rsMove R package update!

rsMove R package update!

January 24, 2018

An update for rsMove is now available on CRAN. rsMove was designed to support movement ecologists in the selection and handling of remote sensing data. This package helps select study sites, guides the choice of satellite data by quantifying the influence of its spatial and temporal resolutions on movement data and quantifies species-environment interactions to help choose environment variables. Additionally, rsMove provides tools to homogenize the scales of remote sensing and animal movement data, select presence/absence samples and model resource suitability from a remote sensing perspective (see Remelgado et al., 2017). Most of the functions in rsMove are accompanied by ggplot objects providing the users with editable plots. This release significantly improves function descriptions and keywords. Moreover, we included a vignette with practical examples! To acces it, click here.

translation of movement data (on the left) to pixels that preserve revisits (center) – to adress pseudo-replication – and the subsequent split of training samples based on their connectivity (right) – to address spatial autocorrelation.

you may also like:

EcoGlob interdisciplinary research with Univ. Bayreuth

EcoGlob interdisciplinary research with Univ. Bayreuth

In the last years our EcoGlob team at the Earth Observation research cluster established various new cooperation with colleagues from the University of Bayreuth as well as the University of Würzburg. One of these numerous activities was the UAS/UAS/drone based...

What it takes to record a forest for an entire year: Insights into one out of many days flying drones in the University Forest

What it takes to record a forest for an entire year: Insights into one out of many days flying drones in the University Forest

This week, EOR Cluster staff carried out a routine flight mission in the University Forest: As every second week, the entire 200 ha were imaged with a multispectral sensor on a Wingtra fixed wing aircraft. Additionally, the mission was augmented by LIDAR on an M300 multicopter (as every month). Initially delayed by rainy weather, the crew managed to acquire high quality data, which helps monitoring the ecosystem.

first BigData@Geo project meeting

first BigData@Geo project meeting

Today we had our first meeting with Andreas Hotho (Informatics) and Heiko Paeth (climatology) about our new BigData@Geo project, funded within the EFRE framework. The project aims to support local SMEs with science based knowledge about georisks, climate change,...