A Machine Learning Framework for the Classification of Natura 2000 Habitat Types at Large Spatial Scales Using MODIS Surface Reflectance Data

Anthropogenic climate and land use change is causing rapid shifts in the distribution and composition of habitats with profound impacts on ecosystem biodiversity. The sustainable management of ecosystems requires monitoring programmes capable of detecting shifts in habitat distribution and composition at large spatial scales. Remote sensing observations facilitate such efforts as they enable cost-efficient modelling approaches that utilize publicly available datasets and can assess the status of habitats over extended periods of time. In this study, we introduce a modelling framework for habitat monitoring in Germany using readily available MODIS surface reflectance data. We developed supervised classification models that allocate (semi-)natural areas to one of 18 classes based on their similarity to Natura 2000 habitat types. Three machine learning classifiers, i.e., Support Vector Machines (SVM), Random Forests (RF), and C5.0, and an ensemble approach were employed to predict habitat type using spectral signatures from MODIS in the visible-to-near-infrared and short-wave infrared. The models were trained on homogenous Special Areas of Conservation that are predominantly covered by a single habitat type with reference data from 2013, 2014, and 2016 and tested against ground truth data from 2010 and 2019 for independent model validation. Individually, the SVM and RF methods achieved better overall classification accuracies (SVM: 0.72–0.93%, RF: 0.72–0.94%) than the C5.0 algorithm (0.66–0.93%), while the ensemble classifier developed from the individual models gave the best performance with overall accuracies of 94.23% for 2010 and 80.34% for 2019 and also allowed a robust detection of non-classifiable pixels. We detected strong variability in the cover of individual habitat types, which were reduced when aggregated based on their similarity. Our methodology is capable to provide quantitative information on the spatial distribution of habitats, differentiate between disturbance events and gradual shifts in ecosystem composition, and could successfully allocate natural areas to Natura 2000 habitat types

Okun's Law in der Krise: Ein Vergleich zwischen Deutschland und den USA. November 2011

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Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in theWater Column of Freshwater Lakes

Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS

Portable Vis-NIR und MIR Spektroskopie zur Erfassung von Bodeneigenschaften im Labor und im Gelände: Ergebnisse einer Fallstudie an Lößböden der Querfurter Platte (Sachsen-Anhalt)

Laborbasierte spektroskopische Verfahren im nahen Infrarot (inklusive dem sichtbaren Bereich; Vis-NIR, 400 – 2500 nm) und im mittleren Infrarot (MIR, 2500 – 25000 nm) stellen erprobte Methoden zur quantitativen Erfassung verschiedenster Bodeneigenschaften in Ergänzung zur klassischen Laboranalytik dar. Im MIR-Bereich hat sich als Technik die DRIFT („Diffuse Reflectance Infrared Fourier Transform“)-Spektroskopie etabliert, die, vergleichbar mit der Vis-NIR Spektroskopie, wenig Aufwand bei der Probenaufbereitung erfordert. Im Gegensatz zum Vis-NIR Bereich fehlen im MIR-Bereich Fallstudien mit „in-situ“ Messungen, da portable FTIR-Spektrometer erst seit wenigen Jahren verfügbar sind. Die vorliegende Untersuchung trägt hier zum Lückenschluss bei. Für 100 Ackerstandorte der Querfurter Platte (Tschernoseme der Mitteldeutschen Trockengebiete) wurden mit portablen Vis-NIR und MIR-Geräten (ASD FieldSpec-4 Wide-Res Feldpektroradiometer mit „Contact Probe“; aktives Agilent 4300 Handheld FTIR-Spektrometer mit „Diffuse Reflectance Sample Interface“) in-situ Messungen durchgeführt. Zusätzlich erfolgten eine Probennahme und erneute spektrale Vermessung der aufbereiteten Proben (gemahlen und luftgetrocknet) im Labor; nasschemisch wurden Referenzwerte für den gesamten und den organischen Kohlenstoff, Gesamt-Stickstoffgehalte und ph-Werte ermittelt. Auf dieser Datenbasis werden folgende Fragestellungen behandelt: 1) Vergleich beider Techniken hinsichtlich ihrer Potenziale zur Quantifizierung der genannten Bodengrößen (sowohl im Labor als auch im Gelände) mit verschiedenen multivariaten Kalibrationsansätzen (insbesondere Partial Least Squares Regression mit und ohne Spektralvariablenselektion); 2) Bewertung von Limitationen beider Techniken bei der praktischen Durchführung von Geländemessungen; 3) Möglichkeiten zur synergistischen Nutzung beider Spektralbereiche für eine verbesserte Abschätzung der Bodeneigenschaften

