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Drosophila suzukii has become a serious pest in Europe attacking many soft-skinned crops such as several berry species and grapevines since its spread in 2008 to Spain and Italy. An efficient and accurate monitoring system to identify the presence of Drosophila suzukii in crops and their surroundings is essential for the prevention of damage to economically valuable fruit crops. Existing methods for monitoring Drosophila suzukii are costly, time and labor intensive, prone to errors, and typically conducted at a low spatial resolution. To overcome current monitoring limitations, we are developing a novel system consisting of sticky traps which are monitored by means of Unmanned Aerial Vehicles (UAVs) and an image processing pipeline that automatically identifies and counts the number of Drosophila suzukii per trap location. To this end, we are currently collecting high resolution RGB imagery of Drosophila suzukii flies in sticky traps taken from both a static position (tripod) and from a UAV, which are then used as input to train deep learning models. Preliminary results show that a large part of the of Drosophila suzukii flies that are caught in the sticky traps can be correctly identified by the trained deep learning models. In the future, an autonomously flying UAV platform will be programmed to capture imagery of the sticky traps under field conditions. The collected imagery will be transferred directly to cloud-based storage for subsequent processing and analysis to identify the presence and count of Drosophila suzukii in near real time. This data will be used as input to a decision support system (DSS) to provide valuable information for farmers.

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In addition to single-angle reflectance data, multi-angular observations can be used as an additional information source for the retrieval of properties of an observed target surface. In this paper, we studied the potential of multi-angular reflectance data for the improvement of leaf area index (LAI) and leaf chlorophyll content (LCC) estimation by numerical inversion of the PROSAIL model. The potential for improvement of LAI and LCC was evaluated for both measured data and simulated data. The measured data was collected on 19 July 2016 by a frame-camera mounted on an unmanned aerial vehicle (UAV) over a potato field, where eight experimental plots of 30 × 30 m were designed with different fertilization levels. Dozens of viewing angles, covering the hemisphere up to around 30° from nadir, were obtained by a large forward and sideways overlap of collected images. Simultaneously to the UAV flight, in situ measurements of LAI and LCC were performed. Inversion of the PROSAIL model was done based on nadir data and based on multi-angular data collected by the UAV. Inversion based on the multi-angular data performed slightly better than inversion based on nadir data, indicated by the decrease in RMSE from 0.70 to 0.65 m2/m2 for the estimation of LAI, and from 17.35 to 17.29 μg/cm2 for the estimation of LCC, when nadir data were used and when multi-angular data were used, respectively. In addition to inversions based on measured data, we simulated several datasets at different multi-angular configurations and compared the accuracy of the inversions of these datasets with the inversion based on data simulated at nadir position. In general, the results based on simulated (synthetic) data indicated that when more viewing angles, more well distributed viewing angles, and viewing angles up to larger zenith angles were available for inversion, the most accurate estimations were obtained. Interestingly, when using spectra simulated at multi-angular sampling configurations as were captured by the UAV platform (view zenith angles up to 30°), already a huge improvement could be obtained when compared to solely using spectra simulated at nadir position. The results of this study show that the estimation of LAI and LCC by numerical inversion of the PROSAIL model can be improved when multi-angular observations are introduced. However, for the potato crop, PROSAIL inversion for measured data only showed moderate accuracy and slight improvements.

Optical remote sensing enables the estimation of crop parameters based on reflected light through empirical-statistical methods or inversion of radiative transfer models. Natural surfaces, however, reflect light anisotropically, which means that the intensity of reflected light depends on the viewing and illumination geometry. Therefore, reflectance anisotropy can be considered as an unwanted effect since it may lead to inaccuracies in parameter estimations. However, it can also be considered as information source due to its unique response to the optical and structural properties of the observed surface. In the past, reflectance anisotropy was studied by multi-angular reflectance measurements from space-borne or ground-based sensors. In this research, the opportunities of Unmanned Aerial Vehicles (UAVs) to collect multi-angular measurements were explored. The main results of this research show that multi-angular measurements can be done with UAVs and that the reflectance anisotropy signal can be used to improve the retrieval of crop parameters.

Surface moisture is an important supply limiting factor for aeolian sand transport, which is the primary driver of coastal dune development. As such, it is critical to account for the control of surface moisture on available sand for dune building. Optical remote sensing has the potential to measure surface moisture at a high spatio-temporal resolution. It is based on the principle that wet sand appears darker than dry sand: it is less reflective. The goals of this study are (1) to measure and model reflectance under controlled laboratory conditions as function of wavelength (l) and surface moisture (h) over the optical domain of 350-2500 nm, and (2) to explore the implications of our laboratory findings for accurately mapping the distribution of surface moisture under natural conditions. A laboratory spectroscopy experiment was conducted to measure spectral reflectance (1 nm interval) under different surface moisture conditions using beach sand. A non-linear increase of reflectance upon drying was observed over the full range of wavelengths. Two models were developed and tested. The first model is grounded in optics and describes the proportional contribution of scattering and absorption of light by pore water in an unsaturated sand matrix. The second model is grounded in soil physics and links the hydraulic behaviour of pore water in an unsaturated sand matrix to its optical properties. The optical model performed well for volumetric moisture content h v 24% (R2 w 0.97), but underestimated reflectance for h between 24-30% (R2 w 0.92), most notable around the 1940 nm water absorption peak. The soil-physical model performed very well (R2 w 0.99) but is limited to 4% w h v 24%. Results from a field experiment show that a short-wave infrared terrestrial laser scanner (l = 1550 nm) can accurately relate surface moisture to reflectance (standard error 2.6%), demonstrating its potential to derive spatially extensive surface moisture maps of a natural coastal beach.