Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data

2009 ◽  
Vol 39 (5) ◽  
pp. 1036-1052 ◽  
Author(s):  
Terje Gobakken ◽  
Erik Næsset
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Laser Scanner ◽  
Position Error ◽  
Canopy Height ◽  
Sample Plot ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Plot Size ◽  
Airborne Laser

Canopy height distributions were created from small-footprint airborne laser scanner data with an average sampling density of 1.1 points·m–2 collected over 132 sample plots and 61 stands. Field measurements of each plot were carried out within two concentric circles (200 m2 and 300 or 400 m2). The plot positions were altered randomly with Monte Carlo simulations. For various metrics derived from the canopy height distributions, the mean and the standard deviation (SD) of the differences between incorrect plot positions and ground-truth positions were compared. In general, SD was smaller for large field plots than for small plots, and the variation in SD among the Monte Carlo repetitions was smaller for large sample plots. The combined effects of field plot size and sample plot position error on the accuracy of mean tree height (hL), stand basal area (G), and stand volume (V) predicted at stand level using a two-stage procedure combining field training data and laser data were assessed. Standard deviation of the differences between predicted and observed hL was quite stable and of similar size for position errors up to 5 m. However, for G and V the influence of plot position error was more pronounced.

Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data

2008 ◽  
Vol 38 (5) ◽  
pp. 1095-1109 ◽  
Author(s):  
Terje Gobakken ◽  
Erik Næsset
Keyword(s):  
Laser Scanner ◽  
Canopy Height ◽  
Stem Density ◽  
Scanner Data ◽  
Field Plot ◽  
Airborne Laser Scanner ◽  
Plot Size ◽  
Airborne Laser ◽  
Point Density ◽  
Field Plots

Canopy height distributions were created from small-footprint airborne laser scanner data with an average sampling density of 1.13 points·m–2 collected over 132 sample plots and 61 forest stands. Field measurements of each plot were carried out within two concentric circles corresponding to fixed areas of 200 m2 and 300 or 400 m2. The laser point clouds were thinned to approximately 0.25, 0.13, and 0.06 point·m–2. For all comparisons, the maximum values of the first as well as last return canopy height distributions differed significantly between the full density and the thinned data. The combined effects of number of field plots, field plot sizes, and point densities on the accuracy of mean tree height, stand basal area, and stand volume predicted at stand level using a two-stage procedure combining field training data and laser data, were assessed using Monte Carlo simulation randomly selecting 75% and 50% of the field plots. The average standard deviation showed only a minor increase by decreasing point density and increased when the number of sample plots was reduced. The effects of field plot size varied with canopy structure and stem density.

Estimation of diameter distributions by means of airborne laser scanner data

2008 ◽  
Vol 38 (6) ◽  
pp. 1611-1620 ◽  
Author(s):  
Johannes Breidenbach ◽  
Christian Gläser ◽  
Matthias Schmidt
Keyword(s):  
Laser Scanner ◽  
Concentric Circle ◽  
Weibull Function ◽  
Sample Plot ◽  
Scanner Data ◽  
Weibull Distributions ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Step Procedure ◽  
Diameter Distributions

Diameter distributions are an important source of information for estimating the timber assortment in forest stands. In this paper, a one-step procedure for deriving the parameters of a Weibull function, itself used to describe diameter distributions, is presented. A generalized linear model (GLM) is employed that allows for an estimation of the shape and scale parameters as functions of different predictors. The GLM was fit using 495 sample plots from a conventional sample-plot inventory. Plotwise height metrics derived from airborne laser scanner data serve as covariates (auxiliary variables). Each sample plot consists of four concentric circle plots, where the largest plot covers an area of 450 m2 (12 m radius). Trees with a diameter at breast height (DBH) <30 cm are measured only on the smaller circle plots. Because of this design, left- and right-truncated Weibull distributions, conditional on the DBH, were used to fit the data. The frequently used two-step procedure — in which the Weibull distribution is firstly fitted via maximum likelihood, and its parameters are then estimated via linear regression — requires an adequate number of observations per sample plot in the first step. Hence, this method would have been unsuitable for the data source at hand, because a mean of just 12 trees per sample plot was recorded. The visual comparison of the predicted Weibull distributions with observed data shows a good fit to the data. The mean of the DBH distributions was estimated with a root mean square error (RMSE) of 2.44 cm and a bias of 0.41 cm.

Change Detection Techniques for Canopy Height Growth Measurements Using Airborne Laser Scanner Data

2006 ◽  
Vol 72 (12) ◽  
pp. 1339-1348 ◽  
Author(s):  
Xiaowei Yu ◽  
Juha Hyyppä ◽  
Antero Kukko ◽  
Matti Maltamo ◽  
Harri Kaartinen
Keyword(s):  
Change Detection ◽  
Laser Scanner ◽  
Height Growth ◽  
Canopy Height ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Detection Techniques ◽  
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.

scholarly journals Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Ernest William Mauya ◽  
Liviu Theodor Ene ◽  
Ole Martin Bollandsås ◽  
Terje Gobakken ◽  
Erik Næsset ◽  
...  
Keyword(s):  
Forest Biomass ◽  
Laser Scanner ◽  
Scanner Data ◽  
Miombo Woodlands ◽  
Airborne Laser Scanner ◽  
Airborne Laser
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