جستجوی مقالات مرتبط با کلیدواژه « مدل رقومی زمین » در نشریات گروه « عمران »

Forest roads are essential for forest management, forest harvesting, wood transportation, recreation, education, research, and forest protection. To meet these needs, forest road networks have been constructed in the northern forests of Iran. Forest road mapping especially over large and mountainous areas is time-consuming and expensive. Today, remote sensing data can be considered as an important tool for forest roads extraction. Therefore, in this research, LiDAR data and UltraCam images were applied in order to extract hidden forest roads. At the first step, noise points in the point cloud data were removed. Then, according to the Central Limit Theorem (CLT), the third statistical moment (amount of skewness) of the data was calculated and the non-ground points were eliminated. At this next step, a number of non-ground points were identified as ground points. In order to eliminate these errors, slope-based algorithm with a radius of 10 meters and a slope of 22 degrees was applied on the points obtained from the first step, these points were eventually removed and the ground points were extracted. Then, extracted ground points were converted to grid. Then the grid was converted to polygon based on the pixel density, by using the DTM as well as UltraCam aerial images, polyglots that were not related to the road were removed. Until this stage, the output was the roads that were not hidden by the forest canopy. Therefore, the hidden parts of the roads were extracted by applying slope-based algorithm with the radius of 10 meters and 65 degrees slope on the whole LiDAR points and interpolating the results by spline interpolation method. By connecting and modifying the polygons, 3m wide dirt roads and 2m wide skidding roads were extracted. The results are evaluated by comparing to manually acquired road data. The quality measures completeness, correctness and quality were 82%, 86% and 72%, respectively.

In order to reduce the effect of the systematic error (bias) in the RPC data in the generated DEM (relative model) from the satellite stereo images, an existing elevation model as an absolute model can replace the need for ground control points. In this research, a DEM matching strategy was introduced based on the development of the slope-based approach.
Unlike other existing DEM matching methods that first apply a projection system, the proposed mathematical model, based on the coordinate system of the input data was developed. In this way, the parameters of the transformation were obtained in this coordinate system. As a result, it is possible to directly improve the RPC data of the stereo images, which are also expressed in the same coordinate system.
Inspiring from the classical absolute orientation of the aerial images, the two-stage transformation was carried out separately. In order to evaluate the proposed method, a Cartosat-1 image pair and an SRTM model were provided from a mountainous region. This method compared with the original slope-based approach and provided a better approximation of the three-dimensional offset values between the relative and absolute models.
The generation of the relative DEM (MATDEM) has been implemented in the MATLAB environment. In this research, it was considered a dense image matching method for generating the relative DEM. Therefore, an area-based solution has been used to reach the desired density of the results.
The least squares image matching (LSM) method, as an area-based method, has the potential to achieve high precision and is mainly used as an alternative to increasing the accuracy of other matching methods. However, the LSM method has a low convergence radius and it is possible to fall into the local minima of the correlation function, which in turn reduces the reliability of the results. Therefore, it is important to produce the proper seed points which are located within the small convergence radius of this method. Here, these seed points were obtained using raw RPC data and the SRTM model. In this regard, a regular grid was assumed on the first image, and after the extraction of the seed points, the precise matching was performed using this method. Also, for comparison, a relative model (PCIDEM) was produced using the PCI-Geomatica software.
The most important achievement was to discover the actual values of the bias of the raw RPCs in the MATDEM case. It was assumed that the systematic errors were propagated from the RPC data in the generated relative DEMs. The estimated values for offset parameters, particularly the offset in the longitude direction, were different for PCIDEM and MATDEM. According to the evaluations, the values obtained from the MATDEM have been more accurate. The reference data for this assessment was the offset calculated using ground control points. In the evaluations using ground control points, the offset values estimated by the proposed method along latitude and longitude directions were 0.77 and 1.23 m, respectively. With regard to the pixel size of Cartosat-1 images, the planimetric offset value was estimated as 0.58 pixels.

