Forest Spectral Recovery and Regeneration Dynamics in Stand-Replacing Wildfires of Central Apennines Derived from Landsat Time Series

Abstract

Understanding post-fire regeneration dynamics is an important task for assessing the resilience of forests and to adequately guide post-disturbance management. The main goal of this research was to compare the ability of different Landsat-derived spectral vegetation indices (SVIs) to track post-fire recovery occurring in burned forests of the central Apennines (Italy) at different development stages. Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2) and a novel index called Forest Recovery Index 2 (FRI2) were used to compute post-fire recovery metrics throughout 11 years (2008–2018). FRI2 achieved the highest significant correlation (Pearson’s r = 0.72) with tree canopy cover estimated by field sampling (year 2017). The Theil–Sen slope estimator of linear regression was employed to assess the rate of change and the direction of SVIs recovery metrics over time (2010–2018) and the Mann–Kendall test was used to evaluate the significance of the spectral trends. NDVI displayed the highest amount of recovered pixels (38%) after 11 years since fire occurrence, whereas the mean value of NDMI, NBR, NBR2, and FRI2 was about 27%. NDVI was more suitable for tracking early stages of the secondary succession, suggesting greater sensitivity toward non-arboreal vegetation development. Predicted spectral recovery timespans based on pixels with a statistically significant monotonic trend did not highlight noticeable differences among normalized SVIs, suggesting similar suitability for monitoring early to mid-stages of post-fire forest succession. FRI2 achieved reliable results in mid- to long-term forest recovery as it produced up to 50% longer periods of spectral recovery compared to normalized SVIs. Further research is needed to understand this modeling approach at advanced stages of post-fire forest recovery.

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

“…Unitless spectral indices can be combined with field survey data to model vegetation cover and other ecosystem traits [72], but it is not clear when such models can be transferred from site to site without additional ground reference data [73]. The NBR correlation with vegetation cover that we observed is similar to the correlation reported by Morresi et al [74], but additional work is needed to validate the consistency of these relationships. Few studies have assessed the ability of NBR and other spectral indices to map fractional cover of vegetation.…”

“…Plots with low burn severity recovered in seven years and plots with moderate burn severity recovered in 13 years. Moressi et al [74] performed a similar analysis using spectral indices. They found that modeled recovery times varied between 8 and 12 years, depending on the forest type, burn severity, and spectral index.…”

Multi-Scale Remote Sensing-Assisted Forest Inventory: A Glimpse of the State-of-the-Art and Future Prospects

“…Moreover, high-resolution canopy-related spectral and ALS-derived attributes were also leveraged by [11] to estimate tree count across canopy cover classes (with r 2 reaching 0.93 when using ALS metrics), whereas medium-resolution but multi-temporal classifications of forest and tree species types by [17] returned promising performances of accuracies >80% by incorporating refinements like topography and stratification. Spectral information from multi-temporal, optical Landsat imagery was also shown by [16] to enable good approximations for forest recovery via the use of multiple vegetation indices, highlighting the tremendous information content within the time series of medium-resolution satellite imagery for monitoring forest stand dynamics on a regional scale and beyond. Finally, [12] followed the important though often underestimated preprocessing step of image co-registration by suggesting a simulated solution based on spatial aggregation over adjacent pixels, which helps with precisely quantifying the co-registration error between the ground control points and satellite data.…”

“…Motivated by the recent trends and global interest, the Remote Sensing special issue "Remote Sensing-Based Forest Inventories from Landscape to Global Scale" hosted nine peer-reviewed papers adopting various modern applications of passive and active remote sensing data for multi-scale forest inventory applications. This special issue is enriched with a series of independent, though contextually related, recent studies from diverse geographical domains of the globe, including the near-Arctic Canada [10], Northern United States [11,12], Northern Japan [13], Southern Spain [14,15], Central Italy [16], Southern Poland [17] and Western Germany [18].…”

“…Moreover, very high resolution, often mono-temporal satellite imagery (UAV and aerial photogrammetry, WorldView-3 data), was employed by [10,11,13], which mainly aimed at quantifying local forest structural details at high geometric resolutions. High-resolution optical data, including RapidEye and SPOT with geometric resolutions <10 m, were applied by [18], whereas medium-resolution (Landsat and Sentinel-2 series) [12,16,17] and low-resolution [14] data were also represented, mainly due to their proven ability for monitoring forest dynamics over timespans. In this realm, further applications of such multi-temporal series of data for regional and statewide inventory and analysis of forest structure and health are expected in the near future, either standalone or in combination with other costly, i.e., high resolution, sources of data.…”