Estimativa da altura de variedades de cana-de-açúcar usando um Veículo Aéreo Não Tripulado (VANT) e integração com imagens de satélite
DOI:
https://doi.org/10.5902/2236499465070Palavras-chave:
VANT, Sensoriamento remoto, Estrutura do movimento, Altura do dosselResumo
O objetivo deste trabalho foi estimar a altura do dossel de três variedades de cana-de-açúcar em diferentes estágios fenológicos, utilizando dados de um VANT e avaliar sua relação com dois índices de vegetação (IVs) (NDVI e EVI) em diferentes resoluções espaciais (3m, 10m e 30m). Para o calcular os índices foram utilizadas imagens dos satélites PlanetScope, Sentinel-2 e Landsat 8, adquiridas o mais próximo possível da data do voo com o VANT. A altura estimada para cada talhão foi obtida pela subtração entre o MDS e MDT construídos a partir das imagens RGB do VANT, por meio da técnica SfM. As médias de cada altura estimada foram comparadas com médias obtidas em campo, a fim de se verificar a acurácia do modelo. Uma análise de correlação de Pearson e o coeficiente de Determinação (R²) foram calculados entre as alturas estimadas e os IVs. As médias de altura estimada e medidas em campo foram diferentes (p<0,05), com o modelo, geralmente, subestimando a altura. Todavia, os modelos de superfície da plantação conseguiram retratar a variabilidade espacial do talhão. É recomendado o uso de GCPs para reduzir os erros na estimativa. Em relação aos índices, a resolução espacial não exerceu influência na análise de correlação, com NDVI apresentando valores maiores que o EVI, com exceção da área A. Contudo, todos os valores, de ambos os coeficientes ficaram abaixo de 0,5 para todas as áreas. Ainda assim, se faz necessária uma análise temporal para compreender melhor a relação entre altura e os IVs. O potencial dos dados de UAV para o gerenciamento zonal deve ser abordado em estudos futuros.
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Referências
AASEN, H., et al. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing, v. 108, p. 245-259. Oct. 2015. DOI: https://doi.org/10.1016/j.isprsjprs.2015.08.002
ABDEL‐RAHMAN, E. M.; AHMED, F. B. The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature. International Journal of Remote Sensing, v. 29, n. 13, p. 3753-3767, 14 jun. 2008. DOI: https://doi.org/10.1080/01431160701874603
ADAMI, M. et al. Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in Brazil. Sustainability, v. 4, n. 4, p. 574-585, 2 apr. 2012. DOI: https://doi.org/10.3390/su4040574
AYOADE, J. O. Introdução à climatologia para os trópicos. 4. ed. Rio de Janeiro: Bertrand Brasil, 1996.
BÉGUÉ, A. et al. Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI. International Journal of Remote Sensing, v. 31, n. 20, p. 5391-5407, oct. 2010. DOI: https://doi.org/10.1080/01431160903349057
BENDIG, J.; BOLTEN, A.; BARETH, G. UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability. Photogrammetrie - Fernerkundung - Geoinformation, v. 2013, n. 6, p.551-562, dec. 2013. DOI: https://doi.org/10.1127/1432-8364/2013/0200
BENDIG, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal Of Applied Earth Observation And Geoinformation. v. 39, p.79-87, jul. 2015. DOI: https://doi.org/10.1016/j.jag.2015.02.012
BIAN, J. et al. Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery. Remote Sensing. v. 11, n. 3, p. 267-284, jan. 2019. DOI: https://doi.org/10.3390/rs11030267
BREUNIG, F. M. et al. Delineation of management zones in agricultural fields using cover–crop biomass estimates from PlanetScope data. International Journal Of Applied Earth Observation And Geoinformation. v. 85, p.102004-102019,mar. 2020a. DOI: https://doi.org/10.1016/j.jag.2019.102004
BREUNIG, F. M., et al. Assessing the Effect of Spatial Resolution on the Delineation of Management Zones for Smallholder Farming in Southern Brazil. Remote Sensing Applications: Society and Environment. , v. 19, p. 100325, aug. 2020b. DOI: https://doi.org/10.1016/j.rsase.2020.100325
CHU, T. et al. Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images. Remote Sensing, v. 9, n. 9, p.923-947, sep. 2017. DOI: https://doi.org/10.3390/rs9090923
CONAB - Companhia Nacional De Abastecimento. Acompanhamento da safra brasileira de cana-de-açúcar- Safra 2019/20. 4. ed. Brasília: Estúdio Nous, 2020. Available in: https://www.conab.gov.br/. Access: Oct. 17th 2020.
