Automatic recognition method of primary path for an anastomosing river based on its typical features,Transactions in GIS

当前位置： X-MOL 学术 › Trans. GIS › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Automatic recognition method of primary path for an anastomosing river based on its typical features
Transactions in GIS ( IF 2.1 ) Pub Date : 2021-06-09 , DOI: 10.1111/tgis.12742
Wei Wu ₁ , Chengming Li _{1,

2,

3} , Zheng Wu _{2,

3} , Yong Yin _{1,

3}

Affiliation

Primary path recognition is a prerequisite for effective river network generalization. In a river network with a large spatial region, anastomosing rivers refer to those with intertwined river channels. Dense reaches with similar semantic information usually form complex spatial structures, which creates special challenges for accurately delineating the primary path. The recognition of the primary path for anastomosing rivers has not received much attention in the literature. Traditional methods rely on empirical thresholds to determine the basin including the primary path, and do not consider the overall spatial structure features of rivers, which lower the flexibility and accuracy of the primary path recognition. Therefore, this article proposes an automatic recognition method for the primary path of an anastomosing river based on its typical features. First, a directed topology of the river network is constructed to detect the key nodes in forming the anastomosing rivers in the river network, which are the nodes with multiple outflow reaches and are defined as redundant nodes. Second, by considering the flow direction, the effective topological boundary of each redundant node is identified to determine the basin of the anastomosing rivers automatically. Finally, a hierarchical tree is constructed to describe the spatial relationships among redundant nodes and based on the hierarchical tree and other typical features of anastomosing rivers, the optimal connection path between each redundant node and downstream node is calculated to identify the primary path. 1:10,000 topographic river data of the experimental region in Hubei Province, China, were used for validation. The experiment reveals that compared with the state-of-the-art Buttenfield method, the primary path identified by the proposed method is more similar to that obtained manually. The inclination angle difference between the result obtained using the Buttenfield method and the artificial identification result is four times larger than that obtained by the proposed method; moreover, the shape of the primary path detected by the proposed method is more natural and smoother.

更新日期：2021-07-09

点击分享查看原文

点击收藏

阅读更多本刊最新论文