Optical remote sensing is an important tool in the study of animal behavior providing ecologists with the means to understand species-environment interactions in combination with animal movement data. However, differences in spatial and temporal resolution between movement and remote sensing data limit their direct assimilation. In this context, we built a data-driven framework to map resource suitability that addresses these differences as well as the limitations of satellite imagery. It combines seasonal composites of multiyear surface reflectances and optimized presence and absence samples acquired with animal movement data within a cross-validation modeling scheme. Moreover, it responds to dynamic, site-specific environmental conditions making it applicable to contrasting landscapes. We tested this framework using five populations of White Storks (Ciconia ciconia) to model resource suitability related to foraging achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for absences. These results were influenced by the temporal composition of the seasonal reflectances indicated by the lower accuracies associated with higher day differences in relation to the target dates. Additionally, population differences in resource selection influenced our results marked by the negative relationship between the model accuracies and the variability of the surface reflectances associated with the presence samples. Our modeling approach spatially splits presences between training and validation. As a result, when these represent different and unique resources, we face a negative bias during validation. Despite these inaccuracies, our framework offers an important basis to analyze speciesenvironment interactions. As it standardizes site-dependent behavioral and environmental characteristics, it can be used in the comparison of intra-and interspecies environmental requirements and improves the analysis of resource selection along migratory paths. Moreover, due to its sensitivity to differences in resource selection, our approach can contribute toward a better understanding of species requirements.
This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a focus on mountain areas. The novelties of the paper are: the extension of an already developed method to coarse resolution data (150 m) in mountain environment with high land heterogeneity, with only VV polarization and the proper selection of input features. During the result analysis, several algorithm characteristics were clearly identified: 1) the performances showed to be strongly related to input features such as topography and vegetation indices; 2) the algorithm needs a training phase; 3) the averaging window needs to be proper selected to take into account both the speckle noise and the characteristics of the area under investigation; and 4) the algorithm, being data driven, can be considered as site dependent. The experimental analysis is carried out on images acquired over the Südtirol/Alto Adige Province in Italy during 2010-2011 from the RADARSAT2 and Envisat ASAR in Wide Swath mode. SMC maps were compared with spatially distributed ground measurements, resulting in a root mean squared error (RMSE) value ranging from 0.045 to 0.07 m 3 /m 3 . Concerning the multiscale analysis, the results indicated that RADARSAT2 maps are able to detect the spatial heterogeneity and soil moisture dynamics at local scale, while ASAR WS SMC maps are able to identify mainly the two main classes of pasture and meadows. When these estimates are compared with SMC values from meteorological stations a RMSE value of 0.10 m 3 /m 3 for both satellites indicated a reduced capability to follow the temporal dynamics.
1. 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. 2. 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. 3. 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. 4. 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. K E Y W O R D S animal tracking, animation, data visualization, movement data, movement ecology, movevis, r, spatio-temporal data | 665 Methods in Ecology and Evoluঞon SCHWALB-WILLMANN et AL. S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Schwalb-Willmann J, Remelgado R, Safi K, Wegmann M. movevis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in r. Methods Ecol Evol. 2020;11:664-669.
Land cover is a key variable in the context of climate change. In particular, crop type information is essential to understand the spatial distribution of water usage and anticipate the risk of water scarcity and the consequent danger of food insecurity. This applies to arid regions such as the Aral Sea Basin (ASB), Central Asia, where agriculture relies heavily on irrigation. Here, remote sensing is valuable to map crop types, but its quality depends on consistent ground-truth data. Yet, in the ASB, such data are missing. Addressing this issue, we collected thousands of polygons on crop types, 97.7% of which in Uzbekistan and the remaining in Tajikistan. We collected 8,196 samples between 2015 and 2018, 213 in 2011 and 26 in 2008. Our data compile samples for 40 crop types and is dominated by "cotton" (40%) and "wheat", (25%). These data were meticulously validated using expert knowledge and remote sensing data and relied on transferable, open-source workflows that will assure the consistency of future sampling campaigns.
Remote sensing is a valuable tool in movement ecology. However, the way it is combined with animal movement data is far from optimal as most studies overlook differences in the spatial, temporal and thematic resolutions between both data types. rsmove uses a pixel‐based approach to link animal tracking and remote sensing data that bridge the gap between these two disciplines while respecting the limitations of the latest. Additionally, if offers standardize methods to pre‐analyse the connection between animal movement and environmental change. The package guides the choice of study sites, satellite data and environmental predictors though data mining and visualization tools. rsmove offers a simple methodology to analyse animal–environment interactions that helps avoid unnecessary, time‐consuming satellite data processing through an informed decision process. The package offers a basis for a better communication between ecologists and remote sensing experts contributing to the definition of clear remote sensing data requirements. Additionally, it provides future studies on animal movement tools for a more critical approach on the selection of environmental data supporting the reproducibility of animal movement studies.
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.
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