Estimating relative lidar accuracy information from overlapping flight lines

Latypov, Damir

doi:10.1016/s0924-2716(02)00047-3

Cited by 60 publications

(44 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model error does not therefore include any assessment of the errors that are introduced during the data capture process. The quantification of this capture error has been investigated extensively in the past (Latypov 2002) and while in any calculation of total errors clearly both the model and the capture errors should be included, the calculation of capture errors is beyond the scope of this investigation.…”

Section: Definition Of Termsmentioning

confidence: 99%

Quantifying Interpolation Errors in Urban Airborne Laser Scanning Models

Smith

Holland

Longley

2005

Geographical Analysis

View full text Add to dashboard Cite

Airborne laser scanning (ALS) is becoming an increasingly popular data capture technique for a variety of applications in urban surface modeling. Raw ALS data are captured and supplied as a 3D point cloud. Many applications require that these data are interpolated onto a regular grid in order that they may be processed. In this article, we identify and analyze the magnitudes and spatial patterning of residuals from ALS models of urban surfaces, at a range of different scales. Previous research has demonstrated the effects of interpolation method and scale upon the nature of error in digital surface models (DSMs), but the size and spatial patterning of such errors have not hitherto been investigated for urban surfaces. The contribution of this analysis is thus to investigate the ways in which different methods may introduce error, and to understand the uncertainty that characterizes urban surface models that are devised for a wide range of applications. The importance of the research is shown using examples of how the different methods may introduce different amounts of error and how the uncertainty information may benefit users of ALS height models. Our analysis uses a range of validation techniques, including split-sample, cross-validation, and jackknifing, to estimate the error created in DSMs of urban areas. IntroductionDigital elevation models (DEMs) are used in many analytical operations such as slope and aspect calculations, and processes such as image segmentation and filtering in the creation of bare-earth digital terrain models (DTMs). When combined in applications, these operations can be highly sensitive to the quality of the elevation data used (Wood 1994;Florinsky 2002). However, many end users of height models in geographical information systems are unaware of the issues surrounding the quality of the underlying height data, partly because of the lack of diagnostic tools for dealing with quality information in many commercial software packages (Desmet 1997). Even where users are aware of quality issues with the DEM, there is a lack of methods available to accommodate them in subsequent spatial analysis (Goodchild and Gopal 1989). The sum consequence is a rather limited understanding of the uncertainties inherent in applications using DEMs based on airborne laser scanning (ALS) data.Previous research on error in DEMs has been restricted to analyzing the nature of the errors within bare-earth surfaces in predominantly natural environments (e.g., MacEachren and Davidson 1987;Xie et al. 2003).Very few studies have begun to consider the problem of understanding error in urban surface models. Assessment of the quality of urban digital surface models (DSMs) poses very different challenges to those of evaluating bare-earth models, because of the inherent complexities of the urban surface-including the frequent discontinuities, the variety of textures and shapes, and the rate of surface change. This paucity of error investigations for urban DEMs arises in spite of the fact that high spatial resolution, accu...

show abstract

Section: Definition Of Termsmentioning

confidence: 99%

Quantifying Interpolation Errors in Urban Airborne Laser Scanning Models

Smith

Holland

Longley

2005

Geographical Analysis

View full text Add to dashboard Cite

show abstract

“…This represents 0.0066 m 3 per pixel volumetric change. Therefore, volumetric changes <0.01 m 3 are disregarded and considered as 'no change areas' to reduce apparent change as a result of data errors (Latypov, 2002).…”

Section: Topographical Changementioning

confidence: 99%

Object-based integrated landscape change analysis: synergy of multi-temporal LiDAR and very high resolution orthophotos

Kamps

Seijmonsbergen

Bouten

2016

GEOBIA 2016: Solutions and Synergies

View full text Add to dashboard Cite

ABSTRACT:Active landslides have three major effects on a landscape: 1. land cover change, 2. topographical change, and 3. above ground biomass change. Data derived from multi-temporal Light Detection and Ranging technology (LiDAR) is used in combination with multi-temporal orthophotos to quantify changes between 2006 and 2012, caused by a landslide near Doren in Austria. Data synergy is used to optimize accuracies of land cover change, and to improve results of topographical change analysis and aboveground biomass estimations. Topographical change is calculated using differencing of digital terrain models. The above ground biomass is quantified by applying a local-maximum algorithm for tree top detection, in combination with allometric equations. The land cover change classification accuracies were improved from 65% (using only LiDAR) and 76% (using only orthophotos) to 90% (using synergy) for 2006. A similar increase from respectively 64% and 75% to 91% was established for 2012. The results of the improved land cover classifications were used to optimize the topographical and above ground biomass change calculations. Fine-scale improvements of the classifications included forest edges and shadows, small open spots in the vegetation, and confusion between land cover classes. The enhanced accuracies of the land cover change analysis demonstrate the effectiveness and advantages of using synergy of LiDAR and orthophotos using OBIA. The method has great potential to be transferred to larger areas for use in monitoring, although data size calls for workflows to operate on cloud-based infrastructures that provide sufficient computational power.

show abstract

“…These overlapping regions of the data sets provide the user of the data with an opportunity to test the geometric quality of the data. As in Latypov, 2002, we are more concerned with providing a method of quantifying the relative accuracy of point cloud by making measurements between the points in the overlapping regions. In this paper, we do not talk about "correcting" the data either by adjustment of calibration parameters (Habib et al, 2010) or by the practice of strip adjustment (e.g., Munjy 2015).…”

Section: Lidar Data Geometric Assessmentmentioning

confidence: 99%

“…The user wants to avoid situations as shown in Figure 1, without having to understand the entire data acquisition process and sensor models. (Habib et al 2010;Latypov 2002;Sande 2010) have discussed methods of reporting registration errors between adjacent strips of Lidar data. The registration errors can be treated as indicators of the quality of calibration.…”

Section: Quality Control (Qc)mentioning

confidence: 99%

Geometric Quality Assessment of Lidar Data Based on Swath Overlap

Sampath

Heidemann

Stensaas

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:This paper provides guidelines on quantifying the relative horizontal and vertical errors observed between conjugate features in the overlapping regions of lidar data. The quantification of these errors is important because their presence quantifies the geometric quality of the data. A data set can be said to have good geometric quality if measurements of identical features, regardless of their position or orientation, yield identical results. Good geometric quality indicates that the data are produced using sensor models that are working as they are mathematically designed, and data acquisition processes are not introducing any unforeseen distortion in the data. High geometric quality also leads to high geolocation accuracy of the data when the data acquisition process includes coupling the sensor with geopositioning systems. Current specifications (e.g. Heidemann 2014) do not provide adequate means to quantitatively measure these errors, even though they are required to be reported. Current accuracy measurement and reporting practices followed in the industry and as recommended by data specification documents also potentially underestimate the interswath errors, including the presence of systematic errors in lidar data.

show abstract

Estimating relative lidar accuracy information from overlapping flight lines

Cited by 60 publications

References 3 publications

Quantifying Interpolation Errors in Urban Airborne Laser Scanning Models

Quantifying Interpolation Errors in Urban Airborne Laser Scanning Models

Object-based integrated landscape change analysis: synergy of multi-temporal LiDAR and very high resolution orthophotos

Geometric Quality Assessment of Lidar Data Based on Swath Overlap

Contact Info

Product

Resources

About