{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T11:25:19Z","timestamp":1769858719450,"version":"3.49.0"},"reference-count":88,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Funds from the FCT-Portuguese Foundation for Science and Technology","award":["UIDB\/04033\/2020"],"award-info":[{"award-number":["UIDB\/04033\/2020"]}]},{"name":"National Funds from the FCT-Portuguese Foundation for Science and Technology","award":["LA\/P\/0126\/2020"],"award-info":[{"award-number":["LA\/P\/0126\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Drones"],"abstract":"<jats:p>Remote-sensing processes based on unmanned aerial vehicles (UAV) have opened up new possibilities to both map and extract individual plant parameters. This is mainly due to the high spatial data resolution and acquisition flexibility of UAVs. Among the possible plant-related metrics is the leaf area index (LAI), which has already been successfully estimated in agronomy and forestry studies using the traditional normalized difference vegetation index from multispectral data or using hyperspectral data. However, the LAI has not been estimated in chestnut trees, and few studies have explored the use of multiple vegetation indices to improve LAI estimation from aerial imagery acquired by UAVs. This study uses multispectral UAV-based data from a chestnut grove to estimate the LAI for each tree by combining vegetation indices computed from different segments of the electromagnetic spectrum with geometrical parameters. Machine-learning techniques were evaluated to predict LAI with robust algorithms that consider dimensionality reduction, avoiding over-fitting, and reduce bias and excess variability. The best achieved coefficient of determination (R2) value of 85%, which shows that the biophysical and geometrical parameters can explain the LAI variability. This result proves that LAI estimation is improved when using multiple variables instead of a single vegetation index. Furthermore, another significant contribution is a simple, reliable, and precise model that relies on only two variables to estimate the LAI in individual chestnut trees.<\/jats:p>","DOI":"10.3390\/drones6120422","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T07:59:21Z","timestamp":1671436761000},"page":"422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Mapping the Leaf Area Index of Castanea sativa Miller Using UAV-Based Multispectral and Geometrical Data"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7570-9773","authenticated-orcid":false,"given":"Lu\u00eds","family":"P\u00e1dua","sequence":"first","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2667-8755","authenticated-orcid":false,"given":"Pamela","family":"Chiroque-Solano","sequence":"additional","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0240-5469","authenticated-orcid":false,"given":"Pedro","family":"Marques","sequence":"additional","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4533-930X","authenticated-orcid":false,"given":"Joaquim","family":"Sousa","sequence":"additional","affiliation":[{"name":"Engineering Department, School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Centre for Robotics in Industry and Intelligent Systems (CRIIS), Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5669-7976","authenticated-orcid":false,"given":"Emanuel","family":"Peres","sequence":"additional","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Engineering Department, School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1093\/oxfordjournals.aob.a083148","article-title":"Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years","volume":"11","author":"Watson","year":"1947","journal-title":"Ann. Bot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1177\/0309133312471367","article-title":"Optical remote sensing of forest leaf area index and biomass","volume":"37","author":"Song","year":"2013","journal-title":"Prog. Phys. Geogr."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Doughty, C.E., and Goulden, M.L. (2008). Seasonal patterns of tropical forest leaf area index and CO2 exchange. J. Geophys. Res. Biogeosci., 113.","DOI":"10.1029\/2007JG000590"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/0168-1923(91)90081-Z","article-title":"A comparison of direct and indirect methods for estimating forest canopy leaf area","volume":"57","author":"Chason","year":"1991","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0002-1571(71)90092-6","article-title":"A theoretical analysis of the frequency of gaps in plant stands","volume":"8","author":"Nilson","year":"1971","journal-title":"Agric. Meteorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1093\/jxb\/erg263","article-title":"Ground-based measurements of leaf area index: A review of methods, instruments and current controversies","volume":"54","year":"2003","journal-title":"J. Exp. Bot."