Spatial Distribution of Soil Heavy Metal Concentrations in Road-Neighboring Areas Using UAV-Based Hyperspectral Remote Sensing and GIS Technology
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area
2.2. Materials
2.2.1. UAV Hyperspectral Remote Sensing Image Data Collection and Image Preprocessing
- (a)
- Geometric registration: Images from different flights were geometrically registered using the quadratic polynomial calculation model to eliminate the geometric distortions.
- (b)
- Radiometrical normalization: This process was used to eliminate the radiometrical inconsistency among the images acquired by different flights.
- (c)
- Image mosaic: The images acquired from different flights were mosaicked into a seamless wide-field image.
- (d)
- Geometric correction: The mosaicked image was geometrically corrected based on the ground control points.
2.2.2. On-Site Soil Data Collection and Processing
3. Method
3.1. Retrieval of Soil Heavy Metal Concentrations
3.1.1. Pretreatment of the Hyperspectral Data
3.1.2. Optimal Spectral Variates Selection
3.1.3. Model Development and Selecting for Retrieving Soil HM Concentrations
- (1)
- The modeling methods.
- 1.
- Multivariate Linear Regressor (MLR)
- 2.
- Decision Tree Regressor (DT)
- 3.
- Gradient-Boosted Decision Trees Regressor (GBDT)
- 4.
- Random Forest Regressor (RF)
- (2)
- Metrics for Evaluating Regression Models
- (3)
- Model selection
3.2. Analysis of Soil Heavy Metal Concentrations Characteristics
- (1)
- Correlation Analysis: Correlation analysis was conducted to reveal the inter-correlations among different HMs. A strong correlation may indicate that two types of heavy metals come from the same source [52,53]. In this study, the Pearson correlation coefficient was calculated to measure the degree of association between the concentrations of Cr and Cu in the soil samples [54].
- (2)
- Spatial Interpolation: Since only the HM concentrations over the bare soil pixels were retrieved, interpolation was employed to map the distribution of HM concentrations in the entire study area to provide a qualitative understanding of spatial trends and patterns, enabling visual interpretation and exploration of the data. The universal kriging (UK) interpolation tool in ArcGIS software (version 10.7.1, ESRI, Redlands, CA, USA) was used for this purpose. Kriging is a geostatistical interpolation method that considers both the distance and degree of variation between known data points when estimating values in unknown areas. UK, a kriging method with a local trend or drift, was used because it is appropriate for analyzing data with a specific trend [55].
- (3)
- Influence of Perpendicular Distance to the Road: The spatial distribution of HMs in the road-neighboring area is affected by various factors. In this study, the perpendicular distance of bare soil points from Jin-Long Avenue was calculated and analyzed as an external environmental factor. The relationship between this distance and soil HM concentrations was analyzed using the Data Management Tool in ArcMap 10.7 and a self-developed Python program.
