LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.2. Methodology
2.2.1. Optimal Fuzzy Clustering of the DTW Distances
Algorithm 1: Data Reduction Process [45,46] |
1: Select values for the parameters and set . |
2: Using Equation (2) calculate the values of the potential of all the N time series. |
3: Set . |
4: Calculate . Select the time series that corresponds to the as the n-th representative time series: . |
5: Remove from the set T all times series for which , and assign them to the n-th group, the representative element of which is the time series. |
6: If T is empty stop; else turn the algorithm to Step 2. |
2.2.2. LSTM Training and Validation
3. Results and Discussion
3.1. Optimal Data Clustering and CORINE Cross Correlation
3.2. LSTM Training and Validation
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Cluster | 4 | 8 | 3 | 7 | 1 | 2 | 9 | 6 | 5 |
No. of LULC types | 16 | 15 | 14 | 13 | 12 | 11 | 11 | 7 | 3 |
% of LULC types | 69.6 | 65.5 | 60.9 | 56.5 | 52.2 | 47.8 | 47.8 | 30.4 | 13 |
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Vasilakos, C.; Tsekouras, G.E.; Kavroudakis, D. LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data. Land 2022, 11, 923. https://doi.org/10.3390/land11060923
Vasilakos C, Tsekouras GE, Kavroudakis D. LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data. Land. 2022; 11(6):923. https://doi.org/10.3390/land11060923
Chicago/Turabian StyleVasilakos, Christos, George E. Tsekouras, and Dimitris Kavroudakis. 2022. "LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data" Land 11, no. 6: 923. https://doi.org/10.3390/land11060923
APA StyleVasilakos, C., Tsekouras, G. E., & Kavroudakis, D. (2022). LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data. Land, 11(6), 923. https://doi.org/10.3390/land11060923