Abstract
Regional terrain complexity assessment (TCA) is an important theoretical foundation for geological feature identification, hydrological information extraction and land resources utilization. However, the previous TCA models have many disadvantages; for example, comprehensive consideration and redundancy information analysis of terrain factors is lacking, and the terrain complexity index is difficult to quantify. To overcome these drawbacks, a TCA model based on principal component analysis (PCA) and a geographic information system (GIS) is proposed. Taking Jiangxi province of China as an example, firstly, ten terrain factors are extracted using a digital elevation model (DEM) in GIS software. Secondly, PCA is used to analyze the information redundancy of these terrain factors and deal with data compression. Then, the comprehensive evaluation of the compressed terrain factors is conducted to obtain quantitative terrain complexity indexes and a terrain complexity map (TCM). Finally, the TCM produced by the PCA method is compared with those produced by the slope-only, the variation coefficient and K-means clustering models based on the topographic map drawn by the Bureau of Land and Resources of Jiangxi province. Meanwhile, the TCM is also verified by the actual three-dimensional aerial images. Results show that the correlation coefficients between the TCMs produced by the PCA, slope-only, variable coefficient and K-means clustering models and the local topographic map are 0.894, 0.763, 0.816 and 0.788, respectively. It is concluded that the TCM of the PCA method matches well with the actual field terrain features, and the PCA method can reflect the regional terrain complexity characteristics more comprehensively and accurately when compared to the other three methods.
Highlights
Regional terrain complexity assessment (TCA) is a meaningful foundation for identifying regional geological features, estimating groundwater level and river information and effectively utilizing the land resources in order to promote social and economic development [1,2,3,4]
Limitations and Research Prospects the principal component analysis (PCA) method used in this study shows better TCA performance than the other three types of methods, there are still some limitations in the processes of PCA modeling
There are some disadvantages in the previous TCA studies, such as few terrain factors used for TCA modeling, the lack of information compression between terrain factors and the qualitative TCA modeling processes
Summary
Regional terrain complexity assessment (TCA) is a meaningful foundation for identifying regional geological features, estimating groundwater level and river information and effectively utilizing the land resources in order to promote social and economic development [1,2,3,4]. In response to this issue, some scholars have proposed a lot of TCA-related models in recent years Some of these models describe the regional terrain complexity only using a single terrain factor; for example, Tianwen, et al [12] and Huaxing, et al [13] selected slope angle as the terrain factor of TCA; Long et al [14] used the fractal dimension value to describe the terrain complexity; Ashenfelter and Eberhard [15] adopted the angle between the space planes to describe the terrain complexity. Chambers, et al [16] pointed out that the regional differentiation laws of terrain complexity can be reflected by the synthesis calculations of terrain factors; Huaxing, Liu and Tang [13] explored the composite terrain factors based on a digital elevation model (DEM) to reflect the change characteristics of topographic relief and folds respectively from the holistic and local perspectives; Rawat and Joshi [17] studied the regional terrain characteristics in the land suitability classification; Thompson, et al [18] analyzed the effects of DEM with different spatial resolutions on TCA and land landscape simulation
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