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The accurate determination of the quality parameters of crops requires a spectral range from 400 nm to 2500 nm (Kawamura et al., 2010, Thenkabail et al., 2002). Presently the hyperspectral imaging systems that cover this wavelength range consist of several separate hyperspectral imagers and the system weight is from 5 to 15 kg. In addition the cost of the Short Wave Infrared (SWIR) cameras is high (∼50 k€). VTT has previously developed compact hyperspectral imagers for drones and Cubesats for Visible and Very near Infrared (VNIR) spectral ranges (Saari et al., 2013, Mannila et al., 2013, Näsilä et al., 2016). Recently VTT has started to develop a hyperspectral imaging system that will enable imaging simultaneously in the Visible, VNIR, and SWIR spectral bands. The system can be operated from a drone, on a camera stand, or attached to a tractor. The targeted main applications of the DroneKnowledge hyperspectral system are grass, peas, and cereals. In this paper the characteristics of the built system are shortly described. The system was used for spectral measurements of wheat, several grass species and pea plants fixed to the camera mount in the test fields in Southern Finland and in the green house. The wheat, grass and pea field measurements were also carried out using the system mounted on the tractor. The work is part of the Finnish nationally funded "DroneKnowledge - Towards knowledge based export of small UAS remote sensing technology" project. [ABSTRACT FROM AUTHOR]

Unmanned airborne systems (UAS) based remote sensing offers flexible tool for environmental monitoring. Novel lightweight Fabry-Perot interferometer (FPI) based, frame format, hyperspectral imaging in the spectral range from 400 to 1600 nm was used for identifying different species of trees in a forest area. To the best of the authors' knowledge, this was the first research where stereoscopic, hyperspectral VIS, NIR, SWIR data is collected for tree species identification using UAS. The first results of the analysis based on fusion of two FPI-based hyperspectral imagers and RGB camera showed that the novel FPI hyperspectral technology provided accurate geometric, radiometric and spectral information in a forested scene and is operational for environmental remote sensing applications. [ABSTRACT FROM AUTHOR]

The unmanned airborne system (UAS) remote sensing using lightweight multi- and hyperspectral imaging sensors offer new possibilities for the environmental monitoring applications. Based on the accurate measurements of the way in which the object reflect and emit energy, wide range of affecting variables can be monitored. Condition for reliable applications is reliable and accurate input data. In many applications, installation of geometric and radiometric reference targets in the object area is challenging, for instance, in forest or water areas. On the other hand, UASs are often operated in very poor conditions, under clouds or under variable cloud cover. Our objective is to develop an autonomous hyperspectral UAS imaging system and data processing chain that does not require any ground reference targets. Prerequisites for this kind of a system are an appropriate sensor setup, stable and well-calibrated instruments and rigorous data processing. In this paper, we will describe the new hyperspectral UAS imaging system that is under development. Important applications for the proposed system include precision agriculture, forest monitoring and water quality monitoring. Finally, we will consider the use of the system in forest inventory application and present the first results of the summer 2014 campaigns. [ABSTRACT FROM AUTHOR]

In an experimental set-up in and around Helsinki, Finland (60°N, 25°E), we have detected pest insect immigration using weather radars and insect traps in the field. This study was part of a project to develop a system to give warning of a possible arrival of long-range migrant insect pests. Bird-cherry aphid, Rhopalosiphum padi, and diamondback moth, Plutella xylostella, were found on the ground following migrations in warm airstreams at the end of May 2007. This migration episode was successfully forecast by the meteorologists in the project team. For the summer 2008, we developed a pest insect immigration alarm system based on SILAM, a Finnish Meteorological Institute atmospheric dispersion model. The first important pest insect immigration occurred in late June, bringing bird-cherry aphids. Our alarm system correctly produced a warning of this immigration. We studied the migration path in the observed events in 2007 and 2008 with the help of the atmospheric dispersion model. Weather radars frequently showed rain echo over the area, but there was also a lot of echoes originating from the migrating insects. Using the polarimetric weather radar in Helsinki, we could differentiate insects from other sources of echoes. Insects were common in layers below 1 km, and were observed up to height of about 2.5 km. Using Doppler weather radars we were able to observe the speed and direction of the migration. The experiment showed that an atmospheric dispersion model is an effective tool for predicting the movement of airborne migrants. The alarm system would work still better, if the sources of the immigrants were known in more detail. In addition, the very simple modelling of airborne migration should be refined. Weather radars, and especially polarimetric systems, are able to detect insect migrations and reveal details of the phenomenon not obtainable by other means. [ABSTRACT FROM AUTHOR]