Skip to main content

Scientist in optical remote sensing of vegetation Forschungszentrum Jülich





Scientist in optical remote sensing of vegetation Forschungszentrum Jülich

The Plant Sciences Subinstitute of the Institute of Bio- and Geosciences (IBG) investigates the dynamics of plant processes and the interaction of plants with the environment. Plant science at Forschungszentrum Jülich plays a leading role at national and international level in the field of plant phenotyping, i.e. in the quantitative and non-invasive recording of structural and functional properties of plants important for agricultural and horticultural plant breeding. In this context, we are developing new sensors and measurement concepts and integrate them into semi- and fully automated systems. One focus of the subinstitute is the use of optical sensors to promote the automated measurement of plant traits under field conditions. Ground-based measurements are complemented by UAV, aircraft and satellite-based remote sensing approaches. Particular focus is on the development of novel non-invasive measurement approaches that include, multi- and hyperspectral imaging as well as different fluorescence retrieval techniques.
We are looking to recruit a
Scientist in optical remote sensing of vegetation
Your Job:
Focus of the research will be on exploiting hyper- and multispectral UAV data from cassava and other crops to derive structural and functional plant properties
Contribution to flight and campaign planning using rotary and fixed-wing unmanned platforms (UAVs) in Nigeria, Taiwan and Germany
Integration of multi- and hyperspectral sensors into existing UAV platforms
Measuring canopy traits in cassava plants in a project collaboration funded by the Bill and Melinda Gates Foundation
Development and refinement of algorithms for the preprocessing, atmospheric correction and georectification of spectrally resolved UAV data
Registration of optical reflectance data with experimental plot set-ups using GIS layers
Retrieval of canopy height models using custom based codes for data processing
Calculation of classical vegetation traits by exploiting the information content of the optical sensors
Radiative transfer inversion of leaf and canopy models to derive structural and functional vegetation traits from the combination of UAV based imaging data and other information sources, such as meteorological and ground based data
Interpretation of the results, correlation of remote sensing data with ground based plant traits and the integration within different synergistic projects
Presentation of the results at scientific conferences and within project reports
Writing of scientific papers in this field by taking advantage of the large body of research data that are available in the group
Contribution to project proposals in this research field
Contribution to supervision of Bachelor, Master and PhD students
Your Profile:
A university degree in Remote Sensing, Geophysics, Plant Biology, Agriculture or a natural scientific discipline with relevant and proven experience in the field of activity
Sound background in the use of UAVs and other unmanned aerial vehicles in research and agricultural practice
Experience in the processing of UAV image data using Agisoft Metashape or Pix4D
Sound background and proven expertise in processing and analyzing multispectral data
Profound knowledge in the field of atmospheric and geometric correction methods applied to ground-based, airborne and satellite data
Wide experience in interpretation and retrieval of vegetation traits from multispectral imagery
Special interest in retrieving and interpreting spectrally resolved UAV data from agricultural settings
Experience with programming languages and software that are used for multi-/hyperspectral image processing, e.g. ENVI/IDL, Python, R, Matlab etc.
Willingness and interest to work in Developing Countries
Ability to work in the field, partly also in remote locations outside of Germany, in specific in Nigeria and Taiwain
Driver license obligatory, already existing licenses to operate UAV platforms are a benefit
Our Offer:
Exciting working environment on an attractive research campus with excellent infrastructure, located between the cities of Cologne, Düsseldorf, and Aachen
Possibility to develop own scientific profile in the emerging topic of ‚remote sensing of vegetation traits using unmanned aerial vehicles'
Integration in a world-leading research group in this field with a stimulating scientific environment
Attendance of national and international conferences and workshops
Possibility for further scientific and technical training through international experts
Flexible working hours and various opportunities to reconcile work and private life
Position initially limited to three years, with the possibility of a longer-term perspective
The position can also be filled as a part-time position; flexible working time models between 50-100% are possible
Salary and social benefits in conformity with the provisions of the Collective Agreement for the Civil Service (TVöD)
Forschungszentrum Jülich promotes equal opportunities and diversity in its employment relations.
We also welcome applications from disabled persons.





Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://g.page/vineeshvc
🌏🌎
🌐🌍

Comments

Popular posts from this blog

Atmospheric Window

The atmospheric window in remote sensing refers to specific wavelength ranges within the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded. These windows are crucial for remote sensing applications because they allow us to observe the Earth's surface and atmosphere without significant interference from the atmosphere's constituents. Key facts and concepts about atmospheric windows: Visible and Near-Infrared (VNIR) window: This window encompasses wavelengths from approximately 0. 4 to 1. 0 micrometers. It is ideal for observing vegetation, water bodies, and land cover types. Shortwave Infrared (SWIR) window: This window covers wavelengths from approximately 1. 0 to 3. 0 micrometers. It is particularly useful for detecting minerals, water content, and vegetation health. Mid-Infrared (MIR) window: This window spans wavelengths from approximately 3. 0 to 8. 0 micrometers. It is valuable for identifying various materials, incl

DRA Disaster Risk Assessment

Disaster Risk Assessment (DRA): A Professional Overview Disaster Risk Assessment (DRA) is a systematic process used to identify, analyze, and evaluate the potential hazards, vulnerabilities, and risks posed by disasters to people, property, infrastructure, and the environment. It is a critical tool for effective disaster risk management, enabling communities, organizations, and governments to make informed decisions and implement appropriate mitigation measures. Key Components of DRA Hazard Identification: Identifying the types of hazards that could potentially affect a specific area, such as natural disasters (earthquakes, floods, cyclones), technological disasters (industrial accidents, infrastructure failures), or man-made disasters (conflicts, pandemics). Vulnerability Assessment: Evaluating the susceptibility of people, infrastructure, and the environment to the identified hazards. This involves assessing factors such as location, construction quality, socio-economic co

Linear Arrays Along-Track Scanners or Pushbroom Scanners

Multispectral Imaging Using Linear Arrays (Along-Track Scanners or Pushbroom Scanners) Multispectral Imaging: As previously defined, this involves capturing images using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation. Linear Array of Detectors (A): This refers to a row of discrete detectors arranged in a straight line. Each detector is responsible for measuring the radiation within a specific wavelength band. Focal Plane (B): This is the plane where the image is formed by the lens system. It is the location where the detectors are placed to capture the focused image. Formed by Lens Systems (C): The lens system is responsible for collecting and focusing the incoming radiation onto the focal plane. It acts like a camera lens, creating a sharp image of the scene. Ground Resolution Cell (D): As previously defined, this is the smallest area on the ground that can be resolved by a remote sensing sensor. In the case of linear array scanne

Discrete Detectors and Scanning mirrors Across the track scanner Whisk broom scanner.

Multispectral Imaging Using Discrete Detectors and Scanning Mirrors (Across-Track Scanner or Whisk Broom Scanner) Multispectral Imaging:  This technique involves capturing images of the Earth's surface using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation.  This allows for the identification of various features and materials based on their spectral signatures. Discrete Detectors:  These are individual sensors that are arranged in a linear or array configuration.  Each detector is responsible for measuring the radiation within a specific wavelength band. Scanning Mirrors:  These are optical components that are used to deflect the incoming radiation onto the discrete detectors.  By moving the mirrors,  the sensor can scan across the scene,  capturing data from different points. Across-Track Scanner or Whisk Broom Scanner:  This refers to the scanning mechanism where the mirror moves perpendicular to the direction of flight.  This allows for t

Hazard Vulnerability Exposure Risk

Key Concepts in Hazard Identification, Vulnerability Assessment, Exposure Assessment, and Risk Analysis Hazard-Exposure-Vulnerability-Risk (HEVR) Framework: Hazard: A potential event or phenomenon that can cause harm. Exposure: People, assets, or environments in harm's way. Vulnerability: Susceptibility to damage or harm from a hazard. Risk: The potential for loss or damage resulting from the interaction of hazards, exposure, and vulnerability. Risk as a Function: Risk can be calculated using the formula: Risk = Hazard × Vulnerability × Exposure. Reducing any of these factors can decrease overall risk. Types of Hazards: Natural hazards: Earthquakes, floods, tsunamis, landslides, hurricanes. Anthropogenic hazards: Industrial accidents, pollution, infrastructure failure, climate change. Technological hazards: Nuclear accidents, chemical spills. Vulnerability Dimensions: Physical: Infrastructure quality, building codes, location. Social: Age, income, disability, gender, acces