DOI: 10.5061/dryad.qv9s4mwfq
title: NDVI time series - early warning signals

description: Many freshwater forested wetlands along the southeastern U.S. coastline
 are rapidly transitioning from forest to marsh or open water, due to
 climate change related disturbances, such as saltwater intrusion and
 increasing flooding frequency. These changes in wetland state are
 considered a regime shift, and the timing and trajectory of change are not
 well understood. Recent studies have found early warning signals (EWS) of
 regime shifts in other ecosystems, but it is unclear if these can be
 detected for coastal wetlands. In this study, we examined the ability to
 detect EWS of regime shifts in coastal wetlands within the Albemarle
 Pamlico peninsula, North Carolina, U.S.A. We used 35 years of the Landsat
 record to examine trends and variance of normalized difference vegetation
 index (NDVI) time series for selected areas known to have undergone regime
 shifts. We found that NDVI time series trends combined with changes in
 standard deviation of NDVI allowed us to identify four scenarios of change
 for coastal wetlands: 1) unstable transitioning; 2) gradual transition
 (declining); 3) unstable re-vegetated (recovering); and 4) stable
 vegetated. At the landscape scale within the Albemarle Pamlico peninsula,
 we found that approximately 114,294 ha (40%) of natural wetlands are
 considered stable, while 77,732 ha (27%) are areas are re-vegetating
 following a disturbance (mostly fires). We also found that 39,828 ha (14%)
 experienced a regime shift with an abrupt change, while 24,092 ha (8.5%)
 are forests gradually shifting to marshes. Syntheses and applications: Our
 results suggest that the transition from a forest to a marsh can occur
 both rapidly and slowly, and remote sensing of NDVI time series can help
 identify ecosystem trajectories. Remote sensing provides the ability to
 measure and monitor the resilience of ecosystems, identify trajectories of
 change, and opens windows of opportunity for intervention, and
 prioritization of conservation/restoration of coastlines, all of which
 will become more important in the face of climate change and sea level
 rise.
description: We used Google Earth Engine (GEE) computing platform for
 processing and extraction of NDVI time series data due to its
 multi-petabyte catalog of satellite imagery and geospatial datasets
 (Google Earth Engine, 2019). Landsat Surface Reflectance Tier 1 (TM, ETM+,
 and OLI) data was used to assess the selected transitioned sites (Fig. 3).
 Sensor differences between Landsat 7 and 8 images were harmonized using
 transformation function following Roy et al. (2016). Images were
 processed, masking out clouds and cloud shadows using QA band. For Landsat
 5 and 7, the ‘sr_atmos_opacity’ band was used to find and mask hazy
 pixels, and for Landsat 8, the ‘sr_aerosol’ band was used to remove pixels
 labelled as high probability of haze. NDVI values were then computed using
 the red and near-infrared bands.  Raster NDVI composites were generated
 for each year from 1985 to 2020 selecting the maximum NDVI per pixel
 during the growing season (May 1 to August 31). It is important to note
 however that the number of images per satellite available varied each year
 and per pixel (Fig. S1).