{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T13:42:36Z","timestamp":1772199756986,"version":"3.50.1"},"reference-count":142,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Department of Agriculture, Natural Resources Conservation Service, Conservation Effects Assessment Project-Grazing Lands","award":["NRC21IRA0010783"],"award-info":[{"award-number":["NRC21IRA0010783"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping the spatial distribution of woody vegetation is important for monitoring, managing, and studying woody encroachment in grasslands. However, in semi-arid regions, remotely sensed discrimination of tree species is difficult primarily due to the tree similarities, small and sparse canopy cover, but may also be due to overlapping woody canopies as well as seasonal leaf retention (deciduous versus evergreen) characteristics. Similar studies in different biomes have achieved low accuracies using coarse spatial resolution image data. The objective of this study was to investigate the use of multi-temporal, airborne hyperspectral imagery and light detection and ranging (LiDAR) derived data for tree species classification in a semi-arid desert region. This study produces highly accurate classifications by combining multi-temporal fine spatial resolution hyperspectral and LiDAR data (~1 m) through a reproducible scripting and machine learning approach that can be applied to larger areas and similar datasets. Combining multi-temporal vegetation indices and canopy height models led to an overall accuracy of 95.28% and kappa of 94.17%. Five woody species were discriminated resulting in producer accuracies ranging from 86.12% to 98.38%. The influence of fusing spectral and structural information in a random forest classifier for tree identification is evident. Additionally, a multi-temporal dataset slightly increases classification accuracies over a single data collection. Our results show a promising methodology for tree species classification in a semi-arid region using multi-temporal hyperspectral and LiDAR remote sensing data.<\/jats:p>","DOI":"10.3390\/rs14122896","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T11:45:44Z","timestamp":1655466344000},"page":"2896","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species"],"prefix":"10.3390","volume":"14","author":[{"given":"Cynthia L.","family":"Norton","sequence":"first","affiliation":[{"name":"University of Arizona School of Natural Resources and the Environment, Arizona Remote Sensing Center, The University of Arizona, 1064 E. Lowell Street, Tucson, AZ 85721, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6782-0589","authenticated-orcid":false,"given":"Kyle","family":"Hartfield","sequence":"additional","affiliation":[{"name":"University of Arizona School of Natural Resources and the Environment, Arizona Remote Sensing Center, The University of Arizona, 1064 E. Lowell Street, Tucson, AZ 85721, USA"}]},{"given":"Chandra D. Holifield","family":"Collins","sequence":"additional","affiliation":[{"name":"Southwest Watershed Research Center, USDA-ARS, Tucson, AZ 85721, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3188-7172","authenticated-orcid":false,"given":"Willem J. D.","family":"van Leeuwen","sequence":"additional","affiliation":[{"name":"University of Arizona School of Natural Resources and the Environment and School of Geography, Development & Environment, Arizona Remote Sensing Center, The University of Arizona, 1064 E. Lowell Street, Tucson, AZ 85721, USA"}]},{"given":"Loretta J.","family":"Metz","sequence":"additional","affiliation":[{"name":"USDA-Natural Resources Conservation Service, Tucson, AZ 85721, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.jaridenv.2006.10.012","article-title":"Woody vegetation expansion in a desert grassland: Prehistoric human impact?","volume":"69","author":"Briggs","year":"2007","journal-title":"J. Arid Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Barger, N.N., Archer, S.R., Campbell, J.L., Huang, C.Y., Morton, J.A., and Knapp, A.K. (2011). Woody plant proliferation in North American drylands: A synthesis of impacts on ecosystem carbon balance. J. Geophys. Res. Biogeosci., 116.","DOI":"10.1029\/2010JG001506"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1890\/03-0583","article-title":"Ecohydrological implications of woody plant encroachment","volume":"86","author":"Huxman","year":"2005","journal-title":"Ecology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1111\/j.1461-0248.2011.01630.x","article-title":"Impacts of shrub encroachment on ecosystem structure and functioning: Towards a global synthesis","volume":"14","author":"Eldridge","year":"2011","journal-title":"Ecol. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/BF00138373","article-title":"Shrubland encroachment in southern New Mexico, USA: An analysis of desertification processes in the American Southwest","volume":"17","author":"Grover","year":"1990","journal-title":"Clim. Chang."},{"key":"ref_6","unstructured":"Goudie, A.S. (2018). Human Impact on the Natural Environment, John Wiley & Sons."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2316","DOI":"10.1126\/science.1057320","article-title":"Consistent land-and atmosphere-based US carbon sink estimates","volume":"292","author":"Pacala","year":"2001","journal-title":"Science"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A large and persistent carbon sink in the world\u2019s forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/nature00910","article-title":"Ecosystem carbon loss with woody plant invasion of grasslands","volume":"418","author":"Jackson","year":"2002","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1126\/science.1094875","article-title":"Ecological linkages between aboveground and belowground biota","volume":"304","author":"Wardle","year":"2004","journal-title":"Science"},{"key":"ref_11","unstructured":"David, J.A., Kari, E.V., Jacob, R.G., Corinna, R., and Truman, P.Y. (2011). Pathways for Positive Cattle\u2013Wildlife Interactions in Semiarid Rangelands. Conserving Wildlife in African Landscapes: Kenya\u2019s Ewaso Ecosystem, Smithsonian Contributions to Zoology."