Integrating unoccupied aerial systems and satellite data to map the patchiness of bare ground at a landscape scale
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Context
Integrating fine-scale measurements with broad-scale monitoring presents a persistent challenge in rangeland ecology, particularly when scaling detailed Unoccupied Aerial System (UAS) observations to satellite-based landscape assessments. This challenge is critical as rangelands face increasing climate variability, requiring reliable methods to detect and monitor ecological changes across landscapes.
Objectives
We investigated how the Largest Patch Index (LPI) of bare ground patches, derived from 3-dimensional UAS observations, can be scaled to landscape levels for mapping bare ground patchiness. Our study aimed to develop and validate methods for integrating UAS and satellite data to support landscape-scale ecological monitoring.
Methods
We conducted our study across a 100 km 2 semi-arid rangeland in southern Arizona during 2019–2023, a period of extraordinary climate variability. We used Random Forest modeling to scale UAS-derived LPI measurements to satellite platforms (Landsat 8 and PlanetScope) with systematic comparison of spatial resolution and sensor data density effects. Our methodology maintained consistency across different sensor platforms while capturing fine-scale ecological processes.
Results
LPI effectively captured vegetation responses to extreme climate events, showing clear sensitivity to severe drought (SPEI -2.47) and wet periods (SPEI + 1.95). LPI values were consistently 30–60% higher in lower elevations, validating detection of known ecological gradients. LPI values increased with larger grid cell sizes in satellite-derived estimates, with the magnitude varying by sensor data density. This data density effect represents a previously unrecognized mechanism that modifies scaling relationships independently of spatial resolution. The approach successfully integrated UAS training data with satellite observations for landscape-scale pattern mapping.
Conclusions
This research provides a practical framework for integrating UAS and satellite observations to support ecological monitoring under increasing climate uncertainty. Our findings challenge fundamental assumptions about scale effects in landscape pattern analysis by revealing the role of sensor data density in scaling relationships.
