Journal of Resources and Ecology >
Can the Soil Erosion in Coastal Mountainous Areas Disturbed by Electrictransmissionline Construction be Estimated with a Deep Learning Model?
LI Xi, Email: lixi_fz@163.com 
Received date: 20230130
Accepted date: 20230325
Online published: 20230802
Supported by
The State Grid Fujian Electric Power Co. Ltd.(52130420002F)
Soil erosion monitoring in coastal mountainous areas is very important during the construction of ElectricTransmissionLine (ETL) because of the impact this disturbance has on the sensitive environment. In this study, highresolution remote sensing data and deep learning models including Dense and Long ShortTerm Memory (LSTM) were used to fit the popular soil erosion equation, which is called the Revised Universal Soil Loss Equation (RUSLE), for the MinYue ETL (in Fujian). The accuracy of soil erosion regression was then evaluated in the transmission line buffer area and sampling spots at two spatial scales in order to obtain the optimized parameters and a suitable model. The results show that the Dense and LSTM models can meet the accuracy requirements by using 10 characteristic values, including soil erodibility, annual rainfall, mountain vegetation index (NDMVI), DEM, slope, four bands gray values of highspectral image, construction attributes. The optimized parameters for the priority machinelearning model LSTM are as follows: the layer depth is 3, the layer capacity is 512, the dropout ratio is 0.1, and the epoch of the LSTM model is 7060. The regression accuracy of the LSTM model decreases with an increase in soil erosion levels, and the average regression accuracy is greater than 0.98 for the slight level of soil erosion. Therefore, the machinelearning model of LSTM can be applied for quickly monitoring the soil erosion using high resolution remote sensing data.
LI Xi , JIANG Shixiong , ZHAO Shanshan , LI Xiaomei , CHEN Yao , WANG Chongqing , WENG Sunxian . Can the Soil Erosion in Coastal Mountainous Areas Disturbed by Electrictransmissionline Construction be Estimated with a Deep Learning Model?[J]. Journal of Resources and Ecology, 2023 , 14(5) : 1026 1033 . DOI: 10.5814/j.issn.1674764x.2023.05.013
Fig. 1 Study area where the MinYue ETL (in Fujian) passes through 
Table 1 Data and preprocessing 
Data  Spatial resolution  Preprocessing 

Skysat image  0.5 m  Obtain precise construction area from an image with 0.5 m spatial resolution and change the pixel scale to 2.5 m spatial resolution for the RULSE factors 
Land use type  10 m  Resample ERSI landuse type dataset to 2.5 m 
2.5 m  Classify the downsampled 2.5 m image  
DEM soil type image GPM  30 m  Bilinear interpolation to 2.5 m 
1 km  Bilinear interpolation to 2.5 m  
0.1° (about 10 km)  202101–202109: MonthlyFinal rainfall data 20211001–20211231: DailyLate rainfall data $\text{Annual}\ \text{Rainfall}\And \And \text{DEM}\left\{ \begin{matrix} P~\text{value}\le 0.05:CoKriging\ \operatorname{int}erpolation \\ P~\text{value}0.05:\text{Kriging}\ \text{interpolation}\ \ \ \ \ \ \ \ \ \\ \end{matrix} \right.$ 
Fig. 2 Main steps in the soil erosion grade estimation by using deep learning models 
Table 2 Values of the P factor with slope angles for the MinYue ETL (in Fujian) 
Land use type  Bare land  Forest  Farmland  Other  

<5°  5°10°  10°15°  15°20°  20°25°  >25°  
Value  1  0.7  0.1  0.221  0.305  0.575  0.705  0.8  0 
Table 3 Characteristic values and sources of soil erosion features 
No.  Feature  Remark 

1  K  From the K value table of Fujian 
2  Rain  $Rain=\left\{ \begin{matrix} 24\times \underset{i=1}{\overset{12}{\mathop \sum }}\,{{D}_{i}}{{P}_{i}},\begin{matrix} {} & {} \\ \end{matrix}\text{for}\ \text{monthly}\ \text{rainfall }\!\!~\!\!\text{ data} \\ \underset{i=1}{\overset{n}{\mathop \sum }}\,{{p}_{i}},\begin{matrix} {} & {} \\ \end{matrix}\text{for}\ \text{daily}\ \text{rainfall}\ \text{data} \\ 24\times \underset{i=1}{\overset{j}{\mathop \sum }}\,{{D}_{i}}{{P}_{i}}+\underset{i=jday+1}{\overset{n}{\mathop \sum }}\,{{p}_{i}},\text{for}\ \text{mixed}\ \text{rainfall}\ \text{data} \\ \end{matrix} \right.$ D_{i} is the day of each month; P_{i} is the monthly rainfall amount per hour (mm h^{–1}); p_{i }is the daily rainfall amount (mm); n is the day of the year; j is the amount of monthly rainfall; and jday is the day of j months. 
3  DEM  ASTER GDEM 
4  Slope  Calculated by DEM^{[18]} 
5  NDMVI  Calculated by the 3^{rd} and the 4^{th} bands of the Skysat image^{[19]} 
6  b1  The blue (1st) band grayscale of the Skysat image 
7  b2  The green (2nd) band grayscale of the Skysat image 
8  b3  The red (3rd) band grayscale of the Skysat image 
9  b4  The near infrared (4th) band grayscale of the Skysat image 
10  Type  Set construction area as 1, and nonconstruction area as 2 
11  A  Reference value from RUSLE 
Fig. 3 Overfitting iteration numbers of the Dense and LSTM models 
Table 4 Deep learning model accuracy of the soil erosion grades in the 400m buffer 
Soil erosion grade  RUSLE reference  LSTM result  Dense result  

Area (km²)  Percent (%)  Area (km²)  Percent (%)  Error (%)  Area (km²)  Percent (%)  Error (%)  
Slight  51.02  96.97  50.80  96.56  0.43  50.09  95.21  1.82 
Mild  1.54  2.92  1.75  3.33  13.79  2.43  4.62  58.14 
Moderate  0.04  0.08  0.06  0.12  46.91  0.09  0.17  111.30 
Intensity  0.01  0.02  0.00  0.00  100.00  0.00  0.00  100.00 
Table 5 Model accuracy of soil erosion grades in the construction area 
Model  Slight grade  Mild grade  Moderate grade  

Min  Max  Average  Min  Max  Average  Min  Max  Average  
Dense  0.83  1.00  0.98  0.07  0.96  0.72  0.01  0.86  0.43 
LSTM  0.83  1.00  0.98  0.29  0.97  0.75  0.23  0.90  0.52 
Fig. 4 Soil erosion grade accuracies of the deep learning model in the construction buffer 
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[4] 

[5] 

[6] 

[7] 

[8] 

[9] 

[10] 

[11] 

[12] 

[13] 
Ministry of Water Resources of the People’s Republic of China. 2008. Soil erosion classification standard SL1902007 (SL1902007 instead of SL19096). Beijing, China: China Water Conservancy and Hydropower Press. (in Chinese)

[14] 

[15] 

[16] 

[17] 

[18] 

[19] 

[20] 

[21] 

[22] 

[23] 

[24] 

[25] 

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