Asghar Hosseini; Zahra Azizi; Saeed Sadeghian
Abstract
Introduction 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 ...
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Introduction 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. Materials & Methods 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 & Discussion 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. Conclusion 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.
Parham Pahlavani; Mahdi Hasanlou
Abstract
Abstract
Nowadays, the combination of data and images obtained from different remote sensing sources is considered as an optimal solution for extracting more information, since these data, with their own wide vision, digital format, their periodically preparation, and high temporal resolution provide ...
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Abstract
Nowadays, the combination of data and images obtained from different remote sensing sources is considered as an optimal solution for extracting more information, since these data, with their own wide vision, digital format, their periodically preparation, and high temporal resolution provide researchers with a variety of information about the land surface. In this regard, the passive optical sensors are widely used in mapping horizontal structures. Given that, radar data can often be collected 24-hours a day and Independent of atmospheric conditions, and also some ground structures and artificial targets have a specific response in the radar frequency, they complete the capabilities of optical images. LiDAR airborne data can also provide sample measurements from vertical structures with very high accuracy. As a result, the simultaneous use of optical, radar and LiDAR data can provide more information in a variety of applications. In this research, by simultaneously applying these three categories of data, we tried to identify the urban specific features in an optimal way. In this regard, by utilizing and producing various descriptors (57 descriptors), and using the feature extraction methods (including PCA and ICA) and estimating the intrinsic dimensions of the data (including SML and NWHFC), an optimal space for the supervised classification was created. After classifying (K-NN method) using the obtained results, descriptors (information layers) produced to identify specific urban features including buildings, roads and vegetation were obtained and grouped according to the classification accuracy. The numerical results indicate the high efficiency of the proposed procedure as well as the applied methods of estimating intrinsic dimension and extracting the features.
Abdollah Seif; Tayyebeh Mahmoodi
Volume 23, Issue 89 , May 2014, , Pages 72-80
Abstract
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 ...
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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.
Hamid Enayati; Shima Toori
Volume 19, Issue 74 , August 2010, , Pages 75-80
Abstract
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 ...
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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.
Ahmad Javaheri; Ebrahim Gholipour
Volume 16, Issue 63 , November 2007, , Pages 22-25
Abstract
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 ...
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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.