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Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

1
InForest JRU (CTFC-CREAF), Carretera de Sant Llorenç de Morunys Km 2, 25280 Solsona, Lleida, Spain
2
Centre for Ecological Research and Forestry Applications (CREAF), 08193 Cerdanyola del Vallès, Spain
3
Spanish National Research Council (CSIC), 08193 Cerdanyola del Vallès, Spain
4
Ebro Delta Natural Park, Government of Catalonia, Av. Catalunya, 46, 43580 Deltebre, Spain
5
Ministry of Territory and Sustainability, Government of Catalonia, Dr. Roux, 80, 08017 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Drones 2019, 3(2), 45; https://doi.org/10.3390/drones3020045
Received: 22 March 2019 / Revised: 23 April 2019 / Accepted: 13 May 2019 / Published: 21 May 2019
(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)
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Abstract

Making agricultural production compatible with the conservation of biological diversity is a priority in areas in which human–wildlife conflicts arise. The threatened Western Swamphen (Porphyrio porphyrio) feeds on rice, inducing crop damage and leading to decreases in rice production. Due to the Swamphen protection status, economic compensation policies have been put in place to compensate farmers for these damages, thus requiring an accurate, quantitative, and cost-effective evaluation of rice crop losses over large territories. We used information captured from a UAV (Unmanned Aerial Vehicle) equipped with a multispectral Parrot SEQUOIA camera as ground-truth information to calibrate Sentinel-2 imagery to quantify damages in the region of Ebro Delta, western Mediterranean. UAV vegetation index NDVI (Normalized Difference Vegetation Index) allowed estimation of damages in rice crops at 10 cm pixel resolution by discriminating no-green vegetation pixels. Once co-registered with Sentinel grid, we predicted the UAV damage proportion at a 10 m resolution as a function of Sentinel-2 NDVI, and then we extrapolated the fitted model to the whole Sentinel-2 Ebro Delta image. Finally, the damage predicted with Sentinel-2 data was quantified at the agricultural plot level and validated with field information compiled on the ground by Rangers Service. We found that Sentinel2-NDVI data explained up to 57% of damage reported with UAV. The final validation with Rangers Service data pointed out some limitations in our procedure that leads the way to improving future development. Sentinel2 imagery calibrated with UAV information proved to be a viable and cost-efficient alternative to quantify damages in rice crops at large scales. View Full-Text
Keywords: Sentinel; UAV; Parrot SEQUOIA; multispectral; vegetation indices; rice crops; western swamphen Sentinel; UAV; Parrot SEQUOIA; multispectral; vegetation indices; rice crops; western swamphen
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Pla, M.; Bota, G.; Duane, A.; Balagué, J.; Curcó, A.; Gutiérrez, R.; Brotons, L. Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio). Drones 2019, 3, 45.

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