Terrestrial laser scanners (TLS) have demonstrated great potential in estimating structural attributes of forest canopy, such as leaf area index (LAI). However, the inversion accuracy of LAI is highly dependent on the measurement configuration of TLS and spatial characteristics of the scanned tree. Therefore, a modified gap fraction model integrating the path length distribution is developed to improve the accuracy of retrieved single-tree leaf area (LA) by considering the shape of a single-tree crown. The sensitivity of TLS measurement configurations on the accuracy of the retrieved LA is also discussed by using the modified gap fraction model based on several groups of simulated and field-measured point clouds. We conclude that (1) the modified gap fraction model has the potential to retrieve LA of an individual tree and (2) scanning distance has the enhanced impact on the accuracy of the retrieved LA than scanning step. A small scanning step for broadleaf trees reduces the scanning time, the storage volume, and postprocessing work in the condition of ensuring the accuracy of the retrieved LA. This work can benefit the design of an optimal survey configuration for the field campaign.
This paper presents a method to reconstruct individual trees from Terrestrial Laser Scanning (TLS) data obtained in leafoff conditions of an experiment plot. It firstly used the point clouds to build the branch structures of trees with a global optimization method. Computer generated needles and shoots were added to the previously constructed branches according to the leaf area (LA) of each individual tree, in consideration of clumping effect of small-scale structures. The LA was determined by the proportion of crown volume in this plot with LAI measured. In this way, several larix trees with different shapes and heights were reconstructed, which is basis of 3D forest scene reconstruction.
Building boundary is important for the urban mapping and real estate industry applications. The reconstruction of building boundary is also a significant but difficult step in generating city building models. As Light detection and
ranging system (Lidar) can acquire large and dense point cloud data fast and easily, it has great advantages for building
reconstruction. In this paper, we combine Lidar data and images to develop a novel building boundary reconstruction
method. We use only one scan of Lidar data and one image to do the reconstruction. The process consists of a sequence of three steps: project boundary Lidar points to image; extract accurate boundary from image; and reconstruct boundary
in Lidar points. We define a relationship between 3D points and the pixel coordinates. Then we extract the boundary in the image and use the relationship to get boundary in the point cloud. The method presented here reduces the difficulty of data acquisition effectively. The theory is not complex so it has low computational complexity. It can also be widely used
in the data acquired by other 3D scanning devices to improve the accuracy. Results of the experiment demonstrate that
this method has a clear advantage and high efficiency over others, particularly in the data with large point spacing.
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.
With the recent availability of commercial high resolution remote sensing panchromatic imagery from sensors such as
IKONOS and QUICKBIRD, it is possible to extract individual objects such as buildings from the imagery. However,
traditional extraction methods cannot get the desired accuracy, because knowledge is not utilized. In this paper, we put
forward a texture-based approach to get building information from the panchromatic imagery. Firstly, the image is
segmented based on texture of variogram feature. Building corner structure knowledge is also combined to detect and
connect building edges. Then we fill interiors of buildings through seed filling algorithm. In the final stage, point noises
and linear noises are eliminated from the imagery through area or shape index value. The accuracy assessment adopted
in this paper is GIS overlay analysis, which shows that 93.9% of building information is extracted correctly. The result
indicates that the approach supplies another new technique for interpreting high spatial resolution remotely sensed
The space-born and airborne platforms are major means to acquire the earth surface information. However, the airborne
and spaceborne are sometime limited in some special cases such as military area, federal agencies. For this reason, this
paper presents study on a blimp-based low altitude remote sensing platform, which has the characters of stability and
safety and is easy to operate and control. The details of the hardware configuration and work flow are first described, and
some key techniques including calibration, synchronization and aerial triangulation bundle adjustment are emphasized.
In this system, low accuracy digital compass is used due to the limitation of blimp payload and cost. With the simulated
study and real data analysis demonstrates that under the current hardware specification, the accuracy of 3D object
coordinates can reach better than 0.5 m. Moreover, this system can reach equality with the airborne platform with less or
without ground control points (GCPs).
