Alonso J., 2011. Modelación de procesos hidrológicos asociados a la forestación con Eucalyptus en el Uruguay. Facultad de Ingeniería, Universidad de la República.
Altmann A., Toloşi L., Sander O., Lengauer T., 2010. Permutation importance: A corrected feature importance measure. Bioinformatics, 26(10): 1340-1347. https://doi.org/10.1093/bioinformatics/btq134
Alvares C.A., Mattos E.M. de, Campoe O.C., Marrichi A.H.C., Stape J.L., 2015. Uso de sensoriamento remoto na estimativa do índice de área foliar em Eucalyptus. XVII Simpósio Brasileiro de Sensoriamento Remoto, pp. 6429-6436.
Ariza Salamanca A.J., Navarro-Cerrillo R.M., Bonet-García F.J., Pérez-Palazón M.J., Polo M.J., 2019. Integration of a Landsat time-series of NBR and hydrological modeling to assess Pinus pinaster Aiton. Forest Defoliation in South-Eastern Spain. Remote Sensing, 11(19): 2291. https://doi.org/10.3390/rs11192291
Castaño J.P., Giménez A., Ceroni M., Furest J., Aunchayna R., Bidegain M., 2011. Caracterización agroclimática del Uruguay 1980-2009. Serie Técnica INIA, 193: 33.
De Almeida A.Q., Ribeiro A., Delgado R.C., Rody Y.P., De Oliveira A.S., Leite F.P., 2015. Índice de área foliar de eucalyptus estimado por índices de vegetação utilizando imagens TM - landsat 5. Floresta e Ambiente, 22: 368-376. https://doi.org/10.1590/2179-8087.103414
De Godoy Goergen L.C., De Vargas Kilca R., Da Silva Narvaes I., Silva M.N., Silva E.A., Pereira R.S., Adami M., 2016. Distinção de espécies de eucalipto de diferentes idades por meio de imagens TM/Landsat 5. Pesquisa Agropecuaria Brasileira, 51: 53-60. https://doi.org/10.1590/S0100-204X2016000100007
Fassnacht F.E., Hartig F., Latifi H., Berger C., Hernández J., Corvalán P., Koch B., 2014. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154: 102-114. https://doi.org/10.1016/j.rse.2014.07.028
Frazier A.E., Hemingway B.L., 2021. A technical review of planet smallsat data: Practical considerations for processing and using planetscope imagery. Remote Sensing, 13(19): 3930. https://doi.org/10.3390/rs13193930
Gitelson A.A., Merzlyak M.N., 1997. Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18: 2691-2697.
Gitelson A.A., Merzlyak M.N., 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22(5): 689-692. https://doi.org/10.1016/S0273-1177(97)01133-2
Gleason C.J., Im J., 2012. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment, 125: 80-91. https://doi.org/10.1016/j.rse.2012.07.006
Hopkinson C., Chasmer L., 2009. Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sensing of Environment, 113(1): 275-288. https://doi.org/10.1016/j.rse.2008.09.012
Huete A., Van Leeuwen W., 1999. MODIS vegetation index (MOD13). Algorithm Theoretical Basis Document, 3(213): 295-309.
Ingram J.C., Dawson T.P., Whittaker R.J., 2005. Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sensing of Environment, 94(4): 491-507. https://doi.org/10.1016/j.rse.2004.12.001
Jain P., Coogan S.C.P., Subramanian S.G., Crowley M., Taylor S., Flannigan M.D., 2020. A review of machine learning applications in wildfire science and management. Environmental Reviews, 28(4): 478-505. https://doi.org/10.1139/er-2020-0019
Jensen J.L.R., Humes K.S., Vierling L.A., Hudak A.T., 2008. Discrete return lidar-based prediction of leaf area index in two conifer forests. Remote Sensing of Environment, 112(10): 3947-3957. https://doi.org/10.1016/j.rse.2008.07.001
Jonckheere I., Fleck S., Nackaerts K., Muys B., Coppin P., Weiss M., Baret F., 2004. Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121(1-2): 19-35. https://doi.org/10.1016/j.agrformet.2003.08.027
Jordan C.F., 1969. Derivation of leaf‐area index from quality of light on the forest floor. Ecology, 50(4): 663-666.
Lary D.J., Alavi A.H., Gandomi A.H., Walker A.L., 2016. Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1): 3-10. https://doi.org/10.1016/j.gsf.2015.07.003
Le Maire G., Marsden C., Nouvellon Y., Stape J.L., Ponzoni F.J., 2012. Calibration of a species-specific spectral vegetation index for leaf area index (LAI) monitoring: Example with MODIS reflectance time-series on eucalyptus Plantations. Remote Sensing, 4(12): 3766-3780. https://doi.org/10.3390/rs4123766
Luo S., Wang C., Xi X., Nie S., Fan X., Chen H., Yang X., Peng D., Lin Y., Zhou G., 2019. Combining hyperspectral imagery and LiDAR pseudo-waveform for predicting crop LAI, canopy height and above-ground biomass. Ecological Indicators, 102: 801-812. https://doi.org/10.1016/j.ecolind.2019.03.011
Megown R.A., Webster M., Jacobs S., 1999. Using Landsat TM imagery to estimate LAI in a Eucalyptus plantation. 1-13.
Mesas-Carrascosa F.J., Castillejo-González I.L., De la Orden M.S., Porras A.G.F., 2012. Combining LiDAR intensity with aerial camera data to discriminate agricultural land uses. Computers and Electronics in Agriculture, 84: 36-46. https://doi.org/10.1016/j.compag.2012.02.020
Morsdorf F., Kötz B., Meier E., Itten K.I., Allgöwer B., 2006. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sensing of Environment, 104(1): 50-61. https://doi.org/10.1016/j.rse.2006.04.019
Mountrakis G., Im J., Ogole C., 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3): 247-259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
Pavithra B., Kalaivani K., Ulagapriya K., 1998. Remote sensing techniques for mangrove mapping. International Journal of Engineering and Advanced Technology, 8: 27-30.
