Talks & Teaching

In this course, ecology students are introduced to remote sensing and geo-analysis for ecological research applications and learn to independently use remote sensing in their own research fields. In the first week, students are introduced to remote sensing and geo-spatial data and methods and learn to use QGIS and R for geo-spatial analysis. In the second week, they apply remote sensing and geo-analysis to answer their own real-world ecological research questions. At the end of the course, they present their remote-sensing-enabled findings.

Duration: 10 sessions (full days, 2-weeks block)

In this guest lecture, Arctic winter ecology MSc. students were introduced to satellite and UAV remote sensing and geo-analysis for ecological research applications in the Arctic. The guest lecture was held at the invitation of Prof. Dr. Simone Lang (UNIS) as part of a six-week international MSc. course on Arctic winter ecology. On the first day, students were introduced to remote sensing research applications, physical principles of remote sensing and fundamental methods for remote sensing data analysis. In an hands-on session, they learned to handle, visualize and analyze remote sensing data from both Earth observation satellites and Unoccupied Aerial Vehicles (UAVs) using geo-spatial software, including QGIS and R. Afterwards, students were introduced to UAV remote sensing sensors and platforms as well as mission planning. The day finished with the design of a real-world field campaign for the next day, including in-situ snow data collection and UAV thermal and LiDAR data collection using two drones. On the second day, students learned in the field how to in-situ collecting snow parameters,, including snow depth, snow wetness, snow temperatures, snow hardness etc. To geo-locate in-situ measurements with high accuracy, students learned to use real-time kinematic (RTK) corrected GPS. Afterwards, two UAV systems were flown to map the research area, equipped with a thermal and a LiDAR sensor. Later-on, students had the chance to experiment with the data they had collected themselves, e.g. by validating UAV-derived snow depth or snow temperature data against their own measurements. The data collected during the course are of high potential value for Arctic ecology research, as they can be used to quantify the impact of snow properties on vegetation phenology and resource development in spring and summer.

Duration: 2 sessions (full days, 2-days block)

Interactive visualizations using shiny for R in combination with leaflet, plotly, ggplot2 and LaTeX. Basics of web design using html, css and javascript. Basics on Declarative Programming and Lazy Execution. Introduction into client-server communication using reactive contexts and event observation. Introduction into basic UI design such as dashboard UIs, interactive maps, interactive plots etc.

Duration: 3 sessions (1.5 hours each)

This methodological course introduces Deep Learning with a practical focus on how to use it for image processing in Earth observation. Students get to know the principles behind the design of Neural Networks and the training thereof using Deep Learning. They learn about loss, backpropagation, optimization, activation functions & vanishing gradients, over- & underfitting, regularization, augmentation, convolutions, layers of state vs. stateless layers, sequential and non-sequential network designs for image processing tasks such as classification and segmentation etc. The course is taught in R and Pyhton, mainly using keras and tensorflow.

Duration: 12 sessions (1.5 hours each)

Remote sensing applications; physical principles (electromagnetic radiation, absorption, emission, reflectance, optics, spectral information); spatial raster & vector data types; coordinate reference systems & projections; QGIS & R; R for spatial & remote sensing data analysis; spectral indices; basic modelling (classifiers, regressions); generating experience-driven ground truth using digitization; supervised classifications; (statistical) validation; accuracy assessment; scale of data vs. scale of observation targets; resolutions.

Duration: 10 sessions (full days, 2-weeks block)

This modelling course introduces concepts and methods to Earth observation students who want to learn to work with animal movement trajectories, a special type of spatio-temporal data, and integrate such with Earth observation analyses. The course aims to find data-driven answers to questions such as: Why do animals move through the landscape the way they do? How are they impacted by their environments? And: Which environmental conditions are tied to what kind of of movement behavior? While the course introduces background knowledge on topics such as movement theory, the effects of discretely observing continuous processes (e.g. sampling rate, autocorrelation, bias etc.), scale-dependencies/matching, tracking approaches and location error, its practical focus lays on methods to handle and analyze tracking data (e.g. using geometric & variance component analyses, behavioral segmentation, area-metrics such as home ranges, remote-sensing driven trajectory analysis incl. corridor analysis & habitat analysis) as well as tracking data in combination with remotely sensed environmental data (e.g. through resource utilization modelling, resource selection modelling and step selection modelling). This leads students to eventually be able to independently spot patterns in movement data, make connections to environmental conditions, and, finally, jointly model movement tracking and remotely sensed environmental data.

