Dataset Information

Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks.

ABSTRACT: Reduced rainfall increases the risk of forest dieback, while in return forest loss might intensify regional droughts. The consequences of this vegetation-atmosphere feedback for the stability of the Amazon forest are still unclear. Here we show that the risk of self-amplified Amazon forest loss increases nonlinearly with dry-season intensification. We apply a novel complex-network approach, in which Amazon forest patches are linked by observation-based atmospheric water fluxes. Our results suggest that the risk of self-amplified forest loss is reduced with increasing heterogeneity in the response of forest patches to reduced rainfall. Under dry-season Amazonian rainfall reductions, comparable to Last Glacial Maximum conditions, additional forest loss due to self-amplified effects occurs in 10-13% of the Amazon basin. Although our findings do not indicate that the projected rainfall changes for the end of the twenty-first century will lead to complete Amazon dieback, they suggest that frequent extreme drought events have the potential to destabilize large parts of the Amazon forest.

SUBMITTER: Zemp DC

PROVIDER: S-EPMC5355804 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks.

Zemp Delphine Clara DC Schleussner Carl-Friedrich CF Barbosa Henrique M J HM Hirota Marina M Montade Vincent V Sampaio Gilvan G Staal Arie A Wang-Erlandsson Lan L Rammig Anja A

Nature communications 20170313

Reduced rainfall increases the risk of forest dieback, while in return forest loss might intensify regional droughts. The consequences of this vegetation-atmosphere feedback for the stability of the Amazon forest are still unclear. Here we show that the risk of self-amplified Amazon forest loss increases nonlinearly with dry-season intensification. We apply a novel complex-network approach, in which Amazon forest patches are linked by observation-based atmospheric water fluxes. Our results sugge ...[more]

PMID: 28287104

Similar Datasets

Project description:Amazon forests are becoming increasingly vulnerable to disturbances such as droughts, fires, windstorms, logging, and forest fragmentation, all of which lead to forest degradation. Nevertheless, quantifying the extent and severity of disturbances and their cumulative impact on forest degradation remains a significant challenge. In this study, we combined multispectral data from Landsat sensors with hyperspectral data from the Earth Observing-One (Hyperion/EO-1) sensor to evaluate the efficacy of multiple vegetation indices in detecting forest responses to disturbances in an experimentally burned forest in southeastern Amazonia. Our experimental area was adjacent to an agricultural field and consisted of three 50-ha treatments - an unburned Control, a plot burned every three years, and a plot burned annually from 2004 to 2010. All plots were monitored to assess vegetation recovery after fire disturbance. These areas were also affected by three drought events (2007, 2010, and 2016) over the study period. We evaluated a total of 18 Vegetation Indices (VI), one unique to Landsat, 12 unique to Hyperion/EO-1, and five commons to both satellites (i.e., 6 total from Landsat and 17 from Hyperion). We used linear models (LM) to evaluate how changes in ground observations of forest structure (biomass, leaf area index [LAI], and litter production) associated with fire were captured by the two VIs most sensitive to forest degradation. Our results indicate that the Plant Senescence Reflectance Index (PSRI) derived from Hyperion/EO-1 was the most sensitive to vegetation changes associated with forest fires, increasing by 94% in burned vs. unburned forests. Of the Landsat-derived VIs, we found that the Green-Red Normalized Difference (GRND) were the most sensitive to forest degradation by fire, showing a marked decline (87%) in the burned plots compared with the unburned Control. However, compared to PSRI, the GRND was a better predictor of changes associated with fire, both in the forest interior or forest edge, for the three ground variables: biomass stocks (r2 = 0.5-0.8), LAI (r2 = 0.8-0.9), and litter production (r2 = 0.4-0.7). This study demonstrate that VIs can detect forest responses to fire and other disturbances over time, highlighting the relative strengths of each VI. In doing so, it shows how the integration of multispectral and hyperspectral data can be useful for monitoring tropical forest degradation and recovery. Moreover, it provides valuable insights into the limitations of existing approaches, which can inform the design of next-generation sensors for global forest monitoring.

Dataset Information

Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks.

Publications

Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets