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Red palm weevil (RPW) is widely considered a key pest of palms, creating extensive damages to the date palm trunk that inevitably leads to palm death if no pest eradication is done. This study evaluates the potential of a remote sensing approach for the timely and reliable detection of RPW infestation on the palm canopy. For two consecutive years, an experimental field with infested and control palms was regularly monitored by an Unmanned Aerial Vehicle (UAV) carrying RGB, multispectral, and thermal sensors. Simultaneously, detailed visual observations of the RPW effects on the palms were made to assess the evolution of infestation from the initial stage until palm death. A UAV-based image processing chain for nondestructive RPW detection was built based on segmentation and vegetation index analysis techniques. These algorithms reveal the potential of thermal data to detect RPW infestation. Maximum temperature values and standard deviations within the palm crown revealed a significant (α = 0.05) difference between infested and non-infested palms at a severe infestation stage but before any visual canopy symptoms were noticed. Furthermore, this proof-of-concept study showed that the temporal monitoring of spectral vegetation index values could contribute to the detection of infested palms before canopy symptoms are visible. The seasonal significant (α = 0.05) increase of greenness index values, as observed in non-infested trees, could not be observed in infested palms. These findings are of added value for steering management practices and future related studies, but further validation of the results is needed. The workflow and resulting maps are accessible through the Mapeo® visualization platform.

This paper gives an overview of the new COmpact hyperSpectral Imaging (COSI) system recently developed at the Flemish Institute for Technological Research (VITO, Belgium) and suitable for remotely piloted aircraft systems. A hyperspectral dataset captured from a multirotor platform over a strawberry field is presented and explored in order to assess spectral bands co-registration quality. Thanks to application of line based interference filters deposited directly on the detector wafer the COSI camera is compact and lightweight (total mass of 500g), and captures 72 narrow (FWHM: 5nm to 10 nm) bands in the spectral range of 600-900 nm. Covering the region of red edge (680 nm to 730 nm) allows for deriving plant chlorophyll content, biomass and hydric status indicators, making the camera suitable for agriculture purposes. Additionally to the orthorectified hypercube digital terrain model can be derived enabling various analyses requiring object height, e.g. plant height in vegetation growth monitoring. Geometric data quality assessment proves that the COSI camera and the dedicated data processing chain are capable to deliver very high resolution data (centimetre level) where spectral information can be correctly derived. Obtained results are comparable or better than results reported in similar studies for an alternative system based on the Fabry–Pérot interferometer.

Phoenix palms cover more than 1.3 million hectares in the Mediterranean, Middle East, and North Africa regions and they represent highly valued assets for economic, environmental, and cultural purposes. Despite their importance, information on the number of palm trees and the palm distribution across different scenes is difficult to obtain and, therefore, limited. In this work, we present the first region-wide spatial inventory of Phoenix dactylifera (date palm) and Phoenix canariensis (canary palm) trees, based on remote imagery from the Alicante province in Spain. A deep learning architecture that was based on convolutional neural networks (CNN) was implemented to generate a detection model able to locate and classify individual palms trees from aerial high-resolution RGB images. When considering that creating large labeled image datasets is a constraint in object detection applied to remote sensing data, as a strategy for pre-training detection models on a similar task, imagery and palm maps from the autonomous community of the Canary Islands were used. Subsequently, these models were transferred for re-training with imagery from Alicante. The best performing model was capable of mapping Phoenix palms in different scenes, with a changeable appearance, and with varied ages, achieving a mean average precision (mAP) value of 0.861. In total, 511,095 Phoenix palms with a probability score above 0.5 were detected over an area of 5816 km2. The detection model, which was obtained from an out-of-the-box object detector, RetinaNet, provides a fast and straightforward method to map isolated and densely distributed date and canary palms—and other Phoenix palms. The inventory of palm trees established here provides quantitative information on Phoenix palms distribution, which could be used as a baseline for long-term monitoring of palms’ conditions. In addition to boosting palm tree inventory across multiple landscapes at a large scale, the detection model demonstrates how image processing techniques that are based on deep learning leverage image understanding from remote sensing data.

Controlling fire blight in pear production areas depends strongly on regular visual inspections of pome fruit orchards, nurseries and other hosts of Erwinia amylovora. In addition, these inspections play an essential role in delineating fire blight free production areas, which has important implications for fruit export. However, visual monitoring is labor intensive and time consuming. As a potential alternative, the performance of spectral sensors on unmanned airborne vehicles (UAV) or drones was evaluated, since this allows the monitoring of larger areas compared to the current field inspections. Unlike more traditional remote sensing platforms such as manned aircrafts and satellites, UAVs offer a higher flexibility and an extremely high level of detail. In this project, a UAV platform carrying a hyperspectral COSI-cam camera was used to map a heavily infected pear orchard. The hyperspectral data were used to assess which wavebands contain information on fire blight infections. In this study, wavelengths 611 nm and 784 nm were found appropriate to detect symptoms associated with fire blight. Vegetation indices that allow to discriminate between healthy and infected trees were identified, too. This manuscript highlights the potential use of the UAV methodology in fire blight detection and remaining difficulties that still need to be overcome for the technique to become fully operational in practice.

