Authors
1
Faculty of Environmental Studies, University Putra Malaysia, 43400 (UPM), Serdang, Selangor D.E., *Corresponding author's E-mail: Ajorlo_m54@yahoo.com
2
Faculty of Agriculture, University Putra Malaysia, 43400 (UPM), Serdang, Selangor D.E., Malaysia.
3
Faculty of Environmental Studies, University Putra Malaysia, 43400 (UPM), Serdang, Selangor D.E.
Abstract
Empirical models are important tools for relating field-measured biophysical variables to remotely sensed data. Regression analysis has been a popular empirical method of linking these two types of data to estimate variables such as biomass, percent vegetation canopy cover, and bare soil. This study was conducted in a semi-arid rangeland ecosystem of Qazvin province, Iran. This paper presents the development of a regression model for predicting rangeland biophysical variables using the original image data of Landsat TM nonthermal bands. The biophysical variables of interest within the rangeland ecosystem were percent vegetation canopy cover, bare soil extent, and stone and gravel which their correlations were analyzed in relation to Landsat TM original data. The results of applying stepwise multiple regression showed that there is a significant correlation between Landsat TM band 2 reflectance values and biophysical variables. The developed models were applied to Landsat TM band 2 and relevant maps were generated. We concluded that such problems as an inexact location of field samples on the image, small size of samples, vegetation heterogeneity may significantly affect the modeling of real rangeland Landsat TM data relationships.
REFERENCES
Ajorlo, M. and Abdullah, B.R. (2007) Develop an Appropriate Vegetation Index for Assessing Rangeland Degradation in Semi-Arid Areas. In: proceedings of 28th Asian Conference on Remote Sensing, 12- 16 Nov, Kuala Lumpur, Malaysia.
Ajorlo, M. (2005) Evaluation and mapping of rangeland degradation using remotely sensed data. In: proceedings of international symposium on land degradation and desertification, 12-17 May, Uberlandia, Brazil.
Cohen, B.W., Thomas K.M., Gower S.T. and Turner P.D. (2003) An improved strategy for regression of biophysical Variables and Landsat ETM+ data. Rem Sens Environ. 84, 561-571.
Danaher, T., Armston J. and Collett L. (2004) A regression model approach for mapping Ajorlo et al., 7 woody foliage projective cover using Landsat Imagery in Queensland, Australia. In: Proceedings of Geoscience and Remote Sensing Symposium, Australia. pp. 514 – 527.
Fazilati, A. and Hosseini E.H. (1984) Rangelands of Iran and their management, development and improvement. Technical Bureau of Rangeland. Tehran, Iran.
Fitzpatrick, B. and Megan, A. (1994) Relationship between vegetation cover field data and Landsat-TM in the pasture development areas of the Douglas-Daily Basin, Northern territory. In: 7th Australian Remote Sensing Conference Proceedings, Melbourne, Australia.
Guo, X., Price K.P. and Stiles J.M. (2000) Modeling Biophysical Factors for Grasslands in Eastern Kansas Using Landsat TM Data. Trans Kans Acad Sci. 103, 122- 138.
Iranian Remote Sensing Center (IRSC) (1998) Landsat 5 TM 10 digital images. Tehran, Iran.
Lillesand, T. M. and Kiefer, R.W. (1994) Remote Sensing and Image Interpretation (3rd Edn). John Wiley & Sons Inc.
Montgomery, D.C. and Peck, E.A. (1992) Introduction to Linear Regression Analysis. New York: Wiley, pp. 270-274.
National Cartographic Center (NCC). (1995) Topographic maps (scale 1:50000). Sheets: Karafs 5860 I, Asian 5861 II, Razak 5961 III, Saman 5960 VI. K753 Series, Tehran, Iran.
Rahman, M. M., Csaplovics E., and Koch B. (2005) An efficient regression strategy for extracting forest biomass information from satellite sensor data. Int. J. Remote Sens. 26, 1511 – 1519.
Rawlings, J.O. (1998) Applied Regression Analysis (a Research Tool). Wadsworth book b and Brooks. pp. 183–184.
Salvador, R. and Pons, X. (1998) On the reliability of Landsat TM for estimating forest variables by regression techniques: a methodological analysis. IEEE Trans Geosci Rem Sens. 36, 1888-1897.
Keywords
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