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Darius S. Culvenor
Researcher at Commonwealth Scientific and Industrial Research Organisation
Publications - 62
Citations - 6037
Darius S. Culvenor is an academic researcher from Commonwealth Scientific and Industrial Research Organisation. The author has contributed to research in topics: Lidar & Forest inventory. The author has an hindex of 31, co-authored 61 publications receiving 5320 citations. Previous affiliations of Darius S. Culvenor include University of Melbourne.
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Detecting trend and seasonal changes in satellite image time series
TL;DR: Breaks For Additive Seasonal and Trend (BFAST) as mentioned in this paper is a change detection approach for time series by detecting and characterizing Breaks for Additive seasonal and trend, which integrates the decomposition of time series into trend, seasonal and remainder components with methods for detecting change within time series.
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Phenological change detection while accounting for abrupt and gradual trends in satellite image time series
TL;DR: BFAST as mentioned in this paper integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series, showing that the phenological change detection is influenced by the signal-to-noise ratio of the time series.
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Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests
TL;DR: In this paper, the capacity of current airborne and ground-based ranging systems to provide data from which useful forest inventory parameters can be derived is investigated and four contrasting study sites were established within an existing study area in the Bago and Maragle State Forests, New South Wales, Australia.
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Nondestructive estimates of above‐ground biomass using terrestrial laser scanning
Kim Calders,Glenn Newnham,Andrew Burt,Simon Murphy,Pasi Raumonen,Martin Herold,Darius S. Culvenor,Valerio Avitabile,Mathias Disney,John Armston,Mikko Kaasalainen +10 more
TL;DR: In this article, the authors developed an approach to estimate AGB from TLS data, which does not need any prior information about allometry and does not rely on indirect relationships with tree parameters or calibration data.
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TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery
TL;DR: TIDA was developed for application to imagery of native Eucalypt forests in Australia, and uses a 'top-down' spatial clustering approach involving key steps designed to reduce the effects of crown segmentation.