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Safaei M, Kleinebecker T, Weis M, Große-Stoltenberg A. Tracking effects of extreme drought on coniferous forests from space using dynamic habitat indices. Heliyon 2024; 10:e27864. [PMID: 38560251 PMCID: PMC10981029 DOI: 10.1016/j.heliyon.2024.e27864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/27/2024] [Accepted: 03/07/2024] [Indexed: 04/04/2024] Open
Abstract
Terrestrial ecosystems such as coniferous forests in Central Europe are experiencing changes in health status following extreme droughts compounding with severe heat waves. The increasing temporal resolution and spatial coverage of earth observation data offer new opportunities to assess these dynamics. Dense time-series of optical satellite data allow for computing Dynamic Habitat Indices (DHIs), which have been predominantly used in biodiversity studies. However, DHIs cover three aspects of vegetation changes that could be affected by drought: annual productivity, minimum cover, and seasonality. Here, we evaluate the health status of coniferous forests in the federal state of Hesse in Germany over the period 2017-2020 including the severe drought year of 2018 using DHIs based on the Normalized Difference Vegetation Index (NDVI) for drought assessment. To identify the most important variables affecting coniferous forest die-off, a series of environmental variables together with the three DHIs components were used in a logistic regression (LR) model. Each DHI component changed significantly across non-damaged and damaged sites in all years (p-value 0.05). When comparing 2017 to 2019, DHI-based annual productivity decreased and seasonality increased. Most importantly, none of the DHI components had reached pre-drought conditions, which likely indicates a change in ecosystem functioning. We also identified spatially explicit areas highly affected by drought. The LR model revealed that in addition to common environmental parameters related to temperature, precipitation, and elevation, DHI components were the most important factors explaining the health status. Our analysis demonstrates the potential of DHIs to capture the effect of drought events on Central European coniferous forest ecosystems. Since the spaceborne data are available at the global level, this approach can be applied to track the dynamics of ecosystem conditions in other regions, at larger spatial scales, and for other Land Use/Land Cover types.
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Affiliation(s)
- Mojdeh Safaei
- Division of Landscape Ecology and Landscape Planning, Institute of Landscape Ecology and Resource Management, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich-Buff Ring 26-32, 35392, Giessen, Germany
| | - Till Kleinebecker
- Division of Landscape Ecology and Landscape Planning, Institute of Landscape Ecology and Resource Management, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich-Buff Ring 26-32, 35392, Giessen, Germany
- Center for International Development and Environmental Research (ZEU), Senckenbergstrasse 3, 35390, Giessen, Germany
| | - Manuel Weis
- Hessian Agency for Nature Conservation, Environment and Geology (HLNUG), Rheingaustraße 186, 65203, Wiesbaden, Germany
| | - André Große-Stoltenberg
- Division of Landscape Ecology and Landscape Planning, Institute of Landscape Ecology and Resource Management, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich-Buff Ring 26-32, 35392, Giessen, Germany
- Center for International Development and Environmental Research (ZEU), Senckenbergstrasse 3, 35390, Giessen, Germany
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Moore CE, Griebel A. A Beginner's Guide to Eddy Covariance: Methodology and Its Applications to Photosynthesis. Methods Mol Biol 2024; 2790:227-256. [PMID: 38649574 DOI: 10.1007/978-1-0716-3790-6_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
The eddy covariance technique, commonly applied using flux towers, enables the investigation of greenhouse gas (e.g., carbon dioxide, methane, nitrous oxide) and energy (latent and sensible heat) fluxes between the biosphere and the atmosphere. Through measuring carbon fluxes in particular, eddy covariance flux towers can give insight into how ecosystem scale photosynthesis (i.e., gross primary productivity) changes over time in response to climate and management. This chapter is designed to be a beginner's guide to understanding the eddy covariance method and how it can be applied in photosynthesis research. It introduces key concepts and assumptions that apply to the method, what materials are required to set up a flux tower, as well as practical advice for site installation, maintenance, data management, and postprocessing considerations. This chapter also includes examples of what can go wrong, with advice on how to correct these errors if they arise. This chapter has been crafted to help new users design, install, and manage the best towers to suit their research needs and includes additional resources throughout to further guide successful eddy covariance research activities.
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Affiliation(s)
- Caitlin E Moore
- UWA School of Agriculture & Environment, The University of Western Australia, Crawley, WA, Australia.
| | - Anne Griebel
- School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
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Cushman KC, Albert LP, Norby RJ, Saatchi S. Innovations in plant science from integrative remote sensing research: an introduction to a Virtual Issue. THE NEW PHYTOLOGIST 2023; 240:1707-1711. [PMID: 37915249 DOI: 10.1111/nph.19237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 11/03/2023]
Abstract
This article is an Editorial to the Virtual issue on ‘Remote sensing’ that includes the following papers Chavana‐Bryant et al. (2017), Coupel‐Ledru et al. (2022), Cushman & Machado (2020), Disney (2019), D'Odorico et al. (2020), Dong et al. (2022), Fischer et al. (2019), Gamon et al. (2023), Gu et al. (2019), Guillemot et al. (2020), Jucker (2021), Koh et al. (2022), Konings et al. (2019), Kothari et al. (2023), Martini et al. (2022), Richardson (2019), Santini et al. (2021), Schimel et al. (2019), Serbin et al. (2019), Smith et al. (2019, 2020), Still et al. (2021), Stovall et al. (2021), Wang et al. (2020), Wong et al. (2020), Wu et al. (2021), Wu et al. (2017), Wu et al. (2018), Wu et al. (2019), Xu et al. (2021), Yan et al. (2021). Access the Virtual Issue at www.newphytologist.com/virtualissues.
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Affiliation(s)
- K C Cushman
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
| | - Loren P Albert
- College of Forestry, Oregon State University, Corvallis, OR, 97331, USA
| | - Richard J Norby
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Sassan Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
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