研究目的
To estimate aboveground carbon reserves in the research area by utilizing remote sensing technology and field data integration.
研究成果
The research successfully estimated aboveground carbon stocks in Batam Island's forest area at 103,397 TonC/Ha with an error rate of 1.8354, demonstrating the feasibility of using remote sensing for efficient carbon stock mapping. Future studies could improve accuracy with higher resolution data and region-specific allometric models.
研究不足
The error rate of 1.8354 indicates inaccuracies in pixel-level estimates, potentially due to limitations in spatial resolution of SPOT 5 imagery and assumptions in allometric equations. The study is specific to Batam Island and may not be generalizable to other regions without validation.
1：Experimental Design and Method Selection:
The research uses a comparative approach integrating remote sensing data (SPOT 5 images) with field data. Methods include radiometric and geometric corrections, multispectral classification using maximum likelihood algorithm, vegetation index transformation (MSAVI), and regression modeling for carbon stock estimation based on allometric equations (Brown's 1997). Accuracy is assessed using RMSE.
2：7). Accuracy is assessed using RMSE.
Sample Selection and Data Sources:
2. Sample Selection and Data Sources: Field data collected from 30 sample plots in Batam Island, Indonesia, selected based on vegetation density classes from MSAVI transformation. Remote sensing data sourced from SPOT 5 satellite imagery with 10-meter spatial resolution.
3：List of Experimental Equipment and Materials:
SPOT 5 satellite imagery, field measurement tools for tree dimensions (e.g., diameter at breast height), software for image processing (not specified), and base maps for geometric correction.
4：Experimental Procedures and Operational Workflow:
Steps include image pre-processing (radiometric and geometric corrections, resizing), multispectral classification to identify forest areas, MSAVI transformation, field sampling for biomass calculation using allometric equations, regression modeling between MSAVI and carbon stock, and accuracy testing with RMSE.
5：Data Analysis Methods:
Statistical analysis includes correlation and regression to model relationships between remote sensing indices and field-measured carbon stocks. Accuracy is evaluated using confusion matrix for classification and RMSE for estimation errors.
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