Climate change may drive the range expansion of invasive species. Integrations of spatially explicit information are useful for preventing and managing. In this study, we used MaxEnt and MigClim modelling approaches to map current and future distribution dynamic of the invasive plant, Mimosa diplotricha. Then, structural and functional connectivity was integrated to develop the prevention strategies under climate change scenarios. The results showed that the suitable niche of M. diplotricha was a warm environment with the large variation in day-night temperature. The range of this species may increase due to a warming climate, with future expansion to the north of current suitable habitats. However, the critical routes of dispersal is likely to vary with the degree of warming. In conclusion, regardless of the warming scenario, the northern edge of the present habitat should be prioritized for control. In addition, as the warming increases, more attention should be paid to the defense of invasion risk areas in the future.

The use of satellite imagery to reconstruct 3D building models has gradually been discussed. Satellite imagery has a wide coverage and high temporal resolution, so there are certain advantages for reconstructing 3D building models. This research focuses on image analysis based on high resolution optical satellite imagery and 3D building models reconstruction with an accuracy of the OGC CityGML LOD1 level. Deep learning technique is applied in this study to automatically extract building footprints from satellite images and remove excess areas that are not buildings. Next, Minimum Bounding Rectangle (MBR), regularization and generalization processing are utilized to shape the more regular and square building polygons. Finally, sub-structures on the upper floor of each building are extracted, and the elevation data is used to fit the height of each polygon with the RANSAC (RANdom SAmple Consensus) algorithm to reconstruct block-based 3D building models. After corrections and removing outliers, the accuracy of the reconstructed 3D building models conforms to the OGC CityGML LOD1 specification.

The study proposes a feature relationship algorithm (FRA) to establish wireframe models from object point clouds. FRA applies a scanning ball to explore local point cloud geometry for determining vertex and edge feature points and further reforms the spatial connections to reconstruct the wireframe model of the object automatically. Experiments on different variables were conducted to show insights into the effeteness of various point quality for FRA parameter configuration. Validations on data acquired from simulated and actual scans showed promising results, in which the RMSE of the model vertices achieved 5 mm in BIM-to-point cloud cases, and the RMSE of the real point cloud with a point interval of 3 cm data case comes to 4.3 cm. The FRA framework is expected to improve the automated level of point cloud modeling.

In this study, we used the supervised image classification to interpret the Formosat-5 image in Yunlin area during the first season of 2018 and 2020. We used the function of QGIS Zonal Statistics to retrieve the spatial information of rice inside and outside the Golden Corridor, respectively. Based on the Land Subsidence Monitoring Wells data collected from the Water Resources Agency, we discussed the relationship between the changes in the rice area and land subsidence. We found that the area of the first crop of 2020 was significantly less than 2018 within the Golden Corridor. We also found that the decrease in the depth of land subsidence of the inner side, was also more significant than that of the outer area. At a 95% confidence level, the results of the t-test shows significant differences in the reduction of rice area and land subsidence on both the inner and outer sides of the Golden Corridor. The results also suggested that the policy of converting rice paddy fields to dry land farming is probably effective in reducing land subsidence.