Airborne and terrestrial lidar are rapid and accurate techniques for remote measurement of terrain elevations with many applications,
such as for digital terrain elevation measurement, volume determination, assessment of forest resources, coastal land erosion and
many more. For disaster monitoring, typical examples of airborne lidar are flood prediction and assessment, monitoring of the
growth of volcanoes and assistance in the prediction of eruption, assessment of crustal elevation changes due to earthquakes, and
monitoring of structural damage after earthquakes. Change detection is an important task in the context of disaster monitoring. The
paper will describe the capability of airborne lidar for rapid change detection in elevations, and methods of assessment of damage in
made-made structures. The idea is to combine change detection techniques with different performance based on Simple Majority
Vote. In order to detect and evaluate changes, a DEM of two epochs has been used. The analysis of changes is rather difficult to
evaluate if only one detection model is applied. In this regard, three different change detection algorithms were used to detect
changes. The used techniques include: Image Differencing, Principal Components Analysis (PCA) and post-classification with
average detection accuracies of 84.7%, 88.3% and 90.2% for post-classification, Image Differencing and PCA respectively. Simple
Majority Vote was then applied for combining votes from the three detection techniques. The proposed fusion algorithm gives an
accuracy of 96.4%. This activity demonstrates the capabilities of lidar data to detect changes, providing a valuable tool for efficient
disaster monitoring and effective management and conservation. |
