Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Study Area
2.2. Data Source
2.3. Data Preprocessing
2.3.1. Landsat 8 OLI Data Processing Method
2.3.2. Sentinel-1A Data Preprocessing Method
2.3.3. Calculation of Tree Biomass in Sample Plot
2.3.4. Correlation Analysis of Remote Sensing Factors and Biomass
2.3.5. Build a Linear Regression Model
2.3.6. Establish BP Neuron Network Model
- , where k is the set size of input data, M and n are the number of hidden layer and input layer nodes, respectively. If i > M, specify = 0;
- , where m is the number of network output layer nodes, n is the number of network input layer nodes and a is a constant between [0 and 10];
- , where n is the number of input layer nodes.
2.3.7. Establishment of BP Neural Network Model Improved by Particle Swarm Optimization
- (1)
- Initialize a group of particles (the group size is N), including random positions and velocities;
- (2)
- Evaluate the fitness of each particle;
- (3)
- For each particle, compare its fitness value with the best position pbest it has passed, and if it is better, use it as the current best position pbest;
- (4)
- For each particle, compare its fitness value with its best position gbest, and if it is better, use it as the current best position gbest;
- (5)
- Adjust particle velocity and position;
- (6)
- If the end condition is not met, go to step 2.
2.3.8. Spatial Biomass Mapping
3. Results
3.1. Plot Data Processing
3.2. Correlation Analysis of Remote Sensing Factors and Biomass
3.3. Linear Regression Model Processing Results
3.4. BP Neuron Network Model Processing Results
3.5. Particle Swarm Optimization Algorithm Improves BP Neural Network Model Processing Results
3.6. Results of Spatial Biomass Mapping
4. Discussion
- (1)
- This experiment was only a preliminary study on a forest estimation model, and there were still some shortcomings and deficiencies in the specific technical processing. More parameter characteristics would be needed for further analysis in order to better understand the impact of biological factors on factors of quantitative inversion accuracy;
- (2)
- Due to limited conditions, the number of training data samples used in this experiment was small, which made the prediction network not stable enough and could cause certain errors in the results;
- (3)
- The data collected with remote sensing technology were greatly affected by factors such as sensors, shooting angles and atmosphere, which may cause the inversion errors.
5. Conclusions
- (1)
- The correlation between altitude and biomass was the highest, and the correlation coefficient was 0.404. The B2 band of Landsat 8 and the NDPI characteristic quantity of the vegetation index have important correlations with forest biomass inversion, and the correlation coefficients are −0.342 and 0.323, respectively. Among the characteristic factors extracted from Sentinel-1A radar images, seven factors were correlated with biomass (p < 0.05), and five factors were negatively correlated, except entropy.
- (2)
- Comparing the inversion accuracy and training speed of the three models, the model based on the linear stepwise regression method has the fastest training speed, but the lowest model accuracy. The BP neural network has a strong fitting ability with complex data, a short training time and high model accuracy. Although the training time of the PSO improved neural network model is longer, the coefficient of determination between the predicted value and the measured value is the highest. The results show that there is a nonlinear relationship between the biomass and the strong correlation factors. The neural network model based on the particle swarm optimization algorithm is the best model for forest biomass inversion in the Liaoning region.
- (3)
- According to the spatial distribution map of biomass, the areas with high forest biomass in Liaoning Province are mainly distributed in areas with high altitudes and steep slopes in the east and southwest, while the areas with low biomass are mainly concentrated in the plain areas with low altitudes and gentle slopes.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Factor | B2 | B3 | B4 | B5 | B6 | B7 | |||||
Correlation coefficient | −0.342 ** | −0.290 ** | −0.302 ** | −0.157 * | −0.337 ** | −0.307 ** | |||||
Factor | ARVI | DVI | EVI | NDPI | NDVI | RVI | |||||
Correlation coefficient | 0.250 ** | 0.249 ** | −0.228 ** | 0.323 ** | 0.313 ** | 0.322 ** | |||||
Factor | SVAI | VH | VV | altitude | slope | canopy closure | |||||
Correlation coefficient | 0.310 ** | −0.070 | −0.004 | 0.404 ** | −0.015 | 0.328 ** | |||||
Mean | Variance | Homogeneity | Contrast | Dissimilarity | Entropy | Second Moment | Correlation | ASM | MAX | Energy | |
B2 | −0.310 ** | −0.056 | 0.069 | −0.036 | −0.063 | −0.087 | 0.083 | 0.107 | |||
B3 | −0.271 ** | −0.092 | 0.162 * | −0.084 | −0.137 | −0.185 * | 0.184 * | −0.040 | |||
B4 | −0.286 ** | −0.052 | 0.171 * | −0.068 | −0.163 * | −0.186 * | 0.169 * | −0.007 | |||
B5 | −0.158 * | −0.046 | 0.004 | 0.001 | −0.006 | 0.023 | −0.063 | 0.004 | |||
B6 | −0.322 ** | −0.168 * | 0.145 | −0.152 | −0.159 * | −0.211 ** | 0.194 * | 0.002 | |||
B7 | −0.285 ** | −0.132 | 0.175 * | −0.156 * | −0.174 * | −0.201 * | 0.184 * | 0.037 | |||
VH | −0.174 * | −0.191 * | −0.197 * | 0.119 | 0.148 | 0.196 * | −0.053 | −0.139 | −0.136 | −0.168 * | |
VV | −0.126 | −0.148 | −0.143 | 0.065 | 0.093 | 0.241 ** | 0.004 | −0.136 | −0.099 | −0.176 * |
Model | Unstandardized Coefficient | Standardized Coefficient | t | Sig. | |
---|---|---|---|---|---|
B | Standard Error | ||||
Constant | −243.422 | 126.015 | −1.932 | 0.055 | |
Altitude | 0.090 | 0.025 | 0.276 | 3.533 | 0.001 |
Canopy closure | 125.943 | 33.185 | 0.267 | 3.795 | 0.000 |
ARVI | −156.917 | 64.266 | −0.472 | −2.442 | 0.016 |
EVI | 73.454 | 35.773 | 0.236 | 2.053 | 0.042 |
RVI | 148.340 | 43.052 | 0.676 | 3.446 | 0.001 |
VVEntropy | 34.447 | 18.391 | 0.127 | 1.873 | 0.063 |
B6Mean | 24.306 | 13.932 | 0.417 | 1.745 | 0.083 |
B6 | −245.049 | 93.419 | −0.656 | −2.623 | 0.010 |
Model | R2 | Time |
---|---|---|
Stepwise regression model | 0.5468 | 0.0862 s |
BP neural network model | 0.78226 | 0.13 s |
POS improved neural network model | 0.83204 | 235.16 s |
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Zhang, D.; Ni, H. Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images. Sensors 2023, 23, 9313. https://doi.org/10.3390/s23239313
Zhang D, Ni H. Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images. Sensors. 2023; 23(23):9313. https://doi.org/10.3390/s23239313
Chicago/Turabian StyleZhang, Danhua, and Hui Ni. 2023. "Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images" Sensors 23, no. 23: 9313. https://doi.org/10.3390/s23239313