Object-based Integrated Landscape Change Analysis : synergy of multi-temporal LiDAR and very high resolution orthophotos
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 bet...
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| Tác giả chính: | |
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| Định dạng: | BB |
| Ngôn ngữ: | eng |
| Thông tin xuất bản: |
2020
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| Chủ đề: | |
| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/5185 |
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| Tóm tắt: | 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. |
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