DAMIAN, J. M. et al. Monitoring variability in cash-crop yield caused by previous cultivation of a cover crop under a no-tillage system. Computers And Electronics In Agriculture, v. 142, p. 607-621, nov. 2017. DOI: https://doi.org/10.1016/j.compag.2017.11.006
DAMIAN, J. M. et al. Applying the NDVI from satellite images in delimiting management zones for annual crops. Scientia Agricola, v. 77, n. 1, p. 1-11, 2020. DOI: https://doi.org/10.1590/1678-992x-2018-0055
DUBREUIL, V., et al. The types of annual climates in Brazil: an application of the classification of Köppen from 1961 to 2015. Confins, n. 37, p.1-20, jan. 2018. In Portuguese. DOI: https://doi.org/10.4000/confins.15738
ELARAB, M. et al. Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. International Journal Of Applied Earth Observation And Geoinformation, v. 43, p.32-42, apr. 2015. DOI: https://doi.org/10.1016/j.jag.2015.03.017
EMPLASA. Região Metropolitana de Ribeirão Preto: estudos técnicos. 2016. Avaliable in: emplasa.sp.gov.br/Cms_Data/Contents/Emplasa/Media/publicacoes/RMRP_estudos_tecnicos.pdf. Acess: Nov. 30th 2020.
GALVÃO, L. S. et al. Crop Type Discrimination Using Hyperspectral Data: Advances and Perspectives, in Biophysical and Biochemical Characterization and Plant Species Studies, ed. by Alfredo Huete Prasad S. Thenkabail, John G. Lyon, 2nd ed. (Boca Raton, FL: CRC Press, 2018), p. 183–211. <https://doi.org/10.1201/9780429431180> DOI: https://doi.org/10.1201/9780429431180-6
GRÜNER, E.; ASTOR, T.; WACHENDORF, M. Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging, Agronomy, v. 9, n. 2, p.54-70, jan. 2019. DOI: https://doi.org/10.3390/agronomy9020054
HOFFMANN, H. et al. Crop water stress maps for an entire growing season from visible and thermal UAV imagery, Biogeosciences, v. 13, n. 24, p.6545-6563, dec. 2016. DOI: https://doi.org/10.5194/bg-13-6545-2016
HUETE, A.et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing Of Environment, v. 83, n. 1-2, p. 195-213, mar. 2002. DOI: https://doi.org/10.1016/S0034-4257(02)00096-2
HUNT E. R.; DAUGHTRY, C. S. T. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal Of Remote Sensing, v. 39, n. 15-16, p.5345-5376, dec. 2017. DOI: https://doi.org/10.1080/01431161.2017.1410300
IEA - INSTITUTO DE ECONOMIA AGRÍCOLA. Estatísticas da Produção Agrícola: Período de 2017 à 2018. 2018. Available at: http://ciagri.iea.sp.gov.br/nia1/subjetiva.aspx?cod_sis=1&idioma=1 Access: Jan.23rd. 2020.
IVUSHKIN, K. et al. UAV based soil salinity assessment of cropland, Geoderma, v. 338, p.502-512, mar. 2019. DOI: https://doi.org/10.1016/j.geoderma.2018.09.046
JUSTICE, C. O. et al. The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing, v.36, n.4, p.1228-1249, jul. 1998. DOI: https://doi.org/10.1109/36.701075
KROSS, A., et al. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops, International Journal of Applied Earth Observation and Geoinformation, v. 34, p. 235-248, feb. 2015. DOI: https://doi.org/10.1016/j.jag.2014.08.002
LEBLON, B.; GRANBERG, H.; CHARLAND, S.D. Shadowing effects on SPOT-HRV and high spectral resolution reflectances in Christmas tree plantations. International Journal Of Remote Sensing, v. 17, n. 2, p. 277-289, jan. 1996. DOI: https://doi.org/10.1080/01431169608949005
LIU, T. et al. Estimates of rice lodging using indices derived from UAV visible and thermal infrared images. Agricultural And Forest Meteorology, v. 252, p.144-154, jan. 2018. DOI: https://doi.org/10.1016/j.agrformet.2018.01.021
LUNA, I.; LOBO, A. Mapping Crop Planting Quality in Sugarcane from UAV Imagery: a pilot study in Nicaragua. Remote Sensing, v. 8, n. 6, p. 500-518, jun. 2016. DOI: https://doi.org/10.3390/rs8060500
MALAMBO, L. et al. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery, International Journal of Applied Earth Observation and Geoinformation, v. 64, fev. 2018. DOI: https://doi.org/10.1016/j.jag.2017.08.014
MARAFON, A. C. Análise Quantitativa de Crescimento em Cana de- açúcar: uma introdução ao procedimento prático. Aracaju: Embrapa Tabuleiros Costeiros, 29 p. (Documentos / Embrapa Tabuleiros Costeiros, ISSN 1678-1953; 168), 2012. Available at: http://www.cpatc.embrapa.br/publicacoes_2012/doc_168.pdf. Access: Jul.15rd. 2020
MULLA, D. J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps, Biosystems Engineering, v. 114, p. 358-371, apr. 2013. DOI: https://doi.org/10.1016/j.biosystemseng.2012.08.009
NEUHÄUSER M. Wilcoxon–Mann–Whitney Test. In: LOVRIC, M. (eds) International Encyclopedia of Statistical Science. Berlin, Heidelberg: Springer, 2011. DOI: https://doi.org/10.1007/978-3-642-04898-2_615
PATURKAR, A.; GUPTA, G.S.; BAILEY, D. Non-destructive and cost-effective 3D plant growth monitoring system in outdoor conditions. Multimedia Tools And Applications. v. 79, n. 47-48, p. 34955-34971, apr. 2020. DOI: https://doi.org/10.1007/s11042-020-08854-1
PAYERO, J. O.; NEALE, C. M. U.; WRIGHT, J. L. Comparison Of Eleven Vegetation Indices For Estimating Plant Height Of Alfalfa And Grass, Applied Engineering in Agriculture, v. 20, p. 385-393, 2004. DOI: https://doi.org/10.13031/2013.16057
QUEBRAJO, L. et al. Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet, Biosystems Engineering, v. 165, p.77-87, jan. 2018. DOI: https://doi.org/10.1016/j.biosystemseng.2017.08.013
ROSSI, M. Mapa pedológico do Estado de São Paulo: revisado e ampliado. São Paulo: Instituto Florestal, v.1. 118 p., 2017. (Inclui Mapas)
ROUSE, J.Wet al. Monitoring vegetation systems in the great plains with ERTS. In: Earth Resources Technology Satellite-1 Symposium, 3., Washington, D.C., 1973. Proceedings. Washington, D.C.: NASA. Goddart Space Flight Center, 1973. v.1, p.309-317. (NASA SP-351).