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0006-3207(76)90049-5","article-title":"The use of remote sensing techniques to identify potential natural areas in oregon","volume":"9","author":"Mairs","year":"1976","journal-title":"Biol. Conserv."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Franklin, S.E. (2001). Remote Sensing for Sustainable Forest Management, CRC Press.","DOI":"10.1201\/9781420032857"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TGRS.2008.916480","article-title":"Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas","volume":"46","author":"Dalponte","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.rse.2006.04.023","article-title":"Remote sensing of woody shrub cover in desert grasslands using MISR with a geometric-optical canopy reflectance model","volume":"112","author":"Chopping","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., and Ciraolo, G. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote Sens., 10.","DOI":"10.20944\/preprints201803.0097.v1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1016\/j.rse.2007.09.009","article-title":"Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest","volume":"112","author":"Anderson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2013.04.005","article-title":"High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision","volume":"136","author":"Dandois","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1093\/jpe\/rtm005","article-title":"Remote sensing imagery in vegetation mapping: A review","volume":"1","author":"Xie","year":"2008","journal-title":"J. Plant Ecol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11273-009-9169-z","article-title":"Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review","volume":"18","author":"Adam","year":"2010","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Franklin, J. (2010). Mapping Species Distributions: Spatial Inference and Prediction, Cambridge University Press.","DOI":"10.1017\/CBO9780511810602"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"311","DOI":"10.5194\/isprsannals-I-3-311-2012","article-title":"Developing species specific vegetation maps using multi-spectral hyperspatial imagery from unmanned aerial vehicles","volume":"3","author":"Strecha","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0034-4257(97)00094-1","article-title":"Conifer species recognition: An exploratory analysis of in situ hyperspectral data","volume":"62","author":"Gong","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mather, P.M., and Koch, M. (2011). Computer Processing of Remotely-Sensed Images: An Introduction, John Wiley & Sons.","DOI":"10.1002\/9780470666517"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"829","DOI":"10.14358\/PERS.73.7.829","article-title":"Estimating species abundance in a northern temperate forest using spectral mixture analysis","volume":"73","author":"Plourde","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_30","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_31","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_32","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote sensing technologies for enhancing forest inventories: A review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1080\/02827589809382966","article-title":"Satellite remote sensing for forestry planning\u2014A review","volume":"13","author":"Holmgren","year":"1998","journal-title":"Scand. J. For. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","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_36","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_37","doi-asserted-by":"crossref","first-page":"819","DOI":"10.14358\/PERS.75.7.819","article-title":"Forest type mapping using object-specific texture measures from multi-spectral Ikonos imagery","volume":"75","author":"Kim","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5236","DOI":"10.1080\/01431161.2017.1363442","article-title":"Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multi-spectral data","volume":"39","author":"Franklin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1002\/eap.1436","article-title":"A hyperspectral image can predict tropical tree growth rates in single-species stands","volume":"26","author":"Caughlin","year":"2016","journal-title":"Ecol. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s12524-014-0392-6","article-title":"Spectral and texture features combined for forest tree species classification with airborne hyperspectral imagery","volume":"43","author":"Dian","year":"2015","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2015.04.015","article-title":"Combining airborne hyperspectral and LiDAR data across local sites for upscaling shrubland structural information: Lessons for HyspIRI","volume":"167","author":"Mitchell","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"47","DOI":"10.14358\/PERS.72.1.47","article-title":"Mapping sagebrush distribution using fusion of hyperspectral and lidar classifications","volume":"72","author":"Mundt","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/JSTARS.2014.2305441","article-title":"Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest","volume":"7","author":"Debes","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Kazakova, A., Moskal, L.M., and Styers, D.M. (2016). Object-based tree species classification in urban ecosystems using LiDAR and hyperspectral data. Forests, 7.","DOI":"10.3390\/f7060122"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Dashti, H., Poley, A., FGlenn, N., Ilangakoon, N., Spaete, L., Roberts, D., Enterkine, J., Flores, A.N., Ustin, S.L., and Mitchell, J.J. (2019). Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach. Remote Sens., 11.","DOI":"10.3390\/rs11182141"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/S0034-4257(01)00207-3","article-title":"Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments","volume":"77","author":"Okin","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1016\/j.rse.2007.11.016","article-title":"Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR","volume":"112","author":"Asner","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.foreco.2014.07.029","article-title":"Contemporary forest restoration: A review emphasizing function","volume":"331","author":"Stanturf","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_53","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_54","first-page":"464","article-title":"Multitemporal hyperspectral tree species classification in the Bia\u0142owie\u017ca Forest World Heritage site","volume":"94","author":"Modzelewska","year":"2021","journal-title":"For. Int. J. For. Res."},{"key":"ref_55","unstructured":"Medina, A.L. (1996). The Santa Rita Experimental Range: History and Annotated Bibliography (1903\u20131988), DIANE Publishing."},{"key":"ref_56","unstructured":"McClaran, M.P., Ffolliott, P.F., and Edminster, C.B. (2022, June 01). Santa Rita Experimental Range: 100 Years (1903 to 2003) of Accomplishments and Contributions, Tucson, AZ, 30 October 2003\u20131 November 2003, A Century of Vegetation Change on the Santa RITA Experimental Range, Available online: https:\/\/www.fs.fed.us\/rm\/pubs\/rmrs_p030\/rmrs_p030_016_033.pdf."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"8112","DOI":"10.1038\/s41598-020-65094-x","article-title":"Woody Plant encroachment has a larger impact than climate change on Dryland water budgets","volume":"10","author":"Vivoni","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_58","unstructured":"NEON (National Ecological Observatory Network) (2021, December 03). Spectrometer Orthorectified Surface Directional Reflectance-Mosaic (DP3.30006.001), RELEASE-2022. Available online: https:\/\/data.neonscience.org\/data-products\/DP3.30006.001."},{"key":"ref_59","unstructured":"NEON (National Ecological Observatory Network) (2021, December 03). Discrete Return LiDAR Point Cloud (DP1.30003.001), RELEASE-2022. Available online: https:\/\/data.neonscience.org\/data-products\/DP1.30003.001."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e03649","DOI":"10.1002\/ecs2.3649","article-title":"Innovations to expand drone data collection and analysis for rangeland monitoring","volume":"12","author":"Gillan","year":"2021","journal-title":"Ecosphere"},{"key":"ref_61","unstructured":"RStudio Team (2015). RStudio: Integrated Development Environment for R, RStudio Team. Available online: http:\/\/www.rstudio.com\/."},{"key":"ref_62","unstructured":"van Leeuwen, W.J. (2009). Visible, Near-IR, and Shortwave IR Spectral Characteristics of Terrestrial Surfaces, SAGE Publications Ltd."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e3992","DOI":"10.1002\/ecs2.3992","article-title":"Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes","volume":"13","author":"Farella","year":"2022","journal-title":"Ecosphere"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"217","DOI":"10.5194\/isprs-annals-III-3-217-2016","article-title":"Airborne Multi-Spectral Lidar Data for Land-Cover Classification and Land\/Water Mapping Using Different Spectral Indexes","volume":"3","author":"Morsy","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A review of vegetation indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"48572","DOI":"10.1109\/ACCESS.2019.2909530","article-title":"Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges","volume":"7","author":"Shakhatreh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0034-4257(02)00011-1","article-title":"Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals","volume":"81","author":"Serrano","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant remote sensing vegetation indices: A review of developments and applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1007\/s004420050337","article-title":"The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels","volume":"112","author":"Gamon","year":"1997","journal-title":"Oecologia"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.rse.2007.05.009","article-title":"Assessing canopy PRI for water stress detection with diurnal airborne imagery","volume":"112","author":"Miller","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2011.10.007","article-title":"Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera","volume":"117","author":"Berni","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Hartfield, K., Gillan, J.K., Norton, C.L., Conley, C., and van Leeuwen, W.J.D. (2022). A Novel Spectral Index to Identify Cacti in the Sonoran Desert at Multiple Scales Using Multi-Sensor Hyperspectral Data Acquisitions. Land, 11.","DOI":"10.3390\/land11060786"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"MTCI: The MERIS terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/0143116031000117065","article-title":"Hyperspectral indices for characterizing upland peat composition","volume":"25","author":"McMorrow","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Lowe, J.J., and Walker, M. (2014). Reconstructing Quaternary Environments, Routledge.","DOI":"10.4324\/9781315844312"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., and Lyon, J.G. (2016). Hyperspectral Remote Sensing of Vegetation, CRC Press.","DOI":"10.1201\/b11222"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Hively, W.D., Lamb, B.T., Daughtry, C.S., Serbin, G., Dennison, P., Kokaly, R.F., Wu, Z., and Masek, J.G. (2021). Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission. Remote Sens., 13.","DOI":"10.3390\/rs13183718"},{"key":"ref_81","unstructured":"Jensen, J.R. (2015). Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall Press."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9780429052729"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.rse.2010.09.020","article-title":"Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies","volume":"115","author":"Brantley","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1016\/j.rse.2008.12.010","article-title":"View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data","volume":"113","author":"Roberts","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2012.09.019","article-title":"Development of spectral indices for detecting and identifying plant diseases","volume":"128","author":"Mahlein","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_86","unstructured":"Thenkabail, P.S., Teluguntla, P., Gumma, M.K., and Dheeravath, V. (2015). Hyperspectral Remote Sensing for Terrestrial Applications. Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, CRC Press. Available online: http:\/\/oar.icrisat.org\/id\/eprint\/8611."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"112061","DOI":"10.1016\/j.rse.2020.112061","article-title":"lidR: An R package for analysis of Airborne Laser Scanning (ALS) data","volume":"251","author":"Roussel","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (2017). Classification and Regression Trees, Routledge.","DOI":"10.1201\/9781315139470"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/01621459.1963.10500855","article-title":"Problems in the analysis of survey data, and a proposal","volume":"58","author":"Morgan","year":"1963","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Hastie, T.J., and Tibshirani, R.J. (2017). Generalized Additive Models, Routledge.","DOI":"10.1201\/9780203753781"},{"key":"ref_92","unstructured":"Therneau, T.M., and Atkinson, E.J. (1997). An Introduction to Recursive Partitioning Using the RPART Routines, Mayo Foundation. Available online: https:\/\/stat.ethz.ch\/R-manual\/R-patched\/library\/rpart\/doc\/longintro.pdf."},{"key":"ref_93","unstructured":"Therneau, T.M., and Atkinson, E.J. (2015). An Introduction to Recursive Partitioning Using the RPART Routines, Mayo Foundation. Available online: https:\/\/cran.r-project.org\/web\/packages\/rpart\/vignettes\/longintro.pdf."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.eswa.2008.02.021","article-title":"Application of data mining techniques in customer relationship management: A literature review and classification","volume":"36","author":"Ngai","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.ijpe.2014.12.031","article-title":"How \u2018big data\u2019can make big impact: Findings from a systematic review and a longitudinal case study","volume":"165","author":"Wamba","year":"2015","journal-title":"Int. J. Prod. Econ."},{"key":"ref_96","unstructured":"Therneau, T.M., Atkinson, B., and Ripley, M.B. (2010). The Rpart Package, R Foundation for Statistical Computing. Available online: https:\/\/cran.r-project.org\/web\/packages\/rpart\/index.html."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_98","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.I. (2014). Combining Pattern Classifiers: Methods and Algorithms, John Wiley & Sons.","DOI":"10.1002\/9781118914564"},{"key":"ref_101","first-page":"e00971","article-title":"Land use\/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms","volume":"22","author":"Ge","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_102","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_103","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1126\/science.aaj1987","article-title":"Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation","volume":"355","author":"Asner","year":"2017","journal-title":"Science"},{"key":"ref_104","unstructured":"Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C.-C., Lin, C.-C., and Meyer, M.D. (2021, August 01). Package \u2018e1071\u2019. R J., Available online: https:\/\/cran.r-project.org\/web\/packages\/e1071\/e1071.pdf."},{"key":"ref_105","unstructured":"Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D., Weingessel, A., and Leisch, M.F. (2022, June 01). The e1071 Package. Misc Functions of Department of Statistics (e1071), TU Wien. Available online: https:\/\/www.researchgate.net\/profile\/Friedrich-Leisch-2\/publication\/221678005_E1071_Misc_Functions_of_the_Department_of_Statistics_E1071_TU_Wien\/links\/547305880cf24bc8ea19ad1d\/E1071-Misc-Functions-of-the-Department-of-Statistics-E1071-TU-Wien.pdf."