scholarly journals Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data

2012 ◽  
Vol 270 ◽  
pp. 54-65 ◽  
Author(s):  
Alicia Peduzzi ◽  
Randolph H. Wynne ◽  
Thomas R. Fox ◽  
Ross F. Nelson ◽  
Valerie A. Thomas
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanner ◽  
Pine Plantations ◽  
Scanner Data ◽  
Area Index ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Intensively Managed

Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests

2004 ◽  
Vol 124 (3-4) ◽  
pp. 269-275 ◽  
Author(s):  
David Riaño ◽  
Fernando Valladares ◽  
Sonia Condés ◽  
Emilio Chuvieco
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanner ◽  
Area Index ◽  
Airborne Laser Scanner ◽  
Airborne Laser

Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators

1998 ◽  
Vol 28 (7) ◽  
pp. 1016-1031 ◽  
Author(s):  
S Magnussen ◽  
P Boudewyn
Keyword(s):  
Leaf Area ◽  
Statistical Tests ◽  
Geometric Model ◽  
Laser Scanner ◽  
Canopy Height ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Wide Range ◽  
The Mean

The distribution of canopy heights obtained with an airborne laser scanner over a field trial with Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) was a function of the vertical distribution of foliage area. Over a wide range of canopy structures, the proportion of laser pulses returned from or above a given reference height was proportional to the fraction of leaf area above it. We hypothesized that the quantile of the laser canopy heights matching in probability the fraction of leaf area above a desired height would be an unbiased estimator of same. This was confirmed in 36 (20 × 20 m) plots and 6 older validation plots. Canopy-based quantiles of the laser canopy height data were within 6% (mean 3%) of the field estimates. Laser and field estimates were strongly correlated (r ~ 0.8), and statistical tests supported the null hypotheses of no difference in mean stand height (P > 0.3). A geometric model successfully predicted the mean difference between the laser canopy heights and the mean tree height. Our results explicate why estimation of stand heights from laser scanner data based on the maximum canopy height value in each cell of a fixed area grid has been successful in practice.

scholarly journals Combining optical satellite data and airborne laser scanner data for vegetation classification

2011 ◽  
Vol 3 (5) ◽  
pp. 393-401 ◽  
Author(s):  
Karin Nordkvist ◽  
Ann-Helen Granholm ◽  
Johan Holmgren ◽  
Håkan Olsson ◽  
Mats Nilsson
Keyword(s):  
Satellite Data ◽  
Laser Scanner ◽  
Vegetation Classification ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Optical Satellite Data

Non-parametric prediction of diameter distributions using airborne laser scanner data

2009 ◽  
Vol 24 (6) ◽  
pp. 541-553 ◽  
Author(s):  
Matti Maltamo ◽  
Erik Næsset ◽  
Ole M. Bollandsås ◽  
Terje Gobakken ◽  
Petteri Packalén
Keyword(s):  
Laser Scanner ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Diameter Distributions ◽  
Non Parametric

Detection of Building in Airborne Laser Scanner Data and Aerial Images

2012 ◽  
Vol 11 ◽  
pp. 7-13
Author(s):  
Dilli Raj Bhandari
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Aerial Images ◽  
Spectral Information ◽  
Surface Model ◽  
World Population ◽  
Data Sets ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Airborne Laser

The automatic extraction of the objects from airborne laser scanner data and aerial images has been a topic of research for decades. Airborne laser scanner data are very efficient source for the detection of the buildings. Half of the world population lives in urban/suburban areas, so detailed, accurate and up-to-date building information is of great importance to every resident, government agencies, and private companies. The main objective of this paper is to extract the features for the detection of building using airborne laser scanner data and aerial images. To achieve this objective, a method of integration both LiDAR and aerial images has been explored: thus the advantages of both data sets are utilized to derive the buildings with high accuracy. Airborne laser scanner data contains accurate elevation information in high resolution which is very important feature to detect the elevated objects like buildings and the aerial image has spectral information and this spectral information is an appropriate feature to separate buildings from the trees. Planner region growing segmentation of LiDAR point cloud has been performed and normalized digital surface model (nDSM) is obtained by subtracting DTM from the DSM. Integration of the nDSM, aerial images and the segmented polygon features from the LiDAR point cloud has been carried out. The optimal features for the building detection have been extracted from the integration result. Mean height value of the nDSM, Normalized difference vegetation index (NDVI) and the standard deviation of the nDSM are the effective features. The accuracy assessment of the classification results obtained using the calculated attributes was done. Assessment result yielded an accuracy of almost 92 % explaining the features which are extracted by integrating the two data sets was large extent, effective for the automatic detection of the buildings.

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