Remotely Sensing Variation in Ecological Strategies and Plant Traits of Willows in Perialpine Floodplains

Milani, Gillian; Kneubühler, Mathias; Tonolla, Diego; Doering, Michael; Schaepman, Michael E.

doi:10.1029/2018jg004969

Cited by 4 publications

(5 citation statements)

References 96 publications

(127 reference statements)

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“…Chl‐a was sequentially extracted with the following solvents: acetone: water (85:15 v/v), pure acetone and isopropanol: hexane (50:50 v/v). The extracts were vacuum concentrated, followed by volume reduction under a gentle stream of nitrogen, and stored at −80°C until their quantification with a U‐HPLC (Agilent 1290 Infinity) coupled with a diode array detector (Milani et al ., 2019). Absorbance at 665 nm was used to measure Chl‐a concentration, identified with a standard (Sigma‐Aldrich).…”

Section: Methodsmentioning

confidence: 99%

The biogeochemical variability of Arctic thermokarst ponds is reflected by stochastic and niche‐driven microbial community assembly processes

Moigne

Bartosiewicz

Schaepman‐Strub

et al. 2020

Environmental Microbiology

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Summary Shallow thermokarst ponds are a conspicuous landscape element of the Arctic Siberian tundra with high biogeochemical variability. Little is known about how microbes from the regional species pool assemble into local pond communities and how the resulting patterns affect functional properties such as dissolved organic carbon (DOC) remineralization and greenhouse gas (GHG) turnover. We analysed the pelagic microbiomes of 20 ponds in north‐eastern Siberia in the context of their physico‐chemical properties. Ponds were categorized as polygonal or trough according to their geomorphological origin. The diversity of bacteria and eukaryotic microbes was assessed by ribosomal gene tag sequencing. Null model analysis revealed an important role of stochastic assembly processes within ponds of identical origin, in particular for genotypes only occurring in few systems. Nevertheless, the two pond types clearly represented distinct niches for both the bacterial and eukaryotic microbial communities. Carbon dioxide concentration, indicative of heterotrophic microbial processes, varied greatly, especially in the trough ponds. Methane concentrations were lower in polygonal ponds and were correlated with the estimated abundance of methanotrophs. Thus, the overall functional variability of Arctic ponds reflects the stochastic assembly of their microbial communities. Distinct functional subcommunities can, nevertheless, be related to GHG concentrations.

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Section: Methodsmentioning

confidence: 99%

The biogeochemical variability of Arctic thermokarst ponds is reflected by stochastic and niche‐driven microbial community assembly processes

Moigne

Bartosiewicz

Schaepman‐Strub

et al. 2020

Environmental Microbiology

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“…Not only are there seasonal differences in the reflectance between individuals of the same species [39] and between different species that do not have the same phenological windows [42], but the temperature and water levels are also seasonal. This study used data collected at the end of summer when the discharge of the Ain River was usually low and the temperature was high, which may bias the spectra towards the signature of hydrological connectivity and potentially plant stress.…”

Section: Discussionmentioning

confidence: 99%

“…Hydrological Connectivity [28] LiDAR X [29] RGB imagery + LiDAR X X [30] Landsat imagery X X [31] RGB imagery + photogrammetry X X X X [32] RGB imagery X [33] RGB imagery + photogrammetry X X X X [34] LiDAR X X [35] RGB imagery + LiDAR X X X X [36] RGB imagery + LiDAR X X X X [37] RGB imagery + LiDAR + photogrammetry X X X [38] RGB imagery + LiDAR X X X X X [39] Hyperspectral imagery X X [40] Hyperspectral imagery + Landsat imagery X X X…”

Section: Topography Andmentioning

confidence: 99%

“…In riparian environments, hyperspectral imagery with Landsat data has been used to provide in situ data for investigating the responses of pioneer species (such as poplars and willows) to groundwater decline following a drought event [40] and to investigate changes in competitor/stress tolerator/ruderal (CSR) strategies in hydrologically-altered reaches [39]. These examples show the potential of hyperspectral imagery for investigating hydrogeomorphic feedback in riparian communities.…”

