Remote Sensing of Environmental Variables and Fluxes

Sadeghi, Morteza; Babaeian, Ebrahim; Ebtehaj, Ardeshir M.; Jones, Scott B.; Tuller, Markus

doi:10.1002/9781119304418.ch9

Cited by 3 publications

(2 citation statements)

References 291 publications

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“…The three approaches we tested (i.e., using single source data, combining multiple medium/high resolution sensor data and fusing these data with high temporal resolution Earth observation data) are not limited to optical satellite imagery, but can be used for other types of Earth observation data captured using a variety of other sensors. For example, different satellite systems are now collecting detailed information on environmental conditions such as, water content, chlorophyll, snow coverage, vegetation type, land or sea surface temperature, humidity, rainfall, air pressure and Earth's magnetic field (Sadeghi et al, 2018), which can be useful for explaining wildlife movement patterns. There is a potential to extend our methodology beyond optical imagery and include all these different types of environmental data.…”

Section: Discussionmentioning

confidence: 99%

Multi-source data fusion of optical satellite imagery to characterize habitat selection from wildlife tracking data

Brum-Bastos

Long

Church

et al. 2020

Ecological Informatics

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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

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Section: Discussionmentioning

confidence: 99%

Multi-source data fusion of optical satellite imagery to characterize habitat selection from wildlife tracking data

Brum-Bastos

Long

Church

et al. 2020

Ecological Informatics

View full text Add to dashboard Cite

show abstract

“…A main reason is the limited availability of soil moisture eld observations (Cassiani et al, 2006). It has long been recognized that remote sensing data can provide estimates of environmental variables and uxes (Moradkhani, 2008;Reichle, 2008;Ma et al, 2015;STOWA, 2016;Zhuo and Han, 2016;Sadeghi et al, 2018). For many years, water managers have been interested in remote sensing as a source of high-resolution spatially distributed data.…”

Section: Soil Moisture Information For Water Managementmentioning

confidence: 99%