Monitoring Post-Fire Recovery of Chaparral and Conifer Species Using Field Surveys and Landsat Time Series

Abstract

Recovery trajectories derived from remote sensing data are widely used to monitor ecosystem recovery after disturbance events, but these trajectories are often retrieved without a precise understanding of the land cover within a scene. As a result, the sources of variability in post-disturbance recovery trajectories are poorly understood. In this study, we monitored the recovery of chaparral and conifer species following the 2007 Zaca Fire, which burned 97,270 ha in Santa Barbara County, California. We combined field survey data with two time series remote sensing products: the relative delta normalized burn ratio (RdNBR) and green vegetation (GV) fractions derived from spectral mixture analysis. Recovery trajectories were retrieved for stands dominated by six different chaparral species. We also retrieved recovery trajectories for stands of mixed conifer forest. We found that the two remote sensing products were equally effective at mapping vegetation cover across the burn scar. The GV fractions (r(78) = 0.552, p < 0.001) and normalized burn ratio (r(78) = 0.555, p < 0.001) had nearly identical correlations with ground reference data of green vegetation cover. Recovery of the chaparral species was substantially affected by the 2011-2017 California drought. GV fractions for the chaparral species generally declined between 2011 and 2016. Physiological responses to fire and drought were important sources of variability between the species. The conifer stands did not exhibit a drought signal that was directly correlated with annual precipitation, but the drought likely delayed the return to pre-fire conditions. As of 2018, 545 of the 756 conifer stands had not recovered to their pre-fire GV fractions. Spatial and temporal variation in species composition were important sources of spectral variability in the chaparral and conifer stands. The chaparral stands in particular had highly heterogeneous species composition. Dominant species accounted for between 30% and 53% of the land cover in the surveyed chaparral patches, so non-dominant land cover types strongly influenced remote sensing signals. Our study reveals that prolonged drought can delay or alter the post-fire recovery of Mediterranean ecosystems. It is also the first study to critically examine how fine-scale variability in land cover affects time series remote sensing analyses.

Long Term Post-Fire Vegetation Dynamics in North-East Mediterranean Ecosystems. The Case of Mount Athos Greece

“…As a result of this property, NDVI is considered an appropriate vegetation index to study vegetation patterns across temporal or spatial scales and it has been extensively used to study post-fire landscape patterns and vegetation dynamics [79,[81][82][83][84]. Although other indices are also reported in the literature as suitable to monitor post-fire vegetation recovery, such us the Normalised Burn Ration (NBR) [77,78,85], NDVI was the first one tried in this study and gave excellent results. The NDVI values theoretically range between -1 and 1 but this range is never achieved since a given pixel will always have some reflectance in both the IR and R bands.…”

“…Since 1982, when Landsat 4 was launched, the spatial resolution is stable at 30 m for the multispectral products. Due to the long timespan of Landsat data availability they are quite extensively used to build time series datasets for long term monitoring of post fire succession and vegetation dynamics, both at the green cover level and at the community level [75][76][77][78][79][80].…”

Influence of topography, vegetation, weather, and climate on Big-cone Douglas-Fir fire refugia and high fire-induced mortality after two large mixed-severity wildfires

“…When comparing the number of BCDF stands that were classified into their respective mortality and burn severity classes, we found that the RdNBR burn severity classes commonly did not associate with the same mortality classification. In both fires, remotely sensed RdNBR significantly overestimated the number of stands with high and moderate mortality and significantly underestimated the number of stands with no and low mortality compared to visual assessment, perhaps due to the sensitivity of RdNBR to fire impacts on the understory (Kibler et al, 2019).…”

Radar and multispectral remote sensing data accurately estimate vegetation vertical structure diversity as a fire resilience indicator

“…Resilience to fire in terms of vertical structure diversity recovery Remote sensing estimates of VSD revealed that none of the plant communities dominated by shrub or tree species have recovered to a prefire state in the short term (4 years after fire disturbance), although their VSD values have increased progressively over the time series. Previous remote sensing research was conducted in similar Mediterranean plant communities in the western Mediterranean Basin (Fernández-Guisuraga et al, 2020; as well as chaparral shrublands in California (Kibler et al, 2019;Storey et al, 2016), evidenced that vegetation cover reached prefire conditions in the short-term after wildfire disturbance. These studies used fractional vegetation cover (FVC) as an engineering resilience indicator retrieved from passive optical data by means of vegetation indices, pixel unmixing models, and radiative transfer models.…”