Hyperspectral imaging in the SWIR wavelength region (1000 nm – 2500 nm) makes it possible to map mechanisms for recombination of photogenerated charge carriers in semiconductors. These mechanisms are linked to imperfections and impurities and lead to decreased performance in solar cells. The hyperspectral camera is mounted with a line laser at wavelength 808 nm, an energy high enough to excite electrons from the valence band to the conduction band in the Silicon material. The camera records radiative photoluminescence from the material, both the Silicon band to band recombination as well as recombinations from trapped electrons due to imperfections. In combination with advanced multivariate techniques for data analysis, this technology have been used to study defects in both multicrystalline and monocrystalline wafers and solar cells. Some of the mapped defects have been linked to well known mechanisms that could previously only be studied by destructive and time consuming methods. So far this technique has only been explored in research laboratories. The goal is however to be able to use this in line in solar cell processing. In this presentation it will also be discussed how this technology can be used to map degradation of outdoor solar panels.
To meet an increasing demand for food production there is a need for faster genetic gains in Norwegian cereal breeding. Yield gains can be improved by use of High-Throughput Phenotyping (HTP) based on multispectral imaging and application of genomic selection. Several spectral indices have been tested to estimate grain yield, such as the Normalized Differential Vegetation Index (NDVI) and MERIS Terrestrial Chlorophyll Index (MTCI). For the present work, data was gathered from a field trial with 96 plots of 24 wheat cultivars laid out in an alpha-lattice split plot design. The design had two levels of nitrogen (N) fertilization, 75 and 150 kg N/ha, applied at sowing. Also, a larger field trial with 301 breeding lines with two reps of high N fertilization was used. Multispectral images where taken in the wavebands green (550 nm), red (660 nm), red edge (735 nm) and near infrared (NIR) (790 nm) with a Parrot Sequoia multispectral camera combined with a sunshine sensor. This allows vegetation indices to be calculated. In addition, 3D models and Digital Surface Models (DSM) are used to estimate plant height. All cameras and sensors were mounted on a light Unmanned Aerial Vehicle (UAV). Images were taken at regular intervals throughout the growth season. The time series of the vegetation indices showed high values during the period of grain filling for wheat plots that received higher dose of fertilization. The values reached their peak around the period of grain filling before declining when plants approached maturity. For site B, the historical cultivars showed significant differences in NDVI and MTCI, but the indices were weakly correlated with grain yield. On site B, however, the large field with breeding lines, both vegetation indices were associated with grain yield with MTCI showing the strongest correlation coefficient of 0.49. The plant heights computed from the DSM showed deviations of 0.1 to 0.2 meters from the manual measurements, indicating that more sophisticated models are needed for reliable prediction of plant height.
The present research approach aims at analyzing the relation between material properties and their thermal behavior using airborne multispectral imaging in VIS/NIR and IR with sensors mounted on Unmanned Aerial Vehicle (UAV). As a follow up to a pilot study from spring 2016, a survey including several flights spanned over three days, from early morning before sunrise until late evening after sunset, was carried out in Athens in June 2017. The camera specifications for the survey in 2017 were different than the ones used in 2016. The performance of the cameras was evaluated, taking into account atmospheric correction. The images have been combined to form maps of surface temperature distribution and material physical properties. The VIS/NIR images were used to classify the different surface materials, to compute a map of estimated albedo, and to construct a 3D-model of the area. By combining thermal maps with material classification, albedo information and local weather data, thermal material properties could be characterized for the various materials. The derived properties from this dataset yield valuable information for improved simulation models of urban climate.
The properties of materials used in the urban fabric play an essential role to the microclimate. Their thermal performance, one of the main impacting factors to urban heat island effects, is mainly determined by the physical characteristics, optical and thermal. The present research approach aims at analyzing the relation between material properties and their thermal behavior using airborne multispectral imaging in VIS/NIR and IR with sensors mounted on Unmanned Aerial Vehicle (UAV), at a survey in Athens. The images have been combined to form maps of surface temperature distribution and of material properties. Normalized Differential Vegetation Index (NDVI) maps were computed from the VIS/NIR images and were used to classify the surface material. Calibration of the temperatures was obtained by applying correct emissivity for different materials from the classified surface material map. Maps of estimated albedo and of apparent thermal inertia were derived from the VIS/NIR images and the temperature images. Combining the surface temperature maps with maps of NDVI, albedo and apparent thermal inertia makes it is possible to identify reflectance characteristics of a variety materials utilized in the urban setting in Athens and to relate these to their thermal properties. The applied multisensory technique demonstrates how novel advances in sensor development combined with advanced data analysis provide new and unique tools for urban climate studies. The results give new perspectives of urban features for revealing the complex mechanisms that lead to microclimatic modifications and to quantify their relative contribution.