Automatic mapping of river canyons using a digital elevation model and vector river data
- PDF / 6,336,776 Bytes
- 15 Pages / 595.276 x 790.866 pts Page_size
- 80 Downloads / 243 Views
METHODOLOGY ARTICLE
Automatic mapping of river canyons using a digital elevation model and vector river data Shi-Yu Xu 1 & An-Bo Li 1,2,3
&
Tian-Tian Dong 1 & Xian-Li Xie 4
Received: 15 May 2020 / Accepted: 29 October 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract River canyons mapping plays an important role in water conservation project construction, tourism resource development, and analysis of fluvial processes. However, the extraction of river canyons via manual interpretation or semi-automatic methods is inefficient and expensive, especially at large spatial scales. Therefore, the objective of this study is to propose a novel method for automatic extraction of river canyons. The method mainly involves (1) extracting the indegree of river segments and generating river buffers based on the indegree, (2) generating topographic profiles at the two riversides of each river segment based on a digital elevation model, (3) extracting peaks from the topographic curves with the assistance of depth curves, (4) matching the peaks from different sides of each river segment based on a distance-priority strategy and then generating peak pairs based on the results, and (5) extracting the geographic range and attributes of river canyons and mapping them into a layer. Results of cases in the Three Gorges and Yarlung Zangbo areas in China illustrate effectiveness and accuracy for the extraction of river canyons. In this case, the false alarm rate and the miss alarm rate of this approach are both no higher than 17%. Keywords River canyon . Geomorphological mapping . DEM . Three gorges . Yarlung Zangbo
Introduction Geomorphological features is valuable in many research areas, such as analyses of surface processes, disaster prevention, natural resources preservation, and project location (Bishop et al. 2012; Kooijman et al. 2018; Owen and Wong 2013; Amiri et al. 2018). Nevertheless, traditional methods of geomorphological mapping, including field investigations and
* An-Bo Li [email protected] 1
Key Laboratory of Virtual Geographic Environment, (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
2
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
4
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
manual interpretation, are inefficient and expensive (Remondo and Oguchi 2009). Therefore, more and more geoscientists have concentrated their attention on automatic/semiautomatic geomorphological mapping using high-resolution remote sensing images or digital elevation models (DEMs). Currently, automatic ways of geomorphological mapping can be mainly classified into two categories: pixel-based methods (Fei et al. 2011) and object-based methods (Embabi and Moawad 2014). The former methods ma
Data Loading...
