جستجوی مقالات مرتبط با کلیدواژه « lidar » در نشریات گروه « جغرافیا »

The height of the building is one of the important factors in organizing the urban landscape. This value is another variable affecting urban density, which is usually considered in regular rules and regulations on projects. In this regard, the use of lidar technology as a new tool in the field of aerial imaging, to calculate the height and building floors, is one of the topics discussed in the field of urban studies. What is considered in this research is to present a new method in urban planning based on extracting the height of buildings and the number of floors using Lidar super points, which includes two general steps. In the first step, the lidar data were filtered and separated from other lidar points related to land and vegetation using the elevation threshold method, and to produce a three dimensional model of the extraction and extraction of the extraction. The dimensions of each circle became two dimensional. Discovering the boundaries of buildings was made possible by creating points in each corner of the extracted building features. In the second stage, after extracting the first building boundaries and determining the points related to the buildings, the digital surface model information was assigned to the extracted building features and according to the assigned heights of the building floors by the nearest height method. The passage to the building was calculated along with the average height of the building above sea level (-26), which is referred to as the average height of the area. To complete the height of the buildings, the difference between the average height of the area and the height of the nearest passage to the building entrance was used. To find the number of floors, taking into account the height of the roof of the building, which according to the prevailing standards includes a range between 2.80 meters to 4 meters, was calculated and the building floors in four sections: one floor, two floors, three floors and four and It became more clear. The data source for the proposed system consisted of air borne Lidar data with rare vision and ultra Cam aerial Images. Were extracted. All the functions used enabled the system to successfully extract the structures from the lidar data. The obtained data and their matching with the samples taken in the field survey, show the accuracy of the boundary and the extracted classes are relatively good and the proposed system in terms of completeness, accuracy, and consistency of the data. It had a good performance. Finally, considering the research of Beilingwei U and et al., Under the title of automatic extraction of density information of urban buildings using lidar data and the results obtained in the present study, it was determined that aerial lidar technology has an extraordinary ability. In collecting very right and dense samples of altitude measurements of the city level has been provided and a new level of detail of right building height information can be automatically and efficiently extracted from the aerial data.

LiDAR (Light Detection and Ranging) employs pulse models which penetrates vegetation cover easilyand provides the possibility of retrieving data related to Digital Terrain Model (DTM).Pulses sent by the Lidar sensorhitdifferent geographical features on the surfaceground and scatter inall directions. Distance to the object is determined by recording the time between transmitted and backscattered pulses and by using the speed of light to calculate the distance traveled by the small portion of pulses backscattered. Most LiDAR receivers at least record the first and last backscattered pulses. The first backscattered pulses are used to produce Digital Surface Models (DSMs) and the last ones are used to produce DTMs. Despite the fact that these data can provide a valuable source for DTM generation, the volume of vegetation (vegetation density) in forest areas reducesthe accuracyof DTMs. Onthe other hand, ground surveying of forest areas is rather expensive and time consuming, especially in largerforests. Aerial images are also used as a source for DTM generation, but this approach requires a 60–80% overlap between images which along with canopy height reduce the potential of this method for DTM generation. Also, low spatial resolution of satellite images collected from forest areas increases errors in DTM generation to a large degree. The present study investigates the accuracy and precision of DTMsproduced from LiDAR data in a forest area. Furthermore, the effect of different methods of filtering and DTM interpolation was explored. Different methods of DTM generation were also closely analyzed and evaluated.

 The case study area is located in Doroodforests, a part of Zagros forests, in the southeastern regions of Lorestan province in Iran (48°51’19’’E to 48°54’31’’E and 33°19’21’’N to 33°21’15’’N). Minimum and maximum altitude above sea level were 1143 and 2413m, respectively. The study area covers 100 hectares of mountains with an average slope of 38%. Approximately 50% of the area is covered by forests in which Brant’s oak (Quercusbrantii Lindley) is the most frequent species. LiDAR data were collected by the National Cartographic Center of Iran (NCC) in 2012 using a Laser scanner system (Litermapper 5600) fixed on an aircraft flying at an average altitude of 1000m. LiDAR data consisted of the first and last returns (backscattered pulses), distance and their intensity value. Collected data had an irregular structure and included an average of more than four points per square meter. A DTM was produced using a two-step filtering. First, a morphological filter removed most of non-ground points, and then a slope-based filter detected remaining points. Inforest areas with rough-surface, DTM was producedthrough processing ofLiDAR data with statistical methods likekriging and inverse distance weighting (IDW). These methods apply third and fourth power to detect and remove non-ground points. To assess the accuracy of DTMs produced by different approached, 5 percent of the LiDAR point cloudswererandomly separated as the test data. Amongst these data sets, 62 points with a suitable dispersion were selected and measured using a GPS-RTK. An error matrix, along with accuracy indices (including correlation and Root Mean Square Error (RMSE)) were calculated based on these data.

