As the second largest island in Japan, Hokkaido provides precious land resources for the Japanese people. Meanwhile, as the food base of Japan, the gradual decrease of the agricultural population and more intensive agricultural practices on Hokkaido have led its arable land use to change year by year, which has also caused changes to the whole land use pattern of the entire island of Hokkaido. To realize the sustainable use of land resources in Hokkaido, past and future changes in land use patterns must be investigated, and target-based land use planning suggestions should be given on this basis. This study uses remote sensing and GIS technology to analyze the temporal and spatial changes of land use in Hokkaido during the past two decades. The types of land use include cultivated land, forest, waterbody, construction, grassland, and others, by using the satellite images of the Landsat images in 2000, 2010, and 2019 to achieve this goal to make classification. In addition, this study used the coupled Markov-FLUS model to simulate and analyze the land use changes in three different scenarios in Hokkaido in the next 20 years. Scenario-based situational analysis shows that the cultivated land in Hokkaido will drop by about 25% in 2040 under the natural development scenario (ND), while the cultivated land area in Hokkaido will remain basically unchanged in cultivated land protection scenario (CP). In forest protection scenario (FP), the area of forest in Hokkaido will increase by 1580.8 km2. It is believed that the findings reveal that the forest land in Hokkaido has been well protected in the past and will be protected well in the next 20 years. However, in land use planning for future, Hokkaido government and enterprises should pay more attention to the protection of cultivated land.

Object matching is critical for updating, maintaining, integrating, and quality assessing spatial data. However, matching data are often obtained from different sources and have problems of positional discrepancy and different levels of detail. To resolve these problems, this article presents a multiscale polygonal object-matching approach, called the minimum bounding rectangle combinatorial optimization (MBRCO) with spatial district (SD). This method starts with the MBRCO algorithm and its enhancement using the SD to find corresponding MBRs of one-to-one, one-to-many, and many-to-many matching pairs. Then, it aligns the MBRs of the matching pairs to identify objectmatching pairs, which are evaluated using a matching criterion to find geometrically corresponding objects. Our approach was experimentally validated using two topographical datasets at 1:2k and 1:10k. The proposed approach outperforms the common two-way area overlap method and another method based on the contextual information and relaxation labeling algorithm. The proposed method achieved accurate aggregation of the many-tomany matching pairs under the positional discrepancy scenario.

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