Predicting the abatement rates of soil organic carbon sequestration management in Western European vineyards using random forest regression

The implementation of soil organic carbon sequestration (SCS) practices on agricultural land has the potential to help to mitigate climate change at the global level. However, our understanding of the extent to which viticultural soils can contribute to this global effort remains limited. In this study, we used a random forest regression to predict the change in soil organic carbon stocks in vineyards of Western Europe under five SCS practices: organic amendments (OA), cover cropping (CC), organic amendments and no-tillage (OA+NT), no-tillage and cover cropping (NT+CC), and a combination of organic amendments, no-tillage and cover cropping (OA+NT+CC). The abatement rate of each SCS practice was modelled and mapped for six countries in Western Europe: Spain, France, Italy, Portugal, Germany and Austria. Overall, the highest abatement rate was reached under OA+NT+CC (8.29 ​Mg CO2-eq. ha−1 yr−1), whereas the lowest was observed under CC (7.03 Mg CO2-eq. ha−1 yr−1). Results showed major differences in abatement rates at the regional and national level. Despite these differences, the adoption of SCS practices was associated with a high abatement potential in the six countries and should be encouraged in the viticulture sector as a way to offset greenhouse gas emissions via soil carbon sequestration

The Chickasha Express

Weekly newspaper from Chickasha, Indian Territory. Coverage includes local, territorial, and United States national news, along with advertising

Don't trust anybody over 30: Youth unemployment and Okun's law in CEE countries Don't trust anybody over 30: Youth unemployment and Okun's law in CEE countries

Abstract In recent years youth unemployment rates across Europe soared, causing the European Commission to take actions through initiatives to counter this development. This article examines youth unemployment development in selected CEE countries and compares them to the EU 15. We use Okun's law and estimate age and country specific Okun coefficients for five different age cohorts. Our results show that young people display much higher Okun coefficients than their older peers, thus confirming that young people are more prone to macroeconomic shocks. This result might be a justification for additional governmental intervention and active labour market policies favouring young people. JEL classification: E24, F50, C23 Abstract In recent years youth unemployment rates across Europe soared, causing the European Commission to take actions through initiatives to counter this development. This article examines youth unemployment development in selected CEE countries and compares them to the EU 15. We use Okun's law and estimate age and country specific Okun coefficients for five different age cohorts. Our results show that young people display much higher Okun coefficients than their older peers, thus confirming that young people are more prone to macroeconomic shocks. This result might be a justification for additional governmental intervention and active labour market policies favouring young people. JEL classification: E24, F50, C2

Youth and gender specific unemployment and Okun's law in Scandinavian countries

The paper investigates Scandinavian countries and its respective male and female youth unemployment rates. Okun's law is used to estimate age-cohort and gender specific Okun coefficients to make inference on the business-cycle dependence of young people across Scandinavian countries

Age effects in the Okun's law within the Eurozone

We estimate Okun coefficients for five different age cohorts for several Eurozone countries. We find a stable pattern for all countries: The relationship between business-cycle fluctuations and the unemployment rate is the strongest for the youngest cohort and gets smaller for the elderly cohorts