Dr. Lena Halounová, Czech Technical University:
Integration of GIS and Remote Sensing for Water Management and Mining Applications

Remote sensing data have a very long history with a larger and larger range of data types and their usage. Environmental applications belong to very important areas. Continental surface water bodies, sources of water for a high number of inhabitants, are very important parts of the environment, which surprises us by very quick change from the time point of view. These changes to be spatially measured can be detected only by remote sensing data. Another example of spatial data collection important for environmental purposes, are mines. There are two main types of mines – underground mines and open-cast ones. Both can be a subject of remote sensing data measurement. Open mines have a development starting from their preparation before their opening, their active period and a closing one followed by reclamation. We can use remote sensing data for all these three parts of their development. Underground mines have generally the same history; however, remote sensing data types and applications are not the same. The lecture will offer variability of analyses using remote sensing data for both spheres of the environment.

Dr. Roger L. King, Mississippi State University:
Semantic based image mining

There is a wealth of accumulated Earth Observations (EO) such as data/images in various databases, files, spreadsheets, video and audio data. However optimal harnessing of these resources has long been recognized as an insurmountable task. The major challenges being the heterogeneous nature of the data due to the diverse procedures and techniques used to collect it, and stored in a variety of formats and at different locations. The EO satellites have been collecting huge amounts of data over the past decades, however, the current methods of searching for useful information is only at the syntactic metadata level, thus the optimal exploitation of the archived data is severely constrained by the lack of content and semantics based knowledge retrieval. This lecture will discuss a semantics driven framework for content-based retrieval from remote sensing data.

Scientific Challenges in Information Retrieval from Earth Observation Imagery
Many challenges exist in the creation of information from Earth observation data. This lecture will present an end-to-end model of the remote sensing information channel and then discuss the scientific challenges confronting the remote sensing analyst. These challenges include such issues as spatial & spectral resolution, BRDF, and time series analysis.

Dr. Martin Isenburg: LiDAR Processing with Examples of Different Projects
Hands-on Workshop: The typical Steps of a LiDAR Processing Workflow

Dr. Isenburg will start with an interactive intro talk on LiDAR processing with examples of different projects such as the Canary Islands (Spain) where the vegetation-penetrating lasers uncovered elevation differences of up to 25 meters between the official government maps and reality, flood mapping in the Philippines, archaeological finds in Polish forests, and mapping biomass in Thailand.
This is followed by a hands-on workshop during which students will perform the typical steps of a LiDAR processing workflow on their own Windows laptops using the software and data provided. This workshop will touch upon parts of (1) LiDAR quality checking, (2) LiDAR preparation (tiling, classifying, cleaning), and (3) LiDAR derivative creation (DTM/DSM/contour/slope maps/CHM/…) similar to what is covered in these tutorials:




Dr. Claudia Notarnicola, EURAC-Institute of Applied Remote Sensing:
Retrieval of Essential Climate Variables (ECV) from remotely sensed data: advanced methodologies, applications and future perspectives

In the last two decades, the increasing number of space-borne sensors, with complete, periodic and synoptic coverage of the Earth’s surface, has determined an increasing interest for the estimation of bio-geophysical surface parameters from remotely sensed data. Among the most interesting and challenging biophysical parameters we shall mention soil moisture, biomass, leaf area index, snow cover. These parameters are mentioned among the Essential Climate Variables (ECV).
The retrieval process is typically a challenging task and it falls in the category of ill-posed problem. This means that beyond the non-linearity of the relationship between input features (sensor measurements) and the target variables (soil moisture, biomass, etc.), more than one combination of soil characteristics may lead to the same electromagnetic response at the sensor. Moreover, given a scene of interest, each system will provide information on a different aspect of the phenomena at the ground (e.g., the spatial patterns or the temporal dynamic) and could be also affected on different extents by different disturbing factors.
This suggests the importance of a synergic use of multiple available remote sensing systems for a comprehensive, accurate and robust understanding and monitoring of the natural processes at the ground. On the other side the proper selection of the retrieval approach is a key issue.
In this context, the seminar will present currently available techniques for the retrieval of bio-physical parameters from remotely sensed data addressing parametric and non-parametric approaches such as Bayesian procedure, Neural Networks and Support Vector Regression.
Each approach will be presented in specific applications such as soil moisture and biomass retrieval indicating advantages, disadvantages and perspectives for the upcoming missions such as Sentinel 1 and 2. In addition the synergic use of different sensors (optical and radar) will be specifically addressed in the context of the retrieval process.

Dr. Marinko Oluić, University of Zagreb:
Remote sensing for the Earth Sciences

Several topics related to remote sensing and its applications will be introduced. My presentation will start with energy and matter interaction and with various imaging sensors and platforms. I will cover remote sensing applications in Earth sciences with emphasis on Geology: tectonic mapping, mineral exploration, and natural hazards –earthquakes and floods. Different scenarios will be analyzed.

Dr. Susan L. Ustin, University of California:
Imaging Spectroscopy: Measurement of Vegetation Properties

Over the past 20 years a significant literature has developed that demonstrates that imaging spectroscopy can detect and quantify concentrations of plant pigments, water, nitrogen, and dry plant materials (like cellulose and lignin) based on physical principles of spectroscopy and that differences in these biochemical traits can be used to identify species and/or plant communities. However, we still lack a fully developed theoretical framework that accounts for the scattering and absorption interactions to explain the observed reflectance of plant canopies. To better understand the information provided by these hyperspectral instruments. I will review examples of patterns from different environments and conditions that illustrate the state of knowledge from both the ecological and the spectroscopy perspectives and suggest some directions for future progress.