Integrating Unoccupied Aerial Systems and Satellite Data to Map the Patchiness of Bare Ground at the Landscape Scale

Integrating fine-scale measurements with broad-scale monitoring is a challenge for environmental monitoring, but it is a critical advancement in the face of increasing climate variability. We addressed this challenge by integrating fine-scale measures from Unoccupied Aerial Systems (UAS) to train broad-scale satellite imagery via machine learning algorithms. We applied this integration to detect how the spatial patchiness of bare ground varies over five years across a 100 km² semi-arid landscape in southern Arizona, USA. We used the Largest Patch Index (LPI) as the measure of spatial patchiness of bare ground. Our findings reveal three key advances in monitoring spatial patchiness over time and across a large landscape. First, the UAS-trained satellite estimates of LPI effectively represented the expected bare ground response to extreme climate events, where LPI increased during severe drought (-2.47 Standardized Precipitation-Evapotranspiration Index (SPEI)) and LPI decreased during exceptional wet periods (+1.95 SPEI). Second, the estimates of LPI were consistently 30-60% greater at lower and drier elevations, validating the ability to represent known ecological gradients. Third, and most notably, we confirmed that LPI is a scale-sensitive measure that differs between 3-m and 30-m grids, and that the magnitude of the differences is inversely related to the density of data in the satellite imagery. LPI was greatest using the 30-m grid Landsat 8 data with a density of 0.02 B/m² and LPI was least when using the 3-m grid PlanetScope data with a density of 0.9 B/m². But we found intermediate LPI values when resampling PlanetScope to 30-m grid while maintaining the greater data density. This previously unrecognized role of data density enriches the understanding of scale effects in landscape pattern analysis. In the end, we demonstrated a practical solution for integrating fine-scale UAS and broad-scale satellite observations via machine learning to support broad-scale environmental monitoring.