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.
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.
KEYWORDS: Global Positioning System, Calibration, Remote sensing, Cameras, 3D modeling, Computer simulations, Control systems, Sensors, Antennas, Imaging systems
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).
A parabola model is employed to fit high voltage power lines by using the remotely identified coordinates of the spacers
and tower corners of power lines. The shortest distance between land surface height at a pixel and heights of power lines
is defined as the warning index for indicating dangerousness of power lines. Three approaches on visualizing the
warning index map are developed and implemented in the Airborne Multi-angle Power Line Inspection System. The
visualization results show that the sliced color warning approach costs a relative low computing time, and can highlight
the dangerous warning sites, the fused color warning approach integrates land surface properties and the warning levels,
and the pseudo-color warning approach costs a relative high computing time and can be applied to visually interpret the
dangerousness by gradual color change.
Advanced technology in airborne detection of crop growth can help optimize the strategies of fertilization, and help
maximize the grain output by adjusting field inputs. In this study, Push-broom Hyperspectral Image sensor (PHI) was
used to investigate the influence of soil nitrogen supplied and variable-rate fertilization to the growth of winter wheat.
The objective was to determine to what extent the reflectance obtained in the 80 visible and near-infrared (NIR)
wavebands (from 410nm to 832nm) might be related to differences of variance of soil nitrogen and variable-rate
fertilization. Management plots were arranged at Beijing Precision Farming Experimental Station. Three flights were
made during the wheat growing season. Several field experiments, including the crop sampling, soil sampling and
variable-rate fertilization were carried out in the field. Data were analyzed for each flight and each band separately.
Some spectrum indices were derived from PHI images and statistical correlation analysis were carried out among the
spectrum indices and soil nitrogen, variable-rate fertilization amount. In addition, the spectrum indices difference
between elongation stage and grain filling stage are calculated and the correlation analysis was also carried out. The
analysis results indicated that the reflectance of winter wheat is significantly influenced at certain wavelength by the soil
nitrogen and the variable-rate fertilization. The soil nitrogen effect was detectable in all the three flights. Differences in
response due to soil nitrogen variance were most evident at spectrum indices, such as dλ red, INFLEX, Green/Red, NIRness,
DVI and RDVI. Furthermore, analysis results also indicated that the variable fertilization can reduce the growth
difference of winter wheat caused by spatial distribution difference of soil nitrogen.
Inversion is an important process in remote sensing. In order to improve the stability and accuracy of inversion, in this
article, we applied kernel forms of AMBRALS (Algorithm for Model Bidirectional Reflectance Anisotropies of the Land
Surface) and PLS (Partial Least Square) regression technique to simplify a canopy reflectance model SAILH
(Scattering by Arbitrarily Inclined Leaves, with Hotspot effect). PLS is a statistical method used for regression highly
collinear variable data. Kernel-driven model is a semi-empirical model with linearity form of "kernels", and these
kernels can be explained in physics. We generated 24 typical canopy cover scenes by combining the canopy parameters
of SAILH model. For each scene, we used PLS regression to estimate the coefficients of our new model. The results
suggest the new model is acceptable in stability and accuracy. Base on the new model, we defined sensitivity matrix to
assess the correlations of directional observations data, which can help to choose appropriate directions when inversion.
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.
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.
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.
Due to the complexity and non-regularity of tree shapes, traditional digital photogrammetry using stereo matching method is difficult to obtain the accurate tree height, This fact therefore limits the application of the aerial digital photogrammetry technology in the power line survey. This paper presents a method of tree height extraction from large viewing aerial image using the knowledge of segmented tree crown. This method is based on a rough digital surface model (DSM) of tree crowns and the exterior orientation of the image. The basic steps of this method is that the DSM is first used to find the region of interest in the image based on the exterior orientation, and then the edges of the distinct trees or branches are extracted using image segmentation technology. An algorithm that uses both the rough DSM height information and exterior orientation data to calculate the accurate heights of the segmented trees or branches is presented. The algorithm assumes that most of the trees are upright, and the projection in the large viewing angle images of the crown and branches can therefore be used to calculate their heights relative to the averaged DSM height. Hence, the accurate height of the trees around the rough DSM can be refined. Some experimental results are given with the image captured from multi-angular imaging system mounted on a helicopter in which a Position and Orientation System (POS) is onboard to record the exterior element of the cameras. The experimental results demonstrated that this algorithm can largely improve the accuracy of tree height extraction. The application in power line monitoring system is promising.
The anisotropic reflectance of vegetation canopy is mainly determined by its spectral and structural features, and can be described by Bidirectional Reflectance Distribution Function (BRDF). In this article, we select the winter wheat from the beginning of April to the beginning of May 2001 at Shunyi county, north of Beijing, as the research object, to study its BRDF changing rule with the changing time. In the process we compute the structural scattering index (SSI) by inverting the semiempirical linear kernel-driven BRDF model, and analyze its relation with the leaf area index (LAI) of winter wheat. The results show that there is a clear linear relationship between SSI and LAI of winter wheat. So SSI can well be used to reflect the seasonal BRDF changing rule of winter wheat.
Construct the transform function model of low-light-level night vision imaging system and its components; Convert the input image with a certain pattern through FT to get the frequency spectrum of it, and filter the frequency spectrum of input image by use of the transform function model. And then, convert the filtered frequency spectrum of input image through IFT to get the filtered image. Thus, implement the digital simulation of low-light-level night vision imaging system by computer.
Topographic effects are the main obstacles to further analysis of satellite spectral data in mountainous area, especially to quantitative remote sensing. To obtain the true reflectance ofthe land surface, we must remove the topographic effects first. A physical model for rugged terrain is developed in this paper. Both atmospheric and topographic effects are considered in the model. 3 illumination sources are expressed analytically which include: 1) direct solar irradiance; 2) diffuse sky irradiance; 3) reflected irradiance from the adjacent terrain. Based on a quick searching algorithm for local horizon. the most complex part in the model—the reflected radiation can be calculated throughout the whole image in an economic computation time. In stead of using the field measured data or standard atmospheric condition obtained from the commercial software, we invert the atmospheric parameters from the image itself based on stochastic programming theory, a priori knowledge is used in the inversion process. To test the model, a Landsat TM-scene is matched to a digital elevation model(DEM) which has a resolution of Im for elevation. The true reflectance map is obtained from the model. It is found that most ofthe topographic effects are removed in the map.
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