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

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.

Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

“…In image processing, the photos were geometrically aligned to build a point cloud, 3D model, digital elevation model (DEM), and digital surface model (DSM). For generating orthomosaic images, those composed of multispectral bands were radiometrically corrected to calibrate the reflectance values corresponding to each band, by using reflectance panels which were captured before each flight specific to the lighting conditions of the date, time, and location of the flight [38,39]. The use of a reflectance panel captured as an image of calibrating a white balance card enabled the PhotoScan to recognize the images' reflectance according to known values of all spectral bands written on the panel for radiometric calibration [38].…”

“…Although the enhancement at spatial and temporal resolution is one of the significant advantages for using UAS imagery, the spectral resolution by Parrot SEQUOIA is limited to capturing RGB, red edge, and NIR bands, with wider bandwidths in the wavelength in comparison to other airborne sensors and satellites which are required to detect more features with narrower and/or specific bandwidths. Nonetheless, the Parrot SEQUOIA has been widely used for a relatively good economic performance trade-off in operational forestry and agriculture applications, with the additional advantage of applying such high-resolution data to calibrate medium-resolution satellite data [39,49,50,70].…”

Editorial of Special Issue “Drones for Biodiversity Conservation and Ecological Monitoring”

“…A set of published articles under this special issue point out the important role of drone-borne multispectral images for validation and upscaling of satellite images and derived products. This is the case for the papers by Díaz-Delgado et al [5], Pla et al [6] and Canisius et al [7]. The first paper takes advantage of the high spatial resolution of multispectral images acquired over Doñana marshes to retrieve relevant monitoring indicators related to ecological integrity of the ecosystem.…”

“…The intermediate scale provided by multispectral drone images are used to enlarge ground-truth for Sentinel-2 images, which exposes a new methodological approach to assess the accuracy of large scale ecosystem essential variables provided by Earth observation satellites, such as the Copernicus European program or the international GEO-GEOSS. Similarly, Pla et al [6], used NDVI (Normalized Difference Vegetation Index) from multispectral images from a multirotor drone to evaluate damages by one threatened bird species on rice paddies. Fine grain damages accounted from drone images was later related to Sentinel-2 NDVI images in order to extend the assessment to larger areas.…”

Mapping of the Canopy Openings in Mixed Beech-Fir Forest at Sentinel-2 Subpixel level Using UAV and Machine Learning Approach

“…It is based on the bisensor approach; it takes advantage of the Sentinel 2 as an auxiliary source of information, very suitable for the large-scale mapping, and derived high-precision forest mask from the UAV imagery as ground truth, obtained on pre-defined training/validation locations inside FMU consisting of the mixed beech-fir forest. This approach of integration of the Sentinel-2 and UAV has been recently broadly implemented in various domains; in forestry, to estimate the aboveground biomass of Mangrove plantations [35], in agriculture to estimate rice crop damages [36] and for water quality monitoring [37]. This study aims to improve the spatial precision of the Sentinel-2, using its enhanced spectral properties that are suitable for capturing and quantifying the very fine shifts in the forest canopy cover at a subpixel level.…”