Duration: 5 sessions (full days, 1-week block)

Basics of R & QGIS; version control using git; R in comparison to other languages (interpreter vs. compiler, memory management etc.); programming paradigms; procedural vs. object-oriented vs. functional programming; types/modes, structures, indexing; implicit vs. explicit type conversion; control flow constructs & vectorization; functions; package building; data visualization; statistics; spatial data analysis; image processing; classification models etc.

Duration: 12 sessions (1.5 hours each)

A practical guide towards building custom GEE processing pipelines natively in R using rgee.

Duration: 1 session (1.5 hours)

This methodological course introduces Deep Learning with a practical focus on how to use it for image processing in Earth observation. Students get to know the principles behind the design of Neural Networks and the training thereof using Deep Learning. They learn about loss, backpropagation, optimization, activation functions & vanishing gradients, over- & underfitting, regularization, augmentation, convolutions, layers of state vs. stateless layers, sequential and non-sequential network designs for image processing tasks such as classification and segmentation etc. The course is taught in R and Pyhton, mainly using keras and tensorflow.

Duration: 12 sessions (1.5 hours each)

This course introduces remote sensing field methods to ecologists. They are get to know field campaigning (sampling methods, routing, positioning etc.), in-situ data sampling (parameters, field devices such as spectrometers, soil moisture probes etc.) and UAS (drone) imagery acquisition (platforms, sensors, flight planning, licensing, training). This prepares them for a field day at a research site where data are collected under real-world research conditions. Afterwards, they learn to handle, process and analyse the recorded data and turn them into interpretable information.

Duration: 10 sessions (full days, 2-weeks block)

This modelling course introduces concepts and methods to Earth observation students who want to learn to work with animal movement trajectories, a special type of spatio-temporal data, and integrate such with Earth observation analyses. The course aims to find data-driven answers to questions such as: Why do animals move through the landscape the way they do? How are they impacted by their environments? And: Which environmental conditions are tied to what kind of of movement behavior? While the course introduces background knowledge on topics such as movement theory, the effects of discretely observing continuous processes (e.g. sampling rate, autocorrelation, bias etc.), scale-dependencies/matching, tracking approaches and location error, its practical focus lays on methods to handle and analyze tracking data (e.g. using geometric & variance component analyses, behavioral segmentation, area-metrics such as home ranges, remote-sensing driven trajectory analysis incl. corridor analysis & habitat analysis) as well as tracking data in combination with remotely sensed environmental data (e.g. through resource utilization modelling, resource selection modelling and step selection modelling). This leads students to eventually be able to independently spot patterns in movement data, make connections to environmental conditions, and, finally, jointly model movement tracking and remotely sensed environmental data.

Duration: 5 sessions (full days, 1-week block)

Remote sensing applications; physical principles (electromagnetic radiation, absorption, emission, reflectance, optics, spectral information); spatial raster & vector data types; coordinate reference systems & projections; QGIS & R; R for spatial & remote sensing data analysis; spectral indices; basic modelling (classifiers, regressions); generating experience-driven ground truth using digitization; supervised classifications; (statistical) validation; accuracy assessment; scale of data vs. scale of observation targets; resolutions.

Duration: 10 sessions (full days, 2-weeks block)

Basics of R & QGIS; version control using git; R in comparison to other languages (interpreter vs. compiler, memory management etc.); programming paradigms; procedural vs. object-oriented vs. functional programming; types/modes, structures, indexing; implicit vs. explicit type conversion; control flow constructs & vectorization; functions; package building; data visualization; statistics; spatial data analysis; image processing; classification models etc.

