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, therefore alternative methods are required to generate remote sensing data that matches the high spatial-temporal resolution of

Context-aware movement analysis in ecology: a systematic review

“…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.…”

“…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).…”

“…This was common when land cover was inferred from NDVI (e.g. Cornélis et al 2011;Avgar et al, 2013;Chynoweth et al, 2015, Brum-Bastos et al 2020.…”

Integrated science of movement

“…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).…”

“…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].…”