PROGRAM:

  • 8:30   Registration
  • 9:00   Opening Session
  • 9:15    Efficient geometric representations of real-world environments
  • 10:30 Coffee break
  • 10:45  Efficient geometric representations of real-world environments
  • 12:30 Lunch
  • 14:00 Efficient geometric representations of real-world environments
  • 15:30  Coffee break
  • 15:45  Efficient geometric representations of real-world environments
  • 17:00  Icebreaker
  • 9:00  UAV based coastal monitoring – UAV Photogrammetry
  • 10:30 Coffee break
  • 10:45 UAV based coastal monitoring – UAV Photogrammetry
  • 12:30 Lunch
  • 14:00 LiDAR data – processing and applications
  • 15:30 Coffee break
  • 15:45 LiDAR data – processing and applications
  • 17:30 Class ends / Poster Session
  • 9:00  Hyperspectral data / Remote sensing for forest inventory
  • 10:30 Coffee break
  • 10:45 Hyperspectral data / ARTMO
  • 12:30 Lunch
  • 14:00 ARTMO toolbox
  • 15:30 Coffee break
  • 15:45 ARTMO toolbox
  • 17:30 Class ends / Poster Session
  • 19:00 Porto @ night
  • Social dinner
  • 9:00 Machine Learning and Deep Learning
  • 10:30 Coffee break
  • 10:45 Machine Learning and Deep Learning
  • 12:30 Lunch
  • 14:00 Machine Learning and Deep Learning
  • 15:30 Coffee break
  • 15:45 Machine Learning and Deep Learning
  • 17:30 Class ends / Poster Session
  • 9:00 SAR technology
  • 10:30 Coffee break
  • 10:45 SAR technology
  • 12:30 Lunch
  • 14:00 Hands-On: SAR and application
  • 15:30 Coffee break
  • 15:45 Hands-On: SAR and application
  • 17:30 – PS-InSAR techniques for landslide susceptibility mapping
  • 18:30 Class ends / Poster Session
  • 11:00 Field Trip to Douro Valley Vineyard
  • 2:00 Lunch
  • 18:00 Return to Porto

TOPICS AND PRESENTERS!

— Efficient geometric representations of real-world environments, encoding remote-sensing data, and simulating their physical-based behavior – Juan Manuel Jurado Rodriguez

–UAV based coastal monitoring – UAV Photogrammetry with an emphasis on UAV RTK positioning and how to effectively eliminate ground control points – José Alberto Gonçalves

–Machine Learning and Deep Learning for Remote Sensing – Luis Paulo Reis

–The ARTMO toolbox for analyzing and processing of hyperspectral data – Jochem Verrelst

–LiDAR data – processing and applications – Xinlian Liang and Yunsheng Wang

–Basics on SAR with a practical session on preprocessing of SAR image – Mattia Callegari

–PS-InSAR techniques for landslide susceptibility mapping – Shibiao Bai

–Remote sensing of Forest inventory – Luka Jurjevic / Ivan Balenovic / Sanja Peric

2023 POSTER CONTEST – BRING YOUR POSTER!

Discuss your project and poster with remote sensing experts.

ABSTRACTS:

UAV based coastal monitoring – UAV Photogrammetry with an emphasis on UAV RTK positioning and how to effectively eliminate ground control points 

UAVs have many applications for environmental monitoring that require high resolution and accurate topographic accuracy. One of them is the assessment of changes in beaches and coastal areas in general. Many of these areas go through significant changes for natural reasons and mainly due to human intervention, and need frequent surveys. UAV photogrammetry is low cost technique that can provide frequent surveys with high accuracy to provide data for coastal studies.

The presentation will focus on general concepts of UAV mapping, describing the methodologies for precise georeferencing. The possible strategies for precise georeferencing will be described: the traditional ground control point based image orientation and the use of RTK GNSS on the UAV. A special emphasis will be given on the latter, which has a much higher efficiency because of the reduction or eventual elimination of GCPs. The effect of camera calibration and image acquisition strategies will be covered in detail.

A practical exercise with images acquired in beaches in the region of Porto will be carried out. Software Agisoft Metashape will be used for photogrammetric processing and QGIS for change assessment.

Machine learning and deep learning for remote sensing

Abstract – coming soon!

Basics on SAR with a practical session on preprocessing of SAR image

Synthetic aperture radar (SAR) are active systems that exploit an electromagnetic wave in the microwave spectrum to characterize the geometric and dielectric characteristics of a target object. The active nature and the working frequency of the SAR systems render them independent from the sunlight and almost insensitive to the presence of clouds. Therefore, SAR is of paramount importance from an application point of view. This lecture provides the basic notions on Synthetic Aperture Radar, including an introduction on SAR interferometry, SAR data processing and examples of applications exploiting past and current SAR satellite missions.

