Landform classification based on landform geospatial structure – a case study on Loess Plateau of China

Lin, Siwei; Xie, Jin; Deng, Jiayin; Meng, Qi; Chen, Nan

doi:10.1080/17538947.2022.2088874

Cited by 10 publications

(3 citation statements)

References 86 publications

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“…DBP is formed by extending the longitudinal profile of the drainage boundary to the plane with the watershed outlet as the focal point. It serves as the highest edge line and the basic skeleton of the basin (Lin et al, 2022c), implying the basic landform evolution process (Condon et al, 2020) and playing a crucial role in driving variations in watershed terrain (Tang et al, 2017). As an erosion‐free zone and overall peripheral structure of the entire basin, DBP is stable and reflects the original geomorphic form of the basin before erosion (Grau Galofre et al, 2018; Liu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Using music and music‐based analysis to model and to classify terrain in geomorphology

Lin,

Yu,

Chen

et al. 2024

Earth Surf Processes Landf

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Music has long served as a bridge between science and nature, allowing for the artistic expression of natural and human‐made phenomena. While music has been used in geomorphology as an engaging teaching strategy, its application in specific scientific inquiries within geomorphology remains relatively unexplored. Drawing on the morphological similarities between music and terrain relief, this work introduced a novel music‐based method for modelling and expressing terrain relief based on the drainage basin profile (DBP). It converts terrain relief into pitches and time values according to mapping rules and then describes terrain relief in an audible form. Based on 5 sample areas and 360 drainage basins on the Loess Plateau, we developed the application of the proposed method on four core geomorphic tasks, including landform interpretation, analysis, recognition and classification. Experimental results show that (1) terrain music can interpret the terrain relief and landform evolution processes through its musical structure and rhythmic variations; (2) music derivatives are related to different terrain features, such as terrain relief, terrain variation intensity, landform evolution degree and terrain complexity, and have a well‐functioning relationship with a series of conventional terrain derivatives; (3) leveraging machine learning techniques, the terrain music method is effective for landform recognition, achieving an overall accuracy of 88.85% and a mean accuracy of 88.85%; and (4) via a case study in Northern Shaanxi, music modelling successfully divided it into 12 distinct landform regions and 8 landform types. Different landform regions exhibit clear regional boundaries and gradual transition zones, while specific landform regions share prominent terrain, spatial clustering and landform processes. Our delineation provides reasonable and effective landform differentiation but captures additional bed‐rock mountain features compared with a traditional method. This study highlights that music is not only an artistic expression but also a valuable research paradigm for a wide range of geomorphic tasks, offering fresh perspectives and enhancing our understanding of loess landforms. The results show a promising effort to integrate music theory into practical geomorphic tasks, demonstrating the potential of using music as a medium for conveying and analysing spatial information in geomorphology.

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Section: Introductionmentioning

confidence: 99%

Using music and music‐based analysis to model and to classify terrain in geomorphology

Lin,

Yu,

Chen

et al. 2024

Earth Surf Processes Landf

Self Cite

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show abstract

“…Therefore, diversified feature selection methods have emerged. Lin et al (2022) measured the importance of terrain features by XGBoost. Zhao et al (2017) constructed a topographic and texture derivatives dataset to quantify terrain features and used the random forest (RF) method to automatically select terrain factors for landform classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Intelligent classification and analysis of regional landforms based on automatic feature selection

Xu,

Zhu,

et al. 2023

Earth Surf Processes Landf

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Terrain features are an important basis for realizing high‐precision landform classification, and feature selection is a key step of machine learning and knowledge mining. However, the feature selection process is facing challenges due to the multidimensionality and correlation of multisource terrain feature datasets and factors. Traditional feature selection methods lack enough consideration for the interpretability and transparency of feature factors, but the transparent decision‐making process of feature selection precisely determines the modelling effect and the reliability of model application results. Current research urgently needs to work out the black holes of visual representation during feature selection. In the process of intelligent landform classification, multiple and effective terrain feature is an essential factor in enhancing the performance and generalisation ability of the network. Therefore, we initially selected 40 terrain feature parameters, including basic terrain factors and digital elevation model (DEM) terrain textures, to calculate the feature contribution degree and sort the parameter importance based on the SHapley Additive exPlanations (SHAP) method, then reserved 10%, 20%, 30%, 40% and 50% terrain features in turn for constructing the landform classification dataset. Because the traditional UNet network cannot completely capture abrupt landform features, the convolutional block attention module (CBAM) was integrated into the UNet, and a deep learning model was established for the fine‐grained classification of regional landforms. Considering the calculation rate, even though there are large regional spatial differences and genetic mechanisms, it is appropriate to retain 20% of terrain features for intelligent landform classification. The classification accuracy of typical regions, namely, the Hanzhong Basin, North China Plain, Yunnan–Guizhou Plateau and Tibetan Plateau, reached 98.76%, 97.36%, 96.3% and 92.78%, respectively, and what's more, some accuracies went up to a higher level under other feature combinations. Meanwhile, given the different feature combinations corresponding to regional landform types, the combinative stability and spatial orderliness characteristics were explored to explain the accuracy variation trend.

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“…The surface of the loess hilly regions is full of gullies and complex in structure. The macro wind speed, wind direction, and other meteorological data are constantly evolving under the influence of the gullies on the surface of the loess hilly regions, which makes it easy to produce various types of turbulence and high-speed cyclones [1][2][3]. In most cases, the wind speed, wind direction, and pulsation characteristics are significantly different from the macro meteorological data in strong convective micrometeorology.…”

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confidence: 99%

Study on Flow Field Simulation at Transmission Towers in Loess Hilly Regions Based on Circular Boundary Constraints

Liu,

Cao,

Zhao

et al. 2023

Energy Engineering

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When using high-voltage transmission lines for energy transmission in loess hilly regions, local extreme wind fields such as turbulence and high-speed cyclones occur from time to time, which can cause many kinds of mechanical and electrical failures, seriously affecting the reliable and stable energy transmission of the power grid. The existing research focuses on the wind field simulation of ideal micro-terrain and actual terrain with mostly single microterrain characteristics. Model boundary constraints and the influence of constrained boundaries are the main problems that need to be solved to accurately model and simulate complex flow fields. In this paper, a flow field simulation method based on circular boundary constraints is carried out. During the study, the influence of the model boundary and the selection conditions of the modeling range are systematically analyzed. It is more suitable to make sure that the air domain is 4 times higher than the height of the hill undulations, in addition to ensuring that there should be a minimum of 400 m between the study region and the boundary. Then, an actual terrain model of a power grid line in Shanxi is established, through the method proposed in this paper, the wind speed at the location of the transmission tower line under different wind directions is analyzed, and it is found that when the incidence direction is 45 degrees north by east the wind speed is the highest. The findings demonstrate that the circular boundary model has the advantage of more easily adjusting the wind incidence direction, in addition to theoretically reducing the errors caused by traditional models in boundary processing. It can accurately obtain the distribution characteristics of the flow field affected by the terrain, and quickly screen out the extreme working conditions that are most harmful to the transmission lines in the actual transmission line for energy transmission in complex loess hilly regions.

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Landform classification based on landform geospatial structure – a case study on Loess Plateau of China

Cited by 10 publications

References 86 publications

Using music and music‐based analysis to model and to classify terrain in geomorphology

Using music and music‐based analysis to model and to classify terrain in geomorphology

Intelligent classification and analysis of regional landforms based on automatic feature selection

Study on Flow Field Simulation at Transmission Towers in Loess Hilly Regions Based on Circular Boundary Constraints

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