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Evaluation of topographic correction methods for LULC preparation based on multi-source DEMs and Landsat-8 imagery

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Abstract

Topographic shadows of irregular mountains obstruct the analysis of satellite images in hilly areas. Due to this effect, there is high variability in the reflectance response of similar vegetation types, i.e. sunny areas show more than actual reflectance, whereas shaded areas show less than expected reflectance. In this study, we have evaluated the performance of five topographic correction methods, namely Cosine, C-Huang Wei, semi empirical C, SCS + C and Variable Empirical Coefficient Algorithm (VECA) depending on the solar incidence angle and exitance angle. The two well-known digital elevation models (DEM) i.e. Shuttle Radar Topography Mission (SRTM) and TanDEM-X have been used for the study. The efficiency of the correction methods is assessed on Landsat-8 satellite image using three criteria: visual interpretation, statistical assessment and classification accuracy assessment. As seen from the statistical analysis, VECA and C-correction method provides good correction of topography for both SRTM and TanDEM-X elevation models. We have used support vector machine (SVM) classifier for classification of topographically corrected images. Our results show that VECA and C-correction method increased classification accuracy from 65.60% (for uncorrected image) to 82.40% for SRTM and 64.00% to 80.00% for TanDEM-X respectively. The highest accuracy of classification is obtained using VECA/C method with SRTM DEM. However, the SCS + C-correction method impressively reduced the visual topography effects.

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(Source: Information extraction using texture analysis, NASA)

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[Source: Satellite Image (Earth Explorer, USGS), Boundary of India (Survey of India)]

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References

  1. Soenen, S. A., Peddle, D. R., & Coburn, C. A. (2005). SCS + C: A modified sun-canopy-sensor topographic correction in forested terrain. IEEE Transactions on Geoscience and Remote Sensing,43, 2148–2159. https://doi.org/10.1109/TGRS.2005.852480.

    Article  Google Scholar 

  2. Colby, J. D. (1991). Topographic normalization in rugged terrain. Photogramm Eng Remote Sensing,57, 531–537. https://doi.org/10.1117/12.529775.

    Article  Google Scholar 

  3. Gu, D., & Gillespie, A. (1998). Topographic normalization of Landsat TM images of forest based on subpixel Sun-canopy-sensor geometry. Remote Sensing of Environment,64, 166–175. https://doi.org/10.1016/S0034-4257(97)00177-6.

    Article  Google Scholar 

  4. Li, H., Xu, L., Shen, H., & Zhang, L. (2016). A general variational framework considering cast shadows for the topographic correction of remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing,117, 161–171. https://doi.org/10.1016/j.isprsjprs.2016.03.021.

    Article  Google Scholar 

  5. Balthazar, V., Vanacker, V., & Lambin, E. F. (2012). Evaluation and parameterization of ATCOR3 topographic correction method for forest cover map** in mountain areas. International Journal of Applied Earth Observation and Geoinformation,18, 436–450. https://doi.org/10.1016/j.jag.2012.03.010.

    Article  Google Scholar 

  6. Civco, D. L. (1989). Reduction of the topographic effect in landsat thematic mapper imagery. Photogrammetric Engineering and Remote Sensing,55, 1303–1309.

    Google Scholar 

  7. Teillet, P. M., Guindon, B., & Goodenough, D. G. (1982). On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing,8, 84–106. https://doi.org/10.1080/07038992.1982.10855028.

    Article  Google Scholar 

  8. Gao, Y., & Zhang, W. (2009). LULC classification and topographic correction of landsat-7 ETM + imagery in the Yangjia River watershed: the influence of DEM resolution. Sensors,9, 1980–1995. https://doi.org/10.3390/s90301980.

    Article  Google Scholar 

  9. Richter, R., Kellenberger, T., & Kaufmann, H. (2009). Comparison of topographic correction methods. Remote Sensing,1, 184–196. https://doi.org/10.3390/rs1030184.

    Article  Google Scholar 

  10. Nichol, J., & Hang, L. K. (2008). The influence of DEM accuracy on topographic correction of Ikonos satellite images. Photogrammetric Engineering and Remote Sensing,74, 47–53. https://doi.org/10.14358/PERS.74.1.47.

    Article  Google Scholar 

  11. Riaño, D., Chuvieco, E., Salas, J., & Aguado, I. (2003). Assesment of different topographic corrections in landsat -TM data for map** vegetation types. IEEE Transactions on Geoscience and Remote Sensing,41, 1056–1061. https://doi.org/10.1109/TGRS.2003.811693.

    Article  Google Scholar 

  12. Meyer, P., Itten, K. I., Kellenberger, T., et al. (1993). Radiometric corrections of topographically induced effects on Landsat TM data in an alpine environment. ISPRS Journal of Photogrammetry and Remote Sensing,48, 17–28. https://doi.org/10.1016/0924-2716(93)90028-L.

