Mapping Quaking Aspen Using Seasonal Sentinel-1 and Sentinel-2 Composite Imagery across the Southern Rockies, USA

Published Article

United States

Publication date: April 30, 2024

View resource

Quaking aspen mapping in the western U.S. improves with Sentinel‑1/2 data. Phenology‑based composites and Random Forests detect small stands with high accuracy (F1 = 0.93), outperforming older maps and supporting forest management.

Subject Tags

  • Forest
  • Ecosystem management
  • Life Sciences

Abstract

Quaking aspen is an important deciduous tree species across interior western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (<0.09 ha or 30 m2), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. Using observed annual phenology of aspen across the Southern Rockies and leveraging the frequent temporal resolution of S1 and S2, ecologically relevant seasonal imagery composites were developed. We derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best-performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing products in both accuracy (0.93 average F1-score) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America.

Citation

Cook, M., Chapman, T., Hart, S., Paudel, A. and Balch, J., 2024. Mapping quaking aspen using seasonal Sentinel-1 and Sentinel-2 composite imagery across the Southern Rockies, USA. Remote Sensing, 16(9), p.1619. https://doi.org/10.3390/rs16091619

TNC Authors

  • Teresa Chapman
    Director, Data Science and Quality
    The Nature Conservancy
    Email: tchapman@tnc.org