Airborne LiDAR Technology: A Review of Data Collection and Processing Systems

Lohani, Bharat; Ghosh, Surajit

doi:10.1007/s40010-017-0435-9

Cited by 68 publications

(34 citation statements)

References 59 publications

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“…The above attributes were calculated directly from a high-resolution digital elevation model (DEM) obtained via airborne laser scanning (ALS). These data are also described as light detection and ranging (LIDAR) data (Lohani and Ghosh 2017). The point density within an ALS-based point cloud in Gdynia was 12 pts/m 2 .…”

Section: Landslide Conditioning Factorsmentioning

confidence: 99%

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Małka

2021

Nat Hazards

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This work aims to prepare a reliable landslide susceptibility model and to analyse the factors contributing to landslides in a dynamic environment by considering the city of Gdynia, Poland as a case study. Geological, geomorphological, hydrological, hydrogeological, and anthropogenic predisposing factors are considered using geographic information systems. Ground types at different depths (1 m and 4 m b.g.l.) are used in the statistical susceptibility assessment for the first time. Landslide susceptibility maps are developed using two techniques in presenting landslides, 13 conditioning factors, and three statistical methods: landslide index, weight of evidence, and logistic regression. The considered factors have an influence on mass movement formation, but their roles are different. Many of these passive factors are interrelated and some of them are also related to active factors, i.e. triggers. Consideration of many thematic layers in the statistical approach allows for the selection of the most appropriate geo-environmental variables. The most significant conditioning factors that affect the likelihood of landsliding include land use and land cover as well as topography. The susceptibility maps generated by the index model and many interrelated passive factors appear to be over-predicted. The logistic regression model and only independent controlling factors (slope angle, slope aspect, and lithology) are sufficient to compile a reliable susceptibility map of Gdynia. Prediction rate curve plots show that the susceptibility map produced using logistic regression exhibits the highest prediction accuracy. The results emphasize the need to check independence in the selection of instability factors and the use of an independent subset of landslides for validation.

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Section: Landslide Conditioning Factorsmentioning

confidence: 99%

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Małka

2021

Nat Hazards

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“…This study also uses airborne Lidar data for building classifications. Lidar data requires the undergoing of quality checks [84,85]. The literature recommends examining the applications of Lidar data in different contexts, since it gives sub-metre accuracy that provides an appropriate source for distribution pattern analysis of buildings' heights [84].…”

Section: Concepts and Proposed Objectives For Urban Analyticsmentioning

confidence: 99%

Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

Shirowzhan

Sepasgozar

2019

IJGI

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Deriving 3D urban development patterns is necessary for urban planners to control the future directions of 3D urban growth considering the availability of infrastructure or being prepared for fundamental infrastructure. Urban metrics have been used so far for quantification of landscape and land-use change. However, these studies focus on the horizontal development of urban form. Therefore, questions remain about 3D growth patterns. Both 3D data and appropriate 3D metrics are fundamentally required for vertical development pattern extraction. Airborne light detection and ranging (Lidar) as an advanced remote-sensing technology provides 3D data required for such studies. Processing of airborne lidar to extract buildings’ heights above a footprint is a major task and current automatic algorithms fail to extract such information on vast urban areas especially in hilly sites. This research focuses on proposing new methods of extraction of ground points in hilly urban areas using autocorrelation-based algorithms. The ground points then would be used for digital elevation model generation and elimination of ground elevation from classified buildings points elevation. Technical novelties in our experimentation lie in choosing a different window direction and also contour lines for the slant area, and applying moving windows and iterating non-ground extraction. The results are validated through calculation of skewness and kurtosis values. The results show that changing the shape of windows and their direction to be narrow long squares parallel to the ground contour lines, respectively, improves the results of classification in slant areas. Four parameters, namely window size, window shape, window direction and cell size are empirically chosen in order to improve initial digital elevation model (DEM) creation, enhancement of the initial DEM, classification of non-ground points and final creation of a normalised digital surface model (NDSM). The results of these enhanced algorithms are robust for generating reliable DEMs and separation of ground and non-ground points in slant urban scenes as evidenced by the results of skewness and kurtosis. Offering the possibility of monitoring urban growth over time with higher accuracy and more reliable information, this work could contribute in drawing the future directions of 3D urban growth for a smarter urban growth in the Smart Cities paradigm.

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“…In the last two decades, single wavelength single pulse linear mode LiDARs with single or multipulse ability have been the dominating technology. However, recently several innovative LiDAR sensors have appeared, enabling new applications in the Earth sciences (Lohani & Ghosh, ).…”

Section: Lidar Backgroundmentioning

confidence: 99%

Estimation of forest biomass from light detection and ranging data by using machine learning

2019

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The use of data driven predictive systems is becoming widespread as innovations in machine learning techniques have allowed the training of increasingly sophisticated models via the available data. The light detection and ranging (LiDAR) remote sensing technique is being increasingly applied to obtain informative terrain maps, due to its ability to collect large amounts of data with satisfactory accuracy. This paper focuses on the application of machine‐learning‐based predictive systems for the extraction of biomass information from LiDAR data. Biomass information has inmense ecological and economical value. We demonstrate the estimation of the Pinus radiata biomass in the Arratia‐Nervión region (Spain). Biomass estimation is considered a regression problem in which the ground truth for some specific sample sites is available. The promising results obtained in this study indicate that LiDAR data can be used to carry out detailed biomass mappings by the extrapolation of the models trained in this study.

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Airborne LiDAR Technology: A Review of Data Collection and Processing Systems

Cited by 68 publications

References 59 publications

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

Estimation of forest biomass from light detection and ranging data by using machine learning

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