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

Magda Pla,Gerard Bota,Jaume Balagué,Andrea Duane,Ricard Gutiérrez,Lluís Brotons,Antoni Curcó

doi:10.3390/drones3020045

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.

Highlights

Addressing human–wildlife conflicts is a fundamental challenge for conservation practitioners.In some areas of the planet, the loss of lives, crops, or live-stock because of wildlife has significantDrones 2019, 3, 45; doi:10.3390/drones3020045 www.mdpi.com/journal/dronesDrones 2019, 3, 45 consequences for people’s livelihoods and their food and agricultural security [1,2]
We found that Sentinel2-Normalized difference vegetation index (NDVI) data explained up to 57% of damage reported with Unmanned Aerial Vehicles (UAV)
We checked for model residuals distribution through a fitted residual plot, and we evaluated model goodness-of-fit through the marginal R2, conditional R2, and the Root Mean Square Error (RMSE)

Summary

Introduction

Addressing human–wildlife conflicts is a fundamental challenge for conservation practitioners.In some areas of the planet, the loss of lives, crops, or live-stock because of wildlife has significantDrones 2019, 3, 45; doi:10.3390/drones3020045 www.mdpi.com/journal/dronesDrones 2019, 3, 45 consequences for people’s livelihoods and their food and agricultural security [1,2]. Quantifying and mapping wildlife-caused damages is essential to carry out both kind of actions. Most methodologies are aimed at developing predictive risk maps [6], but in the context of human–wildlife conflicts, it is crucial to develop accurate protocols for the reliable verification of the authority of the causative species and their relation with damage claims [7]. These protocols are fundamental in creating public trust in the legitimacy of compensation programs, and in avoiding fraud and moral hazards. Medium resolution remote sensing imagery as low-cost Unmanned Aerial Vehicles (UAV) has arisen as an essential tool to meet this challenge

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