Abstract
Landform recognition is one of the most significant aspects of geomorphology research, which is the essential tool for landform classification and understanding geomorphological processes. Watershed object-based landform recognition is a new spot in the field of landform recognition. However, in the relevant studies, the quantitative description of the watershed generally focused on the overall terrain features of the watershed, which ignored the spatial structure and topological relationship, and internal mechanism of the watershed. For the first time, we proposed an effective landform recognition method from the perspective of the watershed spatial structure, which is separated from the previous studies that invariably used terrain indices or texture derivatives. The slope spectrum method was used herein to solve the uncertainty issue of the determination on the watershed area. Complex network and P–N terrain, which are two effective methodologies to describe the spatial structure and topological relationship of the watershed, were adopted to simulate the spatial structure of the watershed. Then, 13 quantitative indices were, respectively, derived from two kinds of watershed spatial structures. With an advanced machine learning algorithm (LightGBM), experiment results showed that the proposed method showed good comprehensive performances. The overall accuracy achieved 91.67% and the Kappa coefficient achieved 0.90. By comparing with the landform recognition using terrain indices or texture derivatives, it showed better performance and robustness. It was noted that, in terms of loess ridge and loess hill, the proposed method can achieve higher accuracy, which may indicate that the proposed method is more effective than the previous methods in alleviating the confusion of the landforms whose morphologies are complex and similar. In addition, the LightGBM is more suitable for the proposed method, since the comprehensive manifestation of their combination is better than other machine learning methods by contrast. Overall, the proposed method is out of the previous landform recognition method and provided new insights for the field of landform recognition; experiments show the new method is an effective and valuable landform recognition method with great potential as well as being more suitable for watershed object-based landform recognition.
Highlights
A segmentation area of a watershed that is too small may mean that the watershed cannot fully contain the terrain features of the sample area and, affect the recognition accuracy
Via the LightGBM, we found that the simulation and quantification of the watershed spatial structure can effectively recognize the geomorphology with high accuracy
Except for not being inferior to the inherent landform recognition methods based on the terrain factor, the proposed method is more effective than the previous methods in some landforms where the spatial structure or morphology is complex
Summary
By simulating the research objects as the network spatial structure, it can effectively analyze the topological relationship, internal composition, and internal spatial structure [44] Since it is very suitable for complex system simulations and the analysis of different scientific disciplines, its growth of application increased rapidly and it has been widely employed in numerous fields such as hydrology [45], earthquakes [46], transport [47,48,49,50,51], agriculture [52], economics [53], the internet [54,55], etc. A series of indexes that quantitatively describe their topological relationship, spatial structure, internal composition, internal mechanism for the P–N terrain, and the watershed weighted complex network was proposed to construct the basic feature matrix. A series of assessment analyses and comparisons with other landform recognition methods and machine learning methods were conducted to prove its good performance
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