RUS Copernicus at the APM - Remote Sensing of environment Workshop
In response to the current situation and restrictions due to the COVID-19 pandemic, this training event will be held online. Attendees will participate from home/office and still get all the benefits of RUS face-2-face practical training.
The session will consist of a live demonstration, followed by a time to repeat the exercise on your pre-configured RUS Virtual Machines with live support from the RUS EO expert team. The session will close with live Q&A.
Wroclaw University of Environmental and Life Sciences (UPWr – Wrocław) is organising a training event on 14 - 18 December 2020. The course will consist of a theoretical part and a practical part including exercises related to environmental applications.
The project is financed by the Polish National Agency for Academic Exchange.
RUS will contribute to the event by providing 3 different hands-on training sessions where participants will access a Virtual Machine from their own laptop to exploit the open source toolboxes available in the RUS environment to download and process Sentinel data.
Please note that the RUS Copernicus Virtual Machines used for this training course can only be provided to citizens/residents of the Copernicus programme member countries (EU plus Iceland and Norway).
Detailed schedule including ESA and Wroclaw APM Partnership presentations will be provided later on.
Day 1 - 14 December 2020
APM Partnership presentation
ESA Presentations - theoretical introduction
Day 2 - 15 December 2020
RUS Exercise 1 - Crop Mapping using Sentinel-1 and Sentinel-2
Day 3 - 16 December 2020
RUS Exercise 2 - Urban mapping using Sentinel-1
Day 4 - 17 December 2020
RUS Exercise 3 - Land Subsidence
Day 5 - 18 December 2020
RUS hands-on exercises
15 December 2020 - Exercise 1: Crop Mapping using Sentinel-1 and Sentinel-2
A wide range of different and complementary data (RADAR, optical, IR) from Sentinel-1,2,3 and now also from Sentinel-5P are nowadays available with an open and free policy. For some applications, such as agriculture, the synergy between these data has been already shown. For several applications, there has been an increasing interest in jointly using both RADAR and optical data to compensate for the limitations of using single data products alone. The combination of the weather and illumination independence and the sensitivity to the size, density, orientation and dielectric properties of SAR sensors together with the multi-spectral information related to the leaf structure, pigmentation and moisture captured by optical sensors can provide greater insight and context in many areas of application.
This tutorial will demonstrate the usage of Open Tools available within the RUS environment to run a supervised classification over an agricultural area using Sentinel-1 GRD and Sentinel-2 products.
A multi-temporal and multi-sensor pixel-based data fusion approach will be used in various scenarios to analyse the influence of different input data in the final classification accuracy.
You will also get familiar with SAR and optical data pre-processing using batch processing in SNAP and its command-line implementation (GPT) as well as using existing R scripts.
16 December 2020 - Exercise 2: Urban mapping using Sentinel-1
As the world is facing a large increase in population, reliable information on urban areas is required to assist and help in the decision-making process. Different methods can be used to gather this information, but satellite earth observation offers a suitable approach based on the coverage and type of data that are provided.
Optical data, such as Sentinel-2 or Landsat are ideal for urban monitoring, but they are limited by cloud cover. In some areas of the world the cloud cover is so persistent that we barely get few semi-cloudless images per year. SAR sensors such as Sentinel-1 are not affected by cloud cover and can be therefore used to overcome this limitation. They however require significantly different processing then optical data. In this exercise, we will use the Sentinel-1 SLC derived coherence to delineate the built-up areas. These areas usually exhibit high coherence values compared to vegetation and other natural surfaces.
17 December 2020 - Exercise 3: Land Subsidence
Land subsidence monitoring is very important for urban areas which are susceptible to ground instabilities for both local and regional scale.
The reasons for this phenomenon can be related to either anthropogenic activities (e.g. excess groundwater pumping, subterranean mining processes, rapid urbanisation) or natural-geological ones (e.g. hydrocompaction in low density soils, liquefaction, crustal deformation).
In this demonstration will use the SAR interferometry (InSAR) technique to identify and map land subsidence over Mexico City.
InSAR is an effective method that allows monitoring of ground displacement over large areas, giving insight into the spatial distribution of the subsidence rates. Land subsidence in Mexico City, which is caused by groundwater over-exploitation, is estimated to be more than 9 meters over the last century, resulting in damages to buildings, streets, sidewalks, sewers, storm water drains and other infrastructure. Previous studies of InSAR using ERS data, showed a maximum subsidence rate larger than 30 cm/year in some parts of the city.
Since the city is partially built on the area of a former lake (Lago Texcoco) and it rests on the heavily saturated clay, it is collapsing due to the over-extraction of groundwater. Current subsidence rates using Sentinel-1 SAR data are around 2.5 cm/month.
Attendance to the training course is free of charge.
Participation, Audience & Selection
Space is limited to 24 Participants. Participants will be selected by the local organizers on the basis of the questionnaire filled in at application with preference to students of: Remote Sensing, GIScience, Geographic Engineering, Geography, Environmental Engineering and similar fields.
Venue & Room
Monday 14th of December 2020 - Friday 18th of December 2020
Application opening - closing
Application is closed