Spatiotemporal Dynamics and Land Cover Drivers of Herbaceous Aboveground Biomass in the Yellow River Delta from 2001 to 2022
Abstract
Highlight
- By mining literature-derived data and incorporating LAI and LGS, the random forest model achieved high-accuracy AGB mapping (R2 = 0.74) in the Yellow River Delta (2001–2022).
- Herbaceous AGB showed an overall increase, primarily driven by cropland and wetland expansion, while grassland contributed little or even negatively.
- Incorporating ecological variables significantly improves large-scale biomass estimation in herbaceous ecosystems.
- The findings support wetland conservation and sustainable land use management in delta regions.
Abstract
1. Introduction
2. Materials
2.1. Study Area
2.2. Field Data
2.3. Remote Sensing Data
2.3.1. Reflectance Information and Vegetation Index
2.3.2. Land Cover Data
2.3.3. Other Auxiliary Data
3. Methods
3.1. Herbaceous Vegetation Extraction
3.2. Herbaceous AGB Estimation
3.3. Trend Analysis
3.4. Driving Forces Detection
4. Results
4.1. Accuracy Assessment of AGB Modeling
4.2. Spatial Distribution of AGB in the YRD
4.3. Interannual Variation in AGB
4.4. Effects of Land Cover Changes on AGB
5. Discussion
5.1. Uncertainty Analysis of Literature-Derived Data
5.2. Effectiveness of LAI and LGS in AGB Estimation
5.3. Drivers of AGB Variation in the YRD
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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VIs | Formula | Reference |
---|---|---|
RVI | [36] | |
EVI | [37] | |
EVI2 | [38] | |
DVI | [39] | |
NDVI | [40] | |
NDPI | [41] | |
MSAVI | [42] | |
OSAVI | [43] |
Reclassified Land Cover | GLC-FCS30 |
---|---|
Cropland | Herbaceous cover cropland, |
Rainfed cropland, | |
Irrigated cropland; | |
Grassland | Grassland, |
Lichens and mosses, | |
Sparse herbaceous (fc < 0.15); | |
Wetland | Swamp, |
Marsh, | |
Flooded flat, | |
Salt marsh, | |
Tidal flat; |
Feature Categories | Variables |
---|---|
spectral variables | BLUE, GREEN, RED, NIR, SWIR1, SWIR2; |
vegetation indices | NDVI, EVI, EVI2, RVI, DVI, OSAVI, MSAVI; |
topographic variables | Elevation, Slope, Aspect; |
ecological variables | LAI, LGS; |
Feature Selection | Features |
---|---|
Selected by LASSO | NIR, SWIR1, Slope, Aspect, LGS, BULE, LAI, Elevation, SWIR2, GREEN, RED, DVI; |
Not Selected | EVI, EVI2, OSAVI, MSAVI, NDVI, RVI; |
Model | R2 (Testing Sets) | RMSE (g/m2) | Precision (%) |
---|---|---|---|
RF | 0.74 | 77.13 | 88.07% |
LASSO-RF | 0.71 | 98.79 | 88.19% |
SVM | 0.67 | 137.33 | 80.63% |
LASSO-SVM | 0.69 | 121.76 | 80.30% |
ANN | 0.55 | 224.75 | 85.96% |
LASSO-ANN | 0.51 | 260.01 | 86.49% |
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Zhang, S.; Song, W.; Huang, N.; Tang, F.; Zhang, Y.; Liu, C.; Liu, Y.; Wang, L. Spatiotemporal Dynamics and Land Cover Drivers of Herbaceous Aboveground Biomass in the Yellow River Delta from 2001 to 2022. Remote Sens. 2025, 17, 3418. https://doi.org/10.3390/rs17203418
Zhang S, Song W, Huang N, Tang F, Zhang Y, Liu C, Liu Y, Wang L. Spatiotemporal Dynamics and Land Cover Drivers of Herbaceous Aboveground Biomass in the Yellow River Delta from 2001 to 2022. Remote Sensing. 2025; 17(20):3418. https://doi.org/10.3390/rs17203418
Chicago/Turabian StyleZhang, Shuo, Wanjuan Song, Ni Huang, Feng Tang, Yuelin Zhang, Chang Liu, Yibo Liu, and Li Wang. 2025. "Spatiotemporal Dynamics and Land Cover Drivers of Herbaceous Aboveground Biomass in the Yellow River Delta from 2001 to 2022" Remote Sensing 17, no. 20: 3418. https://doi.org/10.3390/rs17203418
APA StyleZhang, S., Song, W., Huang, N., Tang, F., Zhang, Y., Liu, C., Liu, Y., & Wang, L. (2025). Spatiotemporal Dynamics and Land Cover Drivers of Herbaceous Aboveground Biomass in the Yellow River Delta from 2001 to 2022. Remote Sensing, 17(20), 3418. https://doi.org/10.3390/rs17203418