Temporal and Spatial Upscaling with PlanetScope Data: Predicting Relative Canopy Dieback in the Piñon-Juniper Woodlands of Utah

Abstract

What are the main findings?

NDVI from PlanetScope imagery significantly explains the sitewide mean of relative canopy dieback in the study area, and this relationship remains robust when upscaled spatially or temporally, even from a modest ground-truthing dataset.

Model outputs indicate that relative canopy dieback has remained stable, with little evidence of substantial regreening in the four years following the 2017–2018 drought.

What are the implications of the main findings?

This approach provides a cost-effective, scalable framework for monitoring canopy dieback and regreening, facilitating forest health assessments without extensive field sampling.

We also identified hotspots of severe canopy dieback in the piñon-juniper forests of Utah, which represent priority areas for further study and management intervention.

Drought-induced forest mortality threatens biodiversity globally, particularly in arid, and semi-arid woodlands. The continual development of remote sensing approaches enables enhanced monitoring of forest health. Herein, we investigate the ability of a limited ground-truthed canopy dieback dataset and satellite image derived Normalised Difference Vegetation Index (NDVI) to make inferences about forest health as temporal and spatial extent from its collection increases. We used ground-truthed observations of relative canopy mortality from the Pinus edulis-Juniperus osteosperma woodlands of southeastern Utah, United States of America, collected after the 2017–2018 drought, and PlanetScope satellite imagery. Through assessing different modelling approaches, we found that NDVI is significantly associated with sitewide mean canopy dieback, with beta regression being the most optimal modelling framework due to the bounded nature of the variable relative canopy dieback. Model performance was further improved by incorporating the proportion of J. osteosperma as an interaction term, matching the reports of species-specific differential dieback. A time-series analysis revealed that NDVI retained its predictive power for our whole testing period; four years after the initial ground-truthing, thus enabling retrospective inference of defoliation and regreening. A spatial random forest model trained on our ground-truthed observations accurately predicted dieback across the broader landscape. These findings demonstrate that modest field campaigns combined with high-resolution satellite data can generate reliable, scalable insights into forest health, offering a cost-effective method for monitoring drought-impacted ecosystems under climate change.