The objective of this study is to assess the distribution and expanding process of an alien-invasive plant: Leucaena leucocephala (LELE) based on remote sensing in the Hengchun Peninsula of Taiwan. This study developed a spatially explicit logistic regression imbedded cellular automaton (CA)-based species expansion model to simulate the invasion of LELE. The first, among the dynamics of LELE for 1988-1997, the invasion sites (from non-LELE to LELE) were 3865 ha, ccupying 6.5% of the study area. Second, 1988-1997 LELE spatial distributions were ingested into a logistic model to build transition rules for CA simulation. The results demonstrated that the enhanced CA model can precisely (an overall accuracy of 95%, with a kappa statistic of 0.8 by comparing with the 2007 LELE distribution) model the population expansion of LELE. The findings may provide insights into the control of LELE, which may be very important for other alien-invasive species research.

Mapping of geospatial earth surfaces has become increasingly important and been needed in many applications, such as disaster prevention, forest management, and monitoring of coastal erosion and sediment transport. Modeling the topography of bare earth and identifying land covers are essential for mapping the earth surfaces. This paper investigates modern small-footprint full-waveform airborne LiDAR systems for the mapping of geospatial earth surfaces in order to provide solutions to difficulties that a conventional discrete-return airborne LiDAR system often encounters. The research begins with the detection of points which are often missed by a discrete-return LiDAR system. The generation of a digital elevation model is then benefited from the enhanced geometry of landscapes. Another significant advantage of full-waveform data is that additional information characterizing different surface types can be extracted. Thus this research continues to identify the earth land covers using information extracted from waveform data.

A portable panoramic image mapping system (PPIMS) is developed to collect spatial data, which is specially designed for some areas that a vehicle-based mapping system has difficulty to enter. PPIMS can capture eight images simultaneously with e-GPS positioning. When images are taken from multiple stations, a large amount of images are needed to handle and measure. Finding targets among images becomes a puzzled task. To resolve this difficulty, this study proposes a new concept of photogrammetry by using panoramic images. Eight images captured by PPIMS can form a spherical panorama image (SPI), which is called PPIMS SPI. Instead of using the original images, PPIMS SPIs are then used for photogrammetric triangulation and mapping. Because the collinearity condition is not rigorously kept that causes the gaps on the cutting edges of overlapped images. We also propose a method to correct this deficiency. Two experiments were done. One is on the test field at Tzu-Chiang Campus, NCKU, and another one is in the Eternal Golden Castle. Both of them confirm bundle adjustment of SPI is possible, and applying corrections for PPIMS SPIs is necessary and effective for bundle adjustment. Besides, SPIs can replace original images that is also confirmed.

The vehicle-based and airborne mobile mapping system become a mature technology in recent years. The smartphone and wearable device are also gradually apply to our daily life. They have the potential to replace the past portable mobile mapping system with various and popular MEMS sensors. However, the inertial navigation is not suitable for pedestrian indoor navigation which generally using in vehicle-based and airborne mobile mapping system。This study proposes the indoor control points to replace the Global Navigation Satellite System in indoor. The indoor control points can be the ground control point, map point, feature point or provided by radio-based positioning technology. In addition, the walking velocity constraint and online smoothing are proposed for improve the accumulative error of inertial integration. The preliminary results show the proposed methods improve the accuracy of pedestrian indoor navigation with smartphone and inertial navigation significantly.

Most images for Image-Based Modeling (IBM) were taken from high resolution cameras in the past. With its widespread, cameras on smart mobile devices can be one type of image sources. The aim of this study is to build up an index of reconstruction quality which can calculate the error of the volume through the focal length. Thus, the index can estimate the accuracy of reconstructed models rapidly by multi-source images. This study uses various kinds of smartphones and a digital single lens reflex (DSLR) to take photographs of concrete specimens and to reconstruct 3D models. Focal lengths calculated by camera calibration are considered as the true value to estimate focal lengths accuracy by structure from motion (SfM) and then the volume error of the 3D models. Finally, the accuracy index of reconstructed models was established through a linear regression based on focal lengths and volume error. Owing to unknown true volume in most actual conditions, it becomes convenient to apply various mobile devices with the camera to take photographs and reconstructed models. According to the accuracy index, the error of earthwork quantity (Y) can be estimated by focal length (X), and the result can be the basis of disaster analysis and prevention measures aprior.