UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitièresNumerous agronomical applications of remote sensing have been proposed in recent years, including water stress assessment at field by thermal imagery. The miniaturization of thermal cameras allows carrying them onboard the unmanned aerial vehicles (UAVs), but these systems have no temperature control and, consequently, drifts during data acquisition have to be carefully corrected. This manuscript presents a comprehensive methodology for radiometric correction of UAV remotely-sensed thermal images to obtain (combined with visible and near-infrared data) multispectral ortho-mosaics, as a previous step for further image-based assessment of tree response to water stress. On summer 2013, UAV flights were performed over an apple tree orchard located in Southern France, and 4 dates and 5 h of the day were tested. The 6400 m2 field plot comprised 520 apple trees, half well-irrigated and half submitted to progressive summer water stress. Temperatures of four different on-ground stable reference targets were continuously measured by thermo-radiometers for radiometric calibration purposes. By using self-developed software, frames were automatically extracted from the thermal video files, and then radiometrically calibrated using the thermal targets data. Once ortho-mosaics were obtained, root mean squared error (RMSE) was calculated. The accuracy obtained allowed multi-temporal mosaic comparison. Results showed a good relationship between calibrated images and on-ground data. Significantly higher canopy temperatures were found in water-stressed trees compared to well-irrigated ones. As high resolution field ortho-mosaics were obtained, comparison between trees opens the possibility of using multispectral data as phenotypic variables for the characterization of individual plant response to drought
The aim of this paper is to assess co-registration errors in remote imagery through the AUGEO system, which consists of geo-referenced coloured tarps acting as terrestrial targets (TT), captured in the imagery and semi-automatically recognised by AUGEO2.0 Ò software. This works as an add-on of ENVI Ò for image co-registration. To validate AUGEO, TT were placed in the ground, and remote images from satellite Quick Bird (QB), airplanes and unmanned aerial vehicles (UAV) were taken at several locations in Andalusia (southern Spain) in 2008 and 2009. Any geo-referencing system tested showed some error in comparison with the Differential Global Positioning System (DGPS)-geo-referenced verification targets. Generally, the AUGEO system provided higher geo-referencing accuracy than the other systems tried. The root mean square errors (RMSE) from the panchromatic and multi-spectral QB images were around 8 and 9 m, respectively and, once co-registered by AUGEO, they were about 1.5 and 2.5 m, for the same images. Overlapping the QB-AUGEO-geo-referenced image and the National Geographic Information System (NGIS) produced a RMSE of 6.5 m, which is hardly acceptable for precision agriculture. The AUGEO system efficiently geo-referenced farm airborne images with a mean accuracy of about 0.5-1.5 m, and the UAV images showed a mean accuracy of 1.0-4.0 m. The geo-referencing accuracy of an image refers to its consistency despite changes in its spatial resolution. A higher number of TT used in the geo-referencing process leads to a lower obtained RMSE. For example, for an image of 80 ha, about 10 and 17 TT were needed to get a RMSE less than about 2 and 1 m. Similarly, with the same number of TT, accuracy was higher for smaller plots as compared to larger plots. Precision agriculture requires high spatial resolution images (i.e., \1.5 m pixel -1 ), accurately geo-referenced (errors \1-2 m). With the current DGPS technology, satellite and airplane images hardly meet this geo-referencing requirement; consequently, additional co-registration effort is needed. This can be achieved using georeferenced TT and AUGEO, mainly in areas where no notable hard points are available.
Wheat and rice are two main staple food crops that may suffer from yield losses due to drought episodes that are increasingly impacted by climate change, in addition to new epidemic outbreaks. Sustainable intensification of production will rely on several strategies, such as efficient use of water and variety improvement. This review updates the latest findings regarding complementary approaches in agronomy, genetics, and phenomics to cope with climate change challenges. The agronomic approach focuses on a case study examining alternative rice water management practices, with their impact on greenhouse gas emissions and biodiversity for ecosystem services. The genetic approach reviews in depth the latest technologies to achieve fungal disease resistance, as well as the use of landraces to increase the genetic diversity of new varieties. The phenomics approach explores recent advances in high-throughput remote sensing technologies useful in detecting both biotic and abiotic stress effects on breeding programs. The complementary nature of all these technologies indicates that only interdisciplinary work will ensure significant steps towards a more sustainable agriculture under future climate change scenarios.
A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method’s efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified.
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