Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System
A Case of Jiangxi Province, China
Bibliographic Data
| ID | 22032222 |
|---|---|
| Authors | Faming Huang (0000-0002-4428-7133, Nanchang University), Jianbo Yang (0000-0002-1368-5294, Nanchang University), Biao Zhang (0009-0000-4520-729X, Nanchang University), Yijing Li (0000-0002-9831-0298, Nanchang University, corresponding author), Jinsong Huang (0000-0002-5159-1635, ARC Centre of Excellence for Enabling Eco-Efficient Beneficiation of Minerals), Na Chen (0009-0000-7743-2117, Hubei University of Technology) |
| Year | 2020 |
| Volume | 9 |
| Issue | 9 |
| Pages | 539 |
| Publication date | 2020-09-08 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9090539 |
| OpenAlex | W3084130488 |
| Language | EN |
| Citations received | 2 |
| References cited | 55 |
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
Cartography · Cluster analysis · Correlation coefficient · Data mining · Digital elevation model · Geographic information system · Geography · Machine learning · Principal component analysis · Remote sensing · Terrain · Computer Science · Geochemistry and Geologic Mapping · Landslides and related hazards · Soil Geostatistics and Mapping · Artificial Intelligence
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| Citations per year | 0.4 |
|---|---|
| Citation span | 2021 - 2021 (1) |
| Highly cited | No |