DocumentCode :
2674237
Title :
Extracting tree crown properties from ground-based scanning laser data
Author :
Moorthy, I. ; Miller, J.R. ; Berni, J.A.J. ; Zarco-Tejada, Pablo J. ; Qingmou Li
Author_Institution :
York Univ., Toronto
fYear :
2007
fDate :
23-28 July 2007
Firstpage :
2830
Lastpage :
2832
Abstract :
The spatial organization of above-ground plant material plays an important role in controlling not only plant functional activities like photosynthesis and evapotranspiration, but also the photo-vegetation interactions. To improve our understanding of such interactions, the acquisition of highly detailed information about the 3D architecture of individual plants and communities of plants is required. Recently, Light detection and ranging (LiDAR) sensors, both at the ground and the airborne-level, have emerged as useful tools for mapping 3D plant structure. One such ground-based instrument is the Intelligent Laser Ranging and Imaging System (ILRIS 3D), which was developed at Optech Incorporated. This laser scanner, generates a 3D digital reconstruction of any scene, by actively emitting laser pulses and recording the time elapsed for the return of a pulse, thereby measuring the distance of any given object. It is the objective of this research to utilize the ILRIS 3D to measure structural, and biophysical information of individual trees for use as direct inputs into complex radiative transfer models. The key parameters under investigation are crown dimensions (i.e. shape, area, and volume), crown-level gap fraction (GF) and crown- level leaf area index (LAI). The ILRIS 3D was used to acquire 3D point clouds of an artificial 6´ Ficus tree, in a controlled laboratory environment. Measured XYZ point cloud data was segmented to retrieve laser pulse return density profiles, which subsequently were used to estimate gap fraction and LAI . Gap fraction estimates were cross-validated with traditional methods of histogram thresholding of digital photographs (r2 = 0.96). Crown LAI estimates were compared with the actual values (r2 = 0.95, RMSE = 0.45). The next challenge was to implement the developed algorithms to real crowns, namely olive (Olea europaea L.) orchards in southern Spain. Individual tree-level ILRIS 3D data was collected from 24 structurall- y diverse crowns. Crown dimensional profiles were extracted for ILRIS data that was collected from a horizontal view (i.e ground-based) and a nadir view (i.e from platform 12 meters above ground). Preliminary retrievals from the olive orchards dataset is described here, while current ongoing field measurements are being conducted to validate the findings. Successful demonstration of extracting crown-level structural parameters like gap fraction and LAI from ground-based LiDAR will be important new information that can be used for detailed radiative transfer modeling in olive orchards and likely lead to more robust inversion algorithms.
Keywords :
measurement by laser beam; optical scanners; radiative transfer; vegetation; vegetation mapping; 3D digital reconstruction; 3D plant structure mapping; 3D point cloud; Ficus tree; ILRIS 3D system; Intelligent Laser Ranging and Imaging System; Olea europaea L; above-ground plant material; biophysical information; crown dimension; crown- level leaf area index; crown-level gap fraction; evapotranspiration; ground-based scanning laser data; laser pulse return density profile; laser scanner; olive orchard; photosynthesis; photovegetation interaction; plant functional activities; radiative transfer model; spatial organization; tree crown property extraction; Clouds; Data mining; Image reconstruction; Information retrieval; Instruments; Intelligent sensors; Laser radar; Optical materials; Optical pulse generation; Pulse measurements;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-1211-2
Electronic_ISBN :
978-1-4244-1212-9
Type :
conf
DOI :
10.1109/IGARSS.2007.4423432
Filename :
4423432
Link To Document :
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