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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (1) : 34-42     DOI: 10.6046/zrzyyg.2022005
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An optimization method of DEM resolution for land type statistical model of coastal zones
JIANG Na1(), CHEN Chao2(), HAN Haifeng1
1. Shandong Provincial Institute of Land Surveying and Mapping, Ji’nan 250013, China;
2. Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
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Abstract  

Accurate, detailed, and three-dimensional land type statistical data with an appropriate resolution is greatly significant for the natural resources monitoring, supervision, and ecological protection in coastal zones. A land type statistical model needs the support of DEM. However, there is little studies on the adaptability between the DEM resolution and the statistical model. Given this, this study proposed an optimization method of DEM resolution for land type statistical model of coastal zones. Specifically, this study systematically explored the impacts of DEM resolution on land type statistical model, selected indices and constructed an assessment model from four aspects, namely statistical accuracy, generality, information amount, and calculation efficiency. Then, this study determined the index weight using the entropy weight method and obtained the optimal DEM resolution through weighted calculation. The results are as follows. ①An increase in the DEM resolution led to the increasingly apparent negative impacts on the statistical accuracy and information amount and the increasingly significant positive effects on the generalization of the model. ②To meet the requirements of statistical accuracy, the DEM resolution should not exceed 30 m. Meanwhile, as required by the landform generalization, the DEM resolution should not be less than 10 m. ③There is a linear positive correlation between the calculation time of spatial operations and the number of DEM grids. ④Based on the comprehensive assessment using the weights calculated by the entropy weight method, the optimal DEM resolution was 10 m. The method of DEM resolution developed in this paper is universal and can be expanded in the natural resource statistics of coastal zones and in the land type statistics of other surveys and monitoring.
Keywords coastal zone      land type statistics      DEM resolution      information entropy      entropy weight method     
ZTFLH:  TP79  
Corresponding Authors: CHEN Chao     E-mail: jiangna123321@163.com;chenchao@zjou.edu.cn
Issue Date: 14 March 2022
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Na JIANG
Chao CHEN
Haifeng HAN
Cite this article:   
Na JIANG,Chao CHEN,Haifeng HAN. An optimization method of DEM resolution for land type statistical model of coastal zones[J]. Remote Sensing for Natural Resources, 2022, 34(1): 34-42.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022005     OR     https://www.gtzyyg.com/EN/Y2022/V34/I1/34
Fig.1  DEM resolution evaluation model
Fig.2  Technical flow chart
Fig.3  Topography of the study area
Fig.4  Topographic performance of 2 m, 5 m, 10 m, 20 m, 30 m and 50 m DEM
DEM空间
尺度/m		 地表平
整系数		 最小值/m 最大值/m 均值/m
2 0.977 6 -79.0 1 130.7 49.563 6
5 0.981 5 -79.0 1 129.6 49.563 6
10 0.985 1 -78.9 1 129.9 49.563 6
15 0.986 7 -79.0 1 127.7 49.563 6
20 0.987 9 -79.0 1 127.5 49.563 7
30 0.989 4 -78.8 1 126.2 49.563 6
40 0.990 5 -78.8 1 111.1 49.564 1
50 0.991 3 -78.8 1 123.3 49.563 5
60 0.992 0 -78.8 1 103.8 49.563 8
70 0.992 6 -78.7 1 109.2 49.561 1
80 0.993 1 -78.7 1 092.1 49.564 5
90 0.993 5 -78.7 1 111.1 49.562 3
100 0.993 9 -78.7 1 098.6 49.566 4
Tab.1  Relationship between topographic statistical features and DEM resolution
Fig.5  Variation of surface flatness coefficient and maximum deviation rate with DEM resolution
DEM空
间尺度/m		 耕地 园地 林地 草地 房屋
建筑区		 铁路与
道路		 构筑物 人工
堆掘地		 荒漠与
裸露地表		 水域 整体
偏差		 偏差率
均方根
2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
5 0.34 0.23 0.28 0.43 0.05 0.84 1.01 0.47 1.77 1.58 0.20 0.89
10 0.54 0.19 0.28 0.03 0.38 3.61 0.69 0.33 8.56 0.34 0.15 2.96
15 0.64 0.64 0.39 0.07 0.94 3.33 0.75 2.50 4.34 0.56 0.18 1.97
20 0.44 1.10 1.21 0.17 3.06 7.58 1.93 0.93 5.37 0.68 0.02 3.22
30 0.66 0.42 1.62 0.06 3.43 8.34 3.43 2.73 8.35 2.54 0.12 4.24
40 1.50 2.83 3.27 3.51 0.99 8.35 10.99 4.46 2.19 2.00 0.08 5.03
50 4.30 7.01 2.81 2.68 4.25 7.52 14.10 3.99 16.26 6.39 0.07 8.23
60 6.17 3.79 1.76 3.58 6.13 3.30 1.36 2.17 6.58 2.52 0.04 4.16
70 7.21 6.98 3.64 0.15 17.34 14.98 2.16 9.98 14.13 5.00 0.03 9.84
80 1.53 9.25 2.71 14.39 11.08 39.75 4.29 2.99 52.30 0.85 0.11 21.84
90 1.64 10.47 2.96 1.75 0.98 22.68 7.02 10.30 31.67 15.14 0.24 14.24
100 4.66 16.06 5.37 14.51 1.09 73.81 14.25 2.34 1.37 16.89 0.08 25.42
Tab.2  Relationship between DEM resolution and statistical accuracy of land types(10-4)
Fig.6  Example of linear figure
Fig.7  Relationship between statistical deviation rate of small target features and DEM resolution
DEM空间尺度/m 0° (0,2]° (2,3]° (3,5]° (5,6]° (6,8]° (8,10]° (10,15]°
2 5.29 71.85 13.19 8.02 0.96 0.52 0.11 0.05
5 0.59 86.83 8.65 3.62 0.25 0.06 0 0
10 0.17 95.73 3.89 0.21 0 0 0 0
15 0 99.63 0.37 0 0 0 0 0
20 0 99.72 0.28 0 0 0 0 0
30 0 100.00 0 0 0 0 0 0
Tab.3  Statistics of distribution in slope zones in residential area at different DEM resolutions (%)
Fig.8  Variation of information entropy of slope zone and elevation zone with DEM resolution
Fig.9  Variation of information entropy and information entropy loss rate of vegetation slope zone with DEM resolution
Fig.10  Variation of number of spots in slope zone and calculation time with DEM resolution
一级指标 二级指标 指标性质/
尺度影响		 熵权
准确性 地表平整系数偏离度 负向/负向 0.070 6
最小值偏差 负向/负向 0.043 0
最大值偏差 负向/负向 0.050 1
地类面积偏差 负向/负向 0.058 6
小目标地形统计偏差 负向/负向 0.072 2
线状地物地形统计偏差 负向/负向 0.081 9
信息量 坡度信息熵 正向/负向 0.069 0
高度带信息熵 正向/负向 0.074 4
地类信息熵 正向/负向 0.055 8
林地坡度信息熵 正向/负向 0.068 4
概括性 微地形概括 负向/正向 0.198 8
计算效率 DEM格网数据量 负向/正向 0.157 1
Tab.4  Determination of index weight by entropy weight method
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