Evaluating the capability of using close-range photogrammetry in measuring desert pavement roughness
محورهای موضوعی : GeomorphologyZahra Ghorbani 1 , Mahdi Tazeh 2 , Saeid Pourmanafi 3 , Saeideh Kalantari 4
1 - MSc of Combating Desertification, Agriculture and Natural Resources Department, Ardakan University, Yazd, Iran.
2 - Associate Professor, Department of Nature Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, P.O. Box 184, Ardakan, Iran.
3 - Assistant Professor of Natural Resources Engineering Department, Isfahan University of Technology, Isfahan, Iran.
4 - Assistant Professor, Department of Nature Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, P.O. Box 184, Ardakan, Iran.
کلید واژه: geomorphology, Three-dimensional model, digital camera,
چکیده مقاله :
Background and objective: There are several methods of measuring desert pavement roughness. Among these methods, one can name laser and sonic rangefinder, 3D photography, and close-range photogrammetry. Remote sensing techniques need less and cheaper equipment than laser and sonic methods. In short-range photogrammetry, the quantitative amount of terrains can be obtained by processing the images of a digital camera using special methods of photography and camera calibration.Materials and methods:This method can be introduced as an accurate and cost-effective measuring method to provide a digital model of complications and a three-dimensional model of objects. The present study aimed to evaluate the possibility of using close-range photogrammetry in measuring desert pavement roughness. In this research, first, the calibration parameter of the camera was calculated by taking photos of standard patterns. Then, the meshed samples of desert pavement were photographed and the photos were three-dimensionally simulated.Results and conclusion:The results showed that since in this method the selected points have more effective height and uniform dispersion, the measurement of the average height of roughness is more accurate. It means that measuring the roughness of the soil surface is done with high accuracy in a short time.
Afrasyabi, S., Tazeh, M., Taghizadeh Mehrjardi, R., & Kalantari, S. (2019). Performance of two measurement methods of pin meter and laser disto meter in the measurement of microtopography Created by desert pavement. Desert Ecosystem Engineering Journal, 8(22), 1-14.https://doi.org/10.22052/deej.2018.7.22.45.
Allmaras, R. R., Burwell, R. E., Larson, W. E., & Holt, R. F. (1966). Total porosity and random roughness of the interrow zone as influenced by tillage. Azad, M., Kalantari, S., Shirmardi, M., & Tazeh, M. (2021). Investigating the Effect of Land Use and Soil’s Physio-chemical properties on Wind Erosion Threshold Velocities via Data Mining. Desert Ecosystem Engineering Journal, 9(29), 1-14.https://doi.org/10.22052/deej.2020.9.29.1
Bretar, F., Arab-Sedze, M., Champion, J., Pierrot-Deseilligny, M., Heggy, E., & Jacquemoud, S. (2013). An advanced photogrammetric method to measure surface roughness: Application to volcanic terrains in the Piton de la Fournaise, Reunion Island. Remote Sensing of Environment, 135, 1-11. https://doi.org/10.1016/j.rse.2013.03.026
Dastorani, M. T., Hakimzadeh, M. A., & Kalantari, S. (2008). Evaluation of the effects of industrial wastewater on soil properties and land desertification. Desert, 13(2), 203-210.
Ewins, N. J., & Pilgrim, D. A. (1997, November). The evaluation of PhotoModeler for use under water. In The Fourth Underwater Science Symposium: Proceedings. OnePetro.
Fathizad, H., Tazeh, M., Kalantari, S., & Shojaei, S. (2017). The investigation of spatiotemporal variations of land surface temperature based on land use changes using NDVI in southwest of Iran. Journal of African Earth Sciences, 134, 249-256. https://doi.org/10.1016/j.jafrearsci.2017.06.007
Gomes, L., Arrue, J. L., Lopez, M. V., Sterk, G., Richard, D., Gracia, R., ... & Frangi, J. P. (2003). Wind erosion in a semiarid agricultural area of Spain: the WELSONS project. Catena, 52(3-4), 235-256. https://doi.org/10.1016/S0341-8162(03)00016-X
He, S., Wang, D., Li, Y., Zhao, P., Lan, H., Chen, W., ... & Chen, X. (2021). Social-ecological system resilience of debris flow alluvial fans in the Awang basin, China. Journal of Environmental Management, 286, 112230. https://doi.org/10.1016/j.jenvman.2021.112230
Jamali, A. A., Naeeni, M. A. M., & Zarei, G. (2020). Assessing the expansion of saline lands through vegetation and wetland loss using remote sensing and GIS. Remote Sensing Applications: Society and Environment, 20, 100428. https://doi.org/10.1016/j.rsase.2020.100428
Jester, W., & Klik, A. (2005). Soil surface roughness measurement—methods, applicability, and surface representation. Catena, 64(2-3), 174-192. https://doi.org/10.1016/j.catena.2005.08.005
Kargaran, F., Kalantari, S., GHANEI, M. J., & TAZEH, M. (2017). The Compare of grading criteria in Coarse ripple Mark on the windward and leeward slopes (Case Study: Hassan Abad erg in Bafg).