Duration: 12 sessions (1.5 hours each)

Remote sensing of forests, vulnerabilities & risks, biodiversity, wildlife ecology, natural resources, fire & burnt areas, coasts, diseases & health, agriculture, soil, land cover & land use, human settlements, policy etc.

Duration: 12 sessions (1.5 hours each)

Duration: 1 session (2.5 hours)

Duration: 2 sessions (full days)

This course introduces remote sensing field methods to ecologists. They are get to know field campaigning (sampling methods, routing, positioning etc.), in-situ data sampling (parameters, field devices such as spectrometers, soil moisture probes etc.) and UAS (drone) imagery acquisition (platforms, sensors, flight planning, licensing, training). This prepares them for a field day at a research site where data are collected under real-world research conditions. Afterwards, they learn to handle, process and analyse the recorded data and turn them into interpretable information.

Duration: 10 sessions (full days, 2-week block)

This methodological course introduces Deep Learning with a practical focus on how to use it for image processing in Earth observation. Students get to know the principles behind the design of Neural Networks and the training thereof using Deep Learning. They learn about loss, backpropagation, optimization, activation functions & vanishing gradients, over- & underfitting, regularization, augmentation, convolutions, layers of state vs. stateless layers, sequential and non-sequential network designs for image processing tasks such as classification and segmentation etc. The course is taught in R and Pyhton, mainly using keras and tensorflow.

Duration: 12 sessions (1.5 hours each)

(Interactive) visualizations using LaTeX, leaflet, plotly, ggplot2, shiny, basics of web design using html, css, javascript (including frameworks such as bootstrap), static site generation frameworks such as hugo, jekyll etc.

Duration: 5 sessions (1.5 hours each)

This modelling course introduces concepts and methods to Earth observation students who want to learn to work with animal movement trajectories, a special type of spatio-temporal data, and integrate such with Earth observation analyses. The course aims to find data-driven answers to questions such as: Why do animals move through the landscape the way they do? How are they impacted by their environments? And: Which environmental conditions are tied to what kind of of movement behavior? While the course introduces background knowledge on topics such as movement theory, the effects of discretely observing continuous processes (e.g. sampling rate, autocorrelation, bias etc.), scale-dependencies/matching, tracking approaches and location error, its practical focus lays on methods to handle and analyze tracking data (e.g. using geometric & variance component analyses, behavioral segmentation, area-metrics such as home ranges, remote-sensing driven trajectory analysis incl. corridor analysis & habitat analysis) as well as tracking data in combination with remotely sensed environmental data (e.g. through resource utilization modelling, resource selection modelling and step selection modelling). This leads students to eventually be able to independently spot patterns in movement data, make connections to environmental conditions, and, finally, jointly model movement tracking and remotely sensed environmental data.

Duration: 5 sessions (full days, 1-week block)

Remote sensing applications; physical principles (electromagnetic radiation, absorption, emission, reflectance, optics, spectral information); spatial raster & vector data types; coordinate reference systems & projections; QGIS & R; R for spatial & remote sensing data analysis; spectral indices; basic modelling (classifiers, regressions); generating experience-driven ground truth using digitization; supervised classifications; (statistical) validation; accuracy assessment; scale of data vs. scale of observation targets; resolutions.

Duration: 10 sessions (full days, 2-weeks block)

Basics of R & QGIS; version control using git; R in comparison to other languages (interpreter vs. compiler, memory management etc.); programming paradigms; procedural vs. object-oriented vs. functional programming; types/modes, structures, indexing; implicit vs. explicit type conversion; control flow constructs & vectorization; functions; package building; data visualization; statistics; spatial data analysis; image processing; classification models etc.

Duration: 12 sessions (1.5 hours each)

Remote sensing of forests, vulnerabilities & risks, biodiversity, wildlife ecology, natural resources, fire & burnt areas, coasts, diseases & health, agriculture, soil, land cover & land use, human settlements, policy etc.

