Approach for Monitoring Spatiotemporal Changes in Fractional Vegetation Cover Through Unmanned Aerial System-Guided-Satellite Survey: A Case Study in Mining Area

Fractional vegetation cover (FVC) is a vital indicator for monitoring regional vegetation and ecology. Although satellite remote sensing is used to monitor long-term changes in regional FVC, its applications are limited by the spatial resolution. Moreover, for unmanned aerial systems (UASs), obtaining long-term and large-scale images is difficult, and the efficiency of the synergy between UAS and satellite data for long-term FVC monitoring is limited. This article considered a mining area with extreme changes in vegetation as an example and proposed an efficient approach called multiple spatiotemporal-scale FVC prediction (MSFP) for long-term FVC monitoring in the region, which is based on the synergy of high spatial-resolution UAS data with high temporal-resolution Landsat data. First, we used the UAS imagery of several typical mining areas in Qianxi County of China collected in 2021, from which the vegetation information was extracted. Second, the 2-D Gaussian sampling was applied to aggregate, that is, to join/connect them into Landsat pixels. The vegetation index (VI) calculated from contemporary Landsat imagery was further used with the aggregated FVC of each satellite pixel. Finally, the VIs from the satellite imagery for different years were calibrated. The analysis demonstrated that: first, the proposed MSFP yielded improved the coefficient of determination (by 0.437) and decreased root-mean-square error (by 0.200) than the traditional dimidiate pixel method based on satellite imagery; second, the UAS imagery for few typical areas was used to predict the FVC of the large-scale area, thereby providing fine-scale vegetation information; third, the MSFP achieved high accuracy and long-term FVC monitoring by interyear calibration of VI calculated from Landsat data. This article paves the way toward accurate long-term monitoring of regional FVC. The demonstrated methodological framework is simple and operable, thereby opening the prospects for its applications in other environments.