Hadi Fadaei; Mahdi Modiri
Abstract
Extended Abstract
Introduction
Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract digital elevation models. In general, there are two types ...
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Extended Abstract
Introduction
Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract digital elevation models. In general, there are two types of resolution in digital ground elevation models.
üArea resolution: The dimensions of the length and width of each cell in the pixel grid is a digital elevation model that shows the minimum dimensions of the topographic features taken on the ground.
ü Height resolution: represents the minimum elevation dimensions that the digital elevation model is able to display. For example, in the digital model of ground elevation with a resolution of 30 meters, elevation features less than 30 meters are not visible.
The digital elevation model can be prepared for a region with different accuracy. The high accuracy of the digital elevation map provides more accurate estimates of the physiographic characteristics of the basin, but the preparation of such maps is very costly. PRISM sensor from ALOS satellite with three cameras: 1- Forward 2- Vertical 3- Forward, which is captured earth surface with the characteristics of the earth (low and high). Therefore, an object that is high above the ground is shown with other points on a flat surface. As a result, by imaging points from different angles, the elevation of those points can be obtained through adaptive mathematical calculations. The purpose of this study is to evaluate the accuracy of the digital elevation model generated by the PRISM sensor of ALOS satellite in comparison with the digital elevation model of ASTER and SRTM for Sarakhs border region (between Iran and Turkmenistan).
Method
The study area is located in north-eastern Iran in the range of 35 to 38 degrees north latitude and 56 to 60 degrees east longitude and on the border between Iran and Turkmenistan in the border region of Sarakhs. The research method in this research has an exploratory aspect that the production and extraction of digital elevation model from PRISM sensor stereo images from Alves satellite and its evaluation is with digital model extracted from ASTER image. The digital SRTM model has a spatial resolution of 90meters, the digital ASTER model has a spatial resolution of 15 meters and the digital elevation model obtained from the PRISM sensor from the ALOS satellite is 5 meters. In this study, elevation control points using Google Earth and GPS have been examined. The algorithms used in this method to extract elevation information are the same as the algorithms used in the photogrammetric method. Elevation digital models are made from satellite images taken in pairs. The accuracy of digital elevation models of this method is perfectly proportional to the scale or resolution of satellite images.
Results & Discussion
In this study, we evaluated the digital elevation model from stereo satellite images of ALOS/PRISM satellite and compared it with the digital model of ASTER elevation and ground observations in the Sarakhs border region located on the border between Iran and Turkmenistan. In this study, the ability to generate a digital elevation model prepared from stereo images extracted from a PRISM sensor with a file of rational polynomial coefficients has been investigated, and we compared it with digital models extracted from stereo ASTER satellite and digital models extracted from SRTM. The results obtained from the digital elevation model are the accuracy of the digital elevation model produced by the pair of ASTER satellite images using a correlation between the two images of 0.47 pixels. Due to the spatial accuracy of the image pixels, which is about 15 meters, the accuracy of the digital model is less than the size of pixels, i.e. less than 15 meters, 6 meters horizontally and 7 meters vertically, which is a total of 13 meters. The results show that RMSE as error index for digital model of elevation extracted from ASTER and PRISM and ground observations are 7.46, 8.77, 3.66 and 6.8 meters, respectively. The results obtained from the stereo images of the PRISM sensor are the standard deviation of the pixels in the longitudinal direction of 1.9 meters and in the transverse direction of 2.3 meters and the distance between the pixels of the digital model is 3 meters high. Therefore, the accuracy of the digital model extracted from PRISM sensor images is higher than SRTM and ASTER. It is recommended to use a high-precision digital elevation model in all borders of the country, which uses a digital elevation model produced from stereo PRISM images from ALOS satellite, which is accompanied by polynomial logical coefficient (RPC) files for geometric correction of images.
Conclusion
The higher the accuracy of the DEM, the more efficient it will be and give border commanders the ability to make better decisions in different situations. The elevation accuracy obtained from the stereo images of the PRISM sensor is 3 meters. The accuracy of the digital model of SRTM elevation in the plains is about 30 meters, which can be used for studies of phase zero and one of the projects, as well as reducing the huge costs of studies. The results of this paper, shows that the accuracy of the digital elevation model produced from the stereo images of the PRISM sensor is higher than the digital elevation and SRTM digital models, i.e. the RMSE error and standard deviation are relatively lower. As a result, it is recommended for border studies that require higher accuracy, and the entire borders of the country, to use the digital elevation model with accuracy.