Duration: 12 sessions (1.5 hours each)

This methodological course introduces Deep Learning with a practical focus on how to use it for image processing in Earth observation. Students get to know the principles behind the design of Neural Networks and the training thereof using Deep Learning. They learn about loss, backpropagation, optimization, activation functions & vanishing gradients, over- & underfitting, regularization, augmentation, convolutions, layers of state vs. stateless layers, sequential and non-sequential network designs for image processing tasks such as classification and segmentation etc. The course is taught in R and Pyhton, mainly using keras and tensorflow.

Duration: 12 sessions (1.5 hours each)

Spectrometric field sampling, hyperspectral image processing, spectral unmixing in R & QGIS etc.

Duration: 2 sessions (full days, 2-days block)

(Interactive) visualizations using LaTeX, leaflet, plotly, ggplot2, shiny, basics of web design using html, css, javascript (including frameworks such as bootstrap), static site generation frameworks such as hugo, jekyll etc.

Duration: 5 sessions (1.5 hours each)

This modelling course introduces concepts and methods to Earth observation students who want to learn to work with animal movement trajectories, a special type of spatio-temporal data, and integrate such with Earth observation analyses. The course aims to find data-driven answers to questions such as: Why do animals move through the landscape the way they do? How are they impacted by their environments? And: Which environmental conditions are tied to what kind of of movement behavior? While the course introduces background knowledge on topics such as movement theory, the effects of discretely observing continuous processes (e.g. sampling rate, autocorrelation, bias etc.), scale-dependencies/matching, tracking approaches and location error, its practical focus lays on methods to handle and analyze tracking data (e.g. using geometric & variance component analyses, behavioral segmentation, area-metrics such as home ranges, remote-sensing driven trajectory analysis incl. corridor analysis & habitat analysis) as well as tracking data in combination with remotely sensed environmental data (e.g. through resource utilization modelling, resource selection modelling and step selection modelling). This leads students to eventually be able to independently spot patterns in movement data, make connections to environmental conditions, and, finally, jointly model movement tracking and remotely sensed environmental data.

Duration: 5 sessions (full days, 1-week block)

Basics of R & QGIS; version control using git; R in comparison to other languages (interpreter vs. compiler, memory management etc.); programming paradigms; procedural vs. object-oriented vs. functional programming; types/modes, structures, indexing; implicit vs. explicit type conversion; control flow constructs & vectorization; functions; package building; data visualization; statistics; spatial data analysis; image processing; classification models etc.

Duration: 12 sessions (1.5 hours each)

Remote sensing of forests, vulnerabilities & risks, biodiversity, wildlife ecology, natural resources, fire & burnt areas, coasts, diseases & health, agriculture, soil, land cover & land use, human settlements, policy etc.

Duration: 12 sessions (1.5 hours each)

introduction into machine learning, deep neural networks, computer vision, discriminative modelling; (web-)APIs, web protocols, machine-to-machine communication; development environments, code maintenance, unit testing, continuous integration, version control etc.

Duration: 12 sessions (1.5 hours each)

Spectrometric field sampling, hyperspectral image processing, spectral unmixing in R & QGIS etc.

Duration: 2 sessions (full days, 2-days block)

(Interactive) visualizations using LaTeX, leaflet, plotly, ggplot2, shiny, basics of web design using html, css, javascript (including frameworks such as bootstrap), static site generation frameworks such as hugo, jekyll etc.

Duration: 5 sessions (1.5 hours each)

Duration: 1 session (2.5 hours)

Duration: 1 session (2.5 hours)

introduction into machine learning, deep neural networks, computer vision, discriminative modelling; (web-)APIs, web protocols, machine-to-machine communication; development environments, code maintenance, unit testing, continuous integration, version control etc.

Duration: 12 sessions (1.5 hours each)

Title of the talk: Potentials of integrating animal movement tracking data with remote sensing.

Duration: 1 session (2.5 hours)

Duration: 1 session (2.5 hours)