Results indicated that 44-degree slope is the best threshold for isolation of non-ground points and inverse distance weighting (IDW) is the best third power interpolation method with the highest correlation (0.9986) and the lowest RMSE (0.204 meter). Amongst the filtering methods, slope-based filter used for separation of ground and non-ground points had the best performance. Since this filter combines two parameters of slope and radius, it can remove cloud points related to the vegetation cover and results in high efficiency for steep forest areas. Slope-based filter is suitable for processing of near-surface vegetation, whilst statistical filter is well-suited for vegetation cover consisting of tall trees.

The present study proposed and investigated different scenarios for the production offorest areas’ DTM using LiDAR data and two interpolation methods. These algorithms were practicallyassessed using LiDAR data collected from Dorood forest areas. The best scenario was slope-based filter with inverse distance weighting (IDW) interpolation. Based on accurate assessment, this approach can produce reliable DTM in forest areas.

Remotely sensed data are used to estimate structural parameters in order to reduce the cost and time required for forest inventory. Nowadays, due to the launch of numerous remote sensing systems, a variety of remotely sensed datasets including LiDAR, radar, and optical images are available in different spatial and spectral resolutions. Therefore, it is important to evaluate and compare the performance of these datasets in retrieving structural parameters. Such comparison, however, has rarely been conducted in previous studies. Consequently, this study aims to compare different remotely sensed datasets used for structural parameter estimation. For this purpose, textural information extracted from Worldview-2 (WV-2) and SPOT-5 images and statistical attributes calculated for LiDAR-derived data were individually utilized to model the structural parameters of a Pinus radiata plantation. Comparing the results, while the WV-2 data are suitable to estimate stocking and mean diameter at breast height (DBH), mean height and stand volume were estimated more accurately by LiDAR-derived data. Meanwhile, there was no significant difference among remotely sensed datasets for basal area estimation. Finally, it was shown that the mean height and mean DBH were estimated more accurately than density-related structural parameters comprising of basal area, stand volume and stocking with more than 20% estimation error.

Nowadays, integrating different kinds of data and images, achieved by the different remote sensing sensors are known as a suitable solution for extracting more useful information. This because of large range of aqucition images, digital format, and high temporal resoulation enable scinentits to extract information form land surface. The passive optical sensors have been used extensively in mapping horizontal structures. However, radar data could be used as a complementary data, since these data would be gathered in different climatic conditions in 24 hours of a day, as well as some geo and manmade structures have a specific response in the radar frequency. Furthermore, LiDAR data could gather precise measurements from vertical structures. On the other hand incorporating these variety of information and data for extracting specific urban features is curucial and challenging task. Hence, by integrating optical, radar, and LiDAR data more features and information would be prepared for different kinds of applications. For example some object may easily find in optic imagery but it is difficult to extract that object form LiDAR or RADAR images. Therefore utilizing procdure for fusing and extracting these object is inevitable. In this research, we used these datasets to detect buildings, roads, and trees in a complex city sense, i.e., San Francisco, by generating 57 features, and also by using the PCA and ICA feature extraction methods, as well as the well-known intrinsic dimension methods, including SML and NWHFC. Finally, the K-NN classifier was utilized in order to detect buildings, roads, and trees and grouping features according to the earned accuracies. Results show the high performance of the proposed approach and support our analyses.

Automatic Building Detection and Extraction in multi-source aerial data are important in many applications, including map updating, city modeling, and urban growth analysis and monitoring of informal settlements. The main purpose of this paper presents a new automated method for building extraction from fusion of Lidar data and aerial images. For this purpose, at first part, we reviewed and evaluated building detection methods (ANN, SVM, MD) in Pixel-base and Object-base levels. The result for study area show a better performance of SVM with an overall accuracy of 95.9% and Omission error of “Building” class equal to 6.2% and Commission error of “Building” class equal to 3.2% which Errors of commission represent pixels that belong to another class that are labeled as belonging to the class of interest. Errors of omission represent pixels that belong to the ground truth class but the classification Technique has failed to classify them into the proper class. The second part of this paper is to extract and reconstruct the building edges based on the proposed method of building detection. For this purpose, building the edges are segmented based on spectral and geometric segmentation. In continue, based on least squares equation, the edge segments of each building are reconstructed. The results extraction show has proved an overall accuracy of 96.¬8% and an Omission error of “Building” class equal to 5.9% and commission error of “Building” class equal to 2.5%.

