Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Preprocessing
2.2.1. Sample Plots Data
- AGB data
- Ratio data of BGB to AGB
2.2.2. Grassland Spatial Distribution Data
2.2.3. Remote Sensing Data and Environmental Variables
- Multispectral bands and spectral indices
- Terrain factors
- Soil factors
- Climate factors
- Location factor
- GLCM
2.3. Analysis Methods
2.3.1. Modeling Algorithms
2.3.2. Model Accuracy Evaluation
2.3.3. Trend Analysis
2.3.4. Driving Factor Analysis
- Optimal parameters-based geographical detector model
- Residual analysis
3. Results
3.1. AGB Model Building and Accuracy Evaluation
3.2. Spatiotemporal Variation of Grassland Biomass Carbon Storage
3.2.1. Spatial Distribution of Grassland Biomass Carbon Density
3.2.2. Change Trend of Grassland Biomass Carbon Storage from 2001 to 2022
3.3. Driving Factors of Spatiotemporal Variation of Biomass Carbon Density
3.3.1. Driving Factors of Grassland Biomass Carbon Density Spatial Distribution
3.3.2. Driving Factors of Time Variations in Grassland Biomass Carbon Density
4. Discussion
4.1. Variables Selection and ML Algorithm Performance Comparison for Grassland AGB Modeling
4.2. Spatiotemporal Characteristics of Biomass Carbon Storage and Its Driving Factors
4.3. Limitations and Prospects
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Grassland Type | Number of Samples | Minimum Value | Maximum Value | Mean Value |
---|---|---|---|---|
Typical steppe | 36 | 10.77 | 44.14 | 22.79 ab |
Meadow steppe | 11 | 7.27 | 33.84 | 19.39 b |
Meadow | 10 | 8.57 | 51.24 | 29.00 a |
Type of Variables | Specific Variables | Number |
---|---|---|
MODIS bands | Band1, Band2, Band3, Band4, Band7 | 5 |
Spectral indices | NDVI, EVI, DVI, GNDVI, MSAVI, OSAVI, RVI, SAVI, NPP, LAI, LST | 11 |
Terrain | Elevation, slope | 2 |
Soil | pH, clay content, bulk density, sand content, water content, soil organic carbon | 6 |
Climate | Temperature, precipitation, ET | 3 |
Location | Longitude | 1 |
GLCM | Angular second moment (ASM), contrast, correlation, entropy, inverse difference moment (IDM), variance | 6 |
Driving Factor | Identification Criterion | Relative Contribution Rate (%) | |||
---|---|---|---|---|---|
CC | HA | ||||
CC and HA | >0 | >0 | >0 | ||
CC | >0 | <0 | 100 | 0 | |
HA | <0 | >0 | 0 | 100 | |
CC and HA | <0 | <0 | <0 | ||
CC | <0 | >0 | 100 | 0 | |
HA | >0 | <0 | 0 | 100 |
Algorithm | Selected Variables | Number of Variables |
---|---|---|
RF | NDVI, EVI, DVI, GNDVI, MSAVI, LAI, longitude, IDM | 8 |
SVM | Band1, Band2, Band7, NDVI, EVI, DVI, GNDVI, MSAVI, OSAVI, RVI, SAVI, NPP, LAI, LST, slope, bulk density, ET, longitude | 18 |
GBDT | NDVI, EVI, DVI, GNDVI, MSAVI, LAI, longitude | 7 |
XGBoost | Band1, Band2, Band3, Band4, Band7, NDVI, EVI, DVI, GNDVI, MSAVI, OSAVI, RVI, SAVI, NPP, LAI, LST, elevation, slope, clay content, bulk density, sand content, soil organic carbon, temperature, precipitation, ET, longitude, ASM, contrast, correlation, entropy, IDM | 31 |
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Zhi, Q.; Hu, X.; Wang, P.; Li, M.; Ding, Y.; Wu, Y.; Peng, T.; Li, W.; Guan, X.; Shi, X.; et al. Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland. Remote Sens. 2024, 16, 3709. https://doi.org/10.3390/rs16193709
Zhi Q, Hu X, Wang P, Li M, Ding Y, Wu Y, Peng T, Li W, Guan X, Shi X, et al. Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland. Remote Sensing. 2024; 16(19):3709. https://doi.org/10.3390/rs16193709
Chicago/Turabian StyleZhi, Qiuying, Xiaosheng Hu, Ping Wang, Ming Li, Yi Ding, Yuxuan Wu, Tiantian Peng, Wenjie Li, Xiao Guan, Xiaoming Shi, and et al. 2024. "Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland" Remote Sensing 16, no. 19: 3709. https://doi.org/10.3390/rs16193709
APA StyleZhi, Q., Hu, X., Wang, P., Li, M., Ding, Y., Wu, Y., Peng, T., Li, W., Guan, X., Shi, X., & Li, J. (2024). Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland. Remote Sensing, 16(19), 3709. https://doi.org/10.3390/rs16193709