We develop a multi-angular imaging power line inspection system. Its main objective is to monitor the relative distance
between high voltage power line and around objects, and alert if the warning threshold is exceeded. Our multi-angular
imaging power line inspection system generates DSM of the power line passage, which comprises ground surface and
ground objects, for example trees and houses, etc. For the purpose of revealing the dangerous regions, where ground
objects are too close to the power line, 3D power line information should be extracted at the same time. In order to
improve the automation level of extraction, reduce labour costs and human errors, an automatic 3D power line
reconstruction method is proposed and implemented. It can be achieved by using epipolar constraint and prior
knowledge of pole tower's height. After that, the proper 3D power line information can be obtained by space intersection
using found homologous projections. The flight experiment result shows that the proposed method can successfully
reconstruct 3D power line, and the measurement accuracy of the relative distance satisfies the user requirement of 0.5m.
Leaf Area Index (LAI) is an important parameter describing the growth status of vegetation canopy and is also critical to
various ecological, biogeochemical and meteorological models. LAI can be conventionally estimated from instantaneous remotely sensed data mainly through Vegetation Indices (VI) and inversion of canopy reflectance models. Data assimilation is a new developed and a promising technique, which can take advantages of time series observations. In this study, the variation algorithm was used to retrieve LAI, by assimilating time series remotely sensed reflectance
data into a simple crop growth model, which was obtained by statistical analysis of more than 600 field samples from
wheat paddock. To overcome the improper assumption that the other inputs except for LAI in the radiative transfer models are known in data assimilation, we proposed a strategy to allow the spectral parameters to be free. This strategy was evaluated by simulation. With this method, we also analyzed the influence of background on the retrieved results by simulation. It was further validated using ground measurements. The results were promising compared with field measured LAI data, with the Root-mean-square-error (RMSE) being 0.51.
LIDAR has revolutionized the acquisition of digital elevation data for large scale mapping applications. Integrated with airborne GPS/IMU, it is possible to compile DTM from an aircraft platform through laser distance measurements. The precision of laserscanner slant distance measurement is primarily determined by the precision of time-of-flight measurement. But the distance measurement accuracy is not equivalent to the final 3D coordinate measurement accuracy. The final accuracy also depends on the precision of airborne GPS and IMU. This accuracy varies with flying height. The height precision of a single ground point is often in the order of 10-15 cm, with a typical planimetric precision in the order of 0.5-1.0 meter at a flying height of 1000 meter. Generally, Airborne LIDAR system integrates a digital camera hard hounded to the LIDAR sensor. Images captured by the integrated digital camera are mainly used to provide the necessary visual coverage of the area and generate the orthimages. We want to find a way to integrate LIDAR and imagery, so that the final 3D measurement errors can be suppressed. A simple simulation system is developed, and the preliminary result shows that the proposed method improves the 3D coordinate measurement accuracy.
In this paper, a mathematic model for POS based bundle adjustment is introduced. The model is made up of four types of linearized observation equations. The intention of the POS based bundle adjustment is to minimizing the error between the four types of observed value and its model value. We use the Levenberg-Marquardt algorithm to achieve this purpose. Our work is supported by China 863 program titled 'airborne multiangular imaging technique in power line inspection' (AMPLI). The purpose of this program is to monitor the relative distance between the power lines and the objects beneath them with accuracy as high as 0.5 meters. A number of high-resolution images must be captured along the power lines to ensure the accuracy. Based on an automatic matching method proposed by other team members in this program, hundreds of homonymous points can be extracted in one image. About 30 to 50 images are used in one block adjustment. As a result, large number of unknowns will contribute to the minimized error, and numerous equations should be solved. So, the minimization algorithm must incur the high computational costs in the problem. Fortunately, the normal equations reconstructed from the observation equations above exhibiting a sparse block structure. Considering the sparse characteristic of the normal equation, we propose a sparse bundle adjustment method based on Levenberg-Marquardt algorithm to save computation cost. A software package is developed based on this algorithm. A comprehension test was performed to investigate the performance of the algorithm. We used a data set provided by a field experiment in Wuhan, China. It is found that our algorithm showed both high accuracy and high efficiency in the test.