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Improving the Temporal and Spatial Accuracy of the Normalized Difference Vegetation Index (NDVI) Map using Satellite Image Fusion Algorithms
Zeinab Zaheri Abdehvand1 , Mostafa Kabolizadeh * 2
1- Faculty of Erath Sciences, Shahid Chamran University of Ahvaz, Ahvaz Iran,
2- Faculty of Erath Sciences, Shahid Chamran University of Ahvaz, Ahvaz Iran, , m.kabolizade@scu.ac.ir
Abstract:   (2418 Views)
In vast areas, the possibility of simultaneous access to satellite images with appropriate spatial resolution, such as Landsat images, is always a challenge. In addition, the temporal resolution of the Landsat satellite does not provide the possibility of examining short-term changes in phenomena such as vegetation. The aim of this research is to use the temporal and spatial fusion techniques of Landsat-8 and MODIS satellite images in preparing the Normalized Vegetation Detection Index (NDVI) map. For this purpose, six image fusion algorithms, including NNDiffuse (Nearest Neighbor Diffusion), PC (Principal Component), Brovey, CN (Color Normalized), Gram-Schmidt, and SFIM, have been used in an experimental area in Khuzestan province. After evaluating the results of the algorithms and choosing the most appropriate fusion algorithm, based on the statistical indicators of the spectral (correlation coefficient) and spatial (Laplacen filter) criteria of each of the algorithms, the spectral and spatial information of the reflection of red and near-infrared of 8 mosaicked Landsat-8 images (30 m) were combined with the red and near-infrared bands of one MODIS image (250 m). In order to investigate the vegetation cover, the NDVI was prepared with the fused satellite image in the Khuzestan province. The results of the research have shown that the NNDiffuse integration fusion algorithm has a very good accuracy among other algorithms in terms of the spatial evaluation index and spectral quality criteria. Therefore, this algorithm was recruited to combine the red and near-infrared bands of Landsat-8 and MODIS images. Compared to the original Landsat-8 image, the NDVI map prepared by this algorithm has the lowest statistical error of RMSE (0.1234) and MAE (0.081), respectively.
 
Keywords: Khuzestan province, NNDiffuse algorithm, image fusion, Normalized Detection Vegetation Index, MODIS.
     
Type of Study: Research | Subject: Rs
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
This work is licensed under a Creative Commons — Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)