This paper presents an example of using the ground-based synthetic aperture radar (GBSAR) technique for the emergency monitoring of a deep foundation excavation. The process includes a quality evaluation for the interferometric data acquired by the IBIS-L radar from Ingegneria Dei Sistemi S.p.A. Atmospheric effects were eliminated through calibration with ground control points (two triangular reflectors) to improve the measuring accuracy of the emergency monitoring. Accuracy of the data obtained was then compared at six check points between the GBSAR technique and a Leica total station TC2003 with prisms. The results indicate that the GBSAR technique is a suitable method of emergency displacement monitoring within a deep foundation excavation and has several significant advantages. These include a higher level of accuracy, near real-time monitoring intervals, and site-wide displacement maps obtained for each observation sampling interval.
Light detection and ranging (LiDAR) point cloud data can contain millions of point returns from a diverse range of surface features, and directly reconstructing buildings from these data is challenging. Trees and other vegetation pose a particular problem in many built environments. This paper investigates several efficient procedures for detecting buildings and excluding vegetation using LiDAR and imagery data. Two general approaches for identifying and filtering out returns from vegetation are investigated: the first uses a normalized difference vegetation index (NDVI) image, while the second uses height differences. The utility of an entropy filter for improving NDVI filter performance as well as two distinct approaches for height-difference modeling are also evaluated. All methods use efficient raster-based algorithms for filtering while retaining the high spatial precision of the vector LiDAR point returns. Following removal of nonbuilding points, remaining points are segmented into distinct building features. In addition, we place particular emphasis on the analysis of processing challenges and special cases as well as the accuracy of these different methods on a large-volume LiDAR dataset covering a challenging build environment.
Main defect of the structured light scanning is that the edge part is lost in the point clouds of scanned object. This
research tried to combine the image processing method to a structured light system in order to improve the quality of the
point cloud. The technique approaches are present, and the results are given as below: after overlying the edge part of
the 3D model to the original point cloud from the structured light system, their hiatus can be restored and the resolution
of the original point cloud can be improved.
Airborne LiDAR data have become cost-effective to produce at local and regional scales across the United States and
internationally. These data are typically collected and processed into surface data products by contractors for state and
local communities. Current algorithms for advanced processing of LiDAR point cloud data are normally implemented in
specialized, expensive software that is not available for many users, and these users are therefore unable to experiment
with the LiDAR point cloud data directly for extracting desired feature classes. The objective of this research is to
identify and assess automated, readily implementable GIS procedures to extract features like buildings, vegetated areas,
parking lots and roads from LiDAR data using standard image processing tools, as such tools are relatively mature with
many effective classification methods. The final procedure adopted employs four distinct stages. First, interpolation is
used to transfer the 3D points to a high-resolution raster. Raster grids of both height and intensity are generated. Second,
multiple raster maps - a normalized surface model (nDSM), difference of returns, slope, and the LiDAR intensity map -
are conflated to generate a multi-channel image. Third, a feature space of this image is created. Finally, supervised
classification on the feature space is implemented. The approach is demonstrated in both a conceptual model and on a
complex real-world case study, and its strengths and limitations are addressed.
The aim of this paper is to present new method that can be used for automatically extracting 3D models in the terrestrial
laser scanning (TLS) point clouds of Chinese traditional architecture. Based on the inherent geometric and topological
constraints in Chinese traditional architectures, spatial direction and topology analysis are used to express the rules. We
develop a rule-based automatic modelling algorithm and apply it to extract the wooden structural elements.
In ground based Lidar system, the targets are used in the process of registration, georeferencing for point cloud, and also
can be used as check points. Generally, the accuracy of capturing the flat target center is influenced by scanning range
and scanning angle. In this research, the experiments are designed to extract accuracy index of the target center with
0-90°scan angles and 100-195 meter scan ranges using a Leica HDS3000 laser scanner. The data of the experiments are
listed in detail and the related results are analyzed.
An application study of Visualization Toolkit (VTK) in three dimension terrain visualization is expatiated in this work.
The research scope contained matching of digital terrain model with individual building model for 3D terrain
visualization, in order to improve the geometric integration of them to optimize the displaying of 3DGIS for construction
objects. A tested example in the research was about Grid DEM matching with an individual building model, which had a
polygon base surface as constraint conditions to the Grid DEM, for enhancing the efficiency of 3D visualization.
Leaf area index (LAI) is one of the most important parameters of canopy structure as it related to many biophysical and
physiological processes, including photosynthesis, respiration, transpiration, carbon cycling, rain intercepting, net
primary productivity, energy exchanging etc. Rapid, accurate and reliable estimations of LAI are required in these
studies above. There are two main categories of procedures to estimate LAI: direct and indirect methods. The objective
of this study is to evaluate LAI estimations obtained by different methods in HeiHe River forest sites. These methods
include the LAI-2000 plant canopy analyzer, HemiView, fifty-seven degree photography method, fisheye photography
method, the tracing radiation and architecture of canopies (TRAC), and Multi-Purpose Canopy Observation System
(MCOS). HemiView shows a large variation on gap fraction measurements compared to LAI-2000, fifty-seven degree
photography method is the superior choice to provide initial LAI values compared to other methods. To determine the
non-photosynthesis elements and foliage clumping effects for optical methods, a new device named MCOS (Multi-
Purpose Canopy Observation System) and TRAC were used. Finally, the results show that with the combination of
MCOS or TRAC and LAI-2000 or hemispherical photography can provide accurate and efficient LAI values.
A method is presented to reconstruct the three-dimensional (3D) canopy and stem based on terrestrial laser scanning
(TLS) data. Multiple range images are obtained and aligned on a 25 m x 25 m plot, where the single specie conifer forests
are growing in Gansu province, China. With regard of organizing the raw point cloud, firstly, a kind of kd-tree structure
is built to make the spatial index of them, and then, a kind of region grow is performed to segment them to smaller size,
and third step, an algorithm is make to extract a single canopy and stem. The segmentation processing is based on
"connected constraint", which can segment each individual tree in one subset. RANSAC and GAUSS image are used to
find cylinder in 3D point cloud to obtain the individual tree measurements, including the tree position and DBH. Each
cylinder is parameterized by its orientation and radius and is estimated iteratively.