},{"key":"ref_7","first-page":"6","article-title":"Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors","volume":"38","author":"Berni","year":"2009","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sishodia, R.P., Ray, R.L., and Singh, S.K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sens., 12.","DOI":"10.3390\/rs12193136"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.procs.2017.11.055","article-title":"Very high resolution aerial data to support multi-temporal precision agriculture information management","volume":"121","author":"Sousa","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s00271-018-0614-8","article-title":"Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals","volume":"37","author":"White","year":"2019","journal-title":"Irrig. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mourad, R., Jaafar, H., Anderson, M., and Gao, F. (2020). Assessment of leaf area index models using harmonized landsat and sentinel-2 surface reflectance data over a semi-arid irrigated landscape. Remote Sens., 12.","DOI":"10.3390\/rs12193121"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/S0168-1699(02)00106-0","article-title":"Mapping vineyard leaf area with multispectral satellite imagery","volume":"38","author":"Johnson","year":"2003","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kang, Y., \u00d6zdo\u011fan, M., Zipper, S.C., Rom\u00e1n, M.O., Walker, J., Hong, S.Y., Marshall, M., Magliulo, V., Moreno, J., and Alonso, L. (2016). How universal is the relationship between remotely sensed vegetation indices and crop leaf area index? A global assessment. Remote Sens., 8.","DOI":"10.3390\/rs8070597"},{"key":"ref_14","unstructured":"Gonz\u00e1lez Piqueras, J. (2006). Evapotranspiraci\u00f3n de la Cubierta Vegetal Mediante la Determinaci\u00f3n del Coeficiente de Cultivo por Teledetecci\u00f3n. Extensi\u00f3n a Escala Regional: Acu\u00edfero 08.29 Mancha Oriental, Facultat de F\u00edsica, Universitat de Val\u00e8ncia."},{"key":"ref_15","first-page":"22","article-title":"Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest","volume":"61","author":"Tian","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1109\/36.649788","article-title":"Estimation of global leaf area index and absorbed PAR using radiative transfer models","volume":"35","author":"Myneni","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.biosystemseng.2014.06.019","article-title":"Satellite-based evapotranspiration of a super-intensive olive orchard: Application of METRIC algorithms","volume":"128","author":"Cunha","year":"2014","journal-title":"Biosyst. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pasqualotto, N., Delegido, J., Van Wittenberghe, S., Rinaldi, M., and Moreno, J. (2019). Multi-crop green LAI estimation with a new simple Sentinel-2 LAI Index (SeLI). Sensors, 19.","DOI":"10.3390\/s19040904"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sun, L., Gao, F., Anderson, M.C., Kustas, W.P., Alsina, M.M., Sanchez, L., Sams, B., McKee, L., Dulaney, W., and White, W.A. (2017). Daily mapping of 30 m LAI and NDVI for grape yield prediction in California vineyards. Remote Sens., 9.","DOI":"10.3390\/rs9040317"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, Z., Jia, K., Xiao, C., Wei, D., Zhao, X., Lan, J., Wei, X., Yao, Y., Wang, B., and Sun, Y. (2020). Leaf area index estimation algorithm for GF-5 hyperspectral data based on different feature selection and machine learning methods. Remote Sens., 12.","DOI":"10.3390\/rs12132110"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, T., Xiao, Z., and Liu, Z. (2017). Performance evaluation of machine learning methods for leaf area index retrieval from time-series MODIS reflectance data. Sensors, 17.","DOI":"10.3390\/s17010081"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"281","DOI":"10.14358\/PERS.81.4.281","article-title":"Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs)","volume":"81","author":"Pajares","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"299","DOI":"10.5194\/isprsarchives-XL-1-W4-299-2015","article-title":"Leaf area index estimation in vineyards from UAV hyperspectral data, 2D image mosaics and 3D canopy surface models","volume":"40","author":"Kalisperakis","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"159","DOI":"10.20870\/oeno-one.2021.55.4.4639","article-title":"Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery","volume":"55","author":"Rubio","year":"2021","journal-title":"OENO One"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ilniyaz, O., Kurban, A., and Du, Q. (2022). Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods. Remote Sens., 14.","DOI":"10.3390\/rs14020415"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-022-00899-7","article-title":"Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery","volume":"18","author":"Wu","year":"2022","journal-title":"Plant Methods"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Caruso, G., Zarco-Tejada, P.J., Gonz\u00e1lez-Dugo, V., Moriondo, M., Tozzini, L., Palai, G., Rallo, G., Hornero, A., Primicerio, J., and Gucci, R. (2019). High-resolution imagery acquired from an unmanned platform to estimate biophysical and geometrical parameters of olive trees under different irrigation regimes. