This study first presents a novel method based on the tensor voting framework for extracting building features from airborne LiDAR data. For the extraction of roof patches, geometric features of LiDAR points are represented by a tensor field. A region-growing method with principal features is developed from the properties of eigenvalues and eigenvectors of the tensor field. The extraction of ridge lines is then inferred from the segmented roof patches. On top of that, three new indicators of the strength of features are proposed to reduce the effect of the number of points on feature identification. Furthermore, Type I errors, Type II errors, fragmentation and discernment are used as quality indicators to represent the effectiveness of the proposed method. Next, we present an algorithm to integrate the LiDAR data and topographic maps for 3D building model reconstruction and develop a quality prediction indicator by the residual tensor analysis. To reduce influence of errors while integrating, a robust least squares method is applied to register boundary points extracted from LiDAR data and building outlines obtained from topographic maps. After registration, a quality indicator based on the tensor analysis of residuals is derived in order to evaluate the correctness of the automatic building model reconstruction. Finally, an actual dataset demonstrates the automatic model reconstruction quality of the predictions. The results show that our method can achieve reliable results and save both time and expense on model reconstruction.

Slope-safety factors associated with topography, geology, and vegetation, which can be considered as indices of the parameters of safety factors, is often quantified to assess their contributions to landslides. However, for large areas, variations in the parameters included for analyzing the safety factors are too large to quantify accurately. With advances in the efficiency and accuracy of investigative techniques, remote sensing technologies have become widely employed in assessing landslide hazards. In this paper, the effectiveness of using airborne laser altimeter retrieved data to evaluate landslide vegetation restoration is analyzed. For this purpose, satellite multi-spectrum image were integrated with airborne laser altimeter surface-roughness data to develop a method to characterize vegetation restoration on landslide areas. The analysis was performed in both 2D and 3D scales, the results of the integrated method suggest that airborne laser altimeter data can be used effectively for evaluating landslide vegetation restoration than using the NDVI, besides, the 3D vegetation index also shows the same characteristics and can describe the features of the vegetation effectively than using spectrum information only; the amplitude of the 3D vegetation index represents the vegetation condition both in the vertical and the horizontal dimension. The classification accuracy is improved from 72.61% for using traditional multispectral classification to 89.75% when using 3D vegetation index integrated with SPOT images. The results of the integrated method suggest that integrated method can be used effectively for evaluating landslide vegetation restoration.

Using the airbone LiDAR high-resolution DEM data to identify lineament structure provides an opportunity to generate more comprehensive structural maps. In this study, we use Mt. Xian-Du Landslide area (Hsiaolin Village) in Kaohsiung city and Guguan – Deji area in Central Cross-Island Highway as example to demonstrate how we applied the LiDAR DEM for field investigation. With the use of high-resolution DEM we identified a series of east-northeast to west-southwest trending structures in Mt. Xian-Du Landslide area. During field investigation we recognized characteristics of these structures, they are generally the strike-slip faults, and track its continuity, as well as other structural relationships. In the high- resolution DEM using of Guguan – Deji area, we observed the north-south and northeast – southwest trending structures. However, the structures in this area are complex in nature, and we cannot recognize the nature of structures directly by DEM. Using the high-resolution DEM can enhance the utility of remote sensing for structural mapping since these data sets enable the extraction of detailed information about both local and regional geological structures. However, field checking is always necessary before the structure.

Airborne LiDAR is an effective tool for recording details of landscape and producing high-resolution digital elevation models (DEM). Therefore, DoD (DEM of Difference) method a straight-forward method for conducting volumetric change of two different time with multitemporal DEMs. And, DoD becomes a basic tool for the analyses of landscape change, erosion and sedimentation, volume of landslides, and others. Nevertheless, the accuracy or quality of a DoD cannot remain the same for every grid pixels. For effective application, the main challenge is to understand and suppress the uncertainties caused by this quality problem. The purpose of this study is to explore the uncertainties caused by un-even distribution of point clouds and thus to propose a method using ground points to assess the uncertainties of DoD. The study area is located in Tseng-Wen Watershed of southern Taiwan. Two Airborne LiDAR surveys were conducted in January and October of 2012, respectively. The results show that 59.49% of the area of a DoD is under high uncertainties within which the number of ground points of a grid cell is low. High uncertainties and high risk can take place if volumetric measures are taken from these areas. An alternative approach for suppress the uncertainties is to apply a stencil of polygons generated from manual edited bare lands. Consequently, only 5.55% of the area of a DoD is under high uncertainties. It is concluded that using ground points of multi-temporal LiDAR survey, uncertainty can be effectively assessed to assure the quality of LiDAR DEMs and applications.

Satellite images have a widespread use in many fields such as natural science, environment detection, military monitoring, land use and land planning. However, the cloud cover is a problem in the usage of images acquired by optical sensors. The ground information in the regions covered by cloud will miss. Thus, cloud detection and removing is a fundamental research issue with many applications in the field of geodesic and remote sensing. In this study, a cloud removal approach based on information cloning is introduced. The approach removes cloud-contaminated portions of a satellite image, and then reconstructs the information of missing data utilizing temporal correlation of multi-temporal images. The basic idea is to clone information from cloud-free patches to their corresponding cloud-contaminated patches under the assumption that land covers change insignificantly over a short period of time. The patch-based information reconstruction is mathematically formulated as a Poisson equation and solved using a global optimization process. Thus, the proposed approach can potentially yield better results in terms of radiometric accuracy and consistency compared with related approaches. Some experimental analyses on sequences of images acquired by the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor are conducted. The experimental results show that the proposed approach can process large clouds in a heterogeneous landscape, which is difficult for cloud removal approaches. In addition, quantitative and qualitative analyses on simulated data with different cloud contamination conditions are conducted using quality index and visual inspection, respectively, to evaluate the performance of the proposed approach.