":"13","author":"Zhang","year":"2005","journal-title":"J. Trop. Subtrop. Bot."},{"key":"ref_70","first-page":"62","article-title":"Relationship between the Accumulation Ability of Photoprotective Substances and the Photosynthetic Capacity in Leaves of Four Woody Plants Grown under Two Light Intensities","volume":"51","author":"Yu","year":"2019","journal-title":"J. South China Normal Univ. (Nat. Sci. Ed.)."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6227\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:36:47Z","timestamp":1760146607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,8]]},"references-count":70,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246227"],"URL":"https:\/\/doi.org\/10.3390\/rs14246227","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,12,8]]}}}