MODELING UNCERTAINTY OF DIGITAL ELEVATION MODELS SRTM AND ASTER AND THEIR IMPACTS ON LANDFORM CLASSIFICATION IN GARM-CHAY BASIN

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

FEIZIZADEH BAKHTIAR; ABDOLAH ABADI SALIMEH; VALIZADEH KAMRAN KHALIL

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: GEOGRAPHICAL DATA Year:2017 | Volume:26 | Issue:103 Page(s): 29-41

Download Full-Text

Persian Verion

View:

762

Download:

Cites:

Information Journal Paper

Title

MODELING UNCERTAINTY OF DIGITAL ELEVATION MODELS SRTM AND ASTER AND THEIR IMPACTS ON LANDFORM CLASSIFICATION IN GARM-CHAY BASIN

Author(s)

FEIZIZADEH BAKHTIAR | ABDOLAH ABADI SALIMEH | VALIZADEH KAMRAN KHALIL | Issue Writer Certificate

Keywords

UNCERTAINTY ANALYSISQ1

DIGITAL ELEVATION MODELQ2

ASTERQ1

SRTMQ1

GARMCHAYQ1

Abstract

Digital Elevation Model (DEM) is one of the main geographical data models which forms the basis of the different spatial analysis. DEM is known as fundamental data for many model ingtasks. 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 interrelated factors, including the methods of data acquisition, the nature of the input data, and the methods employed in generating the DEMs. Analysis of uncertainty in different fields, due to data quality and related issues such as error, uncertainty models, error propagation, error elimination and uncertainties in the data, are felt more than any other times. Of all these factors, data acquisitionis the most critical one. Previous studies on DEM data acquisition have focused either on examination of generation method (s), or on case studies of accuracy testing. These studies are not adequate, however, for the purpose of understanding uncertainty (an indicator used to approximate the discrepancy between geographic data and the geographic reality that these data intend to represent) associated with DEM data and thepropagation of this uncertainty through GIS based analyses. The development of strategies for identifying, quantifying, tracking, reducing, visualizing, and reporting uncertainty in DEM data are called for by the GIS community. In order to apply UNCERTAINTY ANALYSIS on DEMs, this study aimed to evaluate the error rate and uncertainty of elevation data obtained from SRTM and ASTER satellites. The objectives of this study are: (1) to understand the sources and reasons for uncertainty in DEMs produced by cartographic digitizing; (2) to develop methods for quantifying the uncertainty of DEMs using distributional measures and (3) to measure the uncertainty associated with DEMs and minimize the 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 of deviation between two sets of elevation data, conventional ways areto yield statistical expressions of the accuracy, such as the root mean square error, standard deviation, and mean. In fact, all statistical measures that are effective for describing a frequency distribution, including central tendency and dispersion measures, may be used, as long asvarious assumptions for specific methods are satisfied. Our research methodology includes several steps. The first step was, using the statistical indices ME, STD and RMSE, the error rate of DTMs for obtaining the chances of error in ach model. It has to 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 of accounting for spatial variation of errors over the interpolated surface. Inorder to obtain more accurate results, then uncertainty of data errors was also simulated by Monte Carlo method and error propagation pattern was extracted by interpolation of results. The results of this step show that, the DEM derived from pairstereo ASTER despite having better spatial resolution, included more errors and practically lacking the details of DTM 30 meters. Finally, removing the error propagation pattern from DEMs, the secondary DEM was produced. By recalculating indicators describing the error and comparing these values with the initial values, the results indicate that, both DEMs show more accuracy after eliminating the error propagation pattern. TPI Index was used to determine the location of basin topography and the basin is divided into 6 classes and error rate in each class was calculated before and after the simulation. The results showed that, the error ratesin all classes before and after the simulation in both Dems were reduced. In terms of UNCERTAINTY ANALYSIS methods for DEMs, results of our research indicated that the RMSE methods alone is not sufficient for quantifying DEM uncertainty, because this measure rarely addresses the issue of distributional accuracy. To fully understand and quantify the DEM uncertainty, spatial accuracy measures, such as accuracy surfaces, indices for spatial autocorrelation, and variograms, should be used. Results also indicated 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.

Cites

No record.

References

No record.

Cite

APA: Copy

FEIZIZADEH, BAKHTIAR, ABDOLAH ABADI, SALIMEH, & VALIZADEH KAMRAN, KHALIL. (2017). MODELING UNCERTAINTY OF DIGITAL ELEVATION MODELS SRTM AND ASTER AND THEIR IMPACTS ON LANDFORM CLASSIFICATION IN GARM-CHAY BASIN. GEOGRAPHICAL DATA, 26(103 ), 29-41. SID. https://sid.ir/paper/253279/en

Vancouver: Copy

FEIZIZADEH BAKHTIAR, ABDOLAH ABADI SALIMEH, VALIZADEH KAMRAN KHALIL. MODELING UNCERTAINTY OF DIGITAL ELEVATION MODELS SRTM AND ASTER AND THEIR IMPACTS ON LANDFORM CLASSIFICATION IN GARM-CHAY BASIN. GEOGRAPHICAL DATA[Internet]. 2017;26(103 ):29-41. Available from: https://sid.ir/paper/253279/en

IEEE: Copy

BAKHTIAR FEIZIZADEH, SALIMEH ABDOLAH ABADI, and KHALIL VALIZADEH KAMRAN, “MODELING UNCERTAINTY OF DIGITAL ELEVATION MODELS SRTM AND ASTER AND THEIR IMPACTS ON LANDFORM CLASSIFICATION IN GARM-CHAY BASIN,” GEOGRAPHICAL DATA, vol. 26, no. 103 , pp. 29–41, 2017, [Online]. Available: https://sid.ir/paper/253279/en

Related Journal Papers