Pearse G.D., Morgenroth J., Watt M.S., Dash J.P., 2017. Optimising prediction of forest leaf area index from discrete airborne lidar. Remote Sensing of Environment, 200: 220-239. https://doi.org/10.1016/j.rse.2017.08.002
Peduzzi A., Wynne R.H., Fox T.R., Nelson R.F., Thomas V.A., 2012. Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data. Forest Ecology and Management, 270: 54-65. https://doi.org/10.1016/j.foreco.2011.12.048
Planet Labs, 2018. Precision Ag insights from frequent imaging smarter farming throughout the season. 22.
R Core Development Team, 2013. A language and environment for statistical computing. 1.
Rouse jr. J.W., Haas R.H., Schell J.A., Deering D.W., 1973. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation.
Scurlock J.M.O., Asner G.P., Gower S.T., 2001. Worldwide historical estimates of leaf area index, 1932–2000. ORNL/TM-2001/268, 34. https://doi.org/0RNL/TM-2001/268
Shen X., Cao L., Chen D., Sun Y., Wang G., Ruan H., 2018. Prediction of forest structural parameters using airborne full-waveform LiDAR and hyperspectral data in subtropical forests. Remote Sensing, 10(11): 1729. https://doi.org/10.3390/rs10111729
Solberg S., Næsset E., Hanssen K.H., Christiansen E., 2006. Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sensing of Environment, 102(3-4): 364-376. https://doi.org/10.1016/j.rse.2006.03.001
Staben G., Lucieer A., Scarth P., 2018. Modelling LiDAR derived tree canopy height from Landsat TM, ETM+ and OLI satellite imagery - A machine learning approach. International Journal of Applied Earth Observation and Geoinformation, 73: 666-681. https://doi.org/10.1016/j.jag.2018.08.013
Sumnall M., Peduzzi A., Fox T.R., Wynne R.H., Thomas V.A., Cook B., 2016a. Assessing the transferability of statistical predictive models for leaf area index between two airborne discrete return LiDAR sensor designs within multiple intensely managed Loblolly pine forest locations in the south-eastern USA. Remote Sensing of Environment, 176: 308-319. https://doi.org/10.1016/j.rse.2016.02.012
Sumnall M.J., Fox T.R., Wynne R.H., Blinn C., Thomas V.A., 2016b. Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar. International Journal of Remote Sensing, 37: 78-99. https://doi.org/10.1080/01431161.2015.1117683
Tavares Júnior I da S., Torres C.M.M.E., Leite H.G., Castro N.L.M. de, Soares C.P.B., Castro R.V.O., Farias A.A., 2020. Machine learning: Modeling increment in diameter of individual trees on Atlantic Forest fragments. Ecological Indicators, 117: 106685. https://doi.org/10.1016/j.ecolind.2020.106685
Tesfamichael S.G., van Aardt J., Roberts W., Ahmed F., 2018. Retrieval of narrow-range LAI of at multiple lidar point densities: Application on Eucalyptus grandis plantation. International Journal of Applied Earth Observation and Geoinformation, 70: 93-104. https://doi.org/10.1016/j.jag.2018.04.014
Tseng Y.H., Lin L.P., Wang C.K., 2016. Mapping CHM and LAI for heterogeneous forests using airborne full-waveform LiDAR data. Terrestrial, Atmospheric and Oceanic Sciences, 27: 537-548. https://doi.org/10.3319/TAO.2016.01.29.04(ISRS)
Watson D.J., 1947. 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. Annals of Botany, 11: 41-76. https://doi.org/10.1093/oxfordjournals.aob.a083148
Weiss M., Baret F., Smith G.J., Jonckheere I., Coppin P., 2004. Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling. Agricultural and Forest Meteorology, 121: 37-53. https://doi.org/10.1016/j.agrformet.2003.08.001
Whitehead D., Beadle C.L., 2004. Physiological regulation of productivity and water use in Eucalyptus: A review. Forest Ecology and Management, 193: 113-140. https://doi.org/10.1016/j.foreco.2004.01.026
Xue J., Su B., 2017. Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017. https://doi.org/10.1155/2017/1353691
Yan G., Hu R., Luo J., Weiss M., Jiang H., Mu X., Xie D., Zhang W., 2019. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology, 265: 390-411. https://doi.org/10.1016/j.agrformet.2018.11.033
Yuan H., Yang G., Li C., Wang Y., Liu J., Yu H., Feng H., Xu B., Zhao X., Yang X., 2017. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models. Remote Sensing, 9. https://doi.org/10.3390/rs9040309
Zaletnyik P., Laky S., Toth C., 2010. LIDAR waveform classification using self-organizing map. American Society for Photogrammetry and Remote Sensing Annual Conference 2010: Opportunities for Emerging Geospatial Technologies, 2: 1055-1066.
Zhao K., Popescu S., 2009. Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA. Remote Sensing of Environment, 113: 1628-1645. https://doi.org/10.1016/J.RSE.2009.03.006
Zhou H., Wang C., Zhang G., Xue H., Wang J., Wan H., 2020. Generating a spatio-temporal complete 30 m leaf area index from field and remote sensing data. Remote Sensing, 12: 2394. https://doi.org/10.3390/rs12152394
Zhou Y., Qiu F., 2015. Fusion of high spatial resolution WorldView-2 imagery and LiDAR pseudo-waveform for object-based image analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 101: 221-232. https://doi.org/10.1016/j.isprsjprs.2014.12.013