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RSS FeedsRemote Sensing, Vol. 11, Pages 1493: Quantitative Analysis of Anthropogenic Morphologies Based on Multi-Temporal High-Resolution Topography (Remote Sensing)

 
 

25 june 2019 09:00:17

 
Remote Sensing, Vol. 11, Pages 1493: Quantitative Analysis of Anthropogenic Morphologies Based on Multi-Temporal High-Resolution Topography (Remote Sensing)
 


Human activities have reshaped the geomorphology of landscapes and created vast anthropogenic geomorphic features, which have distinct characteristics compared with landforms produced by natural processes. High-resolution topography from LiDAR has opened avenues for the analysis of anthropogenic geomorphic signatures, providing new opportunities for a better understanding of Earth surface processes and landforms. However, quantitative identification and monitoring of such anthropogenic signature still represent a challenge for the Earth science community. The purpose of this contribution is to explore a method for monitoring geomorphic changes and identifying the driving forces of such changes. The study was carried out on the Eibar watershed in Spain. The proposed method is able to quantitatively detect anthropogenic geomorphic changes based on multi-temporal LiDAR topography, and it is based on a combination of two techniques: the DEM of Difference (DoD) and the Slope Local Length of Auto-correlation (SLLAC). First, we tested the capability of the SLLAC and derived parameters to distinguish different types of anthropogenic geomorphologies in 5 study case at a small scale. Second, we calculated the DoD to quantify the geomorphic changes between 2008 and 2016. Based on the proposed approach, we classified the whole basin into three categories of geomorphic changes (natural, urban or mosaic areas). The urban area had the most clustered and largest geomorphic changes, followed by the mosaic area and the natural area. This research might help to identify and monitoring anthropogenic geomorphic changes over large areas, to schedule sustainable environmental planning, and to mitigate the consequences of anthropogenic alteration.


 
59 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 1494: Monitoring Land Surface Displacement over Xuzhou (China) in 2015-2018 through PCA-Based Correction Applied to SAR Interferometry (Remote Sensing)
Remote Sensing, Vol. 11, Pages 1489: Correction: Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E. Evaluating the Performance of a Random Forest Kernel for Land Cover Classification. Remote Sensing 2019, 11, 575 (Remote Sensing)
 
 
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