Section: Topography Andmentioning

confidence: 99%

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Combining Hyperspectral, LiDAR, and Forestry Data to Characterize Riparian Forests along Age and Hydrological Gradients

et al. 2022

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Riparian forests are complex ecosystems shaped by their connectivity to a river system, which produces a mosaic of ages and species. Because of increasing anthropic pressure from factors such as damming or climate change, they are often endangered and suffer from a drop in groundwater accessibility and increased water stress. By combining hyperspectral, LiDAR, and forestry datasets along a 20 km corridor of the Ain River, this paper assesses the ability of remote sensing to characterize and monitor such environments. These datasets are used to investigate changes in site conditions and forest characteristics, such as height and canopy water content, along a gradient of ecosystem ages and for reaches under distinct geomorphic conditions (shifting, sediment-starved, incised). The data show that, over time, forest patches aggrade, and the forest grows and becomes more post-pioneer. However, forest patches that are located in the incised reach aggrade more and appear to be less developed in height, more stressed, and feature species compositions reflecting dryer conditions, in comparison with better-connected patches of the same age. Random forest analysis was applied to predict the indicators of forest connectivity with remotely sensed LIDAR and hyperspectral data, in order to identify the spatial trends at the reach scale and compare them with the geomorphic segmentation of the river. The random forest classifications achieved an accuracy between 80% and 90% and resulted in spatial trends that highlighted the differences in hydrological connectivity between differing geomorphic conditions. Overall, remote sensing appears to be a good tool for characterizing the impact of channel incisions and adjustments on riparian forest conditions by identifying the locations of dryer forest patches. In addition, good accuracy was achieved when attempting to classify these forest patches, even when using hyperspectral data alone, which suggests that satellite data could become a powerful tool for monitoring the health of riparian forests, in the context of increasing anthropic pressures.

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“…The first reach is located along the Sense River next to the village of Plaffeien, an investigation site of previous studies about its hydromorphology [35], floodplain habitats [36] and the use of aerial imagery [37], [38], [39]. The Sense River is one of the last rivers in Switzerland to have a near natural flow and sediment regime.…”

Section: A Study Sitesmentioning

confidence: 99%

Characterizing Flood Impact on Swiss Floodplains Using Interannual Time Series of Satellite Imagery

Milani

Kneubühler

Tonolla

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Pressure on the biodiversity of ecosystems along many rivers is growing continuously due to the increasing number of hydropower facilities regulating downstream flow and sediment regimes. Despite a thorough understanding of the shortterm processes and interactions at this hydro-biosphere interface, long-term analyses of the impacts on floodplain dynamics are lacking. We used inter-annual Landsat 4, 5, 7 and 8 time series to analyze the effects of hydrological events on floodplain vegetation in four mountainous floodplains in the Swiss Alps. Using a spectral mixture analysis approach, we demonstrate that the floodplain vegetation dynamics of mountainous rivers can be recovered at a spatial resolution of 30 meters. Our results suggest that interactions between floods and floodplain vegetation are complex and not exclusively related to flood magnitude. Of the four reaches analyzed, only data gathered along the submountainous reach with a quasi-natural flow regime show a clear link between remotely sensed vegetation indices and floods. In addition, our 29-year time series shows a continuous upward trend in vegetation indices along the floodplains, strongest in the reaches affected by hydropower facilities. The approach presented in this study can be easily replicated in other mountain ranges by providing available flow data to verify the impact of hydropower on floodplain vegetation dynamics.

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Remotely Sensing Variation in Ecological Strategies and Plant Traits of Willows in Perialpine Floodplains

Cited by 4 publications

References 96 publications

The biogeochemical variability of Arctic thermokarst ponds is reflected by stochastic and niche‐driven microbial community assembly processes

The biogeochemical variability of Arctic thermokarst ponds is reflected by stochastic and niche‐driven microbial community assembly processes

Combining Hyperspectral, LiDAR, and Forestry Data to Characterize Riparian Forests along Age and Hydrological Gradients

Characterizing Flood Impact on Swiss Floodplains Using Interannual Time Series of Satellite Imagery

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