High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and remote imagery methods have been proposed to estimate flower cluster numbers, but their overall performance is still far from satisfactory. For other methods, the performance of the method to estimate flower clusters within a tree is unknown since they were only tested on images from one perspective. One of the main reported bottlenecks is the presence of occluded flowers due to limitations of the top-view perspective of the platform-sensor combinations. In order to tackle this problem, the multi-view perspective from the Red−Green−Blue (RGB) colored dense point clouds retrieved from drone imagery are compared and evaluated against the field-based flower cluster number per tree. Experimental results obtained on a dataset of two pear tree orchards (N = 144) demonstrate that our 3D object-based method, a combination of pixel-based classification with the stochastic gradient boosting algorithm and density-based clustering (DBSCAN), significantly outperforms the state-of-the-art in flower cluster estimations from the 2D top-view (R2 = 0.53), with R2 > 0.7 and RRMSE < 15%.

Early and precise spatio-temporal monitoring of tree vitality is key for steering management decisions in pome fruit orchards. Spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while manned aircraft sensor-platform systems are very expensive. In order to address the shortcomings of these platforms, this study investigates the potential of Remotely Piloted Aircraft Systems (RPAS) to facilitate rapid, low cost, and flexible chlorophyll monitoring. Due to the complexity of orchard scenery a robust chlorophyll retrieval model on RPAS level has not yet been developed. In this study, specific focus therefore lies on evaluating the sensitivity of retrieval models to confounding factors. For this study, multispectral and hyperspectral imagery was collected over pome fruit orchards. Sensitivities of both univariate and multivariate retrieval models were demonstrated under different species, phenology, shade, and illumination scenes. Results illustrate that multivariate models have a significantly higher accuracy than univariate models as the former provide accuracies for the canopy chlorophyll content retrieval of R2 = 0.80 and Relative Root Mean Square Error (RRMSE) = 12% for the hyperspectral sensor. Random forest regression on multispectral imagery (R2 > 0.9 for May, June, July, and August, and R2 = 0.5 for October) and hyperspectral imagery (0.6 < R2 < 0.9) led to satisfactory high and consistent accuracies for all months.

Apple scab causes significant losses in the production of this fruit. A timely and more site-specific monitoring and spraying of the disease could reduce the number of applications of fungicides in the fruit industry. The aim of this leaf-scale study therefore lies in the early detection of apple scab infections in a non-invasive and non-destructive way. In order to attain this objective, fluorescence- and hyperspectral imaging techniques were used. An experiment was conducted under controlled environmental conditions, linking hyperspectral reflectance and fluorescence imaging measurements to scab infection symptoms in a susceptible apple cultivar (Malus x domestica Borkh. cv. Braeburn). Plant stress was induced by inoculation of the apple plants with scab spores. The quantum efficiency of Photosystem II (PSII) photochemistry was derived from fluorescence images of leaves under light adapted conditions. Leaves inoculated with scab spores were expected to have lower PSII quantum efficiency than control (mock) leaves. However, besides scab-induced, also immature leaves exhibited low PSII quantum efficiency. Therefore, this study recommends the simultaneous use of fluorescence imaging and hyperspectral techniques. A shortwave infrared narrow-waveband ratio index (R1480/R2135) is presented in this paper as a promising tool to identify scab stress before symptoms become visible to the naked eye. Low PSII quantum efficiency attended by low narrow waveband R1480/R2135 index values points out scab stress in an early stage. Apparent high PSII quantum efficiency together with high overall reflectance in VIS and SWIR spectral domains indicate a severe, well-developed scab infection.

