Volume 25, Issue 78 (9-2025)
jgs 2025, 25(78): 0-0 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Zaheri Abdehvand Z, Kabolizadeh M. (2025). Improving the Temporal and Spatial Accuracy of the Normalized Difference Vegetation Index (NDVI) Map using Satellite Image Fusion Algorithms. jgs. 25(78), doi:10.61186/jgs.25.78.17
URL: http://jgs.khu.ac.ir/article-1-4305-en.html
URL: http://jgs.khu.ac.ir/article-1-4305-en.html
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
2- Faculty of Erath Sciences, Shahid Chamran University of Ahvaz, Ahvaz Iran, ,
Abstract: (3663 Views)
In vast areas, accessing satellite images with appropriate spatial resolution, such as Landsat images, is often challenging. dditionally, the temporal resolution of the Landsat satellite does not allow for the examination of short-term changes in phenomena such as vegetation. The aim of this research is to utilize temporal and spatial fusion techniques of Landsat-8 and MODIS satellite images to prepare a Normalized Difference Vegetation Index (NDVI) map. For this purpose, six image fusion algorithms—NNDiffuse (Nearest Neighbor Diffusion), PC (Principal Component), Brovey, CN (Color Normalized), Gram-Schmidt, and SFIM—were applied in an experimental area in Khuzestan province. After evaluating the results of these algorithms and selecting the most appropriate algorithm based on statistical indicators (spectral criteria such as the correlation coefficient and spatial criteria such as the Laplacian filter), the spectral and spatial information from the red and near-infrared bands of eight mosaic Landsat-8 images (30 m resolution) were combined with the red and near-infrared bands of one MODIS image (250 m resolution). To investigate vegetation cover, the NDVI was calculated using the fused satellite image for Khuzestan province. The results showed that the NNDiffuse fusion algorithm demonstrated very high accuracy among the tested algorithms in terms of spatial evaluation and spectral quality criteria. Consequently, this algorithm was selected 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 using this algorithm had the lowest statistical errors, with an RMSE (Root Mean Square Error) of 0.1234 and an MAE (Mean Absolute Error) of 0.081.
Keywords: Khuzestan province, NNDiffuse algorithm, image fusion, Normalized Detection Vegetation Index, MODIS.
References
1. اژدری، علی؛ حیدریان پیمان؛ فتح‌بار، سمیرا؛ صالحی، حسین و فولادی، علی (1396). اولویت‌های کانون‌های تولید گرد و غبار در استان خوزستان. سازمان زمین‌‌شناسی و اکتشاف معدنی استان خوزستان.
2. شیرازی، میترا؛ اخوان محمد اخوان؛ متین‌فر، حمیدرضا و نخکش، منصور(1399). مقایسه روش‌های کاهش مقیاس تصویر MODIS و OLI برای تشخیص گرد و غبار صنعتی، مجله تحقیقات مرتع و بیابان ایران، شماره 26 (3). [DOI:10.22092/ijrdr.2019.119996]
3. کابلی‌زاده، مصطفی؛ رنگزن، کاظم و محمدی، شاهین (1397). کاربرد تلفیق تصاویر ماهواره ای لندست-8 و سنتینل-2 در پایش محیطی، سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی (سال نهم/ شماره سوم).
4. نخعی‌نژاد فرد، سارا؛ غلامی، حمید؛ اکبری، داود؛ تلفر، مت و رضایی، مرضیه(1398). ارزیابی استفاده از الگوریتم‌های مختلف ادغام تصویر در تهبه نقشه شاخص‌های گیاهی. فصلنامه علمی ـ پژوهشی اطلاعات جغرافیایی سپهر، 28(112)، 199-217. . [DOI:10.22131/sepehr.2020.38616]
5. Acerbi-Junior, F., Clevers, J., & Schaepman, M. (2006). The assessment of multi-sensor image fusion using wavelet transforms for mapping the Brazilian Savanna. International Journal of Applied Earth Observation and Geoinformation, 8(4): 278-288.
https://doi.org/10.1016/j.jag.2006.01.001 [DOI:10.1016/j.jag.2006.01.001.]
6. Al-Wassai, F., Kalyankar, N.V., & Al-Zuky, A A. (2011). Arithmetic and frequency filtering methods of pixel-based image fusion techniques. arXiv preprint arXiv:1107.3348.
