Determination of Landuse Factor in EPM Model by Using Remote Sensing Indices, Tarik Dam Basin, Rudbar, Guilan Province
Subject Areas : ClimatologyAli MohammadiTorkashvand 1 , Reza Bayat 2 , Nasrollah MolaeeHashtjin 3 , Somayyeh Jafari 4
1 - دانشیار دانشکده کشاورزی، دانشگاه آزاد اسلامی، واحد رشت، ایران
2 - عضو هیأت علمی پژوهشکده حفاظت خاک و آبخیزداری، ایران
3 - استاد گروه جغرافیا، دانشگاه آزاد اسلامی، واحد رشت، ایران
4 - پژوهشکده حفاظت خاک و آبخیزداری، ایران
Keywords: Remote Sensing, Erosion, EPM, Landuse,
Abstract :
One of the main problems in the management of watersheds in the country is the lack of information and data. Due to the lack of Sediment Measurement stations in most country's watersheds and insufficient data, empirical models can be important tools for the preparation of these layers. EPM model is used in experimental models, the use of these models to estimate erosion and sediment in basins without data has the costs and difficulties in providing map. In EPM model, the user factor is one of the factors that can be provided through remote sensing with good accuracy and at lower costs. Tarik dam Basin in the west of Rudbar, Guilan province, was considered as study basin. The area of basin was 6102.1 ha with average elevation of 11296 m. Digital elevation model (DEM) from satellite SRTM radar were provided and the required maps were provided from DEM and satellite images. Land use map was prepared based on visual interpretation of Google Earth images and field views. The user factorof EPM model based on the conventional method (field views and corresponding table) and mean opinions of three experienced expert was appointed for each landuse unit. A number of indicators associated with a landuse such as NDVI, PVI, SAVI and MSAV were extracted on ETM+ satellite image in 2011. The average indices for every land use were calculated by Software of remote sensing, and by using a linear regression model, the relationship between the user factor and remote sensing indicators were analyzed. At the next stage, other factors of model based on traditional methods was carried out in determining and estimating of erosion. The erosion of basin was again estimated by using all previous factors and by more appropriate index. The best relationship between vegetation index (VI) and user factor of model was obtained that correlation coefficient was 0.793.
1- رفیعى، ر.، کمانى، ن.، خدا بخش، س. و بزرگزاده، ع. (1389): توسط MPSIAC اجرا و کالیبراسیون مدل تجربی شرایط فیزیکی حاکم بر حوضه، مطالعه موردی: حوضه آبریز رودخانه بختیاری، استان لرستان. فصلنامه زمین شناسی ایران، 4 (14): 63-71.
2- یمانی، م. مزیدی، ا. (1387): بررسی تغییرات سطح و پوشش گیاهی کویر سیاهکو با استفاده از دادههای سنجش از دور، پژوهشهای جغرافیا، 64، صص. 12-1.
_||_3- Chen, X.L., Zhao, H.M., Li, P.X., Yin, Z.Y. (2006): Remote Sensing Image-Based Analysis of The Relationship Between Urban Heat Island and Land Use/Cover Changes. Rem. Sens. Environ. 104: 133-146.
4- Das, P. T., Tajo, L. and Goswami, J. (2009): Assessment of Citrus Crop Condition in Umling Block of Ri-Bhoi District Using RS and GIS Technique. Journal of The Indian Society of Remote Sensing. 37 (2): 317-324.
5- Essa, S. (2004): GIS Modeling of Land Degradation in Northern Jordan Using Landsat Imagery. http://www.isprs.org/istanbul2004/comm4/papers/401.pdf.
6- Fletcher, R.S. (2005): Evaluating High Spatial Resolution Imagery for Detecting Citrus Orchards Affected by Sooty Mould. International Journal of Remote Sensing, 26 (3), pp. 495-502.
7- Gatsis, I., Pavlopoulos, A. and Parcharidis, I. (2001): Geomorphological Observation and Related Natural Hazards Using Merged Remotely Sensed Data: a Case Study in The Corintos Area (NE Peloponnese, S. Greece), Geografiska Annaler: Series A., Physical Geography. 83 (4): 217-228.
8- Koleja, J., N. Y. Manakos and A. Konstadinis. (1997): Getting Standardized Spectral Information About Eroded Soil by Integration of GIS and Remotely Sensed Data. Google Site.
9- Muschen, B., Flugel, W.A., Hochschild, V., Steinnocher, K. and Quiel, F. (2001): Spectral and Spatial Classification Methods in The ARSGISIPproject. Phys. Chem. Earth. 26 (7-8): 613-616.
10- Oetter, D. R., Cohen, W.B., Berterretche, M., Maiersperger, T.K., Kennedy, R.E. (2001): Land Cover Mapping in an Agricultural Setting Using Multiseasonal Thematic Mapper Data. Remote Sensing of Environment. 76 (2): 139-155.
11- Ramos, M. I., Gil, A.J., Feito, F.R. and Garcia-Ferrer, A. (2007): Using GPS and GIS tools to Monitor Olive Tree Movements. Computer Elect. Agr., 57: 135-148.
12- Rembold, F., Carnicelli, S., Nori, M. and Gioranni, A. F. (2000): Use of Aerial Photographs, Landsat TM Imagery and Multidisciplinary Field Survey for Land-Cover Change Analysis in The Lakes Region (Ethiopia). International Journal of Applied Earth Observation and Geoinformation. 2 (3-4): 181-189.
13- Seubert, C. E., Baumgardner, M.F., weismiller, R.A. and Krischner, F.R. (1979): Mapping and Estimating Areal Extent of Severely Eroded Soils of Selected Sites in Northern Indiana, Proc. Symp. Machine Processing of Remotely Sensed Data: 234-238.
14- Unal, E., Mermer, A. and Mete Dogan, H. (2004): Determining Major Orchard (Pistachio, Olive, Vineyard) Areas in Gaziantep Province Using Remote Sensing Techniques. The International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. 34, Part XXX.
15- Zha, Y. J., Gao, S.N. (2003): Use of Normalized Difference Built-Up Index in Automatically Mapping Urban Areas From TM Imagery. Int. J. Rem. Sens. 24: 583-594.