Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates

Abstract

We report large-scale estimates of Amazonian gap dynamics using a novel approach with large datasets of airborne light detection and ranging (lidar), including five multi-temporal and 610 single-date lidar datasets. Specifically, we (1) compared the fixed height and relative height methods for gap delineation and established a relationship between static and dynamic gaps (newly created gaps); (2) explored potential environmental/climate drivers explaining gap occurrence using generalized linear models; and (3) cross-related our findings to mortality estimates from 181 field plots. Our findings suggest that static gaps are significantly correlated to dynamic gaps and can inform about structural changes in the forest canopy. Moreover, the relative height outperformed the fixed height method for gap delineation. Well-defined and consistent spatial patterns of dynamic gaps were found over the Amazon, while also revealing the dynamics of areas never sampled in the field. The predominant pattern indicates 20–35% higher gap dynamics at the west and southeast than at the central-east and north. These estimates were notably consistent with field mortality patterns, but they showed 60% lower magnitude likely due to the predominant detection of the broken/uprooted mode of death. While topographic predictors did not explain gap occurrence, the water deficit, soil fertility, forest flooding and degradation were key drivers of gap variability at the regional scale. These findings highlight the importance of lidar in providing opportunities for large-scale gap dynamics and tree mortality monitoring over the Amazon.

Forest structure and degradation drive canopy gap sizes across the Brazilian Amazon

“…Remote sensing technologies make it possible to map canopy gaps over large areas of tropical forests (Lobo & Dalling 2013, Asner et al 2013, Espírito-Santo et al 2014, Kent et al 2015, Wedeux & Coomes 2015, Goodbody et al 2020, Dalagnol et al 2021). Several studies using airborne lidar datasets have found that gap size-frequency distributions follow a simple power-law function ( f ( x ) = cx − α ) in which small gaps heavily outnumber large gaps in all forest environments (Kellner & Asner 2009, Asner et al 2013, Lobo & Dalling 2013, Espírito-Santo 2014, Silva et al 2019).…”

“…We expect gap fraction and the scaling coefficient α to vary along environmental gradients, since forest dynamics is controlled by environmental variables (Phillips et al 2004, Quesada Phillips, Schwarz, et al 2012). Dalagnol et al (2021) found that gap fraction across the Brazilian Amazon was positively correlated with soil nutrients ( r = 0.46), water deficit ( r = 0.42), dry season length ( r = 0.41), wind speed ( r = 0.21), and floodplains fraction ( r = 0.27); and negatively correlated with distance to the forest edge ( r = -0.43) and precipitation ( r = -0.38). In particular, gap size distribution in primary forests has been linked to broad-scale patterns such as climate, topography and soils (Goulamoussène et al 2017, Goodbody et al 2020), as well as wind and lightning.…”

“…Recent work has shown that large trees are more likely to be directly struck by lightning in tropical forests with strong influences on forest structure and dynamics (Gora, Muller-Landau,). Dalagnol et al (2021) focused on scaling-up tree mortality estimates and did not explore the gap size-frequency distribution and its relationship with environmental factors.…”

“…This relationship depends on the definition of a canopy gap, i.e. whether the cutoff height is defined as a relative number to local canopy height or as a fixed value (Dalagnol et al 2021). In addition, we expect to find larger gaps in forests with taller trees, because big trees generally crush more trees along the path when it falls and produce big gaps (Grubb 1977).…”

“…Therefore, interpreting environmental effects on gap properties across heterogeneous forests can be challenging. For example, a treefall event creates a much smaller gap in a forest with a substantial understory layer, as compared to the same event in sparser forest (Leitold et al 2018, Dalagnol et al 2021). Furthermore, the time it takes for a gap to close depends on the surrounding canopy structure (Grubb 1977, Muscolo et al 2014) and the size of the gap (Dalagnol et al 2019).…”