ORCID Profile
0000-0002-4735-9526
Current Organisations
Australian Plant Phenomics Facility, ANU Node
,
TimeCam.TV
,
TimeScience
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Publisher: Copernicus GmbH
Date: 13-09-2016
Abstract: Abstract. Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its erse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semi-arid scrublands, and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e. drought, flooding, cyclones, and fire) that can alter ecosystem composition, structure, and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology on the continental scale using the enhanced vegetation index (EVI), calculated from MODerate resolution Imaging Spectroradiometer (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e. tropical savannas) to regions where seasonal variation is minimal (i.e. tropical rainforests) or high but irregular (i.e. arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senescing events in tropical savannas and temperate eucalypt understorey, as well as strong seasonal dynamics of in idual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve the current understanding of Australian ecosystems. To facilitate the sharing of this information, we have formed the Australian Phenocam Network (phenocam.org.au/).
Publisher: Elsevier BV
Date: 04-2014
DOI: 10.1016/J.PBI.2014.02.002
Abstract: Agriculture requires a second green revolution to provide increased food, fodder, fiber, fuel and soil fertility for a growing population while being more resilient to extreme weather on finite land, water, and nutrient resources. Advances in phenomics, genomics and environmental control/sensing can now be used to directly select yield and resilience traits from large collections of germplasm if software can integrate among the technologies. Traits could be Captured throughout development and across environments from multi-dimensional phenotypes, by applying Genome Wide Association Studies (GWAS) to identify causal genes and background variation and functional structural plant models (FSPMs) to predict plant growth and reproduction in target environments. TraitCapture should be applicable to both controlled and field environments and would allow breeders to simulate regional variety trials to pre-select for increased productivity under challenging environments.
Publisher: Elsevier BV
Date: 2014
Publisher: Oxford University Press (OUP)
Date: 14-06-2017
DOI: 10.1104/PP.17.00610
Publisher: Walter de Gruyter GmbH
Date: 12-2018
Abstract: The presence or absence of leaves within plant canopies exert a strong influence on the carbon, water and energy balance of ecosystems. Identifying key changes in the timing of leaf elongation and senescence during the year can help to understand the sensitivity of different plant functional types to changes in temperature. When recorded over many years these data can provide information on the response of ecosystems to long-term changes in climate. The installation of digital cameras that take images at regular intervals of plant canopies across the Integrated Carbon Observation System ecosystem stations will provide a reliable and important record of variations in canopy state, colour and the timing of key phenological events. Here, we detail the procedure for the implementation of cameras on Integrated Carbon Observation System flux towers and how these images will help us understand the impact of leaf phenology and ecosystem function, distinguish changes in canopy structure from leaf physiology and at larger scales will assist in the validation of (future) remote sensing products. These data will help us improve the representation of phenological responses to climatic variability across Integrated Carbon Observation System stations and the terrestrial biosphere through the improvement of model algorithms and the provision of validation datasets.
Publisher: Elsevier BV
Date: 03-2012
Publisher: Walter de Gruyter GmbH
Date: 12-2018
Abstract: Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG CO 2 , CH 4 , N 2 O, H 2 O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to s le climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied s ling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.
Publisher: Wiley
Date: 03-2016
DOI: 10.1002/FEE.1222
Publisher: Springer Science and Business Media LLC
Date: 04-08-2018
Publisher: Humana Press
Date: 2012
DOI: 10.1007/978-1-61779-995-2_7
Abstract: The high spatial and temporal resolution of data required for high-throughput phenotyping has typically been all but impossible to obtain in field populations of plants. When studies of in idual and population genetic variation and microclimate sensor data are combined with phenology data, a landscape-level view of how populations respond to changing environments can be obtained. This chapter will discuss the development of a multi-billion pixel ("gigapixel") camera system that enables the collection of phenology data at up to hourly intervals from in situ plant populations. Such gigapixel time-lapse imaging systems represent a key technological advancement for enabling high-throughput phenotyping in field settings. Gigapixel resolution image datasets allow researchers to record life-history (phenology) data across an entire landscape over multiple seasons. Image data can be wirelessly transmitted to a remote server where it can be accessed online within hours of capture. The time-lapse panoramic images are browsable through an interactive web tool that can be used to compare plant phenology with environmental sensor data collected simultaneously from the field. The high spatial and temporal resolution data can be used to identify in idual plant phenology, which can in turn be used to generate complete population level phenotype data. The Gigavision platform is especially powerful when coupled with next-generation population genomic analysis. The Gigavision system permits the rapid identification of the phenotypes and genotypes responding to natural selection in wild populations.
No related grants have been discovered for Tim Brown.