scholarly journals Spatially consistent imputations of forest data under a semivariogram model

2016 ◽  
Vol 46 (9) ◽  
pp. 1145-1156 ◽  
Author(s):  
Victor Felix Strîmbu ◽  
Liviu Teodor Ene ◽  
Erik Næsset
Keyword(s):  
Objective Function ◽  
Laser Scanner ◽  
Data Types ◽  
Semivariogram Model ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Target Values ◽  
The Difference ◽  
Southern Norway

This study proposes a method to perform spatially consistent imputations of forest data to serve simulation studies where spatial autocorrelation is expected to have an effect (e.g., sampling simulations and forest scenario analysis). Starting with a nearest neighbour imputation, an optimization process brings the spatially comprehensive data to a desired state, controlled by a target semivariogram and a target histogram. The target values for both parameters may be approximated using empirical data and are combined in the objective function used by the optimization algorithm. Here, we demonstrate a case study using wall-to-wall airborne laser scanner data, satellite data, and field observations for an 852 ha forest area in southern Norway. Different combinations of data types and target parameters were tested, and the target values were reached in most cases. In some cases, with a more restrictive objective function, the semivariogram did not completely converge to its target values, yet still had a convergence of at least 93%, expressed by the difference reduction between initial and target values. The results recommend the proposed method as a practical means to generate spatially explicit forest data when a particular distribution and well-defined spatial dependence are required.

scholarly journals UNCERTAINTY IN LANDSLIDES VOLUME ESTIMATION USING DEMs GENERATED BY AIRBORNE LASER SCANNER AND PHOTOGRAMMETRY DATA

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 397-404 ◽  
Author(s):  
C. Parente ◽  
M. Pepe
Keyword(s):  
Remote Sensing ◽  
Laser Scanner ◽  
Volume Estimation ◽  
Engineering Project ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Digital Elevation ◽  
Volumetric Accuracy ◽  
Elevation Model

<p><strong>Abstract.</strong> The purpose of this paper is to identify an approach able to estimate the uncertainty related to the measure of terrain volume generated after a landslide. The survey of the area interested of landslide can be performed by Photogrammetry &amp;amp; Remote Sensing (PaRS) techniques. Indeed, depending on the method and technology used for the survey, a different level of accuracy is achievable. The estimate of the quantity of the terrain implicated in the landslide influences the type of geological and geotechnical approach, the civil engineering project on the area and of consequence, the costs to sustain for a community. According to the experiences and recommendations reported in the ASPRS guidelines, an example of the approach used to estimate volumetric accuracy concerning one of the most important landslide in Europe is shown in this paper. In this case study, the dataset is constituted by a Digital Elevation Model (DEM) obtained by photogrammetric (stereo-images) method (pre-landslide) and another by Airborne Laser Scanner (after-landslide). By the comparisons of Airborne Laser Scanner (ALS) and photogrammetry DEMs obtained from successive surveys, it has been possible to produce maps of differences and of consequence, to calculate the volume of the terrain (eroded or accumulated). In order to calculate the uncertainty of volume, a procedure that takes in account even the different accuracy achievable in the vegetation area is explained and discussed.</p>

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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