Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Processing
2.2.1. Field Data
2.2.2. Optical and SAR Data Processing
2.3. Characteristic Variable Selection
2.4. Experimental Models
2.4.1. Random Forest
2.4.2. Convolutional Neural Network (CNN)
2.4.3. CNN-LSTM
2.5. Model Accuracy Assessment
3. Results
3.1. Predicted Variables
3.2. Model Test Results
3.3. Mapping Spatial Distribution of Forest
4. Discussion
4.1. Variable Selection
4.2. Comparison of Different Sensors
4.3. Model Comparison
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Tree Species | Model Expressions and Parameters |
---|---|
Fir (Cunninghamia lanceolata (Lamb.) Hook., belonging to the Cupressaceae) | |
Horsetail pine (Pinus massoniana Lamb. belonging to the Pinaceae) | |
Hard broad | |
Soft broad |
Optical Data | Data Identification | Collection Time | Product Level | Spatial Resolution |
---|---|---|---|---|
GF-6 | GF6_WFV_E118.4_N29.1_20180905_L1A1119836975 | 2018.9.5 | L1A | 16 m |
Sentinel-2 | S2B_MSIL1C_20181029T024819_N0206_R132_T50RPU_20181029T052535 | 2018.10.29 | Level-1C | visible: 10 m near-infrared: 20 m shortwave infrared: 60 m |
S2A_MSIL1C_20181110T023921_N0207_R089_T50RQU_20181110T042743 | 2018.11.10 | Level-1C | ||
Landsat-8 | LC08_L1TP_120039_20181028_20181115_01_T1 | 2018.11.15 | L1T | Bands 1–7, 9–11: 30 m band 8: 15 m |
LC08_L1TP_119039_20180911_20180920_01_T1 | 2018.9.11 | L1T |
Image Type | Acquisition Time | Product Level | Band | Polarization | Spatial Resolution |
---|---|---|---|---|---|
GF-3 | 17 November 2018 | L1A | C band | HH + HV | 1 m |
Sentinel-1 | 1 October 2018 13 October 2018 | Level-1 | C band | VV + VH | IW: 5 × 20 m |
ALOS-2 PALSAR-2 | 25 October 2018 8 November 2018 | Level 1.5 | L band | HH + HV | Spotlight: 1–3 m Stripmap: 3–10 m ScanSAR: 25–100 m |
Variable Type | Name | Description |
---|---|---|
Band Information | GF-6 | B1, 2, 3, 4, 5, 6 |
Sentinel-2 | B2, 3, 4, 5, 6, 7, 8, 8a, 11, 12 | |
Landsat-8 | B2, 3, 4, 5, 6, 7 | |
Vegetation Index | NDVI | NDVI = (NIR − R)/(NIR + R) |
DVI | DVI = NIR −R | |
GNDV | GNDV = (NIR − G)/(NIR + G) | |
RVI | RVI = NIR/R |
Number | Texture Feature Name | Introduction to the Formula |
---|---|---|
1 | Mean | |
2 | Variance | |
3 | Entropy | |
4 | Contrast | |
5 | Homogeneity | |
6 | Dissimilarity | |
7 | Correlation | |
8 | Second Moment |
Image Data Source | Feature Type | Remote Sensing Predictors | |
---|---|---|---|
GF-3, Sentinel-1, ALOS-2 | Backward scattering coefficient | GF-3, ALOS-2 | HH, HV |
Sentinel-1 | VV, VH | ||
Texture features | Mean, Variance, Entropy, Contrast, Homogeneity, Dissimilarity, Correlation, Second Moment | ||
Polarization decomposition features | H, A, α |
Image | Category | Variables | Pearson’s Correlation Coefficient | Variables | Pearson’s Correlation Coefficient |
---|---|---|---|---|---|
GF-3 | Backward scattering coefficient | HH | 0.069 | HV | 0.074 |
Texture feature factor | HH_Mean | 0.051 | HV_Mean | 0.107 | |
HH_Variance | 0.052 | HV_Variance | 0.107 | ||
HH_Entropy | 0.065 | HV_Entropy | 0.107 | ||
HH_Contrast | 0.067 | HV_Contrast | 0.048 | ||
HH_Homogeneity | −0.058 | HV_Homogeneity | −0.098 | ||
HH_Dissimilarity | 0.066 | HV_Dissimilarity | 0.107 | ||
HH_Correlation | 0.029 | HV_Correlation | −0.064 | ||
HH_Second moment | −0.032 | HV_Second moment | −0.127 | ||
Polarization decomposition parameter | H | 0.271 | A | −0.258 | |
α | 0.246 | ||||
Sentinel-1 | Backscattering coefficient | VV | −0.002 | VH | 0.062 |
Texture characteristic factor | VV_Mean | −0.028 | VH_Mean | −0.037 | |
VV_Variance | −0.039 | VH_Variance | −0.083 | ||
VV_Entropy | −0.013 | VH_Entropy | −0.013 | ||
VV_Contrast | 0.32 | VH_Contrast | −0.079 | ||
VV_Homogeneity | −0.013 | VH_Homogeneity | −0.029 | ||
VV_Dissimilarity | 0.021 | VH_Dissimilarity | −0.006 | ||
VV_Correlation | −0.077 | VH_Correlation | −0.037 | ||
VV_Second moment | 0.019 | VH_Second moment | −0.017 | ||
