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SUMMARY:Inferring forest stand structure from LiDAR remote sensing data - 
 Rebecca Spriggs (Coomes lab)
DTSTART:20140508T150000Z
DTEND:20140508T152500Z
UID:TALK51403@talks.cam.ac.uk
CONTACT:Megan Cooper
DESCRIPTION:Forest information at the level of the stand is crucial for de
 riving reliable estimates of the capacity of the future carbon sink\, yet 
 the data are restricted to a small subset of the forest.  Collection of th
 e required datasets can be labour-intensive and costly and thus data avail
 ability is restricted.  Our aim is to develop a model that allows forest s
 tand structure to be derived from LiDAR remote sensing data\; this therefo
 re offers a tool for predicting inventory information across large scales\
 , from which current carbon stocks can be measured and future stocks predi
 cted.\n\nWe have developed a model for predicting the distribution of LiDA
 R first returns retrieved from stand-level data\, demonstrating that expos
 ed\, accumulated and overlapping crown area at a given height are all crit
 ical factors in determining where a return is recorded.  This model predic
 ts to a high degree of accuracy which has allowed it to be effectively imp
 lemented in the extraction of stem diameter distributions from LiDAR retur
 n patterns across whole landscapes. 
LOCATION:Department of Plant Sciences\, Large Lecture Theatre
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