TY - JOUR A1 - Remelgado, Ruben A1 - Safi, Kamran A1 - Wegmann, Martin T1 - From ecology to remote sensing: using animals to map land cover JF - Remote Sensing in Ecology and Conservation N2 - Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence-absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high-resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1-scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non-irrigated land. When both options occurred, the animals disregarded non-irrigated land leading to its misclassification as non-agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non-irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture - with other land-use dependencies and observation times - can contribute to better results in similar applications. KW - Animal Tracking KW - land cover KW - land use KW - movement ecology KW - R KW - remote sensing Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-225200 VL - 6 IS - 1 ER - TY - JOUR A1 - Schwalb‐Willmann, Jakob A1 - Remelgado, Ruben A1 - Safi, Kamran A1 - Wegmann, Martin T1 - moveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in R JF - Methods in Ecology and Evolution N2 - Visualizing movement data is challenging: While traditional spatial data can be sufficiently displayed as two‐dimensional plots or maps, movement trajectories require the representation of time in a third dimension. To address this, we present moveVis, an R package, which provides tools to animate movement trajectories, overlaying simultaneous uni‐ or multi‐temporal raster imagery or vector data. moveVis automates the processing of movement and environmental data to turn such into an animation. This includes (a) the regularization of movement trajectories enforcing uniform time instances and intervals across all trajectories, (b) the frame‐wise mapping of movement trajectories onto temporally static or dynamic environmental layers, (c) the addition of customizations, for example, map elements or colour scales and (d) the rendering of frames into an animation encoded as GIF or video file. moveVis is designed to display interactions and concurrencies of animal movement and environmental data. We present examples and use cases, ranging from data exploration to visualizing scientific findings. Static spatial plots of movement data disregard the temporal dimension that distinguishes movement from other spatial data. In contrast, animations allow to display relocation in both time and space. We deem animations a powerful way to visually explore movement data, frame analytical findings and display potential interactions with spatially continuous and temporally dynamic environmental covariates. KW - animal tracking KW - animation KW - data visualization KW - movement data KW - movement ecology KW - spatio‐temporal data Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-214856 VL - 11 IS - 5 SP - 664 EP - 669 ER -