The age of a shelterbelt is not only an important parameter for determining the function of a shelterbelt, it is also strongly related to the biomass and carbon flux of shelterbelt ecosystems. Therefore, timely and accurate identifications of shelterbelt ages are key for shelterbelt monitoring and management. This study developed a method for estimating shelterbelt age (i.e., years after planting) from a time series of remote sensing images. Firstly, the shelterbelts were divided into three states based on a single remote sensing image of each. Then, a three-stage growth process was established by analysis. Finally, the shelterbelt ages were determined based on time series remote sensing images over a two-year monitoring period in the study area. The actual shelterbelt ages based on field measurements were used to analyze the accuracy of the results. The total number of samples was 243. The results showed that the age identification accuracy was 68.7%. The main factors affecting the identification accuracy were missing images, cloud cover, and the length of the monitoring period. Despite some uncertainties, the proposed method may be used to obtain critical data for shelterbelt management and conducting quick surveys of current shelterbelt conditions over a large area.

Farmland shelterbelt, as a category of shelterbelt in forestry ecological engineering, has an important influence on agricultural sustainability in agricultural systems. Timely and accurate acquisition of farmland shelterbelt age is not only essential to understanding their shelter effects but also directly relates to the adjustment of subsequent shelterbelt projects. In this study, we developed an age identification method using growth pattern to extract the age of shelterbelt (i.e., years after planting) based on Landsat time series images. This method was applied to a typical area of shelterbelt construction in the north of Changchun, China. The results indicated that the accuracy of age identification reached a stable situation when the permissible age error exceeded 3 years, achieving an accuracy of approximately 90%. Moreover, the accuracy at different growth phases (1–3 years, 4–15 years, 16–30 years, and >30 years) decreased with increasing age, and the accuracy of each growth phase can reach more than 80% when the permissible age error is beyond 7 years. Compared to building the typically weak statistical relationship between the shelterbelt age and remote sensing characteristic information to derive age, this method presented a direct age identification method for fine-scale age extraction of the shelterbelt. It introduced a novel perspective for shelterbelt age identification and the assessment of shelterbelt project advancement on the regional scale.