Potential applications of airborne LiDAR for disaster monitoring include 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 in buildings is an important task in the context of disaster monitoring, especially after earthquakes. 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 approach is to combine change detection techniques such as image differencing, principal components analysis (PCA), minimum noise fraction (MNF) and post-classification comparison based on support vector machines (SVM), each of which will perform differently, based on simple majority vote. In order to detect and evaluate changes in buildings, LiDAR-derived DEMsfrom two epochs were used, showing changes in urban buildings due to construction and demolition. To meet the objectives, the detected changes were compared against reference data that was generated manually. The comparison is based on three criteria: overall accuracy; commission and omission errors; and completeness and correctness.The results showed that the average detection accuracies were: 84.7%, 88.3%, 90.2% and 91.6% for post-classification, image differencing, PCA and MNF respectively. On the other hand, the commission and omission errors, and completeness and the correctness of the results improved when the techniques were combined,compared to the best single change detection method. The proposed combination of techniques gives a high accuracy of 97.2% for detection of changes in buildings, which demonstrates the capabilities of LiDAR data to detect changes, thus providing a valuable tool for efficient disaster monitoring and effective management and conservation. |
