# Outlier Reconstruction of NDVI for Vegetation-Cover Dynamic Analyses
## 3 Results  
### Analysis of Reconstructed Results  
Reconstruction results based on different rank sizes using the proposed approach are shown in Figure 3. First, when ranks were \(1\times 3\times 2\), the approximate tensor reconstructed basic topography of the study area. Second, when ranks were upgraded to \(3\times 3\times 3\), topographic features were more clearly portrayed, and the trends and the detailed features of vegetation cover were clearly distinguished. For example, the darker the green areas, the higher the NDVI values, indicating that the vegetation cover was higher in these areas. Third, when ranks were increased to \(3\times 5\times 5\), most vegetation-cover information in 2019 and 2020 was reconstructed and fully predicted, with RMSEs of 0.110 for 2019 and 0.098 for 2020, especially for the vegetation-cover characterization of offshore islands and complex water systems at the estuary. Moreover, Figure 3 demonstrates that the reconstruction results of SWTSA with rank size of \(3\times 10\times 10\) were better than those of RFR, and almost the same as those of MLR. Furthermore, to explore the precision of reconstruction results, we constructed larger ranks. Actual and reconstructed values had a similar accumulation curve (Figure 4), and more than 85% of actual and reconstructed areas overlapped. Meanwhile, cumulative curves with different rank sizes implied that the larger ranks of core tensor may not mean significant improvement in accuracy for reconstruction results. According to the evaluation metrics shown in Table 2, for most of the reconstruction results, values of accuracy, precision, recall, and F\({}_{1}\) score exceeded 0.85, which was considered to be acceptable. When rank sizes increased to \(3\times 5\times 5\), the values of kappa concordance coefficient corresponding to 2019 and 2020 exceeded 0.80 and 0.95, respectively. In comparison with RFR, SWTSA has better accuracy and reconstruction capabilities. Moreover, Table 2 demonstrates that SWTSA and MLR had almost the same reconstruction effect corresponding to rank size of \(3\times 10\times 10\)  
Figure 3: NDVI reconstructed results for different rank sizes. A plot of reconstructed against actual values shows that reconstructed values were closer to actual values with an increase in the number of ranks (Figure 5). Meanwhile, for most reconstruction results, the values of Pearson's correlation coefficient (r) exceeded 0.85. The coefficient of determination (\(R^{2}\)) of MLR and RFR was around 0.8, which was considered unacceptable. Further, reconstruction results for 2020 were better than those for 2019 (Figures 3-5), indicating that optical remote-sensing images from the same platform (Landsat-8) may obtain different results. Lastly, considering the execution time of the proposed algorithm and the accuracy requirements of the reconstructed results, we chose the approximate reconstruction result with ranks of \(3\times 10\times 10\) for 2019, and the approximate reconstruction result with ranks of \(3\times 5\times 5\) for 2020. Both RMSE values for the chosen ranks were below 0.15 (Figure 6). All statistical indicators were consistent across results (Table 3).