Amir Reza Moradi; Mohammad Amin Ghannadi
Abstract
Extended Abstract
Introduction
Digital Elevation Model (DEM) is a physical representation of the earth and a way of determining its topography through a 3D digital model. DEMs with high spatial resolution and appropriate precision and accuracy of elevation are widely used in various applications, such ...
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Extended Abstract
Introduction
Digital Elevation Model (DEM) is a physical representation of the earth and a way of determining its topography through a 3D digital model. DEMs with high spatial resolution and appropriate precision and accuracy of elevation are widely used in various applications, such as natural resource management, engineering, and infrastructure projects, crisis management and risk analysis, archaeology, security, aviation industry, forestry, energy management, surveying and topography, landslide monitoring, subsidence analysis, and spatial information system (Makineci&Karabörk, 2016).
Satellite images are one of the main sources used to produce DEM. In satellite remote sensing, optical and radar imagery are often used to generate DEM. Compared to optical satellite images, the main advantage of using radar satellite images for DEM production is that they are available in different weather conditions and even at nights. Two strategies used to produce DEM from radar satellite images include radar interferometry and radargrammetry(Saadatseresht&Ghannadi, 2018).
Phase information of the images is used in radar interferometry, whereas domain information of the images is used in radargrammetry (Ghannadi, Saadatseresht, &Eftekhary, 2014). Moreover, short baseline image pairs are used in radar interferometry, while long baseline image pairs are useful in radargrammetry. These technologies both have their own advantages and disadvantages,which were investigated in previous studies (Capaldo et al., 2015).
With radar interferometry, it is possible to produce DEM forlarge areas. Sentinel is one of the recent projects in satellite remote sensing. Sentinel constellation collects multi-spectral imagery, radar imagery and thermal imagery from the earth. Sentinel-1 is the radar satellite of the constellation.
Recent studies have investigated the precision of radar interferometry using Sentinel-1 imagery (Yagüe-Martínez et al., 2016) and the precision of DEM produced using these images(Letsios, Faraslis, &Stathakis; Nikolakopoulos &Kyriou, 2015). Generally, DEMs generated through radar interferometry needs to be improved, mainly due tothe phase errors which in many cases turn into outlier points (Zhang, Wang, Huang, Zhou, & Wu, 2012). Various methods have been used to improve DEM generated from SAR imagery, one of which use the information obtained from SRTM DEM. For instance, a previous study used SRTM DEM to improve DEM generated from ESRI/2.Using the information obtained from SRTM, the interferometric phase of areas with lower coherency were improved (Zhang et al., 2012).
The present study proposed a method to improve the accuracy of DEMs generated by Sentinel-1 imagery. In this method, using ascending and descending Sentinel-1 image pairs from the study area, DEM is generated using radar interferometry process. Then, precision is improved using SRTM DEM and a method based on 2D wavelet transform.
Wavelet transform and 2D wavelet transform methods
As a spectral analysis tool, wavelet transform is based on expanding any function like f(t)
(1)
inwhichaiis the expansion coefficient and 𝜓iis the expansion function.
One of the interesting characteristics of discrete wavelet transform is that it can be used as a multi-resolution analysis tool. To do so, a series of scaling functions or are used along with the wavelets to determine coarse data of the signals. Signal detailsare also covered by different wavelets with different scales.
Separatingcoarsedataand details of the signal isthe actual basis of discrete wavelet transform algorithm which wasintroduced by Mallat (Mallat, 1989) and improved by Beylkin et al. (Beylkin, Coifman, &Rokhlin, 1991). As a fast and simple method for discrete wavelet transform,the process is performed based on the followingrecursive relationships betweenahighpass and a lowpass filters with the impulse responses h(n) and g(n), respectively (Primer et al., 1998):
(2)
and
(3)
Where the expansion coefficients h and g are scaling filter and wavelet respectively.
(4)
and
(5)
These formulas can be expanded to calculate 2D discrete wavelet transform.
Proposed Method
This section proposes a method of enhancing DEM generated from Sentinel-1 imagery using SRTM DEM and 2D wavelet transform. Considering the capability of wavelet transform as a multi-resolution analysis tool which can separate coarse data from details, figure 2 shows the proposed process of improving DEM. First, using discrete 2DWT, coarse information and details of each DEMs are separated using the ascending and descending conditions of Sentinel-1 images. Then, two stages are considered based on the nature of these models. First, filtering coefficients usingthresholding and considering the average as the detail or high frequency part of the enhanced model. Second, coarse information derived from wavelet transform method have a resolution of40m and thus data derived from SRTM (30m) has a higher quality. Therefore, inverse 2DWT will improve the results and reach a resolution of 20 m.