7. Boyte, S.P., Wylie, B.K., Rigge, M.B. & Dahal, D. (2017). "Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA", GIScience & Remote Sensing, 1-24. [DOI:10.1080/15481603.2017.1382065]
8. Chen, Sh., Zhang, L., Hu, X., Meng, Q., Qian, J. & Gao, J. (2023). "A Spatiotemporal Fusion Model of Land Surface Temperature Based on Pixel Long Time-Series Regression: Expanding Inputs for Efficient Generation of Robust Fused Results" Remote Sensing 15, no. 21: 5211.
https://doi.org/10.3390/rs15215211 [DOI:10.3390/rs15215211.]
9. Gao, F., Hilker, T., Zhu, X., Anderson, M., Masek, J., Wang, P., & Yang. (2017). Fusing Landsat and MODIS Data for Vegetation Monitoring. IEEE Geoscience and Remote Sensing Magazine, 3(3): 47-60. [DOI:10.1109/MGRS.2015.2434351]
10. Lau, W., King, B.A., & Li, Z. (2000). The influence of image classification by fusion of spatially oriented images. International Archieves of Photogrammetry and Remote Sensing, 33(B7/2; PART 7): 752-759.
11. Moller, M., Gerstmann, h., Gao, F., Dahms, T.C., & Forster, M. (2017). Coupling of phenological information and simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk. CATENA, 150: 192-205. https://doi 10.1016/j.catena.2016.11.016 [DOI:10.1016/j.catena.2016.11.016]
12. Moltó, E. (2022). "Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots" Sensors 22, no. 17: 6642.
https://doi.org/10.3390/s22176642 [DOI:10.3390/s22176642.] [PMID] []
13. Niazi, Y., Moosavi, V., Talebi, A., Mokhtari, M.H., & Shamsi, S.R.F. (2015). A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature. Remote Sensing of Environment, 169, pp.243-254.
https://doi.org/10.1016/j.rse.2015.08.015 [DOI:10/.1016/j.rse.2015.08.015.]
14. Olsoy, P., Mitchell, J., Glenn, N., Flores, A. (2017). Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain. Remote Sensing, 9(10): 981 [DOI:10.3390/rs9100981]
15. Pohl, C., & Van Genderen, J. (2016). Remote sensing image fusion: A practical guide. 1st ed. Crc Press, Boca Raton, 288. [DOI:10.1201/9781315370101]
16. Pushparaj, J., & Hegde, A.V. (2017). Evaluation of pan-sharpening methods for spatial and spectral quality. Applied Geomatics, 9(1): 1-12. [DOI:10.1201/9781315370101]
17. Wang, Q., Blackburn, G.A., Onojeghuo, A.O., Dash, J., Zhou, L., Zhang, Y., & Atkinson, P.M. (2017). Fusion of Landsat 8 OLI and Sentinel-2 MSI data. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3885-3899. [DOI:10.1109/TGRS.2017.2683444]
18. Xu, S., & Ehlers, M. (2017). Hyperspectral image sharpening based on Ehlers fusion.International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W7: 941-947. [DOI:10.1109/TGRS.2017.2683444]
19. Zhang, K., Kimball, J. S., & Running, S.W. (2016). A review of remote sensing based actual evapotranspiration estimation. Wiley Interdisciplinary Reviews: Water, 3(6), 834-853. http://dx.doi.org/10.1002/wat2.1168 [DOI:10.1002/wat2.1168]
20. Zhao, J., Huang, L., Yang, H., Zhang, D., Wu, Z. & Guo, J. (2016). "Fusion and assessment of high-resolution WorldView-3 satellite imagery using NNDiffuse and Brovey algorithms. In Fusion and assessment of high-resolution WorldView-3 satellite imagery using NNDiffuse and Brovey algorithms", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2606-2609. [DOI:10.1109/IGARSS.2016.7729673] [PMID] []
21. Zhou, J., Zhan, W.D., Hu & Zhao, X. (2011). Improvement of mono-window algorithm for retrieving land surface temperature from HJ-1B satellite data. Chinese Geographical Science, 20: 123-131.
https://doi.org/10.1007/s11769-010-0123-z [DOI:1010.1007/s11769-010-0123-z]
22. Zhou, J., Chen, J., Chen, X., Zhu, X., Qiu, Y., Song, H., Rao, Y., Zhang, C., Cao, X., & Cui, X. (2021). Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction. Remote Sensing of Environment, 252, Article 112130. [DOI:10.1016/j.rse.2020.112130]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
This work is licensed under a Creative Commons — Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)