Detecting mortality induced structural and functional changes in a piñon-juniper woodland using Landsat and RapidEye time series

• We used crown-level remote sensing time series to track a PJ woodland disturbance.
• We detected and related changes in canopy LAI to measured canopy productivity.
• We detected changes in structure from woody overstory to herbaceous understory.
• Remote sensing of PJ woodlands is highly dependent on soil water content.
• High resolution data has the potential to improve disturbance recovery monitoring.

Piñon–juniper (PJ) woodlands have recently undergone dramatic drought-induced mortality, triggering broad scale structural changes in this extensive Southwestern US biome. Given that climate projections for the region suggest widespread conifer mortality is likely to continue into the next century, it is critical to better understand how this climate-induced change in vegetation structure alters ecosystem function (e.g., productivity, biogeochemical cycling, energy partitioning) in PJ woodlands. Data from satellite remote sensing could potentially help to gain some of this understanding. However, relatively little is known about the suitability of satellite remote sensing for monitoring mortality induced structural and functional changes in PJ woodlands, a semi-arid biome characterized by sparse vegetation that complicates remote sensing of vegetation properties. Here we examined the potential role of satellite remote sensing to better understand structural and functional changes that take place in PJ woodlands as they respond to and recover from climate induced mortality events. We used time series medium (30 × 30 m) and high (≤ 5 × 5 m) spatial resolution satellite data in concert with eddy covariance-based gross primary productivity (GPP) data collected in a PJ woodland mortality manipulation near Mountainair, NM. Our results showed that both high and medium resolution satellite remote sensing data can provide ecosystem level assessments of overall canopy mortality and subsequent regrowth in these semi-arid woodlands. However, high spatial resolution data allowed detecting the disturbance almost a full year earlier and enabled us to describe the spatially heterogeneous patterns of recovery typical for such complex. The ability of satellite remote sensing to be used to provide information about changes in ecosystem function was conditional on the presence of sufficient soil moisture; when soil moisture was present the strength of the linear relationship between GPP and spectral vegetation indices improved at the manipulated site (e.g. for high resolution NDVI data R2 = 0.13 during drought conditions, R2 = 0.46 when wet, P < .05) and less so at a nearby control site (R2 = 0.09 during drought conditions, compared to R2 = 0.22 during growing conditions, P < .05). Thus, although satellite time series can be used to monitor and improve our understanding of canopy function in both disturbed and undisturbed semi-arid coniferous biomes, combining in situ eddy covariance data with satellite time series demonstrated that the utility of these datasets is highest during periods of peak growing conditions. This study therefore provides a benchmark for understanding how spatial and temporal variation in vegetation structure and function can be best studied as semi-arid coniferous biomes respond to future climate variability.