# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 1 Introduction  
Afforestation and reforestation have been the predominant means of countering forest degradation and mitigating climate change in recent decades [1], and the global forested area increased by 123 million ha between 1990-2020 [2]. Planted forests play valuable roles in reducing losses of natural forests, combating soil erosion and desertification, and boosting poverty alleviation strategies, especially in arid and semi-arid regions, which are among the most fragile ecosystems [3]. However, ecological restoration is a continuous successive process, and afforestation managers are eager to obtain the associated environmental benefits, resulting in negative effects such as increased water scarcity, declining soil quality, and low tree survival rates [4]. Assessments of forest cover often do not discriminate planted forests from natural forests, despite differences in structure and function (e.g., stand composition, biodiversity, and carbon sequestration), which underestimates the impacts of afforestation [5]. Therefore, baseline data depicting the accurate spatiotemporaldistribution of planted forests are urgently needed to evaluate forestry eco-engineering programs and guide forestry management practices. Numerous remote sensing studies have successfully identified planted forests. These studies can be divided into two categories of methods, the first of which is single-phase image classification. This method relies on abundant spectral signatures and texture variables in multi/hyper-spectral images to classify tree species at an individual or stand level, incorporating traditional classifiers (e.g., random forest [RF] and support vector machines [SVM]) or deep learning algorithms (e.g., convolutional neural networks [CNNs]) to optimize classification features [6; 7]. Moreover, structural parameters, such as the canopy height, obtained from LiDAR data are partly complementary to the information from passive optical sensors for achieving higher classification accuracies [8; 9]. Nevertheless, although high- and very-high-resolution images provide more spatial details, they increase the intra-class variability and data-processing costs for the discrimination of planted forests over large areas with high landscape heterogeneity. The second method is multi-temporal image classification based on distinctive periods in the growth process of planted forests. The intra-annual variations derived from phenological periods (i.e., leaf-on and leaf-off) increase the spectral separability [10]; thus, phenology-based algorithms are widely applied for discriminating tree plantations such as rubber, palm oil, and larch from other vegetation [11; 12]. Alternatively, the distribution of plantations such as eucalyptus and tea were delineated according to the gap in the inter-annual time series caused by the selective logging or the artificial pruning phenological phase method [13; 14], which revealed potential change events in planted forest mapping. Here, planted forests are defined as trees or shrubs established by planting or seeding [15; 16]. Accordingly, the main challenges of applying the above approaches to distinguish planted forests from natural forests over large geographical extents, especially in arid and semi-arid regions with high landscape heterogeneity, are the (1) spectral similarities between natural and planted forests and (2) intra-class variability of planted forests. The canopy spectral signals of mature planted forests are similar to those of natural forests, rendering them difficult to separate using single-phase imagery alone. In addition, differences in tree species, stand age, and disturbances (e.g., drought, fire) increase the intra-class variability of planted forests, thereby limiting the usefulness of intra-annual phenological characteristics in classification. In contrast, data over a long time span seem promising for avoiding these adverse impacts in the identification of planted forests [17].