Multi-scale phenology from digital time-lapse camera to Sentinel-2 and MODIS over Australian pastures

Liu, Y; Huete, A; Xie, Q; Nguyen, H

Multi-scale phenology from digital time-lapse camera to Sentinel-2 and MODIS over Australian pastures

Liu, Y Huete, A

Xie, Q

Nguyen, H

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Publication Type:: Conference Proceeding
Citation:: 2020
Issue Date:: 2020-05-08

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Field	Value	Language
dc.contributor.author	Liu, Y
dc.contributor.author	Huete, A https://orcid.org/0000-0003-2809-2376
dc.contributor.author	Xie, Q https://orcid.org/0000-0002-1576-6610
dc.contributor.author	Nguyen, H https://orcid.org/0000-0002-8329-1362
dc.date	2020-05-04
dc.date.accessioned	2022-03-09T01:59:55Z
dc.date.available	2022-03-09T01:59:55Z
dc.date.issued	2020-05-08
dc.identifier.citation	2020
dc.identifier.uri	http://hdl.handle.net/10453/155088
dc.description.abstract	As a natural ecosystem dominated by grasses, phenological studies of pastures have attracted increased attention for their important roles in global carbon cycling, ecosystem biodiversity, and public health. To better understand pasture phenology from in-situ to regional scales, accurate monitoring of pasture greenness variations across different scales is critical. As an alternative approach to labor-intensive field surveys, digital time-lapse cameras (termed phenocams) can provide diurnal and long-term vegetation greenness observation at in-situ scale with less impact from atmospheric effects. Even so, monitoring of phenology at regional to global scales only can be obtained by satellite remote sensing. The data from satellite sensors whether medium-resolution (i.e. Moderate Resolution Imaging Spectrodiometer, MODIS, 250 m) or fine spatial resolution (i.e. Sentinel-2 mission, 10 m) is widely used for vegetation phenology monitoring. However, achieving accurate pasture greenness dynamics using satellite data remains challenging due to limitations resulting from heterogeneity in Australian pastures.
dc.language	en
dc.relation	Australian Technology Network of UniversitiesFAPESP - ATN network
dc.relation	University of Technology Sydney
dc.relation.ispartof	EGU Congress
dc.relation.isbasedon	10.5194/egusphere-egu2020-7261
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Multi-scale phenology from digital time-lapse camera to Sentinel-2 and MODIS over Australian pastures
dc.type	Conference Proceeding
utslib.location.activity	Vienna, Austria
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-03-09T01:59:54Z
pubs.finish-date	2020-05-08
pubs.publication-status	Published
pubs.start-date	2020-05-04

Abstract:

As a natural ecosystem dominated by grasses, phenological studies of pastures have attracted increased attention for their important roles in global carbon cycling, ecosystem biodiversity, and public health. To better understand pasture phenology from in-situ to regional scales, accurate monitoring of pasture greenness variations across different scales is critical. As an alternative approach to labor-intensive field surveys, digital time-lapse cameras (termed phenocams) can provide diurnal and long-term vegetation greenness observation at in-situ scale with less impact from atmospheric effects. Even so, monitoring of phenology at regional to global scales only can be obtained by satellite remote sensing. The data from satellite sensors whether medium-resolution (i.e. Moderate Resolution Imaging Spectrodiometer, MODIS, 250 m) or fine spatial resolution (i.e. Sentinel-2 mission, 10 m) is widely used for vegetation phenology monitoring. However, achieving accurate pasture greenness dynamics using satellite data remains challenging due to limitations resulting from heterogeneity in Australian pastures.

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http://hdl.handle.net/10453/155088