Die räumliche herleitung der Grünland vegetation eines Niedermoorgebietes anhand von Standort‐ und Nutzungskarten

Kaiser, Thomas M.; Leipnitz, Wolfgang; Käding, Horst; Haberstock, Werner; Glemnitz, Michael

doi:10.1080/03650340109366162

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…10b are growing on drained fen soils with varying soil organic matter content and groundwater levels. Such growing conditions cause different species compositions (Kaiser et al, 2001;Schwieder et al, 2020) and therefore heterogeneous drought impacts on grassland vegetation on fine spatial scales. Grasslands in Fig.…”

Section: Spatial Patterns Of Grassland Droughtmentioning

confidence: 99%

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

Kowalski

Okujeni

Brell

et al. 2022

Remote Sensing of Environment

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Severe droughts caused unprecedented impacts on grasslands in Central Europe in 2018 and 2019. Yet, spatially varying drought impacts on grasslands remain poorly understood as they are driven by complex interactions of environmental conditions and land management. Sentinel-2 time series offer untapped potential for improving grassland monitoring during droughts with the required spatial and temporal detail. In this study, we quantified drought effects in a major Central European grassland region from 2017 to 2020 using a regression-based unmixing framework. The Sentinel-2-based intra-annual time series of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and soil fractional cover provide easily interpretable quantities relevant for understanding drought effects on grasslands. Fractional cover estimates from Sentinel-2 matched in-situ conditions observed during field visits. The comparison to a multitemporal reference dataset showed the best agreement for PV cover (MAE = 7.2%). Agreement was lower for soil and NPV, but we observed positive relationships between fractional cover from Sentinel-2 and the reference data with MAE = 10.1% and MAE = 15.4% for soil and NPV, respectively. Based on the fractional cover estimates, we derived a Normalized Difference Fraction Index (NDFI) time series contrasting NPV and soil cover relative to PV. In line with meteorological and soil moisture drought indices, and with the Normalized Difference Vegetation Index (NDVI), NDFI time series showed the most severe drought impacts in 2018, followed by less severe, but persisting effects in 2019. Drought-specific metrics from NDFI time series revealed a high spatial variability of onset, duration, impact, and end of drought effects on grasslands. Evaluating drought metrics on different soil types, we found that grasslands on less productive, sandy Cambisols were strongly affected by the drought in 2018 and 2019. In comparison, grasslands on Gleysols and Histosols were less severely impacted suggesting a higher drought resistance of these grasslands. Our study emphasizes that the high temporal and spatial detail of Sentinel-2 time series is mandatory for capturing relevant vegetation dynamics in Central European lowland grasslands under drought.

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Section: Spatial Patterns Of Grassland Droughtmentioning

confidence: 99%

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

Kowalski

Okujeni

Brell

et al. 2022

Remote Sensing of Environment

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“…The specific approach for deriving regional water supply levels depends on data availability. In an earlier, simplified model, for example, the potential water supply level was mapped by combining water adjustability ratings of the drainage ditches with a digital terrain model (Kaiser et al 2001). The spatial derivation of water supply levels remains a sophisticated problem that cannot be treated exhaustively in this article.…”

Section: Water Supply Level-related Problemsmentioning

confidence: 99%

The derivation of grassland vegetation types on the basis of site and land use characteristics

Kaiser¹,

Käding²,

Kiesel³

et al. 2005

Archives of Agronomy and Soil Science

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This study derives grassland communities from site, climate and land use characteristics, allowing yield estimation within the framework of model scenarios. Based on the dominant species, about 450 relevés, primarily of extensively used grassland from four regions of the North-East German lowlands, were classified into vegetation types using a cluster analysis. These types were then reclassified to known phytosociological communities. The relationship between the vegetational composition and the abiotic explanatory variables were verified by a discriminant analysis. The water supply level proved to be the most important factor influencing grassland vegetation. The impact of historical land use (especially the persistence of formerly sown species) reduced prognostic quality.

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Die räumliche herleitung der Grünland vegetation eines Niedermoorgebietes anhand von Standort‐ und Nutzungskarten

Cited by 2 publications

References 2 publications

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

The derivation of grassland vegetation types on the basis of site and land use characteristics

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