LULC Classification and Topographic Correction of Landsat-7 ETM+ Imagery in the Yangjia River Watershed: the Influence of DEM Resolution

DEM-based topographic corrections on Landsat-7 ETM+ imagery from rugged terrain, as an effective processing techniques to improve the accuracy of Land Use/Land Cover (LULC) classification as well as land surface parameter retrievals with remotely sensed data, has been frequently reported in the literature. However, few studies have investigated the exact effects of DEM with different resolutions on the correction of imagery. Taking the topographic corrections on the Landsat-7 ETM+ images acquired from the rugged terrain of the Yangjiahe river basin (P.R. China) as an example, the present work systematically investigates such issues by means of two commonly used topographic correction algorithms with the support of different spatial resolution DEMs. After the pre-processing procedures, i.e. atmospheric correction and geo-registration, were applied to the ETM+ images, two topographic correction algorithms, namely SCS correction and Minnaert correction, were applied to assess the effects of different spatial resolution DEMs obtained from two sources in the removal of topographic effects and LULC classifications. The results suggested that the topographic effects were tremendously reduced with these two algorithms under the support of different spatial resolution DEMs, and the performance of the topographic correction with the 1:50,000-topographic-map DEM was similar to that achieved using SRTM DEM. Moreover, when the same topographic correction algorithm was applied the accuracy of LULC classification after topographic correction based on 1:50,000-topographic-map DEM was similar as that based on SRTM DEM, which implies that the 90 m SRTM DEM can be used as an alternative for the topographic correction of ETM+ imagery when high resolution DEM is unavailable.

[1]  Dengsheng Lu,et al.  Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research , 2002 .

[2]  P. Teillet,et al.  On the Slope-Aspect Correction of Multispectral Scanner Data , 1982 .

[3]  Craig A. Coburn,et al.  SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Christian Prat,et al.  Classification from Landsat TM of Indurated Volcanic Materials (tepetates) of the Mexican Neo‐volcanique Belt , 2000 .

[5]  Jeffrey D. Colby,et al.  Land cover classification using Landsat TM imagery in the tropical highlands : the influence of anisotropic reflectance , 1998 .

[6]  M. Minnaert The reciprocity principle in lunar photometry , 1941 .

[7]  T. Wassmer 6 , 1900, EXILE.

[8]  Yasushi Yamaguchi,et al.  Evaluation of the Effect of Pre‐processing of the Remotely Sensed Data on the Actual Evapotranspiration, Surface Soil Moisture Mapping by an Approach Using Landsat, DEM and Meteorological Data , 2000 .

[9]  J. Nichol,et al.  Topographic correction for differential illumination effects on IKONOS satellite images , 2004 .

[10]  T. Lin,et al.  The Lambertian assumption and Landsat data. , 1980 .

[11]  D. Reeder Topographic correction of satellite images: Theory and application , 2002 .

[12]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[13]  C. Conese Topographic normalization of TM scenes through the use of an atmospheric correction method and digital terrain model , 1993 .

[14]  T. Tokola,et al.  Use of topographic correction in Landsat TM-based forest interpretation in Nepal , 2001 .

[15]  A. Thomson,et al.  Effects of topography on radiance from upland vegetation in North Wales , 1990 .

[16]  P. Chavez Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[17]  D. Civco Topographic normalization of landsat thematic mapper digital imagery , 1989 .

[18]  Dongmei Chen,et al.  Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case , 2004 .

[19]  A. Gillespie,et al.  Topographic Normalization of Landsat TM Images of Forest Based on Subpixel Sun–Canopy–Sensor Geometry , 1998 .

[20]  Massimo Vincini,et al.  An empirical topographic normalization method for forest TM data , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[21]  David Riaño,et al.  Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003) , 2003, IEEE Trans. Geosci. Remote. Sens..