Spatio-temporal analysis of agroforestry systems in hotan using multi-source remote sensing and deep learning

In the Hotan region of Xinjiang, where arable land is scarce, an agroforestry system integrating walnut trees with crops has been implemented to maximize land-use efficiency. While this dense planting enhances land use, it also limits the availability of light to the understory crops, potentially impacting their yield and quality. To address this issue and enhance the system's sustainability and productivity, precise delineation of the planting structure is critical. This study proposes a novel framework that leverages multi-source remote sensing data combined with advanced deep learning techniques to analyze the agroforestry planting structure. The methodological approach consists of three key phases. First, an instance segmentation model was employed to extract farmland parcels from high-resolution imagery, providing a basis for vegetation classification. Next, a time series model using irregular satellite image time series (irSITS) tracked the growth dynamics of the vegetation. Finally, the spatial planting structure of the walnut trees was quantified using the D-LinkNet model, integrated with a template filling algorithm.The results demonstrated a classification accuracy of 97.85% in extracting parcel-level planting structures, identifying 42,955 farmland parcels, including 21,153 intercropped parcels. The temporal and spatial characteristics of the agroforestry system were then analyzed, leading to a grading of canopy cover and walnut tree density within the intercropped areas. This comprehensive spatiotemporal planting structure offers a valuable foundation for informed local agricultural policy adjustments. In conclusion, this approach advances the understanding of complex agroforestry systems and provides a robust scientific basis for optimizing intercropping practices, contributing to sustainable agricultural development in arid regions.