The increase of unused agricultural land has become an issue in the Kushiro River watershed in eastern Hokkaido. Information on the spatial distribution of unused agricultural land is important so that we may understand the cause of its abandonment and plan its effective utilization. Satellite remote sensing is an effective approach for mapping unused agricultural land. We classified agricultural land into unused agricultural land, grassland, cultivated land, or forest using Landsat 8 operational land imager (OLI) surface reflectance products (1∼7 bands), the normalized difference vegetation index (NDVI) derived from those products, and polygons of agricultural field in Tsurui Village. Landsat 8 OLI surface reflectance products observed during the snowy season, shortly before harvest season, and late in harvest season were used for the classification. We applied two supervised classification methods: Maximum Likelihood and Random Forest. Accuracy validation was performed based on field survey and Google Earth aerial photography and street views at 30 or more random points for each classification class. The best classification was obtained by Random Forest with the data obtained shortly before harvest season and late in harvest season, which showed 0.92 for overall accuracy (OA), 0.79 for Kappa coefficient (κ), and 0.82 and 0.70 for producer’s (PA) and user’s (UA) accuracy, respectively, with regard to the assigned class of the unused agricultural land. Furthermore, we classified all agricultural land in the entire watershed of the Kushiro River using the best classification method in Tsurui Village (under which the OA, κ, and PA and UA of the unused agricultural land class were 0.89, 0.71, 0.81 and 0.54, respectively). Finally, we mapped the percentage of unused agricultural land and renewable unused agricultural land for each 150m×150m mesh.

We investigated the difference in cloud characteristics (fraction and thermodynamic phase) between the Barents Sea (70-80°N, 0-40°E) and East Siberian Sea (70-80°N, 120-180°E) using remote sensing data from a Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in summer (JJA) and winter (DJF). In the Arctic region, the total cloud fraction is larger in summer than winter as a whole. The fraction of ice cloud layers between —25° and 0°Cwas clearly larger in the East Siberian Sea than in the Barents Sea in winter. The difference in the ice cloud fraction between the East Siberian Sea and Barents Sea was remarkable between —25° and —10°C, being about 20% greater in the East Siberian Sea in winter. The ice cloud layer fraction in the lower troposphere (under 2km) was larger in the East Siberian Sea than in the Barents Sea in winter. These results indicate that the ice formation process was promoted more in the lower troposphere in the East Siberian Sea than in the Barents Sea in winter. These are the first results obtained from observations of the cloud internal structure as viewed by the active sensor on board CALIPSO.

We developed a camera system that can acquire image data simultaneously in both visible and infrared bands. Two small-sized single-board computers (Raspberry Pi A+) were used to control the camera modules, obtain position data, and communicate with a PC control system on the ground. The control software on this system was developed and written in the Python programming language. Since the weight of this camera system is less than 300g including the battery, it can be mounted on a small-sized Unmanned Aerial Vehicle (UAV) platform. In order to use this camera system in various research fields, it is useful to convert the output digital number (DN) to represent the reflectance in each band. The reflectance of observed targets can be estimated from the measured relation between the input power and output DN of the camera system. The reflectance extracted from images taken by this camera system was compared with the reflectance measured by the spectral radiometer. Although the correlation of the two reflectances was high, the slope and offset of the regression line in some bands should be improved. This was probably caused by the estimated error of the spectral characteristics in each band, since the spectral characteristics of the camera modules were not available to the public. As an example of the application for this camera system, we introduced a Normalized Difference Vegetation Index (NDVI) measurement of a paddy field during the growth stage from May to October in 2016. It was found that the time series of NDVI change observed in the paddy field was similar to that observed in a past study.