研究目的
Exploring the feasibility of mapping the canopy closures of mangrove forest in the Mekong Delta of Vietnam using Sentinel-2 multispectral composite image.
研究成果
The object-based SVM can obtain satisfactory result using Blue/Green/Red and Green/Red/NIR bands for image segmentation and classification. Pixel-based SVM seems not able to produce satisfactory result which might be due to the effects of mixed pixels and spectral variations in pixels.
研究不足
There was a level of around 11%-24% commission rate and omission rate in the rich, medium, and poor classes of canopy closure. Further research to improve the performance of canopy closure classification is needed.
1：Experimental Design and Method Selection:
The study used an object-based support vector machine (SVM) classifier to derive land use and land cover (LULC) and differentiate canopy closure over the mangrove forest.
2：Sample Selection and Data Sources:
The study site was located in Cape Ca Mau National Park, Southern Mekong Delta, covering an area of approximately 13,400 ha. Sentinel-2 multispectral images were used.
3：List of Experimental Equipment and Materials:
Sentinel-2 images with various resolutions for different bands.
4：Experimental Procedures and Operational Workflow:
A NDVI image was applied to distinguish vegetation pixels from non-vegetation. Then, the object-based SVM and pixel-based SVM methods were applied to map the canopy closure of the mangrove forest.
5：Data Analysis Methods:
Overall accuracy (OA) and kappa coefficient were used to measure the accuracy of classification.
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