The monitoring of migratory geese at known stopover sites is crucial to their habitat conservation but usually requires skilled manpower for counting large flocks of waterfowl. The use of observations from UAVs (unmanned aerial vehicles, a.k.a. drones) is a potential alternative to traditional bird counting methods. We used a multicopter-type UAV with a well-stabilized camera to count greater white-fronted geese (Anser albifrons) that seasonally roost in Lake Miyajima-numa, Hokkaido, Japan. Since the geese roost at sundown, we had to find good camera settings that enabled the detection of geese on the lake under dim light conditions. The key camera setting was a very long explosion time of half a second, which allowed us to detect and count geese up to about 30 minutes after sunset. A single UAV flight could observe the entire lake from an altitude of 100 m above the water surface with little disturbance to the roosting geese.

We used a cascade classifier, which is a machine leaning technique, to automatically count geese in the imagery. The counting accuracy ranged from －4.1 % to＋6.1 % in four validation cases compared with manual counts on the UAV image. We conclude that the combination of UAV and machine leaning methods can yield goose counts with an accuracy of ±15 %. The results suggest that this approach will be useful for monitoring geese or other waterfowl.

Mangrove is a general term for plants that inhabit coastal intertidal zones in the tropical to subtropical regions. We focused on the green infrastructure function of this mangrove wetland. In addition to ecological restoration, we are also supporting restoration activities in the coastal area of Vietnam from the viewpoint of Eco-DRR (Ecosystem-based Disaster Risk Reduction), which contributes to mitigating the effects of climate change. The purpose of using remote sensing (partially GIS database) for this project is to detect primary mangrove habitats by analyzing past satellite images before the coastal area was developed. In this activity, we regarded the native mangrove area as the suitable place for the original mangrove habitat (a place suitable for wetland restoration), and used the image analysis results in the selection of plantations effectively. The most part of the native mangrove area detected as a result of this analysis was concentrated in the coastal delta (especially near the estuary where the island is located offshore). This approach of integrating native (potential) mangrove habitats with huge amounts of big data to detect natural restoration sites in a wide area can reduce the cost of field surveys. Therefore, future application in nature restoration is strongly expected.

Satellite remote sensing data, which has proven to be an effective tool in archaeological studies of Egypt, is currently being applied in the Saudi-Japanese Archaeological Mission at al-Hawra’ on the Red Sea coast. Despite being a well-known historical site of maritime trade and religious pilgrimage, al-Hawra’ has remained basically unsurveyed, and its structure has not been clarified. Therefore, a color-enhanced WorldView-2 image and digital surface model (DSM) generated from the image data were used to develop a hypothesis of how the site was formed. In this process, we focused on terrain features such as channels shaped by flowing rainwater. The analysis results led to the discovery of a previously unknown port area, designated "SJ06", adjacent to the inner area of the present-day port. Based on the archaeological assemblage in the settlement area, SJ06 was dated back to between the 9th and 12th centuries, which seems to agree with historical sources. The empirical method used in this study is expected to be effective for exploring other port city sites along the Red Sea coast that share a common natural environment, including high rocky mountains and low-elevation desert margins.