3.00 credits
22.5 h + 15.0 h
Q2
Teacher(s)
Bontemps Sophie; Defourny Pierre;
Language
English
> French-friendly
> French-friendly
Prerequisites
LBIRE2102 Géomatique appliquée or equivalent introductory class in remote sensing
Pogramming skills (R, python)
Related courses :
LBRAT2102 Modélisation spatiale des dynamiques territoriales
LBRES2101 Smart technologies for environmental engineering
LBRAI2221 Agriculture de précision, géomatique agricole et mécanisation
Pogramming skills (R, python)
Related courses :
LBRAT2102 Modélisation spatiale des dynamiques territoriales
LBRES2101 Smart technologies for environmental engineering
LBRAI2221 Agriculture de précision, géomatique agricole et mécanisation
Main themes
This course aims to develop in-depth understanding and professional skills to process and interpret very high resolution UAV (drone) imagery and Earth Observation satellite time series. Advanced concepts related to signal acquisition, time series quality control and uncertainty characterization are introduced. Radiative transfer modeling and methods for biophysical variables estimation (Leaf Area Index, biomass, nitrogen status, surface temperature, evapotranspiration, soil moisture, height, etc.) and change detection methods are explained and illustrated through practical applications and the European Copernicus Services. Finally, open source tools and systems supporting already operational and forthcoming monitoring systems, including flood monitoring, fire monitoring, forest monitoring and crop monitoring, are discussed in details. The objective of this course is to develop the necessary knowledge and technical skills to use advanced image processing methods (including machine learning and artificial intelligence) and to implement workflow for UAV or satellite monitoring applications in the field of agriculture, forestry, land use land cover change, and water resources management.
Content
The course combines lessons and practicals in computing lab mainly based on open source softwares used to run on your own a case study as a professional.
The lessons address the following topics:
- signal acquisition and preprocessing steps, including quality flags and uncertainty management;
- radiative transfer modelling and retrieval of various biophysical variables;
- optical and SAR time series analysis, features extraction and pixel-based / object-based metrics;
- advanced radar processing including polarimetric and interferometric variables;
- introduction to machine learning and artificial intelligence algorithms for Earth observation mapping, monitoring and change detection;
- critical review of operational monitoring systems (drought, flooding, fire, forest, crop, locust) and of Copernicus Services freely available.
- EO applications related to the environment, agriculture, forestry, water resources and land use planning.
The lessons address the following topics:
- signal acquisition and preprocessing steps, including quality flags and uncertainty management;
- radiative transfer modelling and retrieval of various biophysical variables;
- optical and SAR time series analysis, features extraction and pixel-based / object-based metrics;
- advanced radar processing including polarimetric and interferometric variables;
- introduction to machine learning and artificial intelligence algorithms for Earth observation mapping, monitoring and change detection;
- critical review of operational monitoring systems (drought, flooding, fire, forest, crop, locust) and of Copernicus Services freely available.
- EO applications related to the environment, agriculture, forestry, water resources and land use planning.
Teaching methods
The teachnig introduces the concepts and advanced methods while the praticals in computer lab mobilise them in the context of specific applications. The lessons are quite interactive and thre practicals relies on an inductive approach based on a case study of your choice.
The course and the praticals aims to develop on one hand advanced technical skills in Earth 0bservation data processing and on the other hand, the ability of critical analysis with regards existing solutions, services and products. The student learns not only to use open source packages and Google Earth Engine environment but also to assess the quality and to review the validity of the proposed algorithms and datasets for a given application.
The practical training is closely linked to the course and includes the use of several open source libraries (including QGIS, SNAP, GDAL, ORFEO, Sen4CAP), the exploitation of the Jupyter notebook environment for quality control et time series analysis, and the workflow coding in Python or R.
The course and the praticals aims to develop on one hand advanced technical skills in Earth 0bservation data processing and on the other hand, the ability of critical analysis with regards existing solutions, services and products. The student learns not only to use open source packages and Google Earth Engine environment but also to assess the quality and to review the validity of the proposed algorithms and datasets for a given application.
The practical training is closely linked to the course and includes the use of several open source libraries (including QGIS, SNAP, GDAL, ORFEO, Sen4CAP), the exploitation of the Jupyter notebook environment for quality control et time series analysis, and the workflow coding in Python or R.
Evaluation methods
Evaluation based on a case study (concrete application of your choice) carried out from A to Z throughout the four-month period and presented as a scientific poster.
Other information
This course is part of the Certificate in Applied Geomatics accessible to professionals as part of continuing training.
The theoretical knowledge and practical of this course are mobilized in many other courses in different programs and different faculties.
This course can be given in English.
The theoretical knowledge and practical of this course are mobilized in many other courses in different programs and different faculties.
This course can be given in English.
Online resources
Training material on Moodle and open source libraries available in the computer lab.
Faculty or entity
AGRO
