Calibration of LiDAR histogram databases using a non-linear mathematical model

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Date

2011

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Volume Title

Publisher

University of Dar es Salaam

Abstract

The airborne laser scanning technology (also known as Light Detection and Ranging- LiDAR) in Finland has been proved to be the efficient remote sensing technique used for forest data collection. The data obtained from this technology is used for pre­ diction of forest, stand characteristics for different forest inventories. Using airborne Laser scanning for the new inventory is very expensive. However as different forests have been laser scanned there is a need of utilizing such previous databases in new in­ ventory areas. Since the new site and the database sites have been laser scanned with different scanning instruments with different flying altitudes, there is a need of cali­ brating the databases to match with the new site LiDAR histograms. Therefore, the aim of this study is to the calibrate the LiDAR histogram databases by a non-linear mathematical model using the mean values of the LiDAR histograms. The accuracy of the calibrated LiDAR data is verified by predicting the forest stand characteristics using sparse Bayesian regression. The results obtained show that it is possible to calibrated histogram databases and the calibrated LiDAR histograms can be used in new inventory areas for the forest stand parameters estimation. It is observed that when the small number of calibration set from the new area (approximately 50 plots) is combined with the calibrated plots from the database, the estimation accuracy is almost equal to that when using the whole new area plots. This process is cost effective since instead of scanning the whole new inventory area, only ran- domly selected plots (approximately 50) can be scanned and be complemented with calibrated database plots for prediction of the forest stand characteristics. In some forests the geographical location does not support the aeroplanes to scan the forests, hence using calibrated databases can help to predict the forest characteristics of such areas.

Description

Dissertation

Keywords

Airborne Laser Scanning Technology, Mathematical modelling

Citation