Remotely sensed resilience of tropical forests

Verbesselt, Jan; Umlauf, Nikolaus; Hirota, Marina; Holmgren, Milena; Nes, Egbert H. van; Herold, Martin; Zeileis, Achim; Scheffer, Marten

doi:10.1038/nclimate3108

Cited by 192 publications

(235 citation statements)

References 34 publications

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“…Several of these studies focused on temporal trends of vegetation greenness indicators (i.e., greening or browning; [12][13][14][15][16]), revealing a greening trend in many parts of the world. Other studies quantified changes in phenology (e.g., [17], rainfall use efficiency [18], resilience [10] or the relation between inter-annual temperature variability and northern vegetation activity [19]. However, a study quantifying and related changes of the different aspects of vegetation stability, i.e., resistance, resilience and variance, has not been performed yet to our knowledge.…”

Section: Introductionmentioning

confidence: 96%

“…Moreover, prior to the critical point of divergence when a regime shift occurs, vegetation response may show an altered response [8,9]. Vegetation is assumed to recover more slowly, i.e., critical slowing down, and its variability may increase before the shift [8][9][10]. Quantifying changes in vegetation recovery and variance thus potentially provides an extremely interesting asset for ecosystem management.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

et al. 2017

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Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in vegetation stability. Within this framework, vegetation stability changes were quantified over Australia from 1982 to 2006 using GIMMS NDVI and climate time series (i.e., SPEI (Standardized Precipitation and Evaporation Index)). Starting from a stability assessment on the complete time series, we aim to assess: (i) the magnitude and direction of stability changes; and (ii) the similarity in these changes for different stability metrics, i.e., the standard deviation of the NDVI anomaly (SD), auto-correlation at lag one of the NDVI anomaly (AC) and the correlation of NDVI anomaly with SPEI (CS). Results show high variability in magnitude and direction for the different stability metrics. Large areas and types of Australian vegetation showed an increase in variability (SD) over time; however, vegetation memory (AC) decreased. The association of NDVI anomalies with drought events (CS) showed a mixed response: the association increased in the western part, while it decreased in the eastern part. This methodology shows the potential for quantifying vegetation responses to major climate shifts and land use change, but results could be enhanced with higher resolution time series data.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

et al. 2017

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“…Moreover, a comparison between Figs. 4b and 5b unveils that these causal relationships are highly non-linear, as expected given the distinct resistance and resilience of different ecosystems, which are reflected by a progressive response and recovery of vegetation to these perturbations (Foley et al, 1998;Zeng et al, 2002;Verbesselt et al, 2016).…”

Section: Linear Versus Non-linear Granger Causalitymentioning

confidence: 99%

“…However, as we hypothesize that the relationships between climate and vegetation can be highly non-linear (Foley et al, 1998;Zeng et al, 2002;Verbesselt et al, 2016), we also replace the linear VAR models in the Granger-causality framework with non-linear machine learning models. In other fields, such as neuroscience, kernel methods or other nonlinear models have been used for the investigation of nonlinear Granger-causality relationships between time series (Ancona et al, 2004;Marinazzo et al, 2008).…”

Section: Non-linear Granger Causalitymentioning

confidence: 99%

“…In this article, we show new experimental evidence that advocates the need non-linear methods to study climatevegetation dynamics due to the non-linear nature of these interactions (Foley et al, 1998;Zeng et al, 2002;Verbesselt et al, 2016). To this end, we have assembled a large, comprehensive database, comprising various global data sets of temperature, radiation, and precipitation, originating from multiple online resources.…”

Section: Introductionmentioning

confidence: 99%

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A non-linear Granger-causality framework to investigate climate–vegetation dynamics

et al. 2017

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Abstract. Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate-vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate-vegetation dynamics.

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A Dynamical Systems View of Psychiatric Disorders—Practical Implications

Scheffer,

Bockting,

Borsboom

et al. 2024

JAMA Psychiatry

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ImportanceDynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry.ObservationsEmerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions.Conclusions and RelevanceThose observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.

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Remotely sensed resilience of tropical forests

Cited by 192 publications

References 34 publications

Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

A non-linear Granger-causality framework to investigate climate–vegetation dynamics

A Dynamical Systems View of Psychiatric Disorders—Practical Implications

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