Detection and classification of Lepeophterius salmonis (Krøyer, 1837) using underwater hyperspectral imaging

Salmon louse, or sea lice, (Lepoptherius salmonis) represents practical, economical and fish welfare challenges for salmon farming (Hamre et al., 2013) and for the free-living stocks of salmon. There is an urgent need in the industry for a system that provides reliable numbers of louse on farmed salmon. Underwater hyperspectral imaging represents a potential new technique for louse counting in sea cages. In laboratory studies, the UHI technology could detect and classify pre-adults (pre-adult I and II), adult males and adult females (ovigerous) of sea lice based on the difference in their spectral characteristics. A model was built for detection of lice on the salmon and the UHI had a detection success ranging from 67 to 100 % with an average of 82%. A classification of the detected lice was performed for pre-adults, adult males and ovigerous lice and had a prediction accuracy of 85% when lice were divided into three groups (pre-adults, adult male and ovigerous lice) and 93% when lice were divided in two groups, ovigerous lice and all the other mobile lice (pre-adults and adult male). An automatic procedure for in situ measurements of louse infected salmon could deliver a data basis several times higher than the traditional counting system. The next generation of UHI louse detector should be developed with a higher spatial resolution to be able to detect also the sessile stages of lice. For succeeding with in situ classification of L. salmonis, correction algorithms to compensate for the impact of water between the UHI and lice need also to be developed.     

Evaluating Modified Atmospheric Correction Methods for Landsat Imagery: Image‐Based and Model‐Based Calibration Methods

Abstract

To increase the accuracy of remotely sensed data for agricultural forecasting, pixel values must be corrected for atmospheric effects and converted to spectral reflectance. The objective of this research was to compare two atmospheric correction methods of Landsat imagery under a range of atmospheric conditions. Ground‐based dark‐object subtraction (GDOS) is an image‐based calibration method that used in situ ground data that the dark‐object subtraction (DOS) method did not use, whereas atmospheric calibration (AC) is a model‐based calibration method that required a standard atmospheric profile refined with the use of in situ atmospheric data. GDOS and AC methods improved the reflectance values and had relationships with measured bands, which were approximately 1 to 1 in all bands. However, the GDOS generally had lower root‐mean‐square errors (RMSE) than AC. Data from this study suggest that at the present time the GDOS method may be more accurate than the AC method.     

IMAGE MAGNIFICATION IN DIGITAL FLUOROSCOPY

Image magnification is inherent in radiography. In digital fluoroscopy, the three components of magnification are geometric, electronic and photographic. In this study, the total magnification factor of a digital imaging system was determined by two methods, 1) comparison of measurements of a known object to its image and 2) calculation of geometric, electronic and photographic magnification from the imaging system specifications. Both methods were employed for various focal-film distances, image intensifier tube modes and laser printer formats. Results of these two methods were different due to the detrimental effect of penumbra on image quality with increasing magnification. If a radiographic image is to be used to approximate object size, then a technique should be used that will minimize magnification. In digital fluoroscopy this is achieved with the shortest object-film distance (assuming a fixed focal-object distance), largest image intensifler mode and greatest number of images per sheet of film.     

Differences in the forest landscape structure along the Finnish–Russian border in southern Karelia

Abstract

This study describes forest landscape fragmentation and connectivity along the Finnish–Russian border near the Karelian Isthmus. The landscape pattern was analysed using classification data based on Landsat ETM+ and Landsat TM images in combination with systematic surveys in Finland (Finnish National Forest Inventory) (n=546) and the authors’ own fieldwork data in Russia (n=101). On the Finnish side the forest patches are significantly smaller than on the Russian side. In addition, the Finnish forests landscape is more scattered and distances between patches of the same forest type are longer. The Russian side is more dominated by broadleaved and mixed forest stands. The disparities are due to differences in forestry policy and traditions of forest practices. The growing conditions of the areas are similar. The habitat fragmentation and habitat connectivity are important issues because the Karelian Isthmus is one of three main corridors and migrating routes connecting large Russian boreal taiga forests and their fauna and flora with Finnish isolated boreal forests.