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Remote sensing tools and applications to ecology, biodiversity and natural resources

From 19 Jul 2021 until 21 Jul 2021 - ONLINE COURSE
Remote sensing tools and applications to ecology, biodiversity and natural resources
Monitoring changes in biodiversity driven by widespread environmental alterations in the Anthropocene is a critical international endeavor increasingly supported by remotely-sensed Earth observations (RS/EO) - even more so if we consider the need to track the progress of global conservation initiatives and policy such as the CBD’s Aichi Targets, the UN’s Sustainable Development Goals or the EU’s Habitat Directive.
Recent advances in RS/EO push these technologies into the forefront in biodiversity and ecological assessment and monitoring. The increasing amount, diversity and openness of RS/EO platforms and its data  - from satellites to unmanned aerial vehicles (UAVs) - allow them to cover multiple levels (from species up to biomes) and multiple facets (i.e. composition, structure and functioning) of biodiversity’s multidimensional concept.
This course will introduce students to the fundamental concepts regarding passive optical remote sensing using satellites and UAVs and its applications to assess, monitor and model biodiversity patterns and trends with a special emphasis on the evaluation of change.
The course will also focus on open-data and open-source tools (mainly in R and Python languages) currently available to students and researchers interested in developing their specific studies.

Main objectives:
- Introduce the core/fundamental concepts surrounding (passive optical) RS/EO
- Introduce the main applications of RS/EO in biodiversity assessment, monitoring and modelling
- Showcase different platforms and instruments in RS/EO, from proximal (spectroradiometer), airborne (UAV) up to space-borne (satellites)
- Present the main concepts, methods and software tools used in (pre)processing RS/EO imagery
- Provide access to find and manipulate free and open RS/EO data for ecological applications
- Capacitate students to extract meaningful parameters or indices useful for their own applications from RS/EO imagery
- Introduce core ecoinformatics tools and strategies to process RS/EO data in R, Python and Google’s Earth Engine cloud-processing (GEE)

(preliminary/ may be subject to alterations)

Day 1
- Theoretical topics
Fundamentals of Remote Sensing for Earth Observation (EO)
Physical principles of remote sensing
Sensor types and characteristics
Satellite Remote Sensing (SRS) – orbits and types
Spectral, spatial, temporal and radiometric resolutions
Available satellite platforms in SRS/EO: NASA/USGS missions (e.g., Landsat, Terra/Aqua) and ESA Copernicus (e.g., Sentinel’s)
- Practical topics
First contact with available/’ready-to-use’ RS/EO products
Data searching platforms
Data discovery examples in Earth Explorer and Copernicus Open Access Hub

- Theoretical topics
Applications of SRS in biodiversity assessment and monitoring
- Practical topics (exercises)
Downloading and pre-processing coarse-resolution SRS/EO data: Terra-Aqua/MODIS
Processing MODIS time series in R using the MODIStsp and raster packages
Day 2
- Theoretical topics
Spectral Vegetation and water indices - fundamental principles
The Tasseled Cap Transformation (TCT)
Introduction to the Ecosystem Functioning Attributes (EFAs) framework
- Practical topics (continuation of day 1)
Quantifying EFA’s from different products
Using MODIS imagery/products in habitat suitability models for biodiversity assessment and monitoring

- Theoretical topics
Introduction to Landsat and Sentinel-2 imagery
Image processing levels
Image pre-processing: calibration and radiometric calibration
- Practical topics
Introduction to Google Earth Engine - processing and exporting data
Calculating vegetation and water indices in GEE
Calculating annual indicators and extracting anomalies
Final/wrap-up discussion

Day 3
- Theoretical topics
Unmanned Aerial Vehicles (UAV) fundamentals
Photogrammetry and Structure from Motion (SfS) techniques in 2D/3D image reconstruction
Applications of UAV imagery in assessment and monitoring
- Practical topics
Legal constraints in UAV-based image acquisition
Survey planning
Flight programming
Hands-on session: from UAV field surveys to a full 3D models and multi-spectral image orthomosaics

- Theoretical topics
Image analysis fundamentals
Unsupervised classification
Supervised classification
Pixel-based vs. object-based image analysis (OBIA) and classification
- Practical topics
Hands-on session: supervised classification and OBIA in Sentinel-2 or UAV image classification
Open discussion - integrating RS/EO data

Schedule: 9h30 - 13h00: morning block / 13h00 - 14h30: lunch time / 14h30 - 17h30: afternoon block / Coffee breaks 11h00 - 11h15: morning coffee break / 16h00 - 16h15: afternoon coffee break
32 Hours

João Francisco Gonçalves - CIBIO-InBIO |  ECOCHANGE - Predicting and Managing Ecological Change
Adrián Regos Sanz -  CIBIO-InBIO |  ECOCHANGE - Predicting and Managing Ecological Change
Bruno André Santos MarcosCIBIO-InBIO |  ECOCHANGE - Predicting and Managing Ecological Change
Isabel Pôças (yet to be confirmed) - Centro de Investigação em Ciências Geo-Espaciais (CICGE) / FCUP
João Pradinho Honrado - CIBIO-InBIO |  ECOCHANGE - Predicting and Managing Ecological Change

Salvador Arenas Castro - CIBIO-InBIO |  ECOCHANGE - Predicting and Managing Ecological Change

The course will be open to a maximum number of 15 participants.

75% of available student slots are reserved for BIODIV students.
 Priority will be given to:
• 1st year and other PhD students attending the BIODIV Doctoral Program;
• PhD students attending other courses;
• Other post-graduate students and researchers.

Participation is free of charge for BIODIV students | 65 € (students) | 125 € (other participants). CIBIO-InBIO members will have an additional discount of 20%.

To apply, please fill the form available HERE

Registration deadline: June 11, 2021
All applicants will be notify about whether they are accept until March 19, 2021

Please note that new rules apply for all BIODIV students

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