Abstract
This paper presents a methodology for levee damage detection based on Unmanned Aerial System (UAS) data. In this experiment, the data were acquired from the UAS platform, which was equipped with a laser scanner and a digital RGB (Red, Green, Blue) camera. Airborne laser scanning (ALS) point clouds were used for the generation of the Digital Terrain Model (DTM), and images were used to produce the RGB orthophoto. The main aim of the paper was to present a methodology based on ALS and vegetation index from RGB orthophoto which helps in finding potential places of levee failure. Both types of multi-temporal data collected from the UAS platform are applied separately: elevation and optical data. Two DTM models from different time periods were compared: the first one was generated from the ALS point cloud and the second DTM was delivered from the UAS Laser Scanning (ULS) data. Archival and new orthophotos were converted to Green-Red Vegetation Index (GRVI) raster datasets. From the GRVI raster, change detection for unvegetation ground areas was analysed using a dynamically indicated threshold. The result of this approach is the localisation of places, for which the change in height correlates with the appearance of unvegetation ground. This simple, automatic method provides a tool for specialist monitoring of levees, the critical objects protecting against floods.
Highlights
Hazard monitoring seems to be increasingly more important
RGB (Red, Green, Blue) orthophotos are the primary data that are generated from Unmanned Aerial System (UAS) imagery, UAS data can deliver rasters of vegetation indices such as the Normalised Difference Vegetation Index (NDVI) or Green-Red Vegetation Index (GRVI)
The first part consists in detecting elevation changes between the Digital Terrain Model (DTM) that were generated from the laser scanning data acquired in different time periods
Summary
Hazard monitoring seems to be increasingly more important. Satellite images that are systematically acquired are commonly used for monitoring the phenomena that may devastate large areas, for example, tornadoes, earthquakes, big forest fires and flood hazards [1,2,3]. Levee monitoring can be successfully conducted by means of remote sensing techniques, and in most cases, aerial photogrammetric data is used—especially high-resolution aerial imagery [7,8] and LiDAR (Light Detection and Ranging) point clouds [9]. LiDAR data can deliver reliable information about bare ground; the resolution of these kinds of data (expressed in point cloud density) cannot be compared to aerial images that can be collected with centimetre resolution The comparison of both techniques has been the topic of scientific publications for years [10] and at the beginning it concerned high-altitude manned platforms. The increased temporal and spatial resolution of UAS collections should provide more effective monitoring of levee conditions and identification of potential flood hazards This is especially true since using other moderate resolution airborne data to evaluate changes in small areas are often technologically, resource, or environmentally limited
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