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0210804"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yao, X., Wang, N., Liu, Y., Cheng, T., Tian, Y., Chen, Q., and Zhu, Y. (2017). Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery. Remote Sens., 9.","DOI":"10.3390\/rs9121304"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Peng, X., Han, W., Ao, J., and Wang, Y. (2021). Assimilation of LAI derived from UAV multispectral data into the SAFY model to estimate maize yield. Remote Sens., 13.","DOI":"10.3390\/rs13061094"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106603","DOI":"10.1016\/j.compag.2021.106603","article-title":"Improving estimation of LAI dynamic by fusion of morphological and vegetation indices based on UAV imagery","volume":"192","author":"Qiao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, S., Yuan, F., Ata-UI-Karim, S.T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., and Cao, Q. (2019). Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sens., 11.","DOI":"10.3390\/rs11151763"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-021-00789-4","article-title":"Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season","volume":"17","author":"Gong","year":"2021","journal-title":"Plant Methods"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Martins, L., Castro, J.P., and Gouveia, M.E. (2013, January 9\u201312). Biological control of chestnut blight in Portugal. Proceedings of the II European Congress on Chestnut 1043, Debrecen, Hungary.","DOI":"10.17660\/ActaHortic.2014.1043.5"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"293","DOI":"10.3934\/microbiol.2017.2.293","article-title":"Culturable bacterial diversity from the chestnut (Castanea sativa Mill.) phyllosphere and antagonism against the fungi causing the chestnut blight and ink diseases","volume":"3","author":"Valverde","year":"2017","journal-title":"AIMS Microbiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1111\/mpp.12542","article-title":"Cryphonectria parasitica, the causal agent of chestnut blight: Invasion history, population biology and disease control","volume":"19","author":"Rigling","year":"2018","journal-title":"Mol. Plant Pathol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1081\/PLN-120017662","article-title":"Assessment and description of magnesium deficiencies in chestnut groves","volume":"26","author":"Portela","year":"2003","journal-title":"J. Plant Nutr."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Aebi, A., Sch\u00f6nrogge, K., Melika, G., Alma, A., Bosio, G., Quacchia, A., Picciau, L., Abe, Y., Moriya, S., and Yara, K. (2006). Parasitoid recruitment to the globally invasive chestnut gall wasp Dryocosmus kuriphilus. Galling Arthropods and Their Associates, Springer.","DOI":"10.1007\/4-431-32185-3_9"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10340-017-0857-9","article-title":"Assessing the impact of Dryocosmus kuriphilus on the chestnut tree: Branch architecture matters","volume":"91","author":"Gehring","year":"2018","journal-title":"J. Pest Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.scienta.2006.01.025","article-title":"Non-destructive leaf area estimation in chestnut","volume":"108","author":"Serdar","year":"2006","journal-title":"Sci. Hortic."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1051\/fruits\/2008049","article-title":"Leaf area estimation in some species of fruit tree by using models as a non-destructive method","volume":"64","author":"Demirsoy","year":"2009","journal-title":"Fruits"},{"key":"ref_41","first-page":"111","article-title":"Leaf area index (LAI), production and silvicultural practice in European chestnut (Castanea sativa Mill.) monocultures","volume":"31","year":"2004","journal-title":"Folia Oecologica"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s00468-014-1115-x","article-title":"Estimation of foliage clumping from the LAI-2000 Plant Canopy Analyzer: Effect of view caps","volume":"29","author":"Chianucci","year":"2015","journal-title":"Trees"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/S0378-1127(00)00326-1","article-title":"New management options in chestnut coppices: An evaluation on ecological bases","volume":"141","author":"Cutini","year":"2001","journal-title":"For. Ecol. Manag."},{"key":"ref_44","first-page":"116","article-title":"Production of the aboveground dendromass of European chestnut (Castanea sativa Mill.) in relation to leaf area index and climatic conditions","volume":"32","year":"2005","journal-title":"Folia Oecologica"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1051\/forest:2006002","article-title":"Age-related physiological and structural traits of chestnut coppices at the Castelli Romani Park (Italy)","volume":"63","author":"Covone","year":"2006","journal-title":"Ann. For. