Ground control point (GCP) is an important calibration factor when correcting position information during unmanned aerial vehicle (UAV) remote sensing. Studies of the optimal number and distribution shape of GCPs have been conducted worldwide in recent years. However, when conducting surveys at houses, construction sites, farming lands, forests, and some other locations, it is both difficult and destructive to install GCP inside the subject area. In many cases, it is only possible to install GCP at the outer edge around the area. Therefore, this study aims to suggest the optimal GCP distribution pattern, which can provide the highest accuracy, when only the outer edge of a particular area is available. In this research, 88 GCP patterns have been validated and compared at an 18 ha farm. Results show that the patterns with GCPs distributed evenly around the field provided the best calibration (RMSE = 0.15 m). If this kind of pattern is not achievable because of obstructions, patterns with GCPs distributed evenly around half of the field or forming an evenly distributed triangle can provide moderate accuracy (RMSE = 0.18 m and 0.43 m, respectively). Patterns with GCPs forming a straight line yielded the worst accuracy (RMSE = 2.10 m). This shows that GCP distributions of a two-dimensional shape, even if the surrounding area is small, are better calibrated than a long, straight line. These results strongly suggest that appropriate GCP distribution patterns in the study areas will provide satisfactory accuracy for constructing integrated monitoring systems of diverse resources.

Identification and monitoring of diverse resources or wastes on the ground is important for integrated resource management. The unmanned aerial vehicle (UAV), with its high resolution and facility, is the optimal tool for monitoring ground objects accurately and efficiently. However, previous studies have focused on applying classification methodology on land use and agronomy, and few studies have compared different classification methods using UAV imagery. It is necessary to fully utilize the high resolution of UAV by applying the classification methodology to ground object identification. This study compared three classification methods: A. NDVI threshold, B. RGB image-based machine learning, and C. object-based image analysis (OBIA). Method A was the least time-consuming and could identify vegetation and soil with high accuracy (user’s accuracy > 0.80), but had poor performance at classifying dead vegetation, plastic, and metal (user’s accuracy < 0.50). Both Methods B and C were time- and labor-consuming, but had very high accuracy in separating vegetation, soil, plastic, and metal (user’s accuracy ≥ 0.70 for all classes). Method B showed a good performance in identifying objects with bright colors, whereas Method C showed a high ability in separating objects with similar visual appearances. Scientifically, this study has verified the possibility of using the existing classification methods on identifying small ground objects with a size of less than 1 m, and has discussed the reasons for the different accuracy of the three methods. Practically, these results help users from different fields to choose an appropriate method that suits their target, so that different wastes or multiple resources can be monitored at the same time by combining different methods, which contributes to an improved integrated resource management system.

With reference to the "Shiga Prefecture Municipal Transition Map (1)", old town/village boundaries in Shiga Prefecture between 1868 and 1879 were reconstructed by processing subregional polygon data from three nationwide censuses: National Census, Census of Agriculture and Forestry, and Census of Economy. In all cases based on sub-regional polygon data, more than 90% of the old town/village boundaries could be reconstructed using attribute information alone. For 10% of the old towns and villages, supplementary information was needed to reconstruct the boundaries. There was less need for supplementary information when reconstructing boundaries from the data of Census of Economy and the data of Census of Agriculture and Forestry. The old town/village boundaries created from the Census of Economy data were most similar to the shape of the town/ village boundaries in the "Shiga Prefecture Municipal Transition Map (1)". These results suggest that the sub-regional data of the Census of Economy are the most suitable for reconstructing the old town/village boundaries of the early Meiji period in Shiga Prefecture.