Producing digital elevation models (DTMs) for forested areas is a challenging task. Nowadays, because of the capability of LiDAR pulses to penetrate through the vegetation canopy, airborne laser scanner technology has attracted enormous interest for DEM extraction especially in forested terrain. Producing Digital Terrain Models from airborne LiDAR data consist of two main steps of filtering and interpolation. Several automatic filtering algorithms for laser scanning data have been proposed. The capability of these algorithms to separate ground and non-ground points is not the same. The aim of this study is to combine two algorithms to achieve higher accuracy. In order to separate ground from non-ground points at first, noise was removed from LiDAR data, then the third moment (skewness) was calculated and finally slope-based algorithm was applied. The skew amount of LiDAR data was equal to 0.48; so the points with greater height were recognized as object points and removed from point clouds. Some of the non-ground points were recognized by mistake as the ground points which were removed by applying slope-based algorithm. Slope-based algorithm with five threshold slopes 18, 20, 22, 24 and 26 degrees was applied on point clouds and after calculating type I, type II and total errors the best threshold slope of 22 degrees was found and implemented. Finally, a digital terrain model was produced by B-spline interpolation method. The results showed 21 cm vertical accuracy for produced digital elevation model which comparing to previous works was acceptable. User's and producer's accuracy for ground points was 93.38 and 94.5 and for non-ground points was 94.5 and 95.2, respectively. This accuracy indicates that the ground and non-ground points were separated very carefully.

Nowadays, the developing LiDAR technology has attracted a great deal of attention for generating Digital Terrain Models (DTMs) especially in forest areas, due to great penetration of laser pulses in vegetation. Generating DTM using LiDAR data includes two seteps: filtering and interpolation. Although there is a wide variety of filtering methods, in this study, slope based filter and hybrid filter have been applied in order to filter the cloud of raw lidar data points.
Then Genetic Algorithm ,for improving the interpolation methods like Inverse Distance Weighted (IDW) and polynomials, have been employed. The performance of these intelligent methods has been compared with typical interpolation methods such as Kriging and Radial Basis Functions, etc.
Results of the present study indicated that interpolating by hybrid filter, which used Genetic Algorithm ,for improving the interpolation Inverse Distance Weighted(IDW), RMSE of 0.135 m for the first area having a dense vegetation conopy and RMSE of 0.230 m for the second area having grassland vegetation, had the best performance of all the applied interpolation methods in this study.

Lately, LIDAR technique has been widely used to generate digital terrain model of forest areas.Because,it is not possible to generate digital terrain model in forest areas by satellite and aerial images and also by land surveyor,economicaliy is not possible because of high expenses and taking a long time.The method which is explained in this report for extracting non-terrestrial points from last pulse points in primary step is skewness balancing algorithm.This method is almost capable of removing non-terrestrial points from last pulse points and the remain points can be considered as terrestrial ones.Using this algorithm,most of non-terrestrial points were removed but since the removed points were extracted from last pulse points,it might contained some terrestrial points.For obtaining these points,the points due to primary step must be substracted from last pulse points and then compare the results,with first pulse points by elevation differences.Then this result should be combined with the primary step points to gain new terrestrial points.But because of the ground slope,non-terrestrial points near ground could not be removed by two steps before.For removing them,the slope filter method can be used.The remained points can be accepted as terrestrial points.At last,by using B-Spline method,these points will be interpolated and we get digital terrain model ,with the height accuracy of .2 meter.

Considering ongoing developments in Photogrammetry and Remote Sensing and attending to their applications such as digital true orthophoto generation from high resolution images, have already proven the urgency and necessity of approaches for generating DEM as input of these applications. In this research we describe a novel method for automatic DEM generation. We describe “Semi-Global Matching” algorithm then implement and test it for generating an initial disparity image from high resolution stereo satellite images. Furthermore, post-processing steps for removing outliers, recovering from specific problems of structured environments and the interpolation of gaps are needed, to generate an accurate disparity map. With generating a clear disparity map we can generate 3d point clouds. Then these point clouds are used for generating a precise DEM for other applications.

Nowadays, digital terrain models (DTM) are an important source of spatial data for various applications in many scientific and commercial disciplines. Therefore, special attention is given to their main characteristic ‐ accuracy. These models are infected with various types of errors in the process of sampling, measurement and reconstruction. These errors are divided in to three groups: random, systematic and gross errors. As it is well known, the source data for DEM creation contributes a large amount of errors, including gross errors (blunders), to the final product, which are unacceptable for a practical project. Therefore, the detection and deletion of gross error from DTM data has been becoming a great concern in geospatial data analysis. Most of existing approaches are based on statistical tests and present considerable problems for isolating observations and avoiding their influence. This paper presents an algorithm based on robust estimation with IRLS. Also, the application of robust methods to digital terrain modeling is analyzed versus the classical least-squares approach. Entire dataset is divided into some separate patches. In each patch a bilinear surface is fit to fully surrounded points and the residual for each point is estimated. By the use of robust estimation, it is tried to minimize the sum of squared residuals in order to detect points with gross error. The results showed that the proposed method provides a maximum-resistance solution and therefore the capability of identifying blunders.