Regular course within the MSc Physical Geography at Marburg University.
Basically all field methods bear a tradeoff between grain and extend regardless if the tradeoff is primarily caused by man power, finances, logistics or any combination: one either can study selected processes in detail on a very limited number of observations sites or focus on a more landscape oriented survey using more generalized observation parameters. For a good proportion of such studies, this tradeoff can be overcome with the utilization of reliable but cost-effective, spatially explicit but high-resolution remote sensing. As the only spatially explicit measurement technique, remote sensing offers an amazingly toolbox which - in a modern sense - has been explored for more than 150 years but which is far from fully worn. The advent of high performance computing along with machine learning algorithms has just opened up a new chapter in data mining and information retrieval using a wide variety of ground-based, airborne and satellite sensors.
In this course, we will focus on optical and - to a very limited degree - LiDAR remote sensing. Specialized topics including very high resolution and 3D landscape assessments using UAVs, time series analysis and all sorts of up- and downscaling will be covered in other modules.
The individual sessions can be grouped into four sections:
In this module, we will primarily use QGis with the Orfeo Toolbox extension, SNAP and R.
The course has 1 session per week, 3 hours per session.
|1||Information retrieval using LiDAR observations||Introduction to LiDAR remote sensing, conversion of point to raster layers|
|2||Characteristics of optical sensor data||Introduction to optical remote sensing, sensor signal composition, data I/O|
|Working with very high resolution aerial orthophotos|
|3||Visualization and equalization of sensor data||Digital numbers, grey values, contrast stretch, composites, relative matching|
|4||Computation of artificial images||Data space conversion, dimension reduction, filtering|
|5||Image classification||Training site selection, classification methods, land-cover classification|
|6||Image classification||Machine learning, accuracy assessment|
|7||Image classification with R||Some libraries for R|
|-||T-5 and holding||Build-in hold to finish up the classification sessions|
|8||Image classification revisited||Problems, pitfalls, strategies|
|9||Image classification revisited||Feature selection and model training|
|10||Image classification revisited||Segments and objects|
|11||Image classification revisited||Finish|
|12||Wrap up||Feedback and goodbye|