Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data
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Abstract
The presence of gaps or missing values in time series prevents the practical use of such data. The current research aims at developing a simplified, straightforward technique for gap-filling the time series data of the Normalize Difference Vegetation Index (NDVI) generated using Moderate Resolution Imaging Spectroradiometer (MODIS). This research assumes that a relationship exists between the pixel location, date of acquisition and its NDVI value within a defined timeline. Therefore, two relatively simple methods were tested: the Multiple Linear Regression (MLR) analysis and the Artificial Neural Networks (ANN)to fill the NDVI missing values. While MLR is a well-known simple statistical method, the ANN has been successfully applied for the analysis of various scientific data, including the gap-filling of time series data. Nevertheless, ANN proved its supremacy in such approach. The accuracy of estimation utilizing the developed ANN model reached an average of r2 of 0.8, while the average accuracy of MLR was about 0.3. Nevertheless, the developed model could only be applied within the same timeframe of the images used for developing the model. Otherwise, the accuracy of determination was reduced significantly. The results showed that according to its performance, ANN are promising for filling missing data of NDVI time series and could be applied to any other vegetation indices as well.
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Keywords
Artificial neural networks, Gap filling, Multiple linear regression, MODIS-NDVI, Time series
References
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Chen, Y., Cao, R., Chen, J., Liu, L. & Matsushita, B. (2021). A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter. ISPRS Journal of Photogrammetry and Remote Sensing, 180,174-190. https://doi.org/10.1016/j.isprsjprs.2021.08.015.
Colditz, R.R, Conrad, C. Wehrmann, T., Schmidt, M. & Dech, S. (2008). TiSeG: A flexible software tool for time-series generation of MODIS data utilizing the quality assessment science data set. IEEE Transactions on Geoscience and Remote Sensing, 46(10), 3296-3308. https://doi.org/10.1109/TGRS.2008.921412.
Dada, E., Yakubu, J. & Oyewola, D. (2021). Artificial neural network models for rainfall prediction. EJECE, European Journal of Electrical Engineering & Computer Science, 5(2), 30-35.https://doi.org/10.24018/ejece.2021.5.2.313
Gandhi, G.M., Parthiban, S., Thummalu, N. & Christy, A. (2015). NDVI: Vegetation change detection using remote sensing and GIS – a case study of Vellore district. Procedia Computer Science,57,1199-1210.
Gummadi, J.A. (2013). Comparison of various interpolation techniques for modeling and estimation of radon concentrations in Ohio, Master of Science Degree in Engineering, The University of Toledo, Toledo, USA.
Hornik, K., Stinchcombe, M. & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366.
Huang, S., Tang, L., Hupy, J.P., Wang, Y. & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res., 32, 1-6. https://doi.org/10.1007/s11676-020-01155-1.
Kang, L., Di, L., Deng, M., Yu, E. & Xu., Y. (2016). Forecasting vegetation index based on vegetation-meteorological factor interactions with artificial neural network. In 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 1–6.
Lan, S. & Dong, Z. (2022). Incorporating Vegetation Type Transformation with NDVI time-series to study the vegetation dynamics in Xinjiang. Sustainability, 14,582-296.https://doi.org/10.3390/su14010582.
Liu, X., Ji, L., Zhang, C. & Liu, Y. (2022). A method for reconstructing NDVI time-series based on envelope detection and the Savitzky- Golay filter. International Journal of Digital Earth, 15, 553-584. https://doi.org/10.1080/17538947.2022.2044397.
Makar, R. S. & Faisal, M. (2021). Utilizing neural networks for image downscaling and water quality monitoring. Journal of Applied and Natural Science, 13(4), 1452 - 1461. https://doi.org/10.31018/jans.v13i4.3146 .
Mohanasundaram, S., Baghel, T., Thakur, V., Udmale, P. & Shrestha, S. (2022). Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand. PREPRINT (Version 1) available at Research Square, 25 July 2022, https://doi.org/10..21203/rs.3.rs-1803210/v1.
Moreno-Madrinan, M.J., Al-Hamdan, M.Z., Rickman, D.L. & Muller-Karger, F.E. (2010). Using the Surface Reflectance MODIS Terra Product to Estimate Turbidity in Tampa Bay, Florida. Remote Sensing, 2(12), 2713-2728.
Morgan, R.S., Abd El-Hady, M., Rahim, I.S., Silva, J. & Ribeiro, S. (2017). Evaluation of various interpolation techniques for estimation of selected soil properties. International Journal of GEOMATE, 13 (38), 23-30.
Myneni, R.B., Hall, F.G., Sellers, P.J. & Marshak, A.L. (1995). The interpretation of spectral vegetation indexes. IEEE T Geosci Remote, 33,481-486
Nay, J., Burchfield, E. &Gilligan. J. (2017). A machine-learning approach to forecasting remotely sensed vegetation health. International Journal of Remote Sensing, 39 (6),1800–1816. https://doi.org/10.1080/01431161.2017.14 10296.
Morgan, R. S. & Faisal, M. (2018). Improving land use/ land cover classification utilizing a hybrid method of decision trees and artificial neural networks. Bioscience Research, 15(4),4049-4060.
Reddy, D.S. & Prasad, P.R. (2018). Prediction of vegetation dynamics using NDVI time series data and LSTM. Modeling Earth Systems and Environment, 4,409-419.
Rouse, J.W., Haas, R.H., Schell J.A. & Deering, D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS, In: S.C. Freden, E.P. Mercanti, and M. Becker (eds) Third Earth Resources Technology Satellite–1 Syposium. Volume I: Technical Presentations, NASA SP-351, NASA, Washington, D.C., pp. 309-317
Sarafanov, M., Kazakov, E., Nikitin N.O. and Kalyuzhnaya, A.V. (2020). A machine learning approach for remote sensing data gap-filling with open-source implementation: an example regarding land surface temperature, surface albedo and NDVI. Remote Sensing, 12(23), 3865-3886.https://doi.org/10.3390/rs12233865.
Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J. &Nickless, D. (2004). Terra and Aqua MODIS products available from NASA GES DAAC. Advances in Space Research, 34, 710–714.
Vermote, E. (2015a). MOD09A1 MODIS/Terra surface reflectance 8-Day L3 global 500m SIN grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD09A1.006.
Vermote, E. (2015b). MOD09Q1 MODIS/Terra surface reflectance 8-Day L3 global 250m SIN grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD09Q1.006.
Wei, J. & Fan, Z. (2022). Growing stock volume estimation for Daiyun Mountain reserve based on multiple linear regression and machine learning. sustainability 14,12187. https://doi.org/10.3390/su141912187.
Yu, W., Li, J., Liu, Q., Zhao, J., Dong, Y., Zhu, X., Lin, S., Zhang H. & Zhang, Z. (2021). Historical landsat NDVI time series by integrating climate data. Remote Sensing, 13, 484. https://doi.org/10.3390/rs13030484.
Zhou, Q., Zhu, Z., Xian, G. & Li, C. (2022). A novel regression method for harmonic analysis of time series. ISPRS Journal of Photogrammetry and Remote Sensing, 185, 48-61. https://doi.org/10.1016/j.isprsjprs.2022.01.006.
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Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data . (2022). Journal of Applied and Natural Science, 14(4), 1500-1506. https://doi.org/10.31018/jans.v14i4.4095
