Abstract
Topographic effect in remote sensing images is severe in high mountainous areas. Efficiently to reduce the effects, several topographic normalization models have been proposed. Since the performance of the models is largely dependent on the spectral band and land surface type, the best performance model can vary from image to image in an area as well as from band to band in an image. The normalized difference vegetation index (NDVI) map has been widely used for the vegetation monitoring and assessment. An efficient reduction of the topographic effect in the NDVI map must be required for the spatial analysis of the vegetation monitoring and assessment. In this paper, we propose an efficient method to select the best topographic normalization model in each band to reduce the topographic effect of NDVI maps. The histogram structural similarity (HSSIM) index was used for the model selection because the index allows to select the best model in each band of an image. Five topographic normalization models were used for the test, which include the sun-canopy-sensor (SCS), statistical-empirical, C-correction, Minnaert, and Minnaert + SCS. The performance of the proposed method was validated by using two different season Landsat-8 OLI images including the forest area of northern Malaysia. The standard deviations of the two NDVI maps generated from the test images were reduced by about 53.1% and 28.6% after correction in profile analysis. The coefficient of determination (R 2 ) between the two different NDVI maps increased from 0.626 to 0.759. It indicates that the proposed method effectively reduced the topographic effect of the NDVI maps. This result implies that the proposed method can work well in the topographic normalization. Furthermore, the proposed method would be successfully applied to index maps including the normalized difference snow index (NDSI), normalized difference water index (NDWI), etc.
Highlights
Sensed images have been widely used for the vegetation monitoring and assessment
The histogram structural similarity (HSSIM) index is used to evaluate the performance of the five topographic normalization models such as the SCS, Statisticalempirical, C-correction, Minnaert and Minnaert + SCS
The HSSIM index enables us quantitatively to evaluate the topographic normalization performance based on the similarity between sun-shaded and sunlit slope areas
Summary
Sensed images have been widely used for the vegetation monitoring and assessment. To evaluate topographic normalization methods, we need to determine how similar the pixel values are in the sunlit and sun-shaded areas. The topographic effect of the NDVI map is much lower than the reflectance image, it needs be reduced for the forest type monitoring and classification. Several methods have been proposed to correct the topographic effects of the NDVI map and they has applied to other ratio maps such as leaf area index (LAI), normalized burn ratio (NBR) and normalized difference snow index (NDSI) maps [2], [3], [16], [17]. The best model needs to be differently selected according to spectral band for more precise topographic normalization of the ratio maps. We propose an efficient band-based best model selection method for the topographic normalization of NDVI maps. The reduction of the standard deviation in the sun-shaded and sunlit areas was checked by using the original and normalized NDVI maps and the similarity of the two different season NDVI maps was compared before and after the topographic normalization as well
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
