Battistini, Alessandro

Catani, Filippo

Forzieri, Giovanni

Lagomarsino, Daniela

Tanteri, Luca

English

Florence Research

Geophysical Research AbstractsVol. 14, EGU2012-7375, 2012EGU General Assembly 2012© Author(s) 2012Testing the combined use of LIDAR and High-Resolution Multispectraldata for Automatic detection of Landslides in the Marecchia valley, Italy.D. Lagomarsino, L. Tanteri, G. Forzieri, A. Battistini, and F. CataniUniversity of Firenze, Department of Earth Sciences, Italy (daniela.lagomarsino@unifi.it)The purpose of this work is to test a method to automatically detect landslides using change detection techniqueson airborne sensor data.High spatial resolution multispectral and digital surface model data acquired in two different times are used toidentify landslides on a 80 km transect of the Marecchia valley, Italy.Multispectral ADS40 and Light Detection and Ranging (LiDAR) data, acquired in October 2009 and November2010, were used in this study. High spatial resolution color-infrared digital aerial image data from ADS40 sensorhas 4 spectral bands: Red, Green, Blue and NIR. The imagery data has a 12-bit radiometric depth, with 0.2 meterpixel size. Airborne laser scanning data collected by LiDAR sensors were provided in Digital Terrain Model(DTM) and Digital Surface Model (DSM) form with 1-meter spatial resolution and 0.2 meters vertical accuracy.An expert system for change detection and classification of landscapes has been developed; a pixel-orientedapproach based on multi-temporal tree classification algorithm was implemented. The tool is driven by a prioriknowledge related to the expected temporal variations of remote sensing multispectral and digital surface modeldata.Detection of land use/cover changes focuses on temporal variations of remote sensing data by discriminating:devegetation, rivegetation, urbanization, demolition, landslide and river bed transitions. In order to betteridentify the landslide class, further analyses on morphological factors was performed. For this purpose severalgeo-morphological factors, for which a change can be related to landslide occurrence, were analyzed.A database of the landslides occurred between the two acquisition was built and then statistical analyses wereperformed to correlate the presence of a landslide with the variations of selected morphological features betweenthe two acquisitions.In order to establish wether or not a parameter variation is an exclusive characteristic of a landslide some statistictests, such as t-test and frequency ratio analysis, were performed on parameter data derived from airborne sensors.Considering the results of such analysis, a landslide classification scheme was developed taking into accountincreasing degrees of membership probability.Subsequently, post-processing refinements have been used to reduce misclassification errors and mapping incon-sistencies, which conducted to a lower number of membership classes and a decrease in uncertainty accountingfor the range in probability values of neighboring pixels.

Testing the combined use of LIDAR and High-Resolution Multispectral data for automatic detection of landslides in the Marecchia valley, Italy

https://flore.unifi.it/bitstream/2158/776997/1/Lagomarsino%20et%20al%20GRA%20vol%2014%20EGU%202012%20p.%207375.pdf

Testing the combined use of LIDAR and High-Resolution Multispectral data for automatic detection of landslides in the Marecchia valley, Italy

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