RUDORFF, B. F. T., et al. Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data. Remote Sensing, v. 2, n. 4, p. 1057-1076, apr. 2010. DOI: https://doi.org/10.3390/rs2041057
RUIZ, J.J et al. Evaluating the accuracy of DEM generation algorithms from UAV imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2013, 40, 333–337. DOI: https://doi.org/10.5194/isprsarchives-XL-1-W2-333-2013
SANCHES, G. M. et al. The potential for RGB images obtained using Unmanned Aerial Vehicle to assess and predict yield in sugarcane fields, International Journal Of Remote Sensing, v. 39, n. 15-16, p.5402-5414, mar. 2018. DOI: https://doi.org/10.1080/01431161.2018.1448484
SANTESTEBAN, L. G. et al. High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard, Agricultural Water Management, v. 183, p.49-59, mar. 2017. DOI: https://doi.org/10.1016/j.agwat.2016.08.026
SHAPIRO, S. S.; WILK, M. B. An analysis of variance test for normality (complete samples). Biometrika, 52 (3–4), p. 591–611, 1965. DOI: https://doi.org/10.1093/biomet/52.3-4.591
SOFONIA, J. et al. Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry. International Journal Of Applied Earth Observation And Geoinformation, v. 82, p.101878-101893, oct. 2019. DOI: https://doi.org/10.1016/j.jag.2019.05.011
SOUZA, C. H. W. et al. Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images. Computers and Electronics in Agriculture, v. 143, p.49-56, dec. 2017a. DOI: https://doi.org/10.1016/j.compag.2017.10.006
SOUZA, C. H. W.de et al. Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds. International Journal of Remote Sensing, v. 38, n. 8-10, p.2218-2230, jan. 2017b. DOI: https://doi.org/10.1080/01431161.2017.1285082
TODD, S. W.; HOFFER R. M.; MILCHUNAS D. G. Biomass estimation on grazed and ungrazed rangelands using spectral indices, International Journal of Remote Sensing, p. 427-438, jan. 1998. DOI: https://doi.org/10.1080/014311698216071
TUCKER, C. J. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing Of Environment, n. 8, p. 127-150, 1979. DOI: https://doi.org/10.1016/0034-4257(79)90013-0
TURNER, D.; LUCIEER, A.; WATSON, C. An automated technique for generating georectified mosaics from Ultra-high resolution Unmanned Aerial Vehicle (UAV) imagery, based on Structure from Motion (SfM) point clouds. Remote Sensing, v. 4, 1392–1410, 2012. DOI: https://doi.org/10.3390/rs4051392
WILLKOMM, M.; BOLTEN, A.; BARETH, G. Non-destructive monitoring of rice by hyperspectral in-field spectrometry and UAV-based remote sensing: Case study of field-grown rice in north Rhine-Westphalia, Germany, ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLI-B1, 1071–1077, 2016. DOI: https://doi.org/10.5194/isprsarchives-XLI-B1-1071-2016
YU, D. et al. Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations. European Journal of Agronomy. V. 121(126159), p. 1-16, 2020. DOI: https://doi.org/10.1016/j.eja.2020.126159
ZHANG, N.; WANG, M.; WANG, N. Precision agriculture—a worldwide overview, Computers And Electronics In Agriculture, v. 36, n. 2-3, p.113-132, nov. 2002. DOI: https://doi.org/10.1016/S0168-1699(02)00096-0
ZHOU, X et al. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery, ISPRS Journal Of Photogrammetry And Remote Sensing, v. 130, p.246-255, aug. 2017. DOI: https://doi.org/10.1016/j.isprsjprs.2017.05.003
ZHU, W. et al. Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales. Remote Sensing, v. 11, n. 22, p.2678-2700, nov. 2019. DOI: https://doi.org/10.3390/rs11222678
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