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Manetti, M., Pelleri, F., Becagli, C., Conedera, M., Schleppi, P., and Zingg, A. (2013, January 9\u201312). Growth dynamics and leaf area index in chestnut coppices subjected to a new silvicultural approach: Single-tree-oriented management. Proceedings of the II European Congress on Chestnut 1043, Debrecen, Hungary.","DOI":"10.17660\/ActaHortic.2014.1043.15"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1023\/A:1008997625523","article-title":"Plant species diversity changes in abandoned chestnut (Castanea sativa) groves in southern France","volume":"10","author":"Gondard","year":"2001","journal-title":"Biodivers. Conserv."},{"key":"ref_48","first-page":"357","article-title":"Small format aerial photography to assess chestnut ink disease","volume":"73","author":"Martins","year":"2001","journal-title":"For. Snow Landsc. Res"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Vannini, A., Vettraino, A., Fabi, A., Montaghi, A., Valentini, R., and Belli, C. (2004, January 20\u201323). Monitoring ink disease of chestnut with the airborne multispectral system ASPIS. Proceedings of the III International Chestnut Congress 693, Chaves, Portugal.","DOI":"10.17660\/ActaHortic.2005.693.68"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/10106049709354595","article-title":"Monitoring diseases of chestnut stands by small format aerial photography","volume":"12","author":"Ambrosini","year":"1997","journal-title":"Geocarto Int."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10658-007-9155-3","article-title":"Assessment of the spread of chestnut ink disease using remote sensing and geostatistical methods","volume":"119","author":"Martins","year":"2007","journal-title":"Eur. J. Plant Pathol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Castro, J., Azevedo, J., and Martins, L. (2009, January 13\u201316). Temporal analysis of sweet chestnut decline in northeastern Portugal using geostatistical tools. Proceedings of the I European Congress on Chestnut-Castanea 2009 866, Cuneo-Torino, Italy.","DOI":"10.17660\/ActaHortic.2010.866.53"},{"key":"ref_53","unstructured":"Martins, L., Castro, J.P., Macedo, F., Marques, C., and Abreu, C.G. (2005, January 16). \u00cdndices espectrais em fotografia a\u00e9rea de infravermelho pr\u00f3ximo na monitoriza\u00e7\u00e3o da doen\u00e7a tinta do castanheiro. Proceedings of the 5\u00ba Congresso Florestal Nacional. SPCF-Sociedade Portuguesa de Ci\u00eancias Florestais, Instituto Polit\u00e9cnico de Viseu, Viseu, Portugal."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40663-015-0035-6","article-title":"Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps","volume":"2","author":"Montagnoli","year":"2015","journal-title":"For. Ecosyst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Prada, M., Cabo, C., Hern\u00e1ndez-Clemente, R., Hornero, A., Majada, J., and Mart\u00ednez-Alonso, C. (2020). Assessing Canopy Responses to Thinnings for Sweet Chestnut Coppice with Time-Series Vegetation Indices Derived from Landsat-8 and Sentinel-2 Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12183068"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Marchetti, F., Waske, B., Arbelo, M., Moreno-Ru\u00edz, J.A., and Alonso-Benito, A. (2019). Mapping Chestnut stands using bi-temporal VHR data. Remote Sens., 11.","DOI":"10.3390\/rs11212560"},{"key":"ref_57","first-page":"184","article-title":"Chestnut health monitoring by aerial photographs obtained by unnamed aerial vehicle","volume":"38","author":"Martins","year":"2015","journal-title":"Rev. De Ci\u00eancias Agr\u00e1rias"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Hru\u0161ka, J., Bessa, J., Ad\u00e3o, T., Martins, L.M., Gon\u00e7alves, J.A., Peres, E., Sousa, A.M., Castro, J.P., and Sousa, J.J. (2017). Multi-temporal analysis of forestry and coastal environments using UASs. Remote Sens., 10.","DOI":"10.3390\/rs10010024"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Marques, P., P\u00e1dua, L., Ad\u00e3o, T., Hru\u0161ka, J., Peres, E., Sousa, A., and Sousa, J.J. (2019). UAV-based automatic detection and monitoring of chestnut trees. Remote Sens., 11.","DOI":"10.3390\/rs11070855"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Marques, P., Ad\u00e3o, T., Hru\u0161ka, J., Peres, E., Morais, R., Sousa, A., and Sousa, J.J. (2018, January 20\u201322). UAS-based imagery and photogrammetric processing for tree height and crown diameter extraction. Proceedings of theInternational Conference on Geoinformatics and Data Analysis, Prague, Czech Republic.","DOI":"10.1145\/3220228.3220241"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Marques, P., Martins, L., Sousa, A., Peres, E., and