Monitoring spatially heterogeneous riparian vegetation around permanent waterholes: A method to integrate LiDAR and Landsat data for enhanced ecological interpretation of Landsat fPAR time-series

Abstract

The vegetation dynamics in highly heterogeneous landscapes (e.g., riparian vegetation surrounding waterholes and oases) are difficult to detect from large (e.g., MODIS) and moderate (e.g., Landsat) spatial resolution remote sensing products. Within a “classify-to-monitor” approach, a method to monitor spatially heterogeneous riparian vegetation dynamics is developed by integrating high spatial resolution discrete return airborne LiDAR data (1 m pixels) with moderate resolution Landsat fraction of Photosynthetically Active Radiation absorbed by vegetation (fPAR) data (30 m). LiDAR was used to identify and classify vegetation surrounding permanent waterholes within the Cooper Creek floodplain, in dryland Australia. These waterholes are important areas for ecological conservation given their highly spatially heterogeneous vegetation structure. Landsat fPAR was temporally decomposed into persistent and recurrent components and then integrated with the LiDAR-derived vegetation classes. The LiDAR data were used as a mask to separate the fPAR signal of each vegetation class, capturing their specific dynamics and which fPAR component they are associated with. The newly developed method provides the means to improve the interpretation of Landsat fPAR by monitoring distinct vegetation functional groups within each Landsat pixel. Results showed that LiDAR data provided good estimates of vegetation cover compared to field measurements (R²=0.952). LiDAR data identified different vegetation structural classes within the riparian zone. The integration of LiDAR and Landsat data permitted the distinction of temporal patterns of each vegetation structural class, uncovering the specific temporal and spatial variability of fPAR that would otherwise be undetected. Landsat fPAR provided information on which vegetation component contributed to the fPAR variability in each class, thus providing the means for enhanced ecological interpretation of the temporally decomposed fPAR components. The method can be applied to other similar highly spatially heterogeneous ecosystems to monitor structurally specific vegetation dynamics more accurately than if only using moderate spatial resolution time-series optical satellite imagery.