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Torgo, L. (2011). Data Mining with R: Learning with CASE Studies, Chapman and Hall\/CRC.","DOI":"10.1201\/b10328"},{"key":"ref_107","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_108","doi-asserted-by":"crossref","unstructured":"Hsiao, C. (2022). Analysis of Panel Data, Cambridge University Press.","DOI":"10.1017\/9781009057745"},{"key":"ref_109","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_110","doi-asserted-by":"crossref","first-page":"5166","DOI":"10.1080\/01431161.2013.788261","article-title":"Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests","volume":"34","author":"Guan","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_112","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_113","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Lohr, S.L. (2021). Sampling: Design and Analysis, Chapman and Hall\/CRC.","DOI":"10.1201\/9780429298899"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-validatory choice and assessment of statistical predictions","volume":"36","author":"Stone","year":"1974","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Silverman, B.W. (2018). Density Estimation for Statistics and Data Analysis, Routledge.","DOI":"10.1201\/9781315140919"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Ramezan, C.A., Warner, T.A., and Maxwell, A.E. (2019). Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification. Remote Sens., 11.","DOI":"10.3390\/rs11020185"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s10661-018-6992-9","article-title":"Land subsidence phenomena investigated by spatiotemporal analysis of groundwater resources, remote sensing techniques, and random forest method: The case of Western Thessaly, Greece","volume":"190","author":"Ilia","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2017). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.3844\/ajassp.2009.1031.1035","article-title":"Hyperspectral imagery for mapping disease infection in oil palm plantationusing vegetation indices and red edge techniques","volume":"6","author":"Shafri","year":"2009","journal-title":"Am. J. Appl. Sci."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","article-title":"A review of advanced techniques for detecting plant diseases","volume":"72","author":"Sankaran","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_126","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_127","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1109\/JSTARS.2015.2417859","article-title":"Hyperspectral tree species classification of Japanese complex mixed forest with the aid of LiDAR data","volume":"8","author":"Matsuki","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Weil, G., Lensky, I.M., Resheff, Y.S., and Levin, N. (2017). Optimizing the timing of unmanned aerial vehicle image acquisition for applied mapping of woody vegetation species using feature selection. Remote Sens., 9.","DOI":"10.3390\/rs9111130"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Grybas, H., and Congalton, R.G. (2021). A Comparison of Multi-Temporal RGB and Multi-spectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests. Remote Sens., 13.","DOI":"10.3390\/rs13132631"},{"key":"ref_130","first-page":"1129","article-title":"Using multi-temporal landsat imagery","volume":"61","author":"Wolter","year":"1995","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_131","first-page":"891","article-title":"Delineating forest canopy species in the northeastern United States using multi-temporal TM imagery","volume":"64","author":"Mickelson","year":"1998","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/S0034-4257(00)00159-0","article-title":"A comparison of multi-spectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest","volume":"75","author":"Key","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_133","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_134","doi-asserted-by":"crossref","first-page":"167877","DOI":"10.1016\/j.ijleo.2021.167877","article-title":"Classification of desert steppe species based on unmanned aerial vehicle hyperspectral remote sensing and continuum removal vegetation indices","volume":"247","author":"Yang","year":"2021","journal-title":"Optik"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2019.01.019","article-title":"Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis","volume":"149","author":"Ferreira","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Nezami, S., Khoramshahi, E., Nevalainen, O., P\u00f6l\u00f6nen, I., and Honkavaara, E. (2020). Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks. Remote Sens., 12.","DOI":"10.20944\/preprints202002.0334.v1"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Takahashi Miyoshi, G., Imai, N.N., Garcia Tommaselli, A.M., Antunes de Moraes, M.V., and Honkavaara, E. (2020). Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest. Remote Sensing, 12.","DOI":"10.3390\/rs12020244"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.rse.2004.07.011","article-title":"Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico","volume":"93","author":"Laliberte","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1111\/j.1654-109X.2012.01194.x","article-title":"Mapping invasive woody species in coastal dunes in the N etherlands: A remote sensing approach using LIDAR and high-resolution aerial photographs","volume":"15","author":"Hantson","year":"2012","journal-title":"Appl. Veg. Sci."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Maschler, J., Atzberger, C., and Immitzer, M. (2018). Individual tree crown segmentation and classification of 13 tree species using airborne hyperspectral data. Remote Sens., 10.","DOI":"10.3390\/rs10081218"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Oddi, L., Cremonese, E., Ascari, L., Filippa, G., Galvagno, M., Serafino, D., and Cella, U.M.d. (2021). Using UAV Imagery to Detect and Map Woody Species Encroachment in a Subalpine Grassland: Advantages and Limits. 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