Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion

• Main Authors: Kumar, Jitendra, Langford, Zachary L., Hoffman, Forrest M.
• Other Authors: 10th International Conference on Ecological Informatics- Translating Ecological Data into Knowledge and Decisions in a Rapidly Changing World. 24-28 September, 2018. Jena, Germany.
• Format: Text
• Language: English
• Published: 2018
• Subjects: article; ddc:004; ddc:570; ddc:580; ddc:590; ddc:600; ddc:630; deep learning; arctic vegetation; remote sensing; multi-sensor fusion; Arctic; Hyperion; Seward Peninsula; Alaska
• Online Access: https://doi.org/10.22032/dbt.37838; https://www.db-thueringen.de/receive/dbt_mods_00037838; https://www.db-thueringen.de/servlets/MCRFileNodeServlet/dbt_derivate_00043965/Kumar_S2.4_ICEI.pdf

Land cover datasets are essential for modeling Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, and finding quality satellite remote sensing datasets...