WMS des rechtskräftigen Regionalen Raumordnungsprogramms des Landkreises Rotenburg (Wümme) in zeichnerischer Darstellung. Das Regionale Raumordnungsprogramm ist aus dem Landesraumordnungsprogramm (LROP) 2017 entwickelt und legt die angestrebte räumliche und strukturelle Entwicklung des Planungsraumes fest.
The pattern of plant nutrient uptake in a soil profile is the result of complex processes occurring at the cellular or sub-cellular levels but affecting the whole-plant behaviour in function of the plant environment that varies strongly in time and space. The plant nutrient acquisition depends on root architecture and growth, on soil properties and heterogeneity, and on the 3-D distribution of nutrients and water. Equally important is how these parameters interact, as for instance how the nutrient distribution and soil properties and heterogeneity impact root growth or how nutrient and water limitation affect assimilate allocation. Mathematical modelling using a spatial resolution that resolves the spatial structure of the root structure and the nutrient and water distribution is therefore needed to quantitatively account for these complex and interacting processes and to predict plant nutrient uptake behaviour under environmental constraints. The main goal of the project is to build a modelling platform able to describe 3-D flow and transport processes in the soil to individual roots of an entire root system (WP1). Model parameters will be derived from specific experiments performed at the plant scale in the research group (WP3) and stored in a specific data warehouse (WP2). The impact of different parameters, which describe root growth and nutrient uptake at the single root scale, on nutrient uptake at the soil profile scale, will be investigated based on scenario analyses (WP4). Data on water and nutrient uptake and root growth from plant and field scale experiments will be compared with model predictions to validate the model. Simulations with the 3-D root scale model will be used to validate hypotheses and parameterizations of larger scale 1-D models that do not describe processes at the scale of individual roots (WP5 and SP10).
Ziel der Forschung ist die Formulierung von Bewertungsmodellen des Raumzusammenhangs im weitesten Sinne. Verkehr als Mittel der Raumordnungspolitik ist quantitativ zu erfassen und im Zusammenhang aller Raumstrukturen zu bewerten. Die Raumstruktur (mit den Bereichen 'Siedlungsstruktur' und 'Verkehrsinfrastruktur') weist Komplementaritaeten auf, die Bedingung fuer Kommunikation und, in Verbindung mit der Raumlage, fuer Verkehr sind. Diese gilt es zu durchleuchten und in allgemeinen, verifizierbaren Modellen zu formulieren. Dazu gehoeren nicht nur die ueblicherweise behandelten Wirkungen in der Verkehrsinfrastruktur, sondern auch die Rueckwirkungen des Verkehrsgefueges auf die Raumstruktur.
Dairy farming across Germany displays diverse production systems. Factor endowment, management, technology adoption as well as competitive dynamics in the local or regional land, agribusiness and dairy processing sectors contribute to this differentiation on farm level. These differences impact on the ability of dairy farms and regional dairy production systems to successfully respond to pressures arising from future market and policy changes. The overall objective of the research activities of which this project is a part of, is to develop a thorough understanding of the processes that govern the spatial dynamics of dairy farm development in different regions in Germany. The central hypothesis of this research project is that management system and technological choices differ systematically across local production and market conditions. The empirical approach will focus on the estimation of farm specific nonparametric cost functions for dairy farms located in across Germany differentiated by time and location. A spatially differentiated data base with information on input use, resource availability, as well as local market conditions for land and output markets will be compiled. The nonparametric approach is specifically suited to disclose a more accurate representation of dairy production system heterogeneity across locations and time compared to parametric concepts as it provides the necessary flexibility to accommodate non-linearities relevant for a wide domain of explanatory variables. The methodology employed goes beyond the state of the art of the literature as it combines kernel density estimation with a Bayesian sampling approach to provide theory consistent parameters for each farm in the data sample.The specific methodological hypothesis is that the nonparametric approach is superior to current parametric techniques and this hypothesis is tested using statistical model evaluation. Regarding the farm management and technological choices, we hypothesize that land suitability for feed production determines the farm intensity of dairy production and thus management and technological choices. With respect to the ability of farms to successfully respond to market pressures we hypothesize that farms at the upper and lower tail of the intensity distribution both can generate positive returns from dairy production. These last two hypotheses will be tested using the estimated spatially differentiated farm specific costs and marginal costs.The expected outcomes are of relevance for the agricultural sector and the food supply chain economy as a whole as fundamental market structure changes in the dairy sector are ongoing due to the abolition of the quota regulation in the years 2014/2015. Thus, exact knowledge about differences and development of dairy cost heterogeneity of farms within and between regions are an important factor for the actors involved in the market as well as the political support of this process.
