Skip to main content

Scientific developer for ecological applications of LiDAR Remote Sensing University of Amsterdam / Universiteit van Amsterdam




Scientific developer for ecological applications of LiDAR Remote Sensing University of Amsterdam / Universiteit van Amsterdam


We have a job opening for a two-year LiDAR Remote Sensing Scientist for ecological applications at the University of Amsterdam, The Netherlands. The person will be responsible for developing scientific workflows for ecological applications in biodiversity and ecosystem science using airborne and spaceborne LiDAR data. The person will also closely interact with computer scientists and software engineers to make the workflows and tools available in the context of a Virtual Research Environment (VRE).
What are you going to do?
We are looking for a candidate with exceptionally strong skills in LiDAR processing and ecological applications of LiDAR data in relation to biodiversity and ecosystems. The person will develop workflows using LiDAR data from multiple country-wide airborne laser scanning surveys as well as from spaceborne observations of the new Global Ecosystem Dynamics Investigation (GEDI). Ecological applications can include (but are not restricted to) species distribution or biodiversity models with LiDAR metrics, land cover and habitat classification and mapping using LiDAR with machine learning, or change detection of ecosystem structure using multi-temporal LiDAR datasets. The person should show a passion for biodiversity and ecosystems and a deep understanding of how biodiversity and ecosystems change due to natural processes or human impact. Excellent programming skills in Python and R and experience in GIS and geospatial analyses are required. Additional experience in handling other Earth observation and remote sensing data is advantageous. The candidate should also show strong writing and verbal communication skills and have a successful track-record of publications.
You will develop scientific workflows in the context of establishing a Virtual Lab for ecological applications of LiDAR data. This will include the processing of various national, multi-terabyte airborne LiDAR data using a newly developed point cloud processing software tool Laserchicken, and a related High Performance Computing (HPC) processing pipeline called Laserfarm, both of which are implemented in Python. You will also process LiDAR data from the Global Ecosystem Dynamics Investigation (GEDI) using e.g. rGEDI. Other open source software tools for processing and visualization of point clouds and raster data (e.g. rLiDAR, raster, PDAL, GDAL) are also relevant. Using the derived metrics of ecosystem height, ecosystem cover, and ecosystem structural complexity you will develop ecological applications of LiDAR data with relevance to biodiversity and ecosystem science.
This will include national to global analyses of species distributions, ecosystem structure change, and/or mapping and classification of animal habitats using LiDAR and machine learning.
What do we require?
The ideal candidate should meet the following requirements:
a PhD degree in LiDAR, remote sensing, ecology, biodiversity, Earth science or a related discipline;
interest, passion and experience in LiDAR, biodiversity, computational ecology and Earth observation;
exceptional quantitative skills and profound experience in handling and processing LiDAR data (e.g. scripting/programming in Python and R, geospatial analyses, handling of remote sensing datasets);
experience with computational workflows;
willingness to work in a multidisciplinary team (ecology, computer science; software engineering);
proficiency in scientific writing;
ability to speak and communicate in English at an academic level;
successful and strong track-record of publications.
Of additional advantage:
strong skills in species distribution modelling, change detection or machine learning;
background in biodiversity informatics and open data science;
experience in data and computer science.
Contract and Working environment
The candidate will be contracted by the LifeWatch ERIC Virtual Laboratory and Innovation Center in Amsterdam, The Netherlands and will work within the premises of the University of Amsterdam, Institute for Biodiversity and Ecosystem Dynamics (IBED).
LifeWatch ERIC is a European Infrastructure Consortium providing e-Science research facilities to scientists seeking to increase our knowledge and deepen our understanding of biodiversity organisation and ecosystem functions and services in order to support civil society in addressing key planetary challenges.
LifeWatch ERIC seeks to understand the complex interactions between species and the environment, taking advantage of High-Performance, Grid and Big Data computing systems, and the development of advanced modelling tools to implement management measures aimed at preserving life on Earth.
Combining a wide range of ICT tools and resources with deep knowledge of the domain, LifeWatch ERIC's mission is to be a 'first class' worldwide provider of content and services for the biodiversity research community by:
offering new opportunities for large-scale scientific development;
enabling accelerated data capture with innovative new technologies;
supporting knowledge-based decision-making for biodiversity and ecosystem management;
providing training, dissemination and awareness programmes.
The Institute for Biodiversity and Ecosystem Dynamics (IBED) is one of eight research institutes within the Faculty of Science at the University of Amsterdam. Our scientific work aims at a better understanding of the dynamics of ecosystems at all relevant levels, from genes to ecosystems, using a truly multi-disciplinary approach, and based on both experimental and theoretical research. Scientific focus is on aquatic (both freshwater and marine) and terrestrial ecosystems, evolutionary and population biology, ecosystem and landscape dynamics, and theoretical and computational ecology. We want to unravel how ecosystems function in all their complexity, and how they change due to natural processes and human activities.
You will closely collaborate with computer scientists and software engineers who are developing the technical functionality of Virtual Research Environments (VREs) and the containerization of the applications. It is expected that your work results in scientific papers, and that you participate and present the research at LifeWatch and other international meetings. In your daily work, you will be embedded in the Biogeography & Macroecology (BIOMAC) lab and the Department Theoretical and Computational Ecology, and have close links to the Quality Critical Distributed Computing team of the Multiscale Networked Systems (MNS) research group.
We offer a position for 38 hours a week in an exciting, dynamic and international research environment, starting as soon as possible. The full-time employment contract will be on a temporary basis for a maximum period of 2 years with opportunities for an extension.
The Collective Labour Agreement of Dutch Universities will be applicable for salary. The annual salary will be increased by 8 % holiday allowance and 8.3 % end-of-year bonus. You will participate in the LifeWatch-ERIC Netherlands pension scheme with RESAVER.
Questions?
Do you have questions about this vacancy? Or do you want to know more about our organisation? Please contact:
Dr W. Daniel Kissling, associate professor of quantitative biodiversity science (daily supervisor)
You may also visit his profile page.
Job application
LifeWatch ERIC is committed to a diverse and non-discriminatory workplace. All LifeWatch ERIC Staff shall be treated with equal respect and will have an equal opportunity to contribute fully to the success of LifeWatch based on their individual skills and interests.
Applications should include the following documents, all in one PDF file and in the following order:
motivation letter (max. 1-2 pages, containing your motivation for applying and a description of your previous research experience);
list of your 2-3 most significant publications or systems (incl. a short description in 2-3 sentences what is interesting in each paper);
short outline (max. 1 page) of ideas how to develop LiDAR data products for ecological applications in biodiversity and ecosystem science (will only be used for evaluating the applicants);
CV (with applicant's e-address and telephone number, documentation of education and complete publication list, and contact details of two professional references [name, address, telephone and email]. References will only be contacted if the candidate is short-listed.
Applications should be sent ultimately at 31 August 2020 to Dr W. Daniel Kissling with the job title in the subject field.
#LI-DNP



