scholarly journals A New Method for Positional Accuracy Control for Non-Normal Errors Applied to Airborne Laser Scanner Data

2019 ◽  
Vol 9 (18) ◽  
pp. 3887 ◽  
Author(s):  
Francisco Javier Ariza-López ◽  
José Rodríguez-Avi ◽  
Diego González-Aguilera ◽  
Pablo Rodríguez-Gonzálvez
Keyword(s):  
Quality Control ◽  
Control Method ◽  
Laser Scanner ◽  
Gaussian Model ◽  
Multinomial Distribution ◽  
Type I ◽  
Positional Accuracy ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Wide Range

A new statistical method for the quality control of the positional accuracy, useful in a wide range of data sets, is proposed and its use is illustrated through its application to airborne laser scanner (ALS) data. The quality control method is based on the use of a multinomial distribution that categorizes cases of errors according to metric tolerances. The use of the multinomial distribution is a very novel and powerful approach to the problem of evaluating positional accuracy, since it allows for eliminating the need for a parametric model for positional errors. Three different study cases based on ALS data (infrastructure, urban, and natural cases) that contain non-normal errors were used. Three positional accuracy controls with different tolerances were developed. In two of the control cases, the tolerances were defined by a Gaussian model, and in the third control case, the tolerances were defined from the quantiles of the observed error distribution. The analysis of the test results based on the type I and type II errors show that the method is able to control the positional accuracy of freely distributed data.

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 Estimation of Vegetation-Induced Flow Resistance for Hydraulic Computations Using Airborne Laser Scanning Data

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1864
Author(s):  
Peter Mewis
Keyword(s):  
Laser Scanning ◽  
Flow Resistance ◽  
Laser Scanner ◽  
Airborne Laser Scanning ◽  
Density Parameter ◽  
Vegetation Density ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Bed Roughness ◽  
Available Information

The effect of vegetation in hydraulic computations can be significant. This effect is important for flood computations. Today, the necessary terrain information for flood computations is obtained by airborne laser scanning techniques. The quality and density of the airborne laser scanning information allows for more extensive use of these data in flow computations. In this paper, known methods are improved and combined into a new simple and objective procedure to estimate the hydraulic resistance of vegetation on the flow in the field. State-of-the-art airborne laser scanner information is explored to estimate the vegetation density. The laser scanning information provides the base for the calculation of the vegetation density parameter ωp using the Beer–Lambert law. In a second step, the vegetation density is employed in a flow model to appropriately account for vegetation resistance. The use of this vegetation parameter is superior to the common method of accounting for the vegetation resistance in the bed resistance parameter for bed roughness. The proposed procedure utilizes newly available information and is demonstrated in an example. The obtained values fit very well with the values obtained in the literature. Moreover, the obtained information is very detailed. In the results, the effect of vegetation is estimated objectively without the assignment of typical values. Moreover, a more structured flow field is computed with the flood around denser vegetation, such as groups of bushes. A further thorough study based on observed flow resistance is needed.

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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