Khosravi, F., Tazeh, M., Naeini, S., & Kalantari, S. (2020). Evaluation and comparison of Image J and GIAS softwares with mechanical sieving in automatic particle-size distributions. Journal of Arid Biome, 9(2), 29-42. https://doi.org/10.29252/ARIDBIOM.2020.1814
Lynnerup, N., Andersen, M., & Lauritsen, H. P. (2003). Facial image identification using Photomodeler®. legal Medicine, 5(3), 156-160. https://doi.org/10.1016/S1344-6223(03)00054-3
Niazi, Y., Mendoza, M. E., Talebi, A., & Bidaki, H. (2021). GIS-based support vector machine model in shallow landslide hazards prediction: A case study on Ilam dam watershed, Iran. Journal of Nature and Spatial Sciences (JONASS), 1(1), 59-84. https://dx.doi.org/10.30495/jonass.2021.680329
Nordstrom, K. F., & Hotta, S. (2004). Wind erosion from cropland in the USA: a review of problems, solutions and prospects. Geoderma, 121(3-4), 157-167. https://doi.org/10.1016/j.geoderma.2003.11.012
Romkens, M. J., & Wang, J. Y. (1986). Effect of tillage on surface roughness. Transactions of the ASAE, 29(2), 429-0433. https://doi.org/10.13031/2013.30167
Switzer, D. A., & Candrlic, T. M. (1999). Factors Affecting the Accuracy of Non-Metric Analytical 3-D Photogrammetry, Using PhotoModeler. SAE transactions, 817-831. https://doi.org/10.4271/1999-01-0451
Taghizadeh, R., Ghazali, A., Kalantari, S., & Rahimian, M. H. (2016). Spatial distribution of soil salinity using auxiliary variables and hypercube sampling method in Meybod. Journal of Arid Biome, 6(1), 69-79. https://doi.org/20.1001.1.2008790.1395.6.1.6.9
Tazeh, M., Asadi, M., Taghizadeh, R., Kalantari, S., & Sadeghinia, M. (2018). Evaluation of geomorphometry indices in semi-automatic separation of the geomorphological types in desert areas (case study: west north of Ardekan).
Vulfson, L., Genis, A., Blumberg, D. G., Sprintsin, M., Kotlyar, A., Freilikher, V., & Ben-Asher, J. (2012). Retrieval of surface roughness parameters of bare soil from the radar satellite data. Journal of arid environments, 87, 77-84. https://doi.org/10.1016/j.jaridenv.2012.07.006
Zarei, M., Tazeh, M., & Kalantari, S. (2021). Investigating the Capability of Thermal-Moisture Indices Extracted from MODIS Data in Classification and Trend in Wetlands. Journal of the Indian Society of Remote Sensing, 49(10), 2583-2596. https://doi.org/10.1007/s12524-021-01408-4
Zehtabian, G., Ahmadi, H., Nazari Samani, A., Ehsani, A. H., & Tazeh, M. (2017). Determining the most important geomorphometric parameters in classification of desert plains with using artificial neural networks and sensitivity analysis. Journal of Range and Watershed Managment, 70(1), 197-206. https://doi.org/10.22059/JRWM.2017.61976
Zehtabian, G., Azarnivand, H., Ahmadi, H., & Kalantari, S. (2013). Presentation of Suitable Model to Estimate Vegetation Fraction Using Satellite Images in Arid Region (Case Study: Sadough-Yazd, Iran). Journal of Rangeland Science, 3(2), 108-117.
Zhang, C. L., Zou, X. Y., Gong, J. R., Liu, L. Y., & Liu, Y. Z. (2004). Aerodynamic roughness of cultivated soil and its influences on soil erosion by wind in a wind tunnel. Soil and Tillage Research, 75(1), 53-59. https://doi.org/10.1016/S0167-1987(03)00159-4
Zhang, W., Jiang, T., & Han, M. (2010, October). Digital camera calibration method based on PhotoModeler. In 2010 3rd International Congress on Image and Signal Processing (Vol. 3, pp. 1235-1238). IEEE. https://doi.org/10.1109/CISP.2010.5647253
