2020
DOI: 10.1016/j.ecoinf.2020.101149
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Multi-source data fusion of optical satellite imagery to characterize habitat selection from wildlife tracking data

Abstract: Wildlife tracking data allow monitoring of how organisms respond to spatio-temporal changes in resource availability. Remote sensing data can be used to quantify and qualify these variations to understand how movement is related to these changes. The use of remote sensing data with concurrent high levels of spatial and temporal detail may hold potential to improve our understanding of habitat selection. However, no current orbital sensor produces data with simultaneous high temporal and high spatial resolution… Show more

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Cited by 10 publications
(8 citation statements)
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“…The use of multi-source data, particularly satellite data, to represent contextual variables can improve our capacity to capture the dynamics of the phenomenon that is providing context and its changes at a higher level of both spatial and temporal detail. For example, recent studies employ a multi-source analysis, where NDVI products from several satellite sources and across spatio-temporal scales are used (Berman et al 2019, Brum-Bastos et al 2020. Multi-source data can also enable analysis for which contextual data are required at higher temporal resolution and level of detail than what is readily available.…”
Section: Challenge 2: How Can Cama Methods Properly Account For the Temporal Dynamics Of Contextual Data (Eg Contextual Factors That Chanmentioning
confidence: 99%
See 2 more Smart Citations
“…The use of multi-source data, particularly satellite data, to represent contextual variables can improve our capacity to capture the dynamics of the phenomenon that is providing context and its changes at a higher level of both spatial and temporal detail. For example, recent studies employ a multi-source analysis, where NDVI products from several satellite sources and across spatio-temporal scales are used (Berman et al 2019, Brum-Bastos et al 2020. Multi-source data can also enable analysis for which contextual data are required at higher temporal resolution and level of detail than what is readily available.…”
Section: Challenge 2: How Can Cama Methods Properly Account For the Temporal Dynamics Of Contextual Data (Eg Contextual Factors That Chanmentioning
confidence: 99%
“…Multi-source methods are well developed in other research areas (e.g. environmental monitoring, battlefield surveillance, automatic target detection (Zhang 2010)) but currently remain limited in their application to movement modelling (but see Brum-Bastos et al 2020).…”
Section: Challenge 2: How Can Cama Methods Properly Account For the Temporal Dynamics Of Contextual Data (Eg Contextual Factors That Chanmentioning
confidence: 99%
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“…New methodological approaches are needed to study ways to find the appropriate scale for combining movement and environmental data in a way that preserves the accuracy of environmental information at the location and time of the moving object. Current solutions include multi-source data fusion [3], but new solutions are needed for unusual and geometrically complex environmental data (e.g., satellite data on Earth's magnetic field, dynamic traffic congestion data).…”
Section: Opportunities and Challenges 21 Big Data Opportunities: New Sources And Data Fusionmentioning
confidence: 99%
“…To date, we have made much more progress using movement data to understand pathways-which is not surprising given that the reason we often collect movement data is to understand the movement part of the activity. However, significant opportunities exist for taking a more place-based emphasis, to explore the places individuals visit [31] and the sequences in which they are visited [3].…”
Section: Places and Pathwaysmentioning
confidence: 99%