Analysis of Multi-Spectral Images Acquired by UAVs to Monitor Water Stress of Citrus Orchards in Sicily, Italy
Publication: World Environmental and Water Resources Congress 2021
ABSTRACT
Citrus production is one of the most important agricultural activities in Sicily. Consequently, it also implies significant water consumption. Considering semi-arid climate of the island, it is also prone to droughts, and some areas suffer water scarcity problems. Climate change may also exacerbate the frequency of water shortages; hence, for adaptation, it is fundamental to improve irrigation efficiency. Various technologies are emerging to this aim: installation of tensiometers and flowmeters to compare water needs and consumption so as to suggest a correction of irrigation amounts. Unmanned aerial vehicles (UAVs) mounting thermal or multi-spectral cameras may allow also a rapid assessment of the plants’ stress potentially due to the need for irrigation. In this paper, we illustrate the results of preliminary research aimed at understanding whether multi-spectral images can be useful for monitoring plants’ stress in citrus orchards. In particular, images were acquired through a commercial drone, the Parrot Bluegrass, mounting the Sequoia multi-spectral camera. The four-band images have been combined to obtain several vegetation indexes (VIs): the normalized difference vegetation index (NDVI), the Green NDVI (GNDVI), the leaf chlorophyll index (LCI), the normalized difference red edge (NDRE), the structure intensive pigment index (SIPI2), and the modified chlorophyll absorption in reflectance (MCARI). The maps of the indices were pre-processed in GIS to obtain single averaged values per tree. Results show that the following indices are more suitable for identifying differences in the health of orchards: NDVI, GNDVI, and SIPI2. These three indices may help identifying areas of the orchard that received a surplus or a deficit of irrigation water, or other causes of stress, finally improving orchard management and, possibly, irrigation efficiency.
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REFERENCES
Ampatzidis, Y., and Partel, V. (2019). “UAV-based high throughput phenotyping in citrus utilizing multispectral imaging and artificial intelligence.” Remote Sensing, 11(4).
Ampatzidis, Y., Partel, V., Meyering, B., and Albrecht, U. (2019). “Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence.” Computers and Electronics in Agriculture, Elsevier, 164(7), 104900.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J. (2015). “System for Automated Geoscientific Analyses (SAGA) v. 2.1.4.” Geoscientific Model Development, 8(7), 1991–2007.
Ferrández-Villena, M., and Ruiz-Canales, A. (2017). “Advances on ICTs for water management in agriculture.” Agricultural Water Management, 183(2), 1–3.
Gago, J., Douthe, C., Coopman, R. E., Gallego, P. P., Ribas-Carbo, M., Flexas, J., Escalona, J., and Medrano, H. (2015). “UAVs challenge to assess water stress for sustainable agriculture.” Agricultural Water Management, Elsevier B.V., 153, 9–19.
Gilbert, N. (2012). “Water under pressure.” Nature, 483(7389), 256–257.
Osco, L. P., Marques Ramos, A. P., Saito Moriya, É. A., de Souza, M., Marcato Junior, J., Matsubara, E. T., Imai, N. N., and Creste, J. E. (2019). “Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images.” International Journal of Applied Earth Observation and Geoinformation, Elsevier, 83(5), 101907.
Osco, L. P., Ramos, A. P. M., Pinheiro, M. M. F., Moriya, É. A. S., Imai, N. N., Estrabis, N., Ianczyk, F., de Araújo, F. F., Liesenberg, V., de Castro Jorge, L. A., Li, J., Ma, L., Gonçalves, W. N., Marcato, J., and Creste, J. E. (2020). “A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements.” Remote Sensing, 12(6).
Peres, D. J., Modica, R., and Cancelliere, A. (2019). “Assessing future impacts of climate change on water supply system performance: Application to the Pozzillo Reservoir in Sicily, Italy.” Water (Switzerland), MDPI AG, 11(12).
Peres, D. J., Senatore, A., Nanni, P., Cancelliere, A., and Mendicino, G. (2020). “Evaluation of EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) historical simulations by high-quality observational datasets in southern Italy : insights on drought assessment.” 3057–3082.
Romero-Trigueros, C., Nortes, P. A., Alarcón, J. J., Hunink, J. E., Parra, M., Contreras, S., Droogers, P., and Nicolás, E. (2017). “Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing.” Agricultural Water Management, Elsevier B.V., 183, 60–69.
Santesteban, L. G., Di Gennaro, S. F., Herrero-Langreo, A., Miranda, C., Royo, J. B., and Matese, A. (2017). “High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard.” Agricultural Water Management, Elsevier, 183, 49–59.
Zarco-Tejada, P. J., González-Dugo, V., and Berni, J. A. J. (2012). “Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera.” Remote Sensing of Environment, Elsevier Inc., 117, 322–337.
Zarco-Tejada, P. J., Guillén-Climent, M. L., Hernández-Clemente, R., Catalina, A., González, M. R., and Martín, P. (2013). “Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV).” Agricultural and Forest Meteorology, Elsevier B.V., 171–172, 281–294.
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World Environmental and Water Resources Congress 2021
Pages: 270 - 278
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© 2021 American Society of Civil Engineers.
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Published online: Jun 3, 2021
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