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Effect of seasonal spectral variations on land cover classification

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Abstract

The supervised classification (Maximum likelihood) on three dates of IRS (LISS III) satellite data was performed to study the effect of seasonal spectral variation on land cover classification for the study area falling in the Solan district of Himachal Pradesh at latitude 30° 50’ N to 31° ’N and longitude 77° 00’ E to 77° 15’ E. It was found that the summer dataset was better with overall classification accuracy of 76% as compared to winter and spring dataset with classification accuracy of 49 and 46%, respectively.

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References

  • Chavez, Jr., PS. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data.Remote Sens. Environ.,24: 459–479.

    Article  Google Scholar 

  • Congalton, R., Green, K. and Teply, J. (1993). Mapping old growth forests on national forest and park lands in Pacific Northwest from remotely sensed data.Photogramm. Eng. Remote Sens.,59(4): 529–535.

    Google Scholar 

  • Eder, J.J. (1989). Don’t shoot until it’s autumn.J. of Forestry,87(6): 50–51.

    Google Scholar 

  • Foody, GM., McCulloch, M.B. and Yates, W.B. (1995). Classification of remotely sensed data by an artificial neutral network: Issues related to training data characteristics.Photogramm. Eng. Remote Sens.,61(40): 391–401.

    Google Scholar 

  • Lillesand, T.M. and Kiefer, R.W. (2000). Remote Sensing and Image Interpretation (4th ed.). John Wiley and Sons, New York.

    Google Scholar 

  • Miller, J.R., Wu, J., Boyer, M.G, Berlanger, M. and Hare, E.W. (1991). Seasonal patterns in leaf reflectance rededge characteristics.Int. J. Remote Sens.,12: 1509–1523.

    Article  Google Scholar 

  • Schardt, M.K., Schurek, A. and Winter, R. (1990). Forest mapping using satellite imagery. The Riegenburg map sheet 1: 2000,000 as example.ISPRS J. of Photogrammetry and Remote Sensing,45: 33–46.

    Article  Google Scholar 

  • Schriever, J.R. and R. Congalton, R. (1995). Evaluating seasonal variability as an aid to cover-type mapping from Landsat Thematic Mapper in the Northeast.Photogramm. Eng. Remote Sens.,61(3): 321–327.

    Google Scholar 

  • Skidmore, A.K. and Turner, B.J. (1988). Forest mapping accuracies are improved using a supervised nonparametric classifier with SPOT data.Photogramm. Eng. Remote Sens.,61(10): 1129–1143.

    Google Scholar 

  • Troup, R.S. (1921).The Silviculture of Indian Trees. Vol. II, Clarendon Press Oxford, UK.

    Google Scholar 

  • Wolter, P.T., Miadenoff, D.J., Host, G.E. and Thoman, R.C. (1995). Improved forest classification in the northern lake states using multi-temporal landsat imagery.Photogramm. Eng. Remote Sens.,61(9): 1129–1143.

    Google Scholar 

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Sharma, D.P., Bren, L. Effect of seasonal spectral variations on land cover classification. J Indian Soc Remote Sens 33, 203–209 (2005). https://doi.org/10.1007/BF02990036

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  • DOI: https://doi.org/10.1007/BF02990036

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