# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 3 Results
### Planted and Natural Forest Mapping  
The resultant NHAR map in 2020 generated using the OASB method is displayed in Figure 9a, including three land-cover types: non-forest, planted forest, and natural forest. The distribution of the forests in the map is consistent with the fact that the natural forests are mainly located in the three mountains (see Section 2.1 above), whereas the planted forests are dispersed more widely. The planted forest area was 0.56 million ha, accounting for 67% of the total forest area. Simultaneously, three representative map locations were selected for enlargement to display more details based on the benefits offered by the forests. A natural barrier against sand invasion in the central arid area, formed by the forests on Mount Luo, is shown in Figure 9(b1). Figure 9(b2) displays an exemplary region for the  
Figure 8: **(a)** SNIC superpixels (white polygons) against the background of Landsat composite images in 1991, 2000, 2010, and 2020. **(b)** High-resolution images on Google Earth in 2009 (the earliest) and 2020 as reference data.  
Grain for Green Program (GGP), where ecological rehabilitation has achieved remarkable results over the past 20 years [58]. Moreover, in the region of Mount Liupan (Figure 9(b3)), the planted forests play a role in soil and water conservation. In addition to mountain areas, trees and plantations (mainly economic forests mixed with crops) have been established in plains areas.  
A qualitative validation of the classification was undertaken via visual interpretation, which revealed that the forest map broadly matched the high-resolution images (Figure 9c); the multi-temporal Landsat images in 1991, 2005, and 2020 (Figure 9d-f) demonstrated that the color of the natural forest pixels remained red (or dark red) during the study period, while that of the planted forest pixels changed due to variations in the vegetation cover  
Figure 9: (**a**) Map of the NHAR’s planted and natural forests in 2020. (**b**) The zoomed-in views of three regions corresponding to the labels 1, 2, and 3 in (**a**). (**c**) High-resolution images from Google Earth of the three regions. (**d–f**) Landsat images (in false color composite) in 1991, 2005, and 2020 of the three regions (9 images in total), respectively.  
caused by the planting activities. Moreover, 100 sample points for each land-cover type by field survey and visual interpretation based on historical high-resolution images were screened to label the forest objects and non-forest regions as the reference data for the map validation. The confusion matrix (Table 2) derived from the 300 samples was used for accuracy assessment. The OA of the forest map was 87.3%, with a kappa coefficient of 0.82. For the planted forests, the overestimation error (19.0%) described by PA (81.0%) was greater than the underestimation error (8.0%) described by UA (92.0%), indicating that the proposed framework was slightly over-classified. The overestimation (17 out of 19) of the planted forest was mainly attributed to the high underestimation error (22.0%) of the non-forest area induced by the forest mask generation process.