Experiments and Results
The study area is located in Northern areas of Tehran (Iran) at the UTM coordinates of (542450 ,3964590) northeast to (539010, 3962350) southwest. Two Sentinel-1 satellite image pairs (one ascending and one descending) are used in this study.
In addition, a SRTM DEM with a spatial resolution of30m is used to improve DEM generated from Sentinel-1 images. Sentinel-1 derived DEM is evaluated using the 1m resolutionreference DEM. RMSE values shows the effectiveness of the proposed method in enhancing the Sentinel-1 derived DEM, which means that using information obtained from the SRTM and 2D wavelet transform was reasonable. RMSE values are reduced from 24.2097m to 11.1749m which shows 54% improvement. The proposed method can enhance results to 30 - 82 percentapproximately.
Conclusion
The present study investigates methods used for generating DEM from satellite images especially Sentinel-1 radar imagery. DEM derived from Sentinel-1 data has a high spatial resolution.Yet, it has some outliers or errors in elevation points whichneeds to be modified. Therefore, the present study proposes a method based on 2D wavelet transformfor deriving elevation model witha spatial resolution (20m) equal to that of Sentinel-1 DEM and improved precision and accuracy. In this method, filtering the details of the model using discrete 2D wavelet transform and modifying coarse information using SRTM DEM results in an enhanced DEM with higher spatial resolution.
Bakhtiar Feizizadeh; Salimeh Abdolah Abadei; Khalil Valizadeh Kamran
Abstract
Extended Abstract DigitalElevation Model (DEM) is one of the main geographical datamodels which forms the basis of the different spatial analysis. DEM is known as fundamental data for many modelingtasks. Nowadays, the result validation of GIS spatial analysis, hasbecome a major challenge in the ...
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Extended Abstract DigitalElevation Model (DEM) is one of the main geographical datamodels which forms the basis of the different spatial analysis. DEM is known as fundamental data for many modelingtasks. Nowadays, the result validation of GIS spatial analysis, hasbecome a major challenge in the world of GIS.Thequality of a DEM is dependent upon a number of interrelatedfactors, including the methods of data acquisition, the nature ofthe input data, and the methods employed in generating the DEMs.Analysis of uncertainty in different fields, due to data qualityand related issues such as error, uncertainty models, error propagation, error elimination and uncertainties in the data, are felt morethan any other times. Of all these factors, data acquisitionis the most critical one. Previous studies on DEM dataacquisition have focused either on examination of generation method(s), oron case studies of accuracy testing. These studies are not adequate,however, for the purpose of understanding uncertainty (an indicator used toapproximate the discrepancy between geographic data and the geographic reality thatthese data intend to represent) associated with DEM data and thepropagation of this uncertainty through GIS based analyses. The developmentof strategies for identifying, quantifying, tracking, reducing, visualizing, and reportinguncertainty in DEM data are called for by the GIS community. In order to apply uncertainty analysis on DEMs, this studyaimed to evaluate the error rate and uncertainty of elevationdata obtained from SRTM and ASTER satellites. The objectives ofthis study are: (1) to understand the sources and reasonsfor uncertainty in DEMs produced by cartographic digitizing; (2) to develop methodsfor quantifying the uncertainty of DEMs using distributional measures and (3) to measure the uncertainty associated with DEMs and minimizethe chances of error by means of optimizing models. Quantifying uncertaintyin DEMs requires comparison of the original elevations (e.g. elevations read from topographic maps) with the elevations in aDEM surface. Such a comparison results in height differences (orresiduals) at the tested points. To analyze the pattern ofdeviation between two sets of elevation data, conventional ways areto yield statistical expressions of the accuracy, such as the rootmean square error, standard deviation, and mean. In fact, allstatistical measures that are effective for describing a frequency distribution, including centraltendency and dispersion measures, may be used, as long asvarious assumptions for specific methods are satisfied. Our research methodology includesseveral steps. The first step was, using the statistical indices ME, STD and RMSE, the error rate of DTMsforobtaining the chances of error in ach model. It hasto be mentioned that the main attraction of the RMSElies in its easy computation and straightforward concept. However, this indexis essentially a single global measure of deviations, thus incapable ofaccounting for spatial variation of errors over the interpolated surface. Inorder to obtain more accurate results, then uncertainty of dataerrors was also simulated by Monte Carlo method and errorpropagation pattern was extracted by interpolation of results. The resultsof this step show that, the DEM derived from pairstereo ASTER despite having better spatial resolution, included more errorsand practically lacking the details of DTM 30 meters. Finally,removing the error propagation pattern from DEMs, the secondary DEMwas produced. By recalculating indicators describing the error and comparingthese values with the initial values, the results indicate that,both DEMs show more accuracy after eliminating the error propagationpattern. TPI Index was used to determine the location ofbasin topography and the basin is divided into 6 classesand error rate in each class was calculated before andafter the simulation. The results showed that, the error ratesin all classes before and after the simulation in bothDEMs were reduced. In terms of uncertainty analysis methods forDEMs, results of our research indicated that the RMSE methodsalone is not sufficient for quantifying DEM uncertainty, because this measurerarely addresses the issue of distributional accuracy. To fully understand andquantify the DEM uncertainty, spatial accuracy measures, such as accuracy surfaces, indices for spatial autocorrelation, and variograms, should be used. Results alsoindicated that Monet Carlo simulation is indeed sufficient methods forsimulation error in DEMs. Results of this research are of great importance for uncertainty analysis in domain of Geosciences andcan be used for improving the accuracy of modeling in avariety of applications.
