Abstract
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.
Highlights
Digital topographic data are indispensable for modern modelling needs in many science branches
The objective of this paper is to propose a new and general statistical method for positional accuracy control that can be applied to any kind of data without the need for any underlying statistical hypothesis, e.g., airborne laser scanner (ALS) error data, and in general, for any free-distributed error data or non-normally distributed data
For the type I error analysis, three different quality controls are presented to show that the acceptance of the null hypothesis performs well
Summary
Digital topographic data are indispensable for modern modelling needs in many science branches (e.g., environmental planning, forestry, geology, hydrology, climate change, etc.). Scanner (ALS) systems record terrain elevation, terrain structure lines, buildings, vegetation and, in general, any feature present in the field and that can be detected by its resolution. One type of product of great importance that is derived from ALS are digital terrain models (DTM). DTM generation using only LiDAR data present limitation to simultaneously distinguish complicated terrain situations (e.g., discontinuities and shape ridges), highly fragmented landscapes, and a variety of objects ([1]). The vertical accuracy of LiDAR-derived DTM for uncovered and vegetated areas is assessed in [2]. Related to volume changing (DEM comparison), in [3], the vertical difference of ALS data for vegetated and no vegetated area of a landslide is reported 0.3 and 0.125 m respectively. In the case of the errors caused by the forest vegetation structure, the presence of
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