2021
DOI: 10.1038/s41598-021-90576-x
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Impact of a tropical forest blowdown on aboveground carbon balance

Abstract: Field measurements demonstrate a carbon sink in the Amazon and Congo basins, but the cause of this sink is uncertain. One possibility is that forest landscapes are experiencing transient recovery from previous disturbance. Attributing the carbon sink to transient recovery or other processes is challenging because we do not understand the sensitivity of conventional remote sensing methods to changes in aboveground carbon density (ACD) caused by disturbance events. Here we use ultra-high-density drone lidar to q… Show more

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Cited by 9 publications
(9 citation statements)
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“…First, it appears that at least in certain forest types GSFDs are not well captured by a power-law, which tends to overestimate the frequency of large gaps (Wedeux & Coomes, 2015). Even when a power-law is a good fit to the data, λ has been shown to vary considerably among different forest types (Goodbody et al, 2020), as well as within the same landscape due to differences in disturbance (Cushman et al, 2021;Reis et al, 2021), topography and soil fertility (Lobo & Dalling, 2013;Goulamoussene et al, 2017). For example, using ALS data from 650 sites across the Brazilian Amazon, Reis et al (2021) showed that λ ranges between 1.66 and 2.50 across the basin, primarily reflecting underlying gradients in tree mortality, canopy height and human disturbance.…”
Section: Gap Size Frequency Distributions: Emergent Pattern or White ...mentioning
confidence: 99%
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“…First, it appears that at least in certain forest types GSFDs are not well captured by a power-law, which tends to overestimate the frequency of large gaps (Wedeux & Coomes, 2015). Even when a power-law is a good fit to the data, λ has been shown to vary considerably among different forest types (Goodbody et al, 2020), as well as within the same landscape due to differences in disturbance (Cushman et al, 2021;Reis et al, 2021), topography and soil fertility (Lobo & Dalling, 2013;Goulamoussene et al, 2017). For example, using ALS data from 650 sites across the Brazilian Amazon, Reis et al (2021) showed that λ ranges between 1.66 and 2.50 across the basin, primarily reflecting underlying gradients in tree mortality, canopy height and human disturbance.…”
Section: Gap Size Frequency Distributions: Emergent Pattern or White ...mentioning
confidence: 99%
“…Finally, growing access to repeat ALS surveys of the same location through time provide an opportunity to study gap dynamics in action, mapping when and where new gaps form, and how quickly they close (Silva et al, 2019;Silvério et al, 2019;Cushman et al, 2021). Using repeat ALS from five sites in the Amazon, Dalagnol et al (2021) showed that gap dynamics are closely correlated to rates of tree mortality derived from plot census data, reflecting broadscale gradients in forest dynamics across the region.…”
Section: Beyond Size Structure: Spatiotemporal Patterns Of Gap Format...mentioning
confidence: 99%
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“…Moreover, work using repeat airborne LiDAR acquired before and after the global El Niño event of 2015–2016 has shown that these edge effects can amplify the impacts of drought, as do LiDAR‐detectable fine‐scale topographic features such as steep slopes and ridges (Leitold et al, 2018; Nunes et al, 2021). A similar before–after approach has been used to quantify the impact of tropical storms on forest biomass stocks and show how biomass losses vary predictably across landscapes with flood risk and exposure to high winds (Cushman et al, 2021; Hall et al, 2020). In the near future, repeat airborne surveys may provide a unique opportunity to study other important but historically overlooked drivers of forest disturbance; for example lightning strikes, which at Barro Colorado Island in Panama have been shown to cause 40% of large tree mortality and 20% of annual gap formation (Gora et al, 2021; Yanoviak et al, 2020).…”
Section: Sensing Drivers Of Canopy Structure and Complexity From Loca...mentioning
confidence: 99%
“…For example, canopy tree mortality can be observed from optical satellite platforms, but satellite images are afflicted by high cloud cover in the tropics and the difficulty of automated detection of mortality events (Asner, 2001; Clark et al, 2004). Three‐dimensional forest structure from airborne LiDAR allows detailed characterisation of forest structural change but airborne LiDAR collection is relatively expensive, often preventing repeat measurements needed to detect structural changes (Cushman et al, 2021; Dalagnol et al, 2021; Leitold et al, 2018). Much previous research has classified forest disturbance using the frequency and size structure of ‘standing’ canopy gaps—areas of forest below a threshold height at a single point in time (Brokaw, 1982a; Jucker et al, 2018; Kellner & Asner, 2009; Lobo & Dalling, 2013), but few studies have explored how standing forest structure relates to dynamic measures of new canopy disturbances over time (Dalagnol et al, 2021).…”
Section: Introductionmentioning
confidence: 99%