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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
Open AccessArticle

Quantitative Analysis of Anthropogenic Morphologies Based on Multi-Temporal High-Resolution Topography

1
MNR Key Laboratory of Metallogeny and Mineral Resource Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
2
School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
3
Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
4
Department of Land, Environment, Agriculture and Forestry, University of Padova, Agripolis, viale dell’Università 16, 35020 Legnaro, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1493; https://doi.org/10.3390/rs11121493
Received: 3 May 2019 / Revised: 16 June 2019 / Accepted: 17 June 2019 / Published: 24 June 2019
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. View Full-Text
Keywords: Geomorphology; LiDAR DTM; Anthropogenic signatures; SLLAC; DoD Geomorphology; LiDAR DTM; Anthropogenic signatures; SLLAC; DoD
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Xiang, J.; Li, S.; Xiao, K.; Chen, J.; Sofia, G.; Tarolli, P. Quantitative Analysis of Anthropogenic Morphologies Based on Multi-Temporal High-Resolution Topography. Remote Sens. 2019, 11, 1493.

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