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

Post a Comment

Popular posts from this blog

Spatial feature manipulation in remote sensing

-
Image
Spatial feature manipulation in remote sensing refers to the process of altering or modifying the spatial characteristics of a particular feature in an image or data set. This can be done for a variety of reasons, such as to improve the accuracy or clarity of the image, to enhance the interpretability of the data, or to extract specific information from the image. One common method of spatial feature manipulation is resampling, which involves changing the resolution or spatial extent of an image. This can be done to match the resolution of other data sets, to reduce the size of the image for faster processing, or to increase the resolution for greater detail. Another technique is spatial filtering, which involves applying mathematical operations to the image data to remove noise or highlight specific features. This can be done using convolution filters, which apply a pre-defined mathematical function to the image data, or using image enhancement techniques, such as contrast stretching
Read more

Denman Glacier Losing Some of Its Footing

-
Image
Denman Glacier Losing Some of Its Footing Using a combination of satellite sensors, scientists recently found that Denman Glacier has been retreating both above and below the water line. That one glacier in East Antarctica holds as much ice as half of West Antarctica, so scientists are concerned about its stability. From 1996 to 2018, the grounding line along the western flank of Denman Glacier retreated 5.4 kilometers (3.4 miles), according to a new study by scientists from NASA's Jet Propulsion Laboratory and the University of California, Irvine (UCI). The grounding line is the point at which a glacier last touches the seafloor before it begins to float. Behind the grounding line, the ice is attached to the bedrock; beyond it, glacial ice floats on the ocean as an ice tongue or shelf. The retreat of the grounding line at Denman means more of the glacier's underside is now in contact with water that could warm and melt it from below. If the grounding line continues to retreat,
Read more

Difference between Kriging and IDW

-
Image
Difference between Kriging and IDW. What does kriging mean? least squares estimate. Kriging is a type of regression that gives a least squares estimate of data (Remy et. ... Unlike linear regression or inverse distance weighted interpolation, kriging interpolation is based primarily on empirical observations, the observed sample data points, rather than on a pre-assumed model. What is GIS IDW? Inverse Distance Weighted (IDW) is a method of interpolation that estimates cell values by averaging the values of sample data points in the neighborhood of each processing cell. ... Specifying a lower power will give more influence to the points that are farther away, resulting in a smoother surface. Which interpolation method is best? The most used and promising techniques are universal Kriging and linear regression models in combination with Kriging (residual Kriging) or IDW. E.g.: Air temperature data – Kriging is most likely to produce the best estimation of a continuous surface, followed by
Read more