Active landslides have three major effects on landscapes: (1) land cover change, (2) topographical change, and (3) above ground biomass change. Data derived from multi-temporal Light Detection and Ranging technology (LiDAR) are used in combination with multi-temporal orthophotos to quantify these changes between 2006 and 2012, caused by an active deep-seated landslide near the village of Doren in Austria. Land-cover is classified by applying membership-based classification and contextual improvements based on the synergy of orthophotos and LiDAR-based elevation data. Topographical change is calculated by differencing of LiDAR derived digital terrain models. The above ground biomass is quantified by applying a local-maximum algorithm for tree top detection, in combination with allometric equations. The land cover classification accuracies were improved from 65% (using only LiDAR) and 76% (using only orthophotos) to 90% (using data synergy) for 2006. A similar increase from respectively 64% and 75% to 91% was established for 2012. The increased accuracies demonstrate the effectiveness of using data synergy of LiDAR and orthophotos using object-based image analysis to quantify landscape changes, caused by an active landslide. The method has great potential to be transferred to larger areas for use in landscape change analyses.
ABSTRACT:Active landslides have three major effects on a landscape: 1. land cover change, 2. topographical change, and 3. above ground biomass change. Data derived from multi-temporal Light Detection and Ranging technology (LiDAR) is used in combination with multi-temporal orthophotos to quantify changes between 2006 and 2012, caused by a landslide near Doren in Austria. Data synergy is used to optimize accuracies of land cover change, and to improve results of topographical change analysis and aboveground biomass estimations. Topographical change is calculated using differencing of digital terrain models. The above ground biomass is quantified by applying a local-maximum algorithm for tree top detection, in combination with allometric equations. The land cover change classification accuracies were improved from 65% (using only LiDAR) and 76% (using only orthophotos) to 90% (using synergy) for 2006. A similar increase from respectively 64% and 75% to 91% was established for 2012. The results of the improved land cover classifications were used to optimize the topographical and above ground biomass change calculations. Fine-scale improvements of the classifications included forest edges and shadows, small open spots in the vegetation, and confusion between land cover classes. The enhanced accuracies of the land cover change analysis demonstrate the effectiveness and advantages of using synergy of LiDAR and orthophotos using OBIA. The method has great potential to be transferred to larger areas for use in monitoring, although data size calls for workflows to operate on cloud-based infrastructures that provide sufficient computational power.
Land-cover change could considerably lower landslide triggering rainfall thresholds allowing precipitation events with shorter recurrence intervals to initiate shallow landslides. This research focusses on developing an automated, robust and upscalable workflow to quantitatively assess the effect land-cover change has on initiating rainfall induced shallow landslides in the Laternser Valley. Land-cover is classified using four sets of high resolution orthophotos (198x, 2001, 2006, 2009; 0.25 m spatial resolution) by applying an object-based approach with eCognition software. The correlation between land-cover change and landslide occurrence was assessed by analyzing land-cover change trends in the vicinity (< 25 meters) of mapped shallow landslides. The obtained classification accuracy ranges from 76% for 198x to 88% for 2009. The relative area undergoing land-cover change is 18% in the whole Laternser valley and 34% in the vicinity of landslides. Overall land-cover change trends indicate a shift from grassland to forest in the whole Laternser valley. However, in the vicinity of landslides the opposite is observed, namely a shift from forest to grassland and grassland to bare soil. Even though a general vegetation reduction is detected in the vicinity of landslides no correlation between LCC and landslide occurrence could be established yet.
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