During the last three decades, the process of producing topographic information has observed a development in data producing technology, from traditional and land mapping toward inactive methods of surface measurement and registration (like photogrammetry and remote sensing), and more recently toward active methods (like radar and Lidar). Lidar is a technique used to gather information from the surface which works by measuring distance with laser. Measurement in Lidar is based on this principle: with defined coordinates of the laser sending point, it is possible to measure coordinates of any point on the ground by measuring the oblique distance between pulse sending point and the ground surface and measuring the angle of wave sent between the pulse sending point and ground level. Images produced using Lidar data have a 472*697 pixel dimension. In fact, Lidar is a supplementary tool for collecting 3 dimensional information which aid spatial photogrammetry and remote sensing. The most important information received from this device is the distance between sensor and ground level which is measured by calculating the time period between pulse impact with earth surface and its return to the sensor. Moreover, the distance between ground surface and flying level of the airplane is repeatedly measured which determines ground surface and vegetation. Digital elevation model and digital surface model are products of Lidar. Features like plot parameters, average elevation of trees, surface of vegetation crown, elevation of the vegetation crown, diameter at breast height, single trees and jungle structure can be exploited by Lidar. The present article seeks to introduce Lidar and investigate its functions and applications.

Today, aerial laser scanners (LiDAR) play an important role in 3D data acquisition from features. Filtering is one of the most affecting processes which are done on the LiDAR data. Filtering means distinguish between ground points and object points. Up to now, a lot of algorithms have been developed to automatic filtering of LiDAR data. Each of these algorithms has their own limitations and inefficiencies. One of the weaknesses of these algorithms is disability to remove the large buildings. This is due to their local-based operation. This paper presents a practical approach based on classification techniques to solve this problem. First, the LiDAR data are filtered using slope-based filtering algorithm which is one of the most known filtering algorithms. Afterward, the LiDAR range and intensity data are classified into five classes; road, tree, building, grassland and cement, using three classifiers; Support Vector Machine, Artificial Neural Network and Maximum Likelihood. Finally, the large building points, which had not been filtered by slope-based algorithm, are removed by used classification outputs. The results of accuracy assessment indicate that the Maximum Likelihood gives poor results, but Support Vector Machine and Artificial Neural Network present very good results. Generally, the usage classification techniques in order to improve the results of filtering algorithms causes a negligible increase in type I error and significant decrease in type II and total errors as well. Since, in filtering process, the type II error and total error are more important than type I error, performing of this supplementary process presents an effective result. Quantitative evaluation shows the output of the slope-based algorithm with 20º slope threshold after improving using Artificial Neural Network classifier, presents the best result. In this case type I error increased from 4.98% to 5.04%, type II error reduced from 9.043% to 4.49% and total error decreased from 7.03% to 4.76%.

In recent years the use of aerial laser scanners for determining the topography of water beds has been introduced in the world and has found practical aspect. Depth measurement using laser scanners is a more precise, cost- efficient, and faster method than other depth-measuring methods, which is based on accurate measurement of the travel time of two light signals transmitted to the surface and bed of water. Consequently, the use of appropriate hardware and software, in which the source of the major errors is detected and minimized, is very effective on the result of the flight. This paper presents a variety of depth-measuring laser scanners, various techniques used in each of them, and a description of how depth-measuring operations are performed. In addition to expressing the natural causes of error as well as noise causes in operational data, an algorithm for data correction and a method for noise cancellation is presented.

Recognition and classification of land features on the images have been considered as the base of many applications including the development of a digital model of elevation, identification of changes, updating of maps and many other cases in geomatics. In recent years, researchers have tried to improve the accuracy of this process. By recognizing land features and classification of the image we mean the set of processes and operations which lead to identifying land features and attributing a sticker to each of the pixels entering the classification operation. Based on this, recognition and identification can be achieved by relying on the differences between objects in terms of characteristics recorded by different sensors. The more varied information is available, the more precise and reliable the results will be. Today, with the advancement of technology, various types of information are available by various sensors. But none of these sources provide all the textural, geometric, and spectral properties of an object. That's why it is inevitable to combine the information from different sensors to complete the descriptive space that leads to more accurate extraction of land features. In this study, the integration of digital aerial image information and Lidar data has been evaluated and its role in increasing the accuracy of classification has been tested using a data set from an area in Germany. The results show that the classification accuracy is increased by using digital aerial image and Lidar data simultaneously.