  • SAR basics
  • SAR Interferometry
  • SAR processing
  • SAR missions

At the end of the SAR session, the 1-hour lecture focuses on 1) introductory material related to landslide susceptibility mapping / landslide spatial probability, and 2) ArcGIS SDMtoolbox.  The PS-InSAR techniques are used to monitor displacement and to validate and verify the landslide susceptibility mapping models”.

LiDAR data – processing and applications

This lecture will focus on laser scanning (LiDAR) as a rapidly developing 3D remote sensing technology. Main topics will cover four main topics including:

1) Technical background of laser scanning,

2) Development of laser scanning (LS) and its verities, such as terrestrial laser scanning (TLS), airborne laser scanning (ALS), mobile/portable laser scanning (MLS/PLS), and UAV laser scanning (UAV-LS),

3) Data collection with different LS systems and the basic characters of different types of LS data,

4) Applications of LS and fundamentals of point cloud processing.

The lecture will offer an insight understanding of typical characteristics of LS systems and their datasets, and a general concept of point cloud data processing.

Efficient geometric representations of real-world environments, encoding remote-sensing data, and simulating their physical-based behavior 

The general objective is to introduce the student to multisensory digitization and the use of spatial and geometric algorithms for multi-purpose applications in Agriculture, Ecology, and other environmental sciences. This presentation poses the main steps to develop an Information System (IS) capable of supporting the integration of three-dimensional (3D) data that are enriched with variables extracted from aerial images in the visible (RGB), multispectral, hyperspectral, and thermographic ranges. The acquisition of multisensory information enables an in-depth characterization of the morphological and physiological traits of plants. Recent advances in the production of sensors mounted on unmanned aerial vehicles (UAVs) have changed the methodology for the monitoring and assessment of crop conditions. As a result of the integration of three-dimensional (3D) data, a virtual replica is obtained that can be used to study the evolution of each individual crop.

The ARTMO toolbox for analyzing and processing of hyperspectral data

This tutorial will focus on the use of ARTMO’s (Automated Radiative Transfer Models Operator) radiative transfer models (RTMs), retrieval toolboxes and post-processing tools (https://artmotoolbox.com/) for the generation and interpretation of hyperspectral data. ARTMO brings together a diverse collection of leaf, canopy and atmosphere RTMs into a synchronised user-friendly GUI environment. Essential tools are provided to create all kinds of look-up tables (LUT). These LUTs can then subsequently be used for mapping applications from optical images. A LUT, or user-collected field data, can subsequently be inserted into three types of mapping toolboxes: (1) through parametric regression (e.g. vegetation indices), (2) nonparametric methods (e.g. machine learning methods), or (3) through LUT-based inversion strategies. In each of these toolboxes various optimization algorithms are provided so that the best-performing strategy can be applied for mapping applications. When coupled with an atmosphere RTM retrieval can take place directly from top-of-atmosphere radiance data.

Further, ARTMO’s RTM post-processing tools include: (1) global sensitivity analysis, (2) emulation, i.e. approximating RTMs through machine learning, and (3) synthetic scene generation.

The proposed tutorial will consist of a theoretical introduction and a practical session, where the following topics will be addressed:

  1. Basics of leaf, canopy and atmosphere RTMs: generation of RTM simulations
  2. Overview of retrieval methods: parametric, nonparametric, inversion and hybrid methods.
  3. Principles of emulation of hyperspectral data, and applications such as global sensitivity analysis and scene generation.

In the practical session we will learn to work with the ARTMO toolboxes. They provide practical solutions dealing with the abovementioned topics. Step-by-step tutorials, demonstration cases and demo data will be provided. No prior knowledge is needed.

Remote sensing for forest inventory

Sustainable forest management for the realization of their multiple functions and services requires spatially explicit information about their state and development, which is usually acquired through forest inventories. Although traditional field-based forest inventory can provide relatively accurate information, the process is time-consuming and labor intensive, and in some cases, access to certain areas is not possible. Therefore, the potential of remote sensing application in forest inventory has long been recognized by both researchers and practicing foresters. Over the last few decades, a rapid development of different remote sensing instruments has resulted in the development of various methods and techniques for retrieval of forest information from remote sensing data.

This lecture will provide:

  • General insight in current field-based forest inventory;
  • Overview of various passive and active remote sensing technologies (platforms, sensors, products) that can be applied in forest inventory;
  • Insight in methodologies for remote sensing-based forest inventory;

Examples of previously conducted studies and guidelines for further development of remote sensing-based forest inventory.