Using ETM+ band ratios and principal component analysis for monitoring of vegetation cover in Neyshabour area

بوم شناسی کشاورزی

Article 11, Volume 2, Issue 1 - Serial Number 3, March 2010, Pages 103-110   

Document Type: Scientific - Research

DOI: 10.22067/jag.v2i1.7607

Authors

Seyed Hossein Sanaei Nejad^{* 1}; AliReza Astaraei²; Marjan Ghaemi²

¹Department of Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran

²Department of Soil Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran

Abstract

Remote sensing techniques are known as very useful methods for studying land use management in arid and semi arid areas, where undeveloped soils are dominant. These techniques can be used for determining different environmental characteristics including soil and vegetations. In this study ETM+ band ratios and Principal component analysis (PCA) were used for monitoring of vegetation cover and its disparity in relation to soil characteristics. Neyshabour area was selected as the study area. The results showed that using of vegetation indices and PCA increased regression coefficients for the model. It was also found that when the analysis is restricted to more homogenous areas, the regression coefficients were improved significantly. It was also concluded that it is possible to develop some statistical models for using of ETM+ images for monitoring vegetation cover in arid areas which are covered dispersedly.

Keywords

Landsat; Modelling; RS; Vegetation Index

References

1- Aitkenhead, M.J., and Aalders, I.H. 2008. Classification of Landsat thematic mapper imagery for land cover using neural networks. International Journal of Remote Sensing 1:1–10.

2- Arzani, H., and King, G.W. 2008. Application of remote sensing (landsat TM data) for vegetation parameters measurement in western division of NSW. In: Proceedings of the International Grassland Congress. Hohhot, China. ID NO. 1083.

3- Bahtti, A.U., Mulla, D.J., and Frazier, B.E. 1991. Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and thematic mapper images. Remote Sens. Environment 31: 181- 191.

4- Barnes, E.M., Sudduth, K.A., Hummel, J.W., Lesch, S.M., Corwin, D.L., Yang, C., Daughtry, C.S.T., and Bausch, W.C. 2003. Remote and ground-Based sensore techniques to map soil properties. Photogrammetric Engineering and Remote Sensing 69: 619-630.

5- Bricklemyer, R.S., Lawrence, R.L., and Miller, R.R. 2002. Documenting no-till and conventional till practices using Landsat ETM+ imagery and logistic regression. Soil and Water Conservation 57: 267-271.

6- Buyantuyev, A. 2007. SAVI (Soil Adjusted Vegetation Index) of the 2005 Landsat Thematic Mapper Image. Retrieved May 10, 2008 from http://seinet.asu.edu/DataCatalog/wholeRecord.jsp?id=370&source=ces_dataset

7- Cohen, W.B., Maiersperger, T.K., Gower, S.T., and Turner, D.P. 2003. An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sensing of Environment 84: 561–571.

8- Douaoui, A.E.K., Nicolas, H., and Walteer, C. 2006. Detecting salinity hazards within a semiarid context by means of combining soil and remote sensing data. Geoderma 134: 217- 230.

9- Escadafal , R., Albinet, F., and Simonneaux, V. 2005. Arid Land cover change trend analysis with series of satellite images for desertification monitoring in Northern Africa. http://www.isprs.org/publications/related/ISRSE/html/papers/953.pdf

10- Farifteh, J., and Farshad, A. 2002. Remote sensing and modeling of topsoil properties, a clue for assessing land degrading. In: Proceedings of the 17th–World Congress of soil Science. Bangkok Thailand 14-20 August, 865.

11- Fernandez Buces, N., Siebe, C., Cram, S., and Palacio, J.L. 2006. Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico. Arid Environments 65: 644–667.

12- Foody, G.M., Cutler, M., Mcmorrow, J., Pelz, D., Tangki, H., Boyd, D.S., and Douglas, I. 2001. Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecology and Biogeography 10: 379–387.

13- Frazier, B.E., and Cheng, Y. 1989. Remote sensing of soils in eastern Palouse region with Landsat thematic mapper. Remote sense. Environment 28: 317- 325.

14- Hickler, T., Smith, B., Sykes, M.T., Davis, M.B., Sugita, S., and Walker, K. 2004. Using a generalized vegetation model to simulate vegetation dynamics in northeastern USA. The Ecological Society of America 85: 519–530.

15- Huete, A.R. 1989. Soil influences in remotely-sensed vegetation canopy spectra, Chapter 4. In: G. Asrar (ed.), Theory and Applications of Optical Remote Sensing. John Wiley and Sons, N.Y., pp. 107-141.

16- Khan, N.M., Rastoskuev, V.V., Shalina, E.V. and Sato, Y. 2001. Mapping Salt Affected Soils using remote sensing Indicators a simple approach with the use of GIS IDRISI. In: Proceedings of the 22nd Asian Conference on Remote Sensing, 5-9 November, Singapore. Center for Remote Imaging, Sensing and Processing (CRISP), National University of Singapore; Singapore Institute of Surveyors and Valuers; Asian association on remote sensing

17- Ma, M and Xuemei, W. 2005. Vegetation cover change during 1981 to 2001 in Northwest China. The Eighth International Conference on Dryland Development 25-28 February 2006, Beijing, China.

18- Ma, Y.M., Menenti, M., Tsukamoto, O., Ishikawa, H., Wang, J.M., and Gao, Q.Z. 2004. Remote sensing parameterization of regional land surface heat fluxes over arid area in northwestern China. Arid Environments 57: 117–133.

19- Matsushita, B., Yang, W., Chen, J., Onda1, Y., and Qiu, G. 2001. Sensitivity of the Enhanced Vegetation Index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors 7: 2636-2651.

20- Moleele, N., Ring rose, S., Arenberg, W., Lunden B., and Vanderpost, C. 2001. Assessment of vegetation Indexes useful for browse production in semi-arid ranglands. International Remote Sensing 22: 741-756.

21- Nikolakopoulos, K.G. 2003. Use of Vegetation Indexes with ASTER VNIR Data for Burnt Areas Detection in Western Peloponnese, Greece. In: Proceedings of Geoscience and Remote Sensing Symposium, Athens, IGARSS 03, IEEE International 5: 3287-3289.

22- Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.M., Tucker, C.J., and Stenseth, N.C. 2005. Using the satellite –derived NDVI to assess ecological responses to environmental change. Trends in ecology and evolution 9: 503- 510.

23- Ray, S.S., Singh, J.P., Dasa, G., and Panigrahy, S. 2004. Use of high resolution remote sensing data for generating sitespecific soil mangement plan. In: Proceeding of the 4th International Society for Photogrammetry and Remote sensing congress. July 12-23, Istanbul, Turkey. p. 127 ff.

24- Saxsena, R.K., Verma, K.S., Srivastava, R., Yadav, J., Patel, N.K., Nasre, R.A., Barthwal, A.K., Shiwalkar, A.A., and Londhe, S.L. 2003. Spectral reflectance properties of some dominant soils occurring on different altitudinal zones in Uttarancha Himalayas. Agropedology 13: 35- 43.

25- Taher kia, H., 1996. Remote Sensing. Tehran University. Iran. (In Persian)

26- Xulin G, and Price, P. 2001. Modeling biophysical factors for grasslands in eastern Kansas using Landsat TM data. The Transaction of Kansas Academy of Science 103(3-4):122-138

27- Yuan, J., and Long, L. 1995. Study on forest vegetation classification with remote sensing. In: Proceeding of The Second IFIP International Conference on Computer and Computing Technologies in Agriculture (CCTA2008), October 18-20, 2008, Beijing, China.