....

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

Quantitative Revolution in Geography

The Quantitative Revolution was a major change in Geography during the 1950s and 1960s. It introduced mathematical, statistical and scientific methods to understand geographical phenomena. Before this revolution, geographers mainly described places, regions, landscapes and differences between areas. After the revolution, they increasingly used numerical data, statistical analysis, mathematical models and theories to explain why geographical patterns occur and how they may change. For example: Traditional approach: Describe the distribution of population in different regions. Quantitative approach: Use statistical methods to identify population patterns, explain the factors influencing population distribution and predict future population growth. The revolution helped establish Geography as a spatial science, focusing on the location, distribution, relati...
Post a Comment
Read more

Satellite Remote Sensing: Concepts and Imaging Systems

  Satellite remote sensing relies on detectors (sensors) that measure reflected/emitted electromagnetic radiation from the Earth. How sensors collect data depends on: Spectral coverage → Multispectral (few bands), Hyperspectral (hundreds), Thermal, Microwave. Detector type → Discrete detectors, linear arrays, or area arrays. Scanning mechanism → Scanning mirrors (whiskbroom) vs. linear arrays (pushbroom). Multispectral Imaging Using Discrete Detectors and Scanning Mirrors (Whiskbroom scanners) Principle : A single detector (or a few detectors) measures radiation one pixel at a time. A scanning mirror sweeps across-track (perpendicular to the satellite path) to build up the image line by line. The forward motion of the satellite provides the along-track dimension. Known as a whiskbroom scanner . Characteristics : Good calibration stability. Narrow instantaneous field of view (IFOV). Susceptible to mechanical wear (moving ...
Post a Comment
Read more

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...
Post a Comment
Read more

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types  1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...
Post a Comment
Read more

RADIOMETRIC CORRECTION

  Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance. In other words, it corrects for sensor defects, illumination differences, and atmospheric effects. 1. Detector Response Calibration Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly. This includes: (a) De-Striping Problem: Sometimes images show light and dark vertical or horizontal stripes (banding). Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others. Common in early Landsat MSS data. Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker. Soluti...
Post a Comment
Read more