Information extraction method of mangrove forests based on GF-6 data
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摘要: 红树林具有定期被潮水淹没的特点,这个特点对于利用遥感技术手段提取红树林来说既是机遇也是挑战。为探究在任意潮汐条件下,高分六号(GF-6)卫星数据的红边波段在红树林提取上的贡献度,以海南省最大的红树林区域东寨港东南区为研究区域,利用高分二号(GF-2)卫星数据获取标准样本点。以研究区的标准样本点和高分六号数据为基础,构建典型地物反射光谱曲线图,由植被强吸收的波段建立基线,基线之上反射率的平均值定义了适用于高分六号卫星数据的潮间红树林指数(intertidal mangrove forest index,IMFI),同时建立了红边归一化植被指数(red-edge normalized difference vegetation index,RENDVI),这2种指数与归一化植被指数(normalized difference vegetation index,NDVI)和归一化水体指数(normalized difference water index,NDWI)等常用指数通过箱线图进行对比分析,并基于IMFI和RENDVI构建决策树模型对研究区的典型红树林进行提取,提取结果与高分二号遥感数据目视解译提取的样本进行精度验证。结果表明: ①红树林周期性被潮水淹没的特点,使得潮间红树林的反射光谱曲线在水体与红树林的标准光谱曲线之间分布,且相对分散; ②IMFI和RENDVI可反映红边波段与近红外波段反射光谱的差异性,能够更好地对潮间红树林、红树林和水体进行分离; ③基于IMFI和RENDVI构建的决策树模型可有效提取红树林分布信息,其总体精度为0.95,Kappa系数为0.90。红边波段的引入在红树林提取上发挥了重要作用,具有很大的应用潜力,为国产红边卫星数据在生态方面的应用提供参考。Abstract: Mangrove forests are periodically inundated by tidal water. This characteristic opens up an opportunity but also poses a challenge for the information extraction of mangrove forests using remote sensing technology. To explore the contribution of the red-edge band of GF-6 satellite data in information extraction of mangrove forests under the condition of random tides, this study investigated the southeastern Dongzhaigang area-the largest mangrove forest area in Hainan Province and obtained standard samples using the GF-2 satellite data. The reflectance spectral curves of typical surface features were constructed based on the standard samples and the GF-6 satellite data. Then, a baseline was established based on the bands strongly absorbed by vegetation, and the intertidal mangrove forest index (IMFI) applicable to the GF-6 satellite data was defined using the average reflectance of bands above the baseline. Meanwhile, the red-edge normalized difference vegetation index (RENDVI) was also established. The two indices were compared with commonly used indices, such as the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), using box-whisker plots. Then, using the decision tree model constructed based on IMFI and RENDVI, information on typical mangrove forest in the study area were extracted. The precision of the extraction results was verified through comparison with visual interpretation results of the samples extracted from the GF-2 satellite data. The results show that: ① Because mangrove forests are periodically inundated by tidal water, the reflectance spectral curves of intertidal mangrove forests were relatively scattered between the standard spectral curves of water bodies and mangrove forests; ② IMFI and RENDVI can reflect the differences in the reflectance spectra of the red-edge and near-infrared bands and thus effectively separated the intertidal mangrove forests, mangrove forests, and water bodies; ③ The decision tree model constructed based on IMFI and RENDVI can effectively extract the distribution information of the mangrove forests, with an overall accuracy of 0.95 and a Kappa coefficient of 0.90. The introduction of the red-edge band plays an important role in the information extraction of mangrove forests and has great potential for application. This study can be used as a reference for the ecological applications of red-edge data from domestic satellites.
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Key words:
- GF-6 satellite /
- red-edge band /
- mangrove forest index /
- reflectance spectrum /
- Dongzhaigang
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