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Digitales Oberflächemodell Mesh (DOM‑Mesh)

Ein DOM‑Mesh stellt die sichtbare Oberfläche (Gebäude, Vegetation, Gelände) in 3D dar. Es wird aus Luftbildern und einem digitalen Oberflächenmodell (DOM) abgeleitet und texturiert.

WMS SL GDI Oberflächenmodelle - DOM1 (2025)

Oberflächenmodelle Saarland:Ein DOM ist ein Digitales Höhenmodell, das im freien Gelände dem DGM entspricht, also die natürliche Geländeoberfläche abbildet, ansonsten aber über die Oberflächen der Gebäude und der beständigen Vegetation verläuft. Datengrundlage sind die durch Laserscanning gewonnen dreidimensionalen Messpunkte. DOM bilden die Situation zum Zeitpunkt der Erfassung ab. Bedingt durch unterschiedliche Erfassungszeitpunkte können z.B. bei Vegetations- und Wasserflächen Höhensprünge auftreten. Hohe schmale Objekte wie bspw. Windräder und Strommasten können nur bedingt abgebildet werden.

WMS SL GDI Oberflächenmodelle - DOM Shaded Relief (2025)

Oberflächenmodelle Saarland:Ein DOM ist ein Digitales Höhenmodell, das im freien Gelände dem DGM entspricht, also die natürliche Geländeoberfläche abbildet, ansonsten aber über die Oberflächen der Gebäude und der beständigen Vegetation verläuft. Datengrundlage sind die durch Laserscanning gewonnen dreidimensionalen Messpunkte. DOM bilden die Situation zum Zeitpunkt der Erfassung ab. Bedingt durch unterschiedliche Erfassungszeitpunkte können z.B. bei Vegetations- und Wasserflächen Höhensprünge auftreten. Hohe schmale Objekte wie bspw. Windräder und Strommasten können nur bedingt abgebildet werden.

CDM Market Support Study

The Clean Development Mechanism (CDM) suffers from a price level for certificates that went down to almost zero in a period less than a year. Additionally, no short-term price recovery is expected which could incentivise new projects. A risk is that market participants leave the market and the valuable CDM knowledge base on GHG mitigation and quantification will be lost. The CDM Market Support Study analyses the actual price vulnerability of projects and identifies various financing and project type opportunities for project developers and for (institutional public) investors who intent to support the CDM project continuation and the further development of the CDM framework. The study also shows how the current regulatory framework of the CDM can be maintained by transferring it to future mechanisms. This could be a chance to develop the CDM from a pure market-based instrument towards an integrated part within future market-based and also policy-based instruments. The CDM can provide useful components to currently discussed or tested instruments such as the NMM (New Market Mechanism), the FVA (Framework for Varios Approaches), NAMAs (Nationally Appropriate Mitigation Actions) or results-based financing approaches. The study was financed by the German KfW-managed PoA Support Centre . The aim of the PoA-Support-Centre Germany is to support the development of Programmes of Activities (PoAs) under CDM and JI (Joint Implementation) worldwide.

Programmatic CDM for energy efficiency in the building sector in India

Durchführung von Maßnahmen zur Umsetzung der Klimarahmenkonvention: Ländermaßnahme Chile. Nationale Strategiestudie zum CDM

ASEAN regional network to facilitate sustainable energy investment through Clean Development (CDM)

Modellhafte Beseitigung von Umweltschaeden und Schutz vor Umweltbelastungen am Erfurter Dom (Hoher Chor)

National Strategy Study South Africa

PARAFAC components and fluorescent dissolved organic matter (FDOM) indices on organic matter transformation processes in the sea-surface microlayer and the underlying water during a mesocosm phytoplankton bloom in 2023

The effects of a phytoplankton bloom and photobleaching on colored dissolved organic matter (CDOM) in the sea-surface microlayer (SML) and the underlying water (ULW) were studied in a month-long mesocosm study, in May and June of 2023, at the Institute for Chemistry and Biology of the Marine Environment (ICBM) in Wilhelmshaven, Germany. The mesocosm study was conducted by the DFG research group BASS (Biogeochemical processes and Air–sea exchange in the Sea-Surface microlayer, Bibi et al., 2025) in the Sea Surface Facility (SURF) of the ICBM. The facility contains an 8 m × 1.5 m × 0.8 m large outdoor basin with a retractable roof, which was closed at night and during rain events. The basin was filled with North Sea water from the adjacent Jade Bay. Homogeneity of the ULW in the basin was achieved by constant mixing of the water column. The daily SML and ULW samples were collected alternating in the morning, about 1 h after sunrise, and in the afternoon, about 10 h after sunrise. The alternation of sampling times intended to capture a potential effect of sun-exposure duration on DOM transformations and elucidated the day and night variability of the layers. The SML was collected via glass plate sampling (Cunliffe and Wurl, 2014). The ULW was sampled via a submerged tube and a connected syringe suction system in 0.4 m depth. The removed sample volume was refilled with Jade Bay water every day. SML and ULW samples were filtered through pre-flushed 0.7 µm Whatman GF/F and 0.2 nucleopore filters into clear 40 ml SUPELCO bottles. These bottles were acid-washed twice and combusted at 500 °C for 5 h. The samples were stored dark and at 4 °C and measured within a few days of the study. FDOM was measured using a Aqualog fluorescence spectrometer (Horiba Scientific, Japan) with 10 seconds integration time and high gain of the CCD (charge-coupled device) sensor within an excitation range from 240 to 500 nm, and an emission range from 209.15 to 618.53 nm. The Aqualog measures fluorescence as well as absorption. The resulting data includes an excitation-emission-matrix (EEM) of the blank (MilliQ Starna cuvette), an EEM of the sample, and the absorption values of the sample. The raw exported Aqualog data was corrected for errors and lamp shifts. The corrected EEM data is then decomposed by PARAFAC (Murphy et al., 2013) for its underlying fluorophore components. Before running the PARAFAC routine, the corrected data needed to undergo a correction process by subtracting the blank from the sample EEM and canceling the influences of the inner-filter effect (IFE, Parker & Rees, 1962; Kothawala et al., 2013). The fluorescence intensity of the IFE-corrected EEM is calibrated by using the Raman scatter peak of water (Lawaetz & Stedmon, 2009). For PARAFAC the corrected data was processed using the drEEM and NWAY toolbox (version 0.6.5; Murphy et al., 2013) in MATLAB (R2020b). A 4-component model was validated with the validation style S4C6T3 for the split half analysis with nonnegativity constraints and 1-8e as the convergence criteria with 50 random starts and a maximum number of 2500 iterations. The resulting final model had a core consistency of 88.11 and the explained percentage was 99.55%. Furthermore, four fluorescence indices were calculated from the corrected EEM data (HIX – Humification index, Zsolnay et al., 1999; BIX – Biological index, Huguet et al., 2009; REPIX – Recently produced index, Parlanti et al., 2000, Drozdowska et al., 2015; ARIX, Murphy, 2025).

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