Mohammad Baaghideh; Gholamabbas Fallah Ghalhari; Hasan Hajimohammadi; hasan rezaei
Abstract
Extended Abstract Introduction The climatic conditions of each site play an important role in the dispersion of humans, animals and plants. Therefore, any activity or planning in different economic, agricultural and industrial fields at the ground level is not feasible without the knowledge of ...
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Extended Abstract Introduction The climatic conditions of each site play an important role in the dispersion of humans, animals and plants. Therefore, any activity or planning in different economic, agricultural and industrial fields at the ground level is not feasible without the knowledge of the climate. For this reason, climatic zoning and recognition of the most important factors and factors affecting each area is one of the ways of recognizing the climatic identity of the area. Lack of knowledge of the sub-regions of the country fails to meet the economic and agricultural plans of mankind. In general, the climate of a region is the average of the weather conditions in the region. Access to the average weather conditions in a specific location requires long-term weather information. Data and Methods In order to obtain the correct and comprehensive knowledge of the climate of Hamedan province, climatic zoning was performed with new statistical methods such as factor analysis and cluster analysis during the 20 years period (1993-2013). For this purpose, 23 variables were selected from 8 meteorological stations. Then, using a digital elevation model, a multivariable regression was applied between the meteorological parameters and the digital elevation model. Finally, a zonal matrix with a dimension of 23 × 88 was obtained. Since the aim of this research was the climate zone of Hamadan province based on altitude, a digital elevation layer (DEM) was used with a resolution of 90 meters. In the following, for climatic zoning, a regression relationship was made between climate parameters and length, width and height of the area. To identify the climatic sub-regions of Hamedan province, the raster data obtained from the zoning were converted to point data. Then, based on the analysis of the main components, the points were analyzed by clustering method and the dominant factors were identified. In this research, the resolution of each of the pixel was 15 × 15 km and a matrix with dimensions of 23 × 88 was developed. Finally, this matrix was clustered into the MATLAB software using the Wardclustering method. Results and discussion By studying 23 climatic elements, 5 climatic factors were identified and their maps were drawn. These factors include temperature, visibility, rainfall, thunder storm and radiation. Among these factors, the first factor with 37% of the variance of the total data has the most important role in determining the climate diversity of the province. This factor is most commonly observed in the South and Southwest of the province and with moving to the North and Northeast of the province, this factor is severely reduced. Conclusion According to the dendrogram, 6 climatic regions were identified and the characteristics of each separate area were investigated.
Abbas Khosravi
Volume 23, Issue 89 , May 2014, , Pages 26-31
Abstract
Since the avalanche hazard map is a very time-consuming task, our goal is to improve the development of risk mapping models in vast remote areas. This model is based on satellite imagery and digital elevation model at two points of the Swiss Alps. To simulate avalanche hazards, the model (DEM) is programmed ...
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Since the avalanche hazard map is a very time-consuming task, our goal is to improve the development of risk mapping models in vast remote areas. This model is based on satellite imagery and digital elevation model at two points of the Swiss Alps. To simulate avalanche hazards, the model (DEM) is programmed in a computer that includes the determination of avalanche coordinates and parameters in the forest environment. Forests and pastures were classified according to thematic maps (TM) data. So far only a single forest classification has been made. While separating forests, bushes, and areas near the separating lane (the hypothetical line near which no tree grows) creates problems. The classification of small water springs, and the effect of avalanches within the forest was successful. The comparison of Bahman land and land use maps shows that 85 percent of risk and risk areas are correctly categorized. But for scientific applications, the separation of the red and blue lines was not satisfactory, and more needs to be done for operational applications that need to be addressed. The overall policy is very promising and should be led toward our goals, which is to provide more reliable risk maps with better and newer gateways to mutual conversion of snow and forest.