    Article  Google Scholar 

  13. Pimple, U., Sitthi, A., Simonetti, D., et al. (2017). Topographic Correction of landsat TM-5 and landsat OLI-8 imagery to improve the performance of forest classification in the mountainous terrain of northeast Thailand. Sustainability,9, 258. https://doi.org/10.3390/su9020258.

    Article  Google Scholar 

  14. Smith, J. A., Lin, T. L., & Ranson, K. J. (1980). The lambertian assumption and landsat data the lambertian assumption I and landsat data. Photogrammetric Engineering and Remote Sensing,46, 1183–1189.

    Google Scholar 

  15. Fan, Y., Koukal, T., & Weisberg, P. J. (2014). A sun-crown-sensor model and adapted C-correction logic for topographic correction of high resolution forest imagery. ISPRS Journal of Photogrammetry and Remote Sensing,96, 94–105. https://doi.org/10.1016/j.isprsjprs.2014.07.005.

    Article  Google Scholar 

  16. Hantson, S., & Chuvieco, E. (2011). Evaluation of different topographic correction methods for landsat imagery. International Journal of Applied Earth Observation and Geoinformation,13, 691–700. https://doi.org/10.1016/j.jag.2011.05.001.

    Article  Google Scholar 

  17. Szantoi, Z., & Simonetti, D. (2013). Fast and robust topographic correction method for medium resolution satellite imagery using a stratified approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,6, 1921–1933.

    Article  Google Scholar 

  18. Gao, Y., & Zhang, W. (2009). A simple empirical topographic correction method for ETM + imagery. International Journal of Remote Sensing,30, 2259–2275. https://doi.org/10.1080/01431160802549336.

    Article  Google Scholar 

  19. Gao, Y., & Zhang, W. (2007). Variable empirical coefficient algorithm for removal of topographic effects on remotely sensed data from rugged terrain. International Geoscience and Remote Sensing Symposium,1, 4733–4736. https://doi.org/10.1109/IGARSS.2007.4423917.

    Article  Google Scholar 

  20. United State Geological Survey (2018) Landsat 8 Data Users Handbook.

  21. Jensen, J. R. (2005). Introductory digital image processing: a remote sensing perspective: Pearson Prentice Hall. New Jersey: Prentice Hall.

    Google Scholar 

  22. Ekstrand, S. (1996). Landsat TM-based forest damage assessment: correction for topographic effects. Photogrammetric Engineering and Remote Sensing,62, 151–161.

    Google Scholar 

  23. Shukla, D. P., Gupta, S., Dubey, C. S., & Thakur, M. (2016). Geo-spatial technology for landslide hazard zonation and prediction. In M. Marghany (Ed.), Environmental applications of remote sensing (pp. 281–308). Rijeka: InTech.

    Google Scholar 

  24. Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data principles and practices (2nd ed.). Florida: CRC Press/Taylor & Francis.

    Book  Google Scholar 

  25. Congalton, R. G. (1991). A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing Environment,4257, 34–46. https://doi.org/10.1016/0034-4257(91)90048-B.

    Article  Google Scholar 

  26. Wu, Q., **, Y., & Fan, H. (2016). Evaluating and comparing performances of topographic correction methods based on multi-source DEMs and Landsat-8 OLI data. International Journal of Remote Sensing,37, 4712–4730. https://doi.org/10.1080/01431161.2016.1222101.

    Article  Google Scholar 

  27. Gupta SK, Shukla DP (2017) Utilization of Tandem-X Dem for topographic correction of Sentinel-2 Satellite image. In: Pecora 20 -Observing a Changing Earth; Science for Decisions—Monitoring, Assessment, and Projection. Sioux Falls, South Dakota.

  28. Vanonckelen, S., Lhermitte, S., & Van, Rompaey A. (2015). The effect of atmospheric and topographic correction on pixel-basedimage composites: Improved forest cover detection in mountainenvironments. International Journal of Applied Earth Observation and Geoinformation,35, 320–328. https://doi.org/10.1016/j.jag.2014.10.006.

    Article  Google Scholar 

  29. Tan, B., Masek, J. G., Wolfe, R., et al. (2013). Improved forest change detection with terrain illumination corrected Landsat images. Remote Sensing Environment,136, 469–483. https://doi.org/10.1016/j.rse.2013.05.013.

    Article  Google Scholar 

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Acknowledgements

The authors are greatly thankful to German Aerospace Center (DLR) for providing TanDEM-X digital elevation model of the study area through the project (Project No. DEM_HYDR1925) sanctioned to Dr. Dericks P. Shukla. We would like to thank United States Geological Survey and European Space Agency for providing Landsat-8 and Sentinel-2 images free of cost.

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  1. School of Engineering, Indian Institute of Technology Mandi, Kamand, Himachal Pradesh, 175005, India

    Sharad Kumar Gupta & Dericks P. Shukla

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  1. Sharad Kumar Gupta
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Gupta, S.K., Shukla, D.P. Evaluation of topographic correction methods for LULC preparation based on multi-source DEMs and Landsat-8 imagery. Spat. Inf. Res. 28, 113–127 (2020). https://doi.org/10.1007/s41324-019-00274-0

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