Abstract
Research Highlights: Fire-frequented savannas are dominated by plant species that regrow quickly following fires that mainly burn through the understory. To detect post-fire vegetation recovery in these ecosystems, particularly during warm, rainy seasons, data are needed on a small, temporal scale. In the past, the measurement of vegetation regrowth in fire-frequented systems has been labor-intensive, but with the availability of daily satellite imagery, it should be possible to easily determine vegetation recovery on a small timescale using Normalized Difference Vegetation Index (NDVI) in ecosystems with a sparse overstory. Background and Objectives: We explore whether it is possible to use NDVI calculated from satellite imagery to detect time-to-vegetation recovery. Additionally, we determine the time-to-vegetation recovery after fires in different seasons. This represents one of very few studies that have used satellite imagery to examine vegetation recovery after fire in southeastern U.S.A. pine savannas. We test the efficacy of using this method by examining whether there are detectable differences between time-to-vegetation recovery in subtropical savannas burned during different seasons. Materials and Methods: NDVI was calculated from satellite imagery approximately monthly over two years in a subtropical savanna with units burned during dry, dormant and wet, growing seasons. Results: Despite the availability of daily satellite images, we were unable to precisely determine when vegetation recovered, because clouds frequently obscured our range of interest. We found that, in general, vegetation recovered in less time after fire during the wet, growing, as compared to dry, dormant, season, albeit there were some discrepancies in our results. Although these general patterns were clear, variation in fire heterogeneity and canopy type and cover skewed NDVI in some units. Conclusions: Although there are some challenges to using satellite-derived NDVI, the availability of satellite imagery continues to improve on both temporal and spatial scales, which should allow us to continue finding new and efficient ways to monitor and model forests in the future.
Highlights
Differences in fire management, including the frequency and season of fire, cause changes in forest structure and composition in the savanna ecosystems of tropical and subtropical regions. [1,2,3,4].Immediately following fires, the perennial plants that dominate the understory in these fire-frequented habitats begin resprouting from belowground buds, often returning to pre-fire stature within months [5,6]
The objectives of this paper are to (1) examine the efficacy of using Normalized Difference Vegetation Index (NDVI) derived from satellite imagery to detect rapid vegetation recovery after fire; and (2) understand the temporal changes in vegetation recovery after fire by relating NDVI to season changes in weather
NDVI values derived fromrecovery satellite after imagery and tree height or expensive ground-based imagery, because vegetation fire iscould swiftbe in used gain ecological information in pine savannas where a high temporal is needed to theseto ecosystems
Summary
Differences in fire management, including the frequency and season of fire, cause changes in forest structure and composition in the savanna ecosystems of tropical and subtropical regions. [1,2,3,4].Immediately following fires, the perennial plants that dominate the understory in these fire-frequented habitats begin resprouting from belowground buds, often returning to pre-fire stature within months [5,6]. Normalized Difference Vegetation Index (NDVI), a measure of vegetation productivity using remote sensing, has been used to examine vegetation recovery after fire over a limited range of temporal and spatial scales. To date, this metric has primarily been used to study vegetation recovery after infrequent, stand-replacing fires where yearly satellite images are sufficient to detect changes over time [9,10,11,12,13]. In burned savannas, where frequent imagery is needed to detect vegetation recovery, researchers often employ ground-based remote sensing [14], because differences in canopy density and phenology may skew NDVI [15,16]. Satellite imagery typically does not have the spatial and temporal resolution needed to accurately detect rapid changes in understory vegetation [17]
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