Polarization decomposition parameter | H | 0.241 | A | −0.238 | |
α | 0.226 | ||||
ALOS-2 PALSAR-2 | Backscattering coefficient | HH | −0.024 | HV | 0.079 |
Texture factor | HH_Mean | −0.017 | HV_Mean | 0.082 | |
HH_Variance | −0.005 | HV_Variance | −0.034 | ||
HH_Entropy | −0.013 | HV_Entropy | −0.077 | ||
HH_Contrast | −0.005 | HV_Contrast | 0.015 | ||
HH_Homogeneity | −0.013 | HV_Homogeneity | 0.040 | ||
HH_Dissimilarity | −0.004 | HV_Dissimilarity | −0.007 | ||
HH_Correlation | −0.045 | HV_Correlation | 0.006 | ||
HH_Second moment | 0.031 | HV_Second moment | 0.053 | ||
Polarization decomposition parameter | H | 0.312 | A | −0.319 | |
α | 0.235 |
Image | Selection of Characteristic Variables |
---|---|
GF-3 | HH_dB, HV_dB, HH_Contrast, H |
Sentinel-1 | VH_dB, VH_Contrast, VH_Variance, A |
ALOS-2 PALSAR-2 | HV_dB, HV_Entropy, HV_Mean, H |
Image | Category | Feature Variable | Pearson’s Correlation Coefficient | Feature Variable | Pearson’s Correlation Coefficient |
---|---|---|---|---|---|
GF-6 | Band information | B1 | 0.080 | B2 | 0.036 |
B3 | 0.020 | B4 | −0.131 | ||
B5 | −0.118 | B6 | −0.092 | ||
Vegetation index | NDVI | 0.091 | DVI | −0.128 | |
GNDV | 0.01 | RVI | 0.206 | ||
Texture factors | Mean | 0.084 | Variance | 0.141 | |
Entropy | −0.048 | Contrast | 0.122 | ||
Homogeneity | 0.006 | Dissimilarity | 0.041 | ||
Correlation | 0.069 | Second moment | 0.083 | ||
Principal component analysis | PCA1 | 0.090 | PCA2 | 0.066 | |
PCA3 | 0.343 | ||||
Sentinel-2 | Band information | B2 | −0.291 | B3 | −0.332 |
B4 | −0.374 | B5 | −0.377 | ||
B6 | −0.225 | B7 | −0.12 | ||
B8 | −0.157 | B8a | −0.138 | ||
B11 | −0.330 | B12 | −0.422 | ||
Vegetation index | NDVI | 0.282 | DVI | 0.075 | |
GNDV | 0.117 | RVI | 0.249 | ||
Texture factors | Mean | −0.405 | Variance | −0.111 | |
Entropy | −0.216 | Contrast | −0.180 | ||
Homogeneity | Dissimilarity | −0.182 | |||
Correlation | −0.136 | Second moment | 0.155 | ||
Principal component analysis | PCA1 | −0.275 | PCA2 | −0.368 | |
PCA3 | 0.420 | ||||
Landsat-8 | Band information | B2 | −0.273 | B3 | −0.402 |
B4 | −0.472 | B5 | −0.173 | ||
B6 | B7 | −0.464 | |||
Vegetation index | NDVI | 0.273 | DVI | −0.028 | |
GNDV | 0.273 | RVI | 0.396 | ||
Texture factors | Mean | −0.348 | Variance | −0.260 | |
Entropy | −0.398 | Contrast | −0.211 | ||
Homogeneity | 0.360 | Dissimilarity | −0.324 | ||
Correlation | 0.064 | Second moment | 0.374 | ||
Principal component analysis | PCA1 | −0.381 | PCA2 | −0.371 | |
PCA3 | −0.406 |
Image | Selection of Characteristic Variables |
---|---|
GF-6 | B4, B5, B6, Contrast, Variance, RVI, GNDV, PCA3 |
Sentinel-2 | B3, B4, B5, B12, Entropy, Mean, NDVI, PCA3 |
Landsat-8 | B3, B4, B7, Second, Entropy, NDVI, RVI, PCA3 |
Remote Sensing Data | Estimation Methods | RMSE | R2 |
---|---|---|---|
GF-3 | RF | 32.9378 | 0.4088 |
CNN | 36.5828 | 0.3073 | |
CNN-LSTM | 31.5704 | 0.4355 | |
Sentinel-1 | RF | 32.9558 | 0.4058 |
CNN | 35.0048 | 0.2933 | |
CNN-LSTM | 32.7193 | 0.4184 | |
ALOS-2 | RF | 34.5347 | 0.3025 |
CNN | 31.8198 | 0.4124 | |
CNN-LSTM | 31.7333 | 0.4285 |
Image | Selection of Feature Variables |
---|---|
GF-3 | HV_dB, HH_Contrast, H |
Sentinel-1 | VH_dB, VH_Variance, A |
ALOS-2 PALSAR-2 | HV_dB, HV_Mean, H |
GF-6 | B5, Variance, RVI, PCA3 |
Sentinel-2 | Mean, B12, B5, PCA3 |
Landsat-8 | B4, Entropy, RVI, PCA3 |
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Tian, X.; Li, J.; Zhang, F.; Zhang, H.; Jiang, M. Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China. Remote Sens. 2024, 16, 1074. https://doi.org/10.3390/rs16061074
Tian X, Li J, Zhang F, Zhang H, Jiang M. Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China. Remote Sensing. 2024; 16(6):1074. https://doi.org/10.3390/rs16061074
Chicago/Turabian StyleTian, Xin, Jiejie Li, Fanyi Zhang, Haibo Zhang, and Mi Jiang. 2024. "Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China" Remote Sensing 16, no. 6: 1074. https://doi.org/10.3390/rs16061074