Sousa, J.J. (2020). Monitoring of chestnut trees using machine learning techniques applied to UAV-based multispectral data. Remote Sens., 12.","DOI":"10.3390\/rs12183032"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Fernandez-Manso, A., Cifuentes, J., Sanz-Ablanero, E., and Quintano, C. (2021, January 1\u20135). Forest damage monitoring in South-Western Europe based on data from Unmanned Aerial Vehicles (UAV). Proceedings of the Applications of Digital Image Processing XLIV, San Diego, CA, USA.","DOI":"10.1117\/12.2593300"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating multispectral images and vegetation indices for precision farming applications from UAV images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_64","unstructured":"Instituto Nacional de Estat\u00edstica, I. P. (2019). Estat\u00edsticas Agr\u00edcolas 2018."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Albetis, J., Duthoit, S., Guttler, F., Jacquin, A., Goulard, M., Poilv\u00e9, H., F\u00e9ret, J.B., and Dedieu, G. (2017). Detection of Flavescence dor\u00e9e grapevine disease using unmanned aerial vehicle (UAV) multispectral imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040308"},{"key":"ref_66","unstructured":"Rouse, J.W., Haas, R.H., Schell, J., and Deering, D. (1973). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, Texas A & M University, Remote Sensing Center. Contractor Report."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_69","unstructured":"Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T. (2000, January 16\u201319). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating PAR absorbed by vegetation from bidirectional reflectance measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_72","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/2151237X.2007.10129236","article-title":"Adaptive thresholding using the integral image","volume":"12","author":"Bradley","year":"2007","journal-title":"J. Graph. Tools"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TPAMI.2003.1177156","article-title":"A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions","volume":"25","author":"Maurer","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0165-1684(94)90060-4","article-title":"Topographic distance and watershed lines","volume":"38","author":"Meyer","year":"1994","journal-title":"Signal Process."},{"key":"ref_76","unstructured":"R Development Core Team (2022). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_77","unstructured":"Kuhn, M. (2022). Caret: Classification and Regression Training, Available online: https:\/\/cran.r-project.org\/package=caret."},{"key":"ref_78","unstructured":"Hamner, B., and Frasco, M. (2018). Metrics: Evaluation Metrics for Machine Learning, Available online: https:\/\/cran.r-project.org\/package=Metrics."},{"key":"ref_79","unstructured":"Solla, S., Leen, T., and M\u00fcller, K. (1999). The Relevance Vector Machine. Proceedings of the Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Livingstone, D.J. (2009). Bayesian Regularization of Neural Networks. Artificial Neural Networks: Methods and Applications, Humana Press.","DOI":"10.1007\/978-1-60327-101-1"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1214\/009053604000001147","article-title":"Spike and slab variable selection: Frequentist and Bayesian strategies","volume":"33","author":"Ishwaran","year":"2005","journal-title":"Ann. Stat."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Marques, P., Martins, L., Sousa, A., Peres, E., and Sousa, J.J. (October, January 26). Estimation of Leaf Area Index in Chestnut Trees using Multispectral Data from an Unmanned Aerial Vehicle. Proceedings of the IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324614"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_84","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_85","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","article-title":"Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method","volume":"165","author":"Liang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_88","first-page":"102","article-title":"Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index","volume":"32","author":"Tian","year":"2016","journal-title":"Trans. Chin. Soc. Agric. Eng."}],"container-title":["Drones"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-446X\/6\/12\/422\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:42:44Z","timestamp":1760146964000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-446X\/6\/12\/422"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":88,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["drones6120422"],"URL":"https:\/\/doi.org\/10.3390\/drones6120422","relation":{},"ISSN":["2504-446X"],"issn-type":[{"value":"2504-446X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,16]]}}}