Essential Variables for Environmental Monitoring: What Are the Possible Contributions of Earth Observation Data Cubes?

Giuliani, Gaston; Egger, Elvire; Italiano, Julie; Poussin, Charlotte; Richard, Jean-Philippe; Chatenoux, Bruno

doi:10.3390/data5040100

Cited by 29 publications

(25 citation statements)

References 152 publications

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“…And it is increasingly recognized that one has to consider the functional dimension of biodiversity in its own right, which requires working with high temporal resolutions. In particular, remote sensing observations are of uttermost importance for the analysis of the EBVs (Skidmore et al, 2015;Pettorelli et al, 2016;Giuliani et al, 2020;Randin et al, 2020). Recently, the EBVs for Species Population started to develop a framework for working with space-time-species cubes and proposed it as a suitable model for the oncoming challenges of big data (Jetz et al, 2019) (European BON http://biodiversity.eubon.eu/essential-biodiversityvariables).…”

Section: Discussionmentioning

confidence: 99%

A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America

et al. 2021

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Tropical ecosystems experience particularly fast transformations largely as a consequence of land use and climate change. Consequences for ecosystem functioning and services are hard to predict and require analyzing multiple data sets simultaneously. Today, we are equipped with a wide range of spatio-temporal observation-based data streams that monitor the rapid transformations of tropical ecosystems in terms of state variables (e.g., biomass, leaf area, soil moisture) but also in terms of ecosystem processes (e.g., gross primary production, evapotranspiration, runoff). However, the underexplored joint potential of such data streams, combined with deficient access to data and processing, constrain our understanding of ecosystem functioning, despite the importance of tropical ecosystems in the regional-to-global carbon and water cycling. Our objectives are: 1. To facilitate access to regional “Analysis Ready Data Cubes” and enable efficient processing 2. To contribute to the understanding of ecosystem functioning and atmosphere-biosphere interactions. 3. To get a dynamic perspective of environmental conditions for biodiversity. To achieve our objectives, we developed a regional variant of an “Earth System Data Lab” (RegESDL) tailored to address the challenges of northern South America. The study region extensively covers natural ecosystems such as rainforest and savannas, and includes strong topographic gradients (0–6,500 masl). Currently, environmental threats such as deforestation and ecosystem degradation continue to increase. In this contribution, we show the value of the approach for characterizing ecosystem functioning through the efficient implementation of time series and dimensionality reduction analysis at pixel level. Specifically, we present an analysis of seasonality as it is manifested in multiple indicators of ecosystem primary production. We demonstrate that the RegESDL has the ability to underscore contrasting patterns of ecosystem seasonality and therefore has the potential to contribute to the characterization of ecosystem function. These results illustrate the potential of the RegESDL to explore complex land-surface processes and the need for further exploration. The paper concludes with some suggestions for developing future big-data infrastructures and its applications in the tropics.

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Section: Discussionmentioning

confidence: 99%

A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America

et al. 2021

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“…These variables are strongly related to the ability of drylands to provide essential ecosystem services such as climate regulation and nutrient cycling, and they largely determine ecosystem response and resistance to ongoing environmental change such as increasing aridity (Maestre et al, 2016). In order to systematically monitor ecosystems, the concept of essential variables has emerged in the scientific discourse to characterize the state and dynamics of environmental change through a set of measurements, for instance in relation to climate or biodiversity (Giuliani et al, 2020). Essential Climate Variables (ECVs) (Table S1) are physical, chemical, or biological variables or a group of linked variables that critically contribute to the characterization of Earth's climate and are grouped into atmosphere, land, or ocean related variables (GCOS, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Essential Climate Variables (ECVs) (Table S1) are physical, chemical, or biological variables or a group of linked variables that critically contribute to the characterization of Earth's climate and are grouped into atmosphere, land, or ocean related variables (GCOS, 2022). The list of ECVs is long, but spatiotemporal data are only available for a subset of these variables (see Table 1 in Giuliani et al (2020) for details). Many studies have focused on individual, or the combination of a few, ecosystem state variables to investigate their past, present, and future states at different scales ranging from local to global (Bernardino et al, 2020; D'Adamo et al, 2021; Dang et al, 2022; de Jong et al, 2011; Fensholt et al, 2015; Liu et al, 2013; Piao et al, 2020; Qiu et al, 2016; Wild et al, 2022; Zhao et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Contrasting ecosystem vegetation response in global drylands under drying and wetting conditions

Abel

Abdi

Tagesson

et al. 2023

Global Change Biology

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Increasing aridity is one major consequence of ongoing global climate change and is expected to cause widespread changes in key ecosystem attributes, functions, and dynamics. This is especially the case in naturally vulnerable ecosystems, such as drylands. While we have an overall understanding of past aridity trends, the linkage between temporal dynamics in aridity and dryland ecosystem responses remain largely unknown. Here, we examined recent trends in aridity over the past two decades within global drylands as a basis for exploring the response of ecosystem state variables associated with land and atmosphere processes (e.g., vegetation cover, vegetation functioning, soil water availability, land cover, burned area, and vapor-pressure deficit) to these trends. We identified five clusters, characterizing spatiotemporal patterns in aridity between 2000 and 2020. Overall, we observe that 44.5% of all areas are getting dryer, 31.6% getting wetter, and 23.8% have no trends in aridity. Our results show strongest correlations between trends in ecosystem state variables and aridity in clusters with increasing aridity, which matches expectations of systemic acclimatization of the ecosystem to a reduction in water availability/water stress. Trends in vegetation (expressed by leaf area index [LAI]) are affected differently by potential driving factors (e.g., environmental, and climatic factors, soil properties, and population density) in areas experiencing water-related stress as compared to areas not exposed to water-related stress. Canopy height for example, has a positive impact on trends in LAI when the system is stressed but does not impact the trends in nonstressed systems. Conversely, opposite relationships were found for soil parameters such as root-zone water storage capacity and organic carbon density. How potential driving factors impact dryland vegetation differently depending on water-related stress (or no stress) is important, for example within management strategies to maintain and restore dryland vegetation.

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“…Earth Observation (EO) satellite data is a crucial enabler for facilitating environmental monitoring [1]. The growing volume and complexity of EO data are making it harder to store, manage, and process [2].…”

Section: Introductionmentioning

confidence: 99%

Scalable Data Processing Platform for Earth Observation Data Repositories

Astsatryan

Lalayan

Giuliani

2023

SCPE

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Earth observation (EO) satellite data is essential to environmental monitoring. At a national and regional level, the open data cubes harness the power of satellite data by providing application programming interfaces and services to the end-users. The volume and the complexity of satellite observations are increasing, demanding novel approaches for data storing, managing, and processing. High-performance computing (HPC) and cloud platforms may improve Big EO data processing performance. However, it is necessary to consider several vital aspects for efficient and flexible EO data processing, such as the interoperability from cloud-HPC and EO data repositories, automatic provisioning and scaling of cloud-HPC resources, cost-effectiveness, support of new EO data formats and open-source packages, or linkage with data cube platforms. The article proposes a scalable EO data processing platform interoperable from cloud-HPC and EO data repositories. The platform enables linking any data repository supporting web coverage service or SpatioTemporal Asset Catalog Application Programming Interfaces (STAC-API), and any cloud or HPC resource supporting scheduling system API for providing access to the cluster backends.

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Essential Variables for Environmental Monitoring: What Are the Possible Contributions of Earth Observation Data Cubes?

Cited by 29 publications

References 152 publications

A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America

A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America

Contrasting ecosystem vegetation response in global drylands under drying and wetting conditions

Scalable Data Processing Platform for Earth Observation Data Repositories

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