PhD Position (f/m/x) – Remote Sensing of Biodiversity Helmholtz-Gemeinschaft Deutscher Forschungszentren

https://recruitingapp-5128.de.umantis.com/Vacancies/2019/Description/2

The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources.

The newly established Remote Sensing Centre for Earth System Research (RSC4Earth) - a joint initiative of UFZ and the Faculty of Physics and Earth Sciences at Leipzig University - conducts innovative research to advance the understanding of the Earth system via the integration of various remote sensing, data science, and process-oriented modelling techniques. It has extensive research experience in quantifying land surface dynamics from multi-source Earth observations across scales.

Within the PhD framework "MoDEV - Towards novel model-data fusion for understanding environmental variability in space and time from high-resolution remote sensing" we are seeking to appoint a highly motivated candidate (m/f/x) for the PhD project "Remote Sensing of Biodiversity".

PhD Position (f/m/x) – Remote Sensing of Biodiversity

Working time: 65% (25.35 hours per week), limited to 3 years

Your tasks:

The ability of remote sensing technologies to estimate the biodiversity of ecosystems is frequently discussed. One approach is the exploitation of the spectral variation in remotely sensed images towards an assessment of species and trait diversity. These attempts are, however, correlative and the actual drivers of the spectral variation are still unexplored.

This PhD project aims to combine in-situ trait measurements in temperate ecosystems, radiative transfer modelling as well as multi- and hyperspectral image analysis to address the following questions:

Which leaf and canopy biophysical properties are the drivers of spectral variation in a remotely-sensed image data set?

How stable and reliable are remotely sensed biodiversity indicators in space, time and across sensor systems?

Can we use Earth observation data to assess the diversity of grass- and farmlands across Germany?

Your profile:

MSc degree in ecology, biology, (bio)geography, environmental science, remote sensing, statistics, computer sciences, or a related field

Interest in biodiversity research and EO data analysis

Good programming skills (preferably R, but likewise Python or Matlab

Experience in field vegetation sampling, trait measurements, statistical analysis, spectroscopy and EO data processing are advantageous

Good communication skills in English, and strong interest to work in an interdisciplinary research team

Willingness to publish results in peer-reviewed journals and present at conference

We offer:

Excellent technical facilities which are without parallel

The freedom you need to bridge the difficult gap between basic research and close to being ready for application

Work in interdisciplinary, multinational teams

Excellent links with national and international research networks

Excellent support and optimal subject-specific and general training with our HIGRADE graduate school

Remuneration in accordance with the TVöD public-sector pay grade 13 (65%)

Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://www.facebook.com/Applied.Geography
http://geogisgeo.blogspot.com

Comments

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Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process : - The algorithm starts by divid...

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...

REMOTE SENSING INDICES

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Vineesh V, Geography

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