A model-based approach for mapping rangelands covers using Landsat TM image data

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 ۲ reflectance values and biophysical variables. The developed models were applied to Landsat TM band ۲ 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. (۲۰۰۷) Develop an Appropriate Vegetation Index for Assessing Rangeland Degradation in Semi-Arid Areas. In: proceedings of ۲۸th Asian Conference on Remote Sensing, ۱۲- ۱۶ Nov, Kuala Lumpur, Malaysia. Ajorlo, M. (۲۰۰۵) Evaluation and mapping of rangeland degradation using remotely sensed data. In: proceedings of international symposium on land degradation and desertification, ۱۲-۱۷ May, Uberlandia, Brazil. Cohen, B.W., Thomas K.M., Gower S.T. and Turner P.D. (۲۰۰۳) An improved strategy for regression of biophysical Variables and Landsat ETM+ data. Rem Sens Environ. ۸۴, ۵۶۱-۵۷۱. Danaher, T., Armston J. and Collett L. (۲۰۰۴) A regression model approach for mapping Ajorlo et al., ۷ woody foliage projective cover using Landsat Imagery in Queensland, Australia. In: Proceedings of Geoscience and Remote Sensing Symposium, Australia. pp. ۵۱۴ – ۵۲۷. Fazilati, A. and Hosseini E.H. (۱۹۸۴) Rangelands of Iran and their management, development and improvement. Technical Bureau of Rangeland. Tehran, Iran. Fitzpatrick, B. and Megan, A. (۱۹۹۴) Relationship between vegetation cover field data and Landsat-TM in the pasture development areas of the Douglas-Daily Basin, Northern territory. In: ۷th Australian Remote Sensing Conference Proceedings, Melbourne, Australia. Guo, X., Price K.P. and Stiles J.M. (۲۰۰۰) Modeling Biophysical Factors for Grasslands in Eastern Kansas Using Landsat TM Data. Trans Kans Acad Sci. ۱۰۳, ۱۲۲- ۱۳۸.  Iranian Remote Sensing Center (IRSC) (۱۹۹۸) Landsat ۵ TM ۱۰ digital images. Tehran, Iran. Lillesand, T. M. and Kiefer, R.W. (۱۹۹۴) Remote Sensing and Image Interpretation (۳rd Edn). John Wiley & Sons Inc. Montgomery, D.C. and Peck, E.A. (۱۹۹۲) Introduction to Linear Regression Analysis. New York: Wiley, pp. ۲۷۰-۲۷۴. National Cartographic Center (NCC). (۱۹۹۵) Topographic maps (scale ۱:۵۰۰۰۰). Sheets: Karafs ۵۸۶۰ I, Asian ۵۸۶۱ II, Razak ۵۹۶۱ III, Saman ۵۹۶۰ VI. K۷۵۳ Series, Tehran, Iran. Rahman, M. M., Csaplovics E., and Koch B. (۲۰۰۵) An efficient regression strategy for extracting forest biomass information from satellite sensor data. Int. J. Remote Sens. ۲۶, ۱۵۱۱ – ۱۵۱۹. Rawlings, J.O. (۱۹۹۸) Applied Regression Analysis (a Research Tool). Wadsworth book b and Brooks. pp. ۱۸۳–۱۸۴. Salvador, R. and Pons, X. (۱۹۹۸) On the reliability of Landsat TM for estimating forest variables by regression techniques: a methodological analysis. IEEE Trans Geosci Rem Sens. ۳۶, ۱۸۸۸-۱۸۹۷.

Keywords:

Biophysical variables , Empirical model , Multiple regression , Rangeland , Remotely , sensed data