4. Results and Discussion
4.1. Descriptive Statistics of Soil HMs Concentrations
4.2. Model Development and Selection for Heavy Metal Concentration Retrieval
4.2.1. Spectral Transformations
4.2.2. Selection of Optimal Spectral Variates
4.2.3. Model Development and Selection for Heavy Metal Concentration Retrieval
4.3. The Spatial Character of the Soil HMs Concentrations
4.3.1. Spatial Distribution of HMs Concentrations
4.3.2. Influence of the Perpendicular Distance to the Road
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Spectral Transformation | Formula |
---|---|
Reciprocal transformation | |
Logarithmic transformation | |
Square Root transformation | |
Exponential transformation |
Heavy Metal | n 1 | Min (mg/kg) | Max (mg/kg) | Mean (mg/kg) | Standard Deviation | C.V. 2 (%) | SBV 3 | Pearson Correlation Coefficients | |
---|---|---|---|---|---|---|---|---|---|
Cr | Total | 70 | 68.034 | 133.843 | 104.987 | 11.091 | 10.564% | 86.000 | 0.770 * |
<SBV | 4 | 68.034 | 85.686 | 74.315 | 6.740 | 9.070% | |||
>SBV | 66 | 88.975 | 133.843 | 105.539 | 11.314 | 10.720% | |||
Cu | Total | 70 | 24.952 | 41.109 | 33.920 | 3.349 | 9.879% | 30.700 | |
<SBV | 12 | 24.952 | 29.879 | 28.144 | 1.464 | 5.202% | |||
>SBV | 58 | 30.918 | 41.109 | 34.729 | 2.688 | 7.740% |
Soil Heavy Metals | The Optimal Spectral Variables | Spearman Correlation Coefficient |
---|---|---|
Cr | Sqrt, FOD-1.2, 518 nm | 0.433 |
Exponential, 946 nm | 0.405 | |
Reciprocal, FOD-1.0, 658 nm | −0.478 | |
Reciprocal, FOD-1.2, 742 nm | −0.427 | |
Reciprocal, FOD-1.4, 658 nm | −0.331 | |
Reciprocal, FOD-1.6, 710 nm | −0.514 | |
Cu | Sqrt, FOD-1.8, 706 nm | −0.399 |
Sqrt, FOD-2.0, 946 nm | −0.365 | |
Reciprocal, FOD-1.8, 510 nm | 0.362 | |
Reciprocal, FOD-1.8, 666 nm | 0.447 | |
Reciprocal, FOD-1.8, 702 nm | −0.421 |
Parameter | Method | R2 | MAE | RMSE | MARE | |
---|---|---|---|---|---|---|
Cr | RF | Train | 0.621 | 5.535 | 6.766 | 0.053 |
Test | 0.325 | 7.322 | 9.043 | 0.070 | ||
Decision Tree | Train | 0.338 | 7.242 | 8.930 | 0.069 | |
Test | 0.252 | 7.925 | 9.516 | 0.076 | ||
MLR | Train | 0.397 | 7.011 | 8.546 | 0.067 | |
Test | 0.264 | 7.738 | 9.442 | 0.074 | ||
GBDT | Train | 0.668 | 5.224 | 6.315 | 0.050 | |
Test | 0.351 | 7.371 | 8.868 | 0.070 |
Parameter | Method | R2 | MAE | RMSE | MARE | |
---|---|---|---|---|---|---|
Cu | RF | Train | 0.608 | 1.655 | 2.078 | 0.050 |
Test | 0.324 | 2.185 | 2.733 | 0.065 | ||
Decision Tree | Train | 0.311 | 2.237 | 2.752 | 0.067 | |
Test | 0.116 | 2.479 | 3.124 | 0.074 | ||
MLR | Train | 0.380 | 2.139 | 2.615 | 0.064 | |
Test | 0.246 | 2.377 | 2.886 | 0.071 | ||
GBDT | Train | 0.643 | 1.610 | 1.978 | 0.048 | |
Test | 0.325 | 2.226 | 2.730 | 0.067 |
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Gan, W.; Zhang, Y.; Xu, J.; Yang, R.; Xiao, A.; Hu, X. Spatial Distribution of Soil Heavy Metal Concentrations in Road-Neighboring Areas Using UAV-Based Hyperspectral Remote Sensing and GIS Technology. Sustainability 2023, 15, 10043. https://doi.org/10.3390/su151310043
Gan W, Zhang Y, Xu J, Yang R, Xiao A, Hu X. Spatial Distribution of Soil Heavy Metal Concentrations in Road-Neighboring Areas Using UAV-Based Hyperspectral Remote Sensing and GIS Technology. Sustainability. 2023; 15(13):10043. https://doi.org/10.3390/su151310043
Chicago/Turabian StyleGan, Wenxia, Yuxuan Zhang, Jinying Xu, Ruqin Yang, Anna Xiao, and Xiaodi Hu. 2023. "Spatial Distribution of Soil Heavy Metal Concentrations in Road-Neighboring Areas Using UAV-Based Hyperspectral Remote Sensing and GIS Technology" Sustainability 15, no. 13: 10043. https://doi.org/10.3390/su151310043