Phenology Patterns Indicate Recovery Trajectories of Ponderosa Pine Forests After High-Severity Fires

Using Satellite NDVI Time-Series to Monitor Grazing Effects on Vegetation Productivity and Phenology in Heterogeneous Mediterranean Forests

The reintroduction of livestock grazing to regulate biomass load is being tested for large-scale restoration in Mediterranean landscapes affected by rural abandonment. Concurrently, there is a need to develop cost-effective methods to monitor such interventions. Here, we investigate if satellite data can be used to monitor the response of vegetation phenology and productivity to grazing disturbance in a heterogenous forest mosaic with herbaceous, shrub, and tree cover. We identify which vegetation seasonal metrics respond most to grazing disturbances and are relevant to monitoring efforts. The study follows a BACI (Before-After-Control-Impact) design applied to a grazing intervention in a Pyrenean oak forest (Quercus pyrenaica) in central Portugal. Using NDVI time-series from Sentinel-2 imagery for the period between June 2016 and June 2021, we observed that each type of vegetation exhibited a distinct phenology curve. Herbaceous vegetation was the most responsive to moderate grazing disturbances with respect to changes in phenology and productivity metrics, namely an anticipation of seasonal events. Results for shrubs and trees suggest a decline in peak productivity in grazed areas but no changes in phenology patterns. The techniques demonstrated in this study are relevant to a broad range of use cases in the large-scale monitoring of fine-grained heterogeneous landscapes.

Tracking rates of postfire conifer regeneration vs. deciduous vegetation recovery across the western United States

Postfire shifts in vegetation composition will have broad ecological impacts. However, information characterizing postfire recovery patterns and their drivers are lacking over large spatial extents. In this analysis, we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of postfire, dual-season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally specific drivers of postfire rates of NDVI recovery. Rates of postfire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n = 230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in postfire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track postfire forest recovery across the western United States. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Analyzing Ecological Vulnerability and Vegetation Phenology Response Using NDVI Time Series Data and the BFAST Algorithm

Identifying ecologically vulnerable areas and understanding the responses of phenology to negative changes in vegetation growth are important bases for ecological restoration. However, identifying ecologically vulnerable areas is difficult because it requires high spatial resolution and dense temporal resolution data over a long time period. In this study, a novel method is presented to identify ecologically vulnerable areas based on the normalized difference vegetation index (NDVI) time series from MOD09A1. Here, ecologically vulnerable areas are defined as those that experienced negative changes frequently and greatly in vegetation growth after the disturbances during 2000–2018. The number and magnitude of negative changes detected by the Breaks for Additive Season and Trend (BFAST) algorithm based on the NDVI time-series data were combined to identify ecologically vulnerable areas. TIMESAT was then used to extract the phenology metrics from an NDVI time series dataset to characterize the vegetation responses due to the abrupt negative changes detected by the BFAST algorithm. Focus was given to Jilin Province, a region of China known to be ecologically vulnerable because of frequent drought. The results showed that 13.52% of the study area, mostly in Jilin Province, is ecologically vulnerable. The vulnerability of trees is the lowest, while that of sparse vegetation is the highest. The response of phenology is such that the relative amount of vegetation biomass and length of the growing period were decreased by negative changes in growth for dense vegetation types. The present research results will be useful for the protection of environments being disturbed by regional ecological restoration.