Water, carbon and nitrogen are key elements in all ecosystem turnover processes and they are related to a variety of environmental problems, including eutrophication, greenhouse gas emissions or carbon sequestration. An in-depth knowledge of the interaction of water, carbon and nitrogen on the landscape scale is required to improve land use and management while at the same time mitigating environmental impact. This is even more important under the light of future climate and land use changes.In the frame of the proposal 'Uncertainty of predicted hydro-biogeochemical fluxes and trace gas emissions on the landscape scale under climate and land use change' we advocate the development of fully coupled, process-oriented models that explicitly simulate the dynamic interaction of water, carbon and nitrogen turnover processes on the landscape scale. We will use the Catchment Modelling Framework CMF, a modular toolbox to implement and test hypothesis of hydrologic behaviour and couple this to the biogeochemical LandscapeDNDC model, a process-based dynamic model for the simulation of greenhouse gas emissions from soils and their associated turnover processes.Due to the intrinsic complexity of the models in use, the predictive uncertainty of the coupled models is unknown. This predictive (global) uncertainty is composed of stochastic and structural components. Stochastic uncertainty results from errors in parameter estimation, poorly known initial states of the model, mismatching boundary conditions or inaccuracies in model input and validation data. Structural uncertainty is related to the flawed or simplified description of natural processes in a model.The objective of this proposal is therefore to quantify the global uncertainty of the coupled hydro-biogeochemical models and investigate the uncertainty chain from parameter uncertainty over forcing data uncertainty up the structural model uncertainty be setting up different combinations of CMF and LandscapeDNDC. A comprehensive work program has been developed structured in 4 work packages, that consist of (1) model set up, calibration and uncertainty assessment on site scale followed by (2) an application and uncertainty assessment of the coupled model structures on regional scale, (3) global change scenario analyses and finally (4) evaluating model results in an ensemble fashion.Last but not least, a further motivation of this proposal is to provide project results in a manner that they support planning and decision taking under uncertainty, as this proposal is part of the package proposal on 'Methodologies for dealing with uncertainties in landscape planning and related modelling'.
Regionaler Entwicklungsplan für die Planungsregion Anhalt-Bitterfeld-Wittenberg mit den Planinhalten „Raumstruktur, Standortpotenziale, technische Infrastruktur und Freiraumstruktur“ Die oberste Landesentwicklungsbehörde hat am 21.12.2018 die Genehmigung unter einer Maßgabe erteilt. Am 29.03.2019 trat die Regionalversammlung mit Beschluss Nr. 03/2019 der Maßgabe bei. Mit Bekanntmachung der Genehmigung trat der Regionale Entwicklungsplan am 27.04.2019 in Kraft.
Der Datensatz beinhaltet Daten des LBGR über die Hydrogeologische Raumgliederung Brandenburgs und wird über je einen Darstellungs- und Downloaddienst bereitgestellt. Die Karte gibt einen Überblick zu den hydrogeologischen Raumgliederungen Brandenburgs. Die Gliederungseinheiten tragen den angewandten Charakter von Nutzungsräumen. Sie werden anhand von Wassereinzugsgebieten und Charakteristiken dazugehöriger Grundwasserdynamik beschrieben. Für das Territorium einer hydrogeologischen Einheit werden vergleichbare Grundwasserverhältnisse vorausgesetzt.
Regionaler Entwicklungsplan für die Planungsregion Anhalt-Bitterfeld-Wittenberg mit den Planinhalten „Raumstruktur, Standortpotenziale, technische Infrastruktur und Freiraumstruktur“ Die oberste Landesentwicklungsbehörde hat am 21.12.2018 die Genehmigung unter einer Maßgabe erteilt. Am 29.03.2019 trat die Regionalversammlung mit Beschluss Nr. 03/2019 der Maßgabe bei. Mit Bekanntmachung der Genehmigung trat der Regionale Entwicklungsplan am 27.04.2019 in Kraft.
Dieser Dienst enthält Daten der Planungsregionen Oberes Elbtal/Osterzgebirge, Region Chemnitz und Oberlausitz-Niederschlesien und deckt im Endausbau den gesamten Freistaat Sachsen ab. Entsprechend des Landesentwicklungsplanes 2013 als fachübergreifendes Gesamtkonzept zur räumlichen Entwicklung, Ordnung und Sicherung des Freistaates Sachsen stellen die Regionalpläne einen verbindlichen Rahmen für die räumliche Entwicklung, Ordnung und Sicherung des Raumes dar. Im Dienst sind regionalplanerische Festlegungen des Komplexes Raumstruktur enthalten. Die rechtsverbindlichen Karten Raumstruktur werden in der Regel in Maßstäben zwischen 1:300.000 und 1:450.000 erstellt. Eine Darstellung der Inhalte der Regionalpläne erfolgt in diesem Dienst nur im Maßstab kleiner 1:10.000.
