# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 4 Discussion  
The lack of an accurate and comprehensive assessment of planted forest cover leads to uncertainty in the impacts of large-scale afforestation on ecosystems. Therefore, we developed the OASB method to monitor afforestation expansion by identifying the planted forest distribution and estimating the planting years. The new framework presented in this study is an integration of the SNIC and SBTSC. The annual Landsat-NDVI time series from 1991 to 2020 was built to characterize the changes in forest cover, so that the planted forest could be distinguished from the natural forest according to the discrepancies in the temporal features caused by the planting activities. Given the significant performance of the SBTSC approach proposed by [20] for the identification of rubber plantations (OA = 89%, kappa = 0.85), we adapted the approach to discern planted forests from natural forests in arid and semi-arid regions. In addition, the homogenous objects yielded via the SNIC segmentation are more consistent with planted forests in patches compared to pixels, while reducing the impact of spectral noise. The improved annual NDVI time series was reconstructed at the object level to better characterize the changes in forest cover during the three decades, which was utilized for identifying the planted forests via the SBTSC. [PERSON] et al. [64] also proved the superiority of introducing OBIA into time-series classification for glacial environmental monitoring. In summary, the OASB has two advantages. (1) It is an automatic procedure for extracting object-level time-series shapelets with no sample requirements. Only two parameters (min and max) that constrain the length of the shapelet and a significance level (\(\alpha\)) that determines the classification threshold need to be set for this procedure. Although [PERSON] et al. [65] identified planted forests from other vegetation with high accuracy (93%) using multi-variate features incorporated with supervised classification, it was largely dependent on the training set, which led to the poor transferability of the approach. (2) The method offers the advantage of being interpretable. As the shapelet here is a sub-sequence characterizing planting activities, in addition to category information, the establishment years of the planted forests were also estimated for mapping their expansion. In essence, this is a temporal segmentation approach. It enables researchers to understand data better than algorithms such as LandTrendr [66] and BFAST [67], which focus on important breakpoints. Figure 11: Comparison between the mapped and observed years of forest planting. The colorful dots simultaneously represent the two data sources for the planted forest sample, and the color represents the number of dots in the same position; the straight red line is the linear regression between the two data sources. Affected by the uniform interval of initialized centroids, superpixels derived by SNIC segmentation are regularly shaped and compactly arranged, which leads to over-segmentation for narrow strips or large homogenous areas. Despite little loss of accuracy being observed in the final maps, further work will attempt to assess the performance of other methods, such as multiscale segmentation [68; 69], to better outline boundaries while minimizing intra-segment variation. Moreover, the procedure for detecting time-series shapelets is a hotspot for the optimization of the shapelet-based algorithms. Although an exhaustive algorithm is detailed, its computational efficiency needs to be further improved. The symbolization of the time series before detection can improve the computational efficiency of the shapelet extraction [70; 71], or else the ensemble algorithms could be taken into account; for example, [PERSON] et al. [68] achieved the integration of the shapelet and deep learning. For the 2020 NHAR planted forest map, the major errors can be attributed to the forest mask instead of the OASB.