Debris Flow Source Identification in Tropical Dense Forest Using Airborne Laser Scanning Data and Flow-R Model

2017 ◽  
pp. 85-112 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Suzana Binti Abu Bakar
Keyword(s):  
Debris Flow ◽  
Source Identification ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Dense Forest

scholarly journals Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover

2020 ◽  
Vol 9 (4) ◽  
pp. 224
Author(s):  
Mihnea Cățeanu ◽  
Arcadie Ciubotaru
Keyword(s):  
Thin Plate ◽  
Laser Scanning ◽  
Ground Surface ◽  
Forest Vegetation ◽  
Airborne Laser Scanning ◽  
Canopy Density ◽  
Digital Terrain ◽  
Airborne Laser ◽  
Dense Forest

A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by interpolation, a process which is affected by various errors. Using LiDAR data to collect ground surface data for forestry applications is a challenging scenario because the presence of forest vegetation will hinder the ability of laser pulses to reach the ground. The density of ground observations will be therefore reduced and not homogenous (as it is affected by the variations in canopy density). Furthermore, forest areas are generally present in mountainous areas, in which case the interpolation of the ground surface is more challenging. In this paper, we present a comparative analysis of interpolation accuracy for nine algorithms, which are used for generating Digital Terrain Models from Airborne Laser Scanning (ALS) data, in mountainous terrain covered by dense forest vegetation. For most of the algorithms we find a similar performance in terms of general accuracy, with RMSE values between 0.11 and 0.28 m (when model resolution is set to 0.5 m). Five of the algorithms (Natural Neighbour, Delauney Triangulation, Multilevel B-Spline, Thin-Plate Spline and Thin-Plate Spline by TIN) have vertical errors of less than 0.20 m for over 90 percent of validation points. Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs.

A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data

Landslides ◽  
2018 ◽  
Vol 15 (9) ◽  
pp. 1833-1850 ◽  
Author(s):  
Ali Mutar Fanos ◽  
Biswajeet Pradhan ◽  
Shattri Mansor ◽  
Zainuddin Md Yusoff ◽  
Ahmad Fikri bin Abdullah
Keyword(s):  
Machine Learning ◽  
Hybrid Model ◽  
Source Identification ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Airborne Laser

scholarly journals New Advances in Automated Urban Modelling from Airborne Laser Scanning Data

2011 ◽  
Vol 5 (3) ◽  
pp. 196-208 ◽  
Author(s):  
D. F. Laefer ◽  
T. Hinks ◽  
H. Carr ◽  
L. Truong-Hong
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Urban Modelling

scholarly journals Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data

2021 ◽  
Vol 13 (4) ◽  
pp. 1917
Author(s):  
Alma Elizabeth Thuestad ◽  
Ole Risbøl ◽  
Jan Ingolf Kleppe ◽  
Stine Barlindhaug ◽  
Elin Rose Myrvoll
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Image Data ◽  
Remote Sensing Image ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Detection Rates ◽  
Airborne Laser ◽  
Pros And Cons

What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.

scholarly journals Airborne laser scanning reveals large tree trunks on forest floor

2021 ◽  
Vol 491 ◽  
pp. 119225
Author(s):  
Einari Heinaro ◽  
Topi Tanhuanpää ◽  
Tuomas Yrttimaa ◽  
Markus Holopainen ◽  
Mikko Vastaranta
Keyword(s):  
Forest Floor ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Large Tree ◽  
Airborne Laser

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.

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