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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (2) : 231-237     DOI: 10.6046/gtzyyg.2018.02.31
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Topographic variable analysis and lithologic classification based on DEM
Ting WANG(), Jun PAN(), Lijun JIANG, Lixin XING, Yifan YU, Pengju WANG
College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China
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Abstract  

Lithologic identification and classification can provide important basic information for regional geological survey and mineral resource exploration. Topographic variables constitute the quantitative parameters of digital expression for topography, and are very important in improving the accuracy. Based on the classification validity and correlation of 10 topographic variables such as elevation, slope, profile curvature, surface roughness, and surface cutting depth in the known lithologic area, the authors screened the topographic variables and used the variables under the best scale for the classification of lithology. The result shows that the combination of elevation, profile curvature, surface cutting depth, surface roughness and plane curvature is very useful and, in terms of the capability of identification, each variable has the corresponding lithology. The adding of the best terrain variables combination to fully express terrain characteristics in identifying each type of lithology is helpful to improving the recognition and classification of lithology.
Keywords geological lithology classification      topography      topographic variables      mean change point analysis      unsupervised classification     
:  TP75  
Corresponding Authors: Jun PAN     E-mail: 1362258925@qq.com;Panj@jlu.edu.cn
Issue Date: 30 May 2018
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Ting WANG
Jun PAN
Lijun JIANG
Lixin XING
Yifan YU
Pengju WANG
Cite this article:   
Ting WANG,Jun PAN,Lijun JIANG, et al. Topographic variable analysis and lithologic classification based on DEM[J]. Remote Sensing for Land & Resources, 2018, 30(2): 231-237.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.31     OR     https://www.gtzyyg.com/EN/Y2018/V30/I2/231
Fig.1  Remote sensing image in Songling
Fig.2  Lithologic unit distribution in Songling
Fig.3  DEM image in Songling
序号 地形因子 区分情况① 分开数量
1 高程 (3,11)(5,11)(2,6 7 9 10 11)(6,7 9 11)(9,11)(11,7 10) 13
2 剖面曲率 (3,5 7 9)(5,10 11)(1,10 11)(6,10)(9,10 11)(11,7)(10,7) 12
3 地表切割深度 (5,6 7 10 11)(1,6 10 11)(4,10)(9,10)(10,7) 10
4 纵向曲率 (3,9 10)(5, 10)(1,10)(6,10)(9,10 11)(11,7)(10,7) 9
5 地表粗糙度 (3,7)(5, 9)(2,7)(1,7)(4,7)(6,7)(9,7)(11,7)(10,7) 9
6 坡度 (3,7)(2,7)(4,7)(6,7)(9,7)(11,7)(10,7) 7
7 地形起伏度 (3,10)(5,10)(1,10)(4,10 11)(9,10) 6
8 高程变异系数 (3,11)(5,11)(4,11)(9,10 11)(11,7) 6
9 平面曲率 (5,10)(1,10 11)(9,10 11) 4
10 横向曲率 (5,10)(9,10) 2
Tab.1  Distinction between the factors
Fig.4  Patterns of topographic variables range of different lithologies
窗口大小 面积/(100m2) 地表切割深度均值/m 地表粗糙度均值 高程均值/m 平面曲率均值 剖面曲率均值
3 81 6.66 1.021 182 657.775 16 -0.012 682 -0.005 103
11 1 089 28.60 1.021 183 657.768 86 -0.012 759 -0.005 099
19 3 249 45.46 1.021 183 657.755 86 -0.012 719 -0.005 095
27 6 561 58.88 1.021 183 657.736 16 -0.012 651 -0.005 104
35 11 025 69.81 1.021 185 657.711 20 -0.012 465 -0.005 130
43 16 641 78.98 1.021 189 657.684 66 -0.012 358 -0.005 145
51 23 409 86.88 1.021 194 657.658 69 -0.012 229 -0.005 155
67 40 401 100.14 1.021 211 657.613 29 -0.012 155 -0.005 156
101 91 809 121.94 1.021 244 657.527 59 -0.012 169 -0.005 144
Tab.2  Correspondence between the unit size of the terrain factor and the mean value
窗口大小 面积/(100 m2) 地表切割深度均值/m
3 81 6.66
5 225 12.79
7 441 18.45
9 729 23.70
11 1 089 28.60
13 1 521 33.21
15 2 025 37.54
17 2 601 41.61
19 3 249 45.46
21 3 969 49.09
23 4 761 52.53
25 5 625 55.79
27 6 561 58.88
31 8 649 64.61
35 11 025 69.81
39 13 689 74.58
43 16 641 78.98
47 19 881 83.06
51 23 409 86.88
55 27 225 90.47
Tab.3  Correspondence between the unit size of the terrain factor and the mean value of the surface cutting depth
Fig.5  Fitting curve of the average surface cutting depth and the unit area
K SK S-SK
2 12.589 3.678
3 9.960 6.307
4 8.102 8.165
5 6.799 9.468
6 5.909 10.358
7 5.336 10.931
8 5.015 11.252
9 4.897 11.370
10 4.956 11.321
11 5.134 11.133
12 5.437 10.830
13 5.836 10.431
14 6.313 9.954
15 7.112 9.155
16 8.182 8.085
17 9.477 6.790
18 10.961 5.306
19 12.603 3.664
20 14.379 1.888
Tab.4  Statistical results of mean change point method
Fig.6  Image classification result
分布图中
岩性编号		 分类结果图中岩性编号
1 2 3 4 5 6 7 8
1 5 949 761 2 086 659 30 010 953 33 918 272 18 239 900 7 215 781 3 493 717 2 376 494
2 19 346 740 18 394 248 58 110 957 18 599 059 56 231 876 23 124 459 4 909 170 9 350 201
3 48 945 930 34 583 857 25 967 775 10 214 733 84 839 723 84 188 991 32 131 963 62 783 966
4 5 626 219 9 654 631 25 371 820 10 171 135 30 800 045 15 106 756 5 931 198 1 100 251
5 12 454 452 3 169 551 55 132 367 32 902 075 44 977 491 39 889 654 5 682 589 10 919 553
6 16 342 165 13 487 423 9 719 016 0 38 424 795 13 590 921 10 025 250 10 328 643
7 0 1 206 611 2 390 134 900 670 1 704 757 2 589 176 1 153 986 0
8 787 456 5 478 334 21 0 25 523 1 213 225 526 591 80 577 218
9 2 310 757 4 577 790 4 407 416 0 8 329 258 2 721 464 3 564 971 0
10 545 952 2 520 577 3 485 273 0 5 038 708 0 2 222 177 692 278
11 0 0 3 416 612 3 705 695 691 222 1 144 187 0 0
Tab.5  Corresponds result of the lithologic unit area (m2)
分类结果
图中编号		 对应面积/m2 岩性单元分布
图中各类总
面积/m2		 对应面积占各类
的百分比/%
1 16 342 165.11 116 775 952.60 13.99
2 9 654 631.46 98 813 007.72 9.77
3 58 110 957.00 218 991 255.80 26.54
4 33 918 271.69 110 411 638.10 30.72
5 84 839 722.93 292 867 380.20 28.97
6 39 889 653.55 197 008 668.20 20.25
7 3 564 971.06 70 808 773.97 5.03
8 80 577 217.73 186 747 411.80 43.15
Tab.6  Corresponds result of lithologic unit
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