Appropriate management of (semi-)natural areas requires detailed knowledge of the ecosystems present and their status. Remote sensing can provide a systematic, synoptic view at regular time intervals, and is therefore often suggested as a powerful tool to assist with the mapping and monitoring of protected habitats and vegetation. In this study, we present a multi-step mapping framework that enables detailed NATURA 2000 (N2000) heathland habitat patch mapping and the assessment of their conservation status at patch level. The method comprises three consecutive steps: (1) a hierarchical land/vegetation type (LVT) classification using airborne AHS imaging spectroscopy and field reference data; (2) a spatial re-classification to convert the LVT map to a patch map based on life forms; and (3) identification of the N2000 habitat type and conservation status parameters for each of the patches. Based on a multivariate analysis of 1325 vegetation reference plots acquired in 2006–2007, 24 LVT classes were identified that were considered relevant for the assessment of heathland conservation status. These labelled data were then used as ground reference for the supervised classification of the AHS image data to an LVT classification map, using Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Search feature selection. Overall classification accuracies for the LVT mapping varied from 83% to 92% (Kappa ≈ 0.82–0.91), depending on the level of detail in the hierarchical classification. After converting the LVT map to a N2000 habitat type patch map, an overall accuracy of 89% was obtained. By combining the N2000 habitat type patch map with the LVT map, two important conservation status parameters were directly deduced per patch: tree and shrub cover, and grass cover, showing a strong similarity to an independent dataset with estimates made in the field in 2009. The results of this study indicate the potential of imaging spectroscopy for detailed heathland habitat characterization of N2000 sites in a way that matches the current field-based workflows of the user.

Very few attempts have been made to qualify and quantify the contribution of fruits to the hyperspectral canopy signal. The interference of fruit may influence the effectiveness of spectral indices and radiative transfer models used to monitor leaf-related biochemical and biophysical parameters. In a series of experiments, the spectral characteristics (350 to 2,500 nm range) of fruit and its contributions to the canopy signature were investigated. Leaf, fruit, and canopy spectra were collected in a "Midknight Valencia" orange orchard, evaluated, and cross-referenced against biophysical and biochemical tree characteristics in order to gain a better insight in the contribution of citrus fruits to canopy spectral reflectance. Results indicate that the presence of fruits leads to major reflectance decreases in the infrared regions (700 to 2,500 nm) of the electromagnetic spectrum. This may be explained by, on the one hand a partial obstruction of the volume scattering in the canopy, and on the other hand the significant contribution of the orange fruits to the overall equivalent canopy water thickness. In the visible (vis: 350 to 700 nm) domain, the impact of the presence of fruit is much less evident. The occurrence of medium and heavy crop loads did not affect the visible reflectance signature significantly but was indirectly evidenced by leaf chlorosis mainly due to competition for nitrogen between the canopy elements. The knowledge gained from this study will contribute to better hyperspectral vegetation index design and improved radiative transfer models for fruit bearing perennial woody crops. What is more, the direct biochemical linkages between water, nitrogen, fruit biomass, and spectral characteristics are an important precursor for the development and implementation of an air- or space-borne crop load monitoring system for citrus orchards. [ABSTRACT FROM AUTHOR]

The sub-pixel spectral contribution of background soils and shadows hampers the accurate site-specific monitoring of agricultural crop characteristics from aerial or satellite images. To address this problem, the present study combines measured in situ and hyperspectral data in an alternative unmixing algorithm. The proposed algorithm, referred to as Soil Modeling Mixture Analysis (SMMA), incorporates a soil reflectance model in a traditional unmixing algorithm and as such opens up the opportunity to simultaneously extract the sub-pixel spatial extent of crops as well as its pure spectral information. The performance of the algorithm is evaluated using a soil moisture reflectance model, calibrated for an in situ measured Albic Luvisol dataset. Synthetic mixtures, i.e., compiled from in situ measured hyperspectral bare soil and citrus tree canopy spectra, were decomposed and the sub-pixel crop cover fractions (R² > 0.94, RMSE <0.03) and pure vegetation signals (average extraction error 350 to 2,500 nm = 0.017, RMSE = 0.02) were adequately extracted from the mixtures. [ABSTRACT FROM AUTHOR]

Abstract: This contribution describes the distribution of marine salt aerosols in Belgium, France and Spain, as obtained from applying a kriging model to a set of data. The data was collected over a period of nine years and included wet as well as dry deposition results. It was found that the concentration of the salt particles decreased the fastest in Spain and the slowest in Belgium with increasing inland distance from the sea. These findings have implications for the degradation of monuments and historical buildings as a result of salt attack and ingress that often accompany degradation due to air pollution. [Copyright &y& Elsevier]

A paradoxical embolus associated with orthodeoxia-platypnea syndrome and intracardiac shunting is extremely uncommon. We present a patient who was found to have a positional change in desaturation after a right pneumonectomy who suffered from gangrene of the right foot and simultaneous deep venous thrombosis of the left arm. Workup revealed a patent foramen ovale as a cause for both the right-to-left shunt and the paradoxical emboli. After percutaneous closure the orthodeoxia resolved. This case highlights the necessity of heightened awareness of this syndrome in case of severe hypoxemia after pneumonectomy and the importance of an occult patent foramen ovale. [Copyright &y& Elsevier]