Next Article in Journal
Monitoring Land Surface Displacement over Xuzhou (China) in 2015–2018 through PCA-Based Correction Applied to SAR Interferometry
Next Article in Special Issue
Multiplatform-SfM and TLS Data Fusion for Monitoring Agricultural Terraces in Complex Topographic and Landcover Conditions
Previous Article in Journal
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

MNR Key Laboratory of Metallogeny and Mineral Resource Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
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;
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop