Workshop Report at the Department of Remote Sensing – April 11, 2018

Workshop Report at the Department of Remote Sensing – April 11, 2018

m

March 27, 2018

Remote sensing for phenological monitoring: How to derive phenological information using MODIS time series for Germany?

On April 11, 2018, our colleague Carina Kübert will address different questions on how to derive phenological information from time series analysis using MODIS data.

Abstract

“Phenology is perhaps the simplest process for changing the ecology of species as a response to climate change.” (Rosenzweig et al. 2007)

There are several approaches to track phenology in the context of global change. Land Surface Phenology (LSP) deals with methods to monitor vegetation phenology using time series data from Earth Observation satellites. Getting deeper into this topic, it becomes clear that it is not as simple as stated by Rosenzweig et al.: Which data should I use? Which method should I apply concerning filtering, which for outlier detection, which for deriving phenological metrics? What is the meaning of those metrics in an ecological point of view? This talk will give an overview of the modeling framework that helps answering those questions and will give recommendations on how to derive LSP for Germany.

Title of presentation: Remote sensing for phenological monitoring: How to derive phenological information using MODIS time series for Germany?

Presenter: Carina Kübert

Date: Wednesday, 11th April 2018 Time: 10 – 12 am s.t. Place: Josef-Martin-Weg 52, Campus Hubland Nord

 

All interested persons are cordially invited!

 

What are the workshop reports about? Presentation of ongoing PhD studies and projects, as well as new modules for the scientific exchange but also for preparation to presenting at conferences. Topics presented will be discussed in an environment of different expertise.

follow us and share it on:

you may also like:

Setting Up for the 2nd EO4CAM Nutzerworkshop

Setting Up for the 2nd EO4CAM Nutzerworkshop

Right now our staff are busy hauling cases, mounting displays and testing every cable for the 2nd EO4CAM Nutzerworkshop, “Erdbeobachtung für die Klimaanpassung, behördliche Akteure in Bayern”. The doors open tomorrow, and the exhibition...

A dashboard for multi-UAS thermal data

A dashboard for multi-UAS thermal data

Mapping an active fire with various drones flying in parallel is hard enough. But getting the data processed, evaluated and turned into research datasets is a different story. The publication is on its way, and for now we’d like to share a dashboard by Antonio José...

Have you ever wondered how many trees there are on our campus?

Have you ever wondered how many trees there are on our campus?

Sometimes a chat during a formal event like the one for the Urban:Pulse funding celebration, results in something totally different. Following a discussion with the vice-president of our University, we promised to look into the research by one of our PhD students...

Earth Observation at the DFG BETAFOR workshop

Earth Observation at the DFG BETAFOR workshop

Jakob Schwalb-Willmann represented our team at the DFG BETAFOR workshop in the Bavarian Forest National Park. The room was full of biologists, which is the whole point of an interdisciplinary project, and our UAS multisensor time series drew a lot of interest from the...

Thermal Drone Data Acquisitions: CalVal project with Informatics

Thermal Drone Data Acquisitions: CalVal project with Informatics

Thermal cameras on drones look very good - visually. They show us temperature of our study objects be it stressed trees, warm patches in a city or active fires. But we do not yet know for sure how far we can trust the temperature values they deliver especially when...

bzgl. Art. 50 Verordnung (EU) 2024/1689 (AI Act):

Überwiegend werden eigene originäre Texte und Bilder des EORC genutzt, jedoch sind einige Inhalte von blog post Texten teilweise mit Hilfe von KI überarbeitet worden und einige Bilder sind ganz mit KI erstellt, die jedoch deutlich keine photorealistische Darstellungen abbilden. Aussnahmen sind KI generierte fernerkundliche Datensätze, die explizit für Forschungszwecke mit KI erstellt wurden, hier wird aber durch den assoziierten Text der wissenschaftliche Grund der KI generierten Bilder erläutert.

Share This