KEYWORDS: LIDAR, Radar, Data processing, Calibration, Reflectivity, Data modeling, Signal attenuation, Sensors, Atmospheric laser remote sensing, Data acquisition
LiDAR provides intensity data that reflect the material characteristics of objects. However, intensity values need to be corrected before they can be reliably used for applications because of the error during data acquisition. This study proposed an automatic and overlap based method for intensity correction. Firstly, a radar equation based method was employed for removal of main errors. Then, nearest neighbor algorithm was used to find out homologous points of overlap regions and assumption was made that homologous points should have same intensity. Finally, an improved model was utilized to eliminate overlap discrepancies. This method can be considered as a potential aid to enhance the accuracy of LiDAR intensity data and improve the automation of data process.
Wetlands have received intensive interdisciplinary attention as a unique ecosystem and valuable resources. As a new technology, the airborne LiDAR system has been applied in wetland research these years. However, most of the studies used only one or two LiDAR observations to extract either terrain or vegetation in wetlands. This research aims at integrating LiDAR’s multiple attributes (DSM, DTM, off-ground features, Slop map, multiple pulse returns, and normalized intensity) to improve mapping and classification of wetlands based on a multi-level object-oriented classification method. By using this method, we are able to classify the Yellow River Delta wetland into eight classes with overall classification accuracy of 92.5%
As the cost of LiDAR equipment is high and different projects prefer different goals, the flying scheme needs to be strict
planned to save cost and energy. In this study, LiDAR's ability at different flying height was tested. Two trials with
different flying height are compared. Analysis was conducted by considering the relative accuracy, intensity and
penetration ability to find out the difference of two trials. The result shows the relative accuracy between these two trials
is 0.37 m. True relative accuracy is achieved when removing canopy as large interpolation errors often exist at places
with great slope changes like the canopy. Intensity values decrease greatly with the increase of flying height due to the
longer travel distance and more energy loss in atmosphere. Statistical results showed that in each trial high intensity is
apt to be from low canopy and only return. LiDAR vegetation penetration ability decreased greatly at low vegetation due
to the enlarged footprint and weakened energy, but it doesn't change on large vegetation.
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