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Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive
Minh Hai Pham1, Thi Hoai Do1, Van-Manh Pham2
1Vietnam Institute of Geodesy and Cartography, Ha Noi, Viet Nam.
Plos One
|May 22, 2020
Summary
A new hybrid model combining the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) offers improved forest classification and aboveground biomass (AGB) estimation. This advanced approach outperforms conventional methods for accurate forest monitoring.
Area of Science:
- Remote Sensing
- Machine Learning
- Forestry
Background:
- Earth observation and machine learning advance forest monitoring capabilities.
- Classifying forest cover and estimating aboveground biomass (AGB) are key applications.
Purpose of the Study:
- Introduce a novel hybrid model integrating the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS).
- Apply the model for mangrove forest classification and AGB estimation in Vietnam's Ca Mau coastal area.
Main Methods:
- Utilized Sentinel-1A (VH, VV polarizations) and SPOT multispectral imagery.
- Implemented feature selection for optimal predictor variables.
- Benchmarked against Support Vector Regression, Multilayer Perceptron, Random Subspace, and Random Forest.
Main Results:
- The proposed hybrid model demonstrated statistically superior performance across all indicators (RMSE, MAE, R2).
- Achieved RMSE = 70.882, MAE = 55.458, and R2 = 0.577 for AGB estimation.
Conclusions:
- The hybrid ASO-ANFIS model is a recommended alternative for biomass estimation.
- Metaheuristic algorithms offer scientifically sound solutions for forest cover analysis.
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