scholarly journals Estimating Single Tree Stem Volume of Pinus sylvestris Using Airborne Laser Scanner and Multispectral Line Scanner Data

2011 ◽  
Vol 3 (5) ◽  
pp. 929-944 ◽  
Author(s):  
Christoph Straub ◽  
Barbara Koch
Keyword(s):  
Pinus Sylvestris ◽  
Laser Scanner ◽  
Stem Volume ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Single Tree ◽  
Airborne Laser ◽  
Line Scanner ◽  
Tree Stem

Using Airborne Laser Scanner Data and CIR Orthophotos to Estimate the Stem Volume of Forest Stands

2009 ◽  
Vol 2009 (3) ◽  
pp. 277-287 ◽  
Author(s):  
Christoph Straub ◽  
Matthias Dees ◽  
Holger Weinacker ◽  
Barbara Koch
Keyword(s):  
Laser Scanner ◽  
Stem Volume ◽  
Scanner Data ◽  
Forest Stands ◽  
Airborne Laser Scanner ◽  
Airborne Laser

Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data

2006 ◽  
Vol 36 (2) ◽  
pp. 426-436 ◽  
Author(s):  
M Maltamo ◽  
J Malinen ◽  
P Packalén ◽  
A Suvanto ◽  
J Kangas
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Airborne Laser Scanning ◽  
Data Sources ◽  
Aerial Images ◽  
Stem Volume ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Other Information

In forest management planning and forestry decision-making there is a continuous need for higher quality information on forest resources. The aim of this study was to improve the quality of forest resource information acquired by airborne laser scanning by combining it with aerial images and current stand-register data. A k-MSN (most similar neighbor) application was constructed for the prediction of the plot and stand volumes of standing trees. The application constructed used various data sources, including laser scanner data, aerial digital photographs, class variables describing a stand, and updated old stand volumes. The ability of these data sources to predict stem volume was tested together and separately. In the airborne laser scanner data based k-MSN application, characteristics of canopy quantiles were used as independent variables. The results show that with respect to individual plot and stand volume estimation approaches, the laser-based technique is a superior one. The results were improved further when other information sources were used together with the laser scanner data. Using a combination of laser scanner data, aerial images, and class variables (on the grounds of the current forest database) improved the root mean square error (RMSE) of the estimated plot volume by 15% (from 16% to 13%) as compared to using laser scanner data on their own. When the results were averaged at the stand level, the accuracy improved considerably, but the use of other information sources together with airborne laser scanner data did not further improve the results as it did at the plot level. The RMSE of stand volume was about 6% in all data combinations where airborne laser scanning information was used. One conclusion is that making use of additional available data sources together with laser material improves the reliability of plot volume estimates. As these additional data typically mean no extra material costs (since they are available in any case), making combined use of these data and laser scanner data improves the cost efficiency of a forest inventory.

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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