# Faszination Nächtlicher Vogelzug A web component for visualizing migratory bird detections on an interactive map. Built with React, MapLibre GL, and the BirdWeather GraphQL API. Designed for embedding into CMS platforms like Contao. ## Tech Stack - **React 19** + **TypeScript** (Vite) - **MapLibre GL** -- WebGL map rendering (Stadia Maps dark theme) - **Supercluster** -- per-species spatial clustering - **Apollo Client 4** -- GraphQL data fetching with caching - **GraphQL Code Generation** -- type-safe queries from BirdWeather schema - **SunCalc** -- astronomical day/night calculations - **Tailwind CSS 4** + **Ant Design 6** -- UI - **Vitest** -- testing ## Features - **Interactive map** with color-coded detection clusters per species - **Timeline animation** with autoplay, step controls, and throttled slider - **Night-only mode** that compresses inactive daytime hours using SunCalc sunrise/sunset calculations - **Day/night overlay** showing the terminator (day/night boundary) as a real-time GeoJSON polygon - **Species search** with autocomplete and availability checking per map viewport - **Supplementary layers** (light pollution, noise mapping via WMS) - **Web component** (`<zug-birdnet>`) for CMS embedding without routing ## Project Structure ``` src/ main.tsx Web component registration App.tsx Root component, species selection state api/ fragments.ts GraphQL fragments (DetectionItem, SpeciesItem) queries.ts GraphQL queries (detections, species, search) useDetections.ts Detection fetch hook with prefetching components/ DatesProvider.tsx Time state context (date range, animation, night mode) MapProvider.tsx MapLibre GL instance context SpeciesDropdown.tsx Species selection with search autocomplete Timeline.tsx Date picker, animation slider, playback controls LayersDropdown.tsx Toggle info layers (light pollution, noise) InfoPopup.tsx Map info marker popups map/ Map.tsx MapLibre GL initialization and rendering clusterUtils.ts Per-species Supercluster index creation colorUtils.ts MapLibre paint expression builder mapStyles.ts Map layer definitions usePersistentColors.ts Stable color assignment per species infopoints.ts Static info marker data lib/ apollo-client.ts Apollo Client with cache type policies buildAvailableSpeciesQuery.ts Dynamic aliased query generation getDayPolygon.ts Day/night terminator polygon calculation getTranslatedSpeciesName.ts i18n species name lookup isNotNull.ts, hasNonNullProp.ts Type guard utilities throttle.ts Throttle utility gql/ Auto-generated GraphQL types (do not edit) ``` ## Architecture Three React context providers compose the application: ``` ApolloProvider GraphQL caching and data fetching DatesProvider Date range, animation state, night-only time segments MapProvider MapLibre GL map instance App Species selection, filtered detections, color mapping ``` **Data flow:** Apollo fetches detections for the current bounding box and date range. Detections are filtered client-side by the visualisation time window (controlled by the timeline slider). Each species gets its own Supercluster index for independent color-coded clustering. Cluster features are rendered via MapLibre GL layers with dynamic `match` paint expressions. **GraphQL:** Queries and fragments are defined in `src/api/` and typed via `@graphql-codegen/client-preset`. Run `npm run codegen` after schema changes to regenerate `src/gql/`. ## Development ```sh npm install npm run dev ``` The dev server uses a self-signed SSL certificate via `@vitejs/plugin-basic-ssl`. Accept the browser warning on first visit. Other commands: ```sh npm run build # Production build npm run test # Run tests npm run lint # ESLint npm run codegen # Regenerate GraphQL types ``` ## Build & Integration Run `npm run build` to produce the `dist/` folder. The build outputs stable filenames (no hashes) and splits vendor dependencies into separate chunks for caching: ``` dist/ index.html assets/ index.css App styles (Tailwind + Ant Design) index.js Application code, React, Supercluster, dayjs, SunCalc maplibre.js MapLibre GL antd.js Ant Design + icons apollo.js Apollo Client + graphql ``` Only `index.js` changes on application updates. Vendor chunks are cache-stable between deploys. To embed the web component, include the built CSS and JS, then use the custom element: ```html <link rel="stylesheet" href="/assets/index.css"> <script type="module" src="/assets/index.js"></script> <zug-birdnet></zug-birdnet> ``` No routing. The component is self-contained and can be placed anywhere on the page. Third-party CMS integration (e.g., Contao) only needs to include the built assets and the custom element tag. ## Configuration App-level settings are in `src/config.ts`: | Option | Default | Description | |---|---|---| | `SHOW_DEMO_INFOPOINTS` | `false` | Show static info markers on the map (demo/development only) |
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