A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery
A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery
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使用单潮 Sentinel-2 图像检测定期淹没的红树林的新植被指数
DOI：
10.3390/rs11172043
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发表时间：
2019-09-01
期刊：
影响因子：
5
通讯作者：
Zhang, Yuanzhi
中科院分区：
文献类型：
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作者：
Jia, Mingming;Wang, Zongming;Zhang, Yuanzhi
红树林是生长在受保护的潮间带的热带树木和灌木。红树林的精确制图是遥感的一个巨大挑战，因为红树林周期性地被潮汐洪水淹没。传统上，需要多潮汐图像来消除水的影响;然而，由于多雨的气候和不确定的当地潮汐条件，这种图像往往无法获得。因此，从单潮图像中提取红树林具有重要意义。在这项研究中，红边波段的Sentinel-2影像的反射率被用来建立一个新的植被指数，是敏感的淹没红树林。具体而言，红和短波近红外波段被用来建立一个线性基线，基线以上的4个红边波段的平均反射率值被定义为红树林森林指数（MFI）。为了评价MFI，定量评估了MFI和四个广泛使用的植被指数（维斯）之间的检测红树林森林的能力。此外，通过对全球三个红树林样地的应用，验证了MFI的实际作用。结果表明：（1）理论上，Jensen-Shannon散度表明，水下红树林与水体像元之间的MFI距离最大。此外，箱形图表明，所有淹没红树林森林可以从MFI图像的水背景分离。此外，在MFI图像中，为了分离红树林和水，阈值是等于零的常数。(2)实际上，在将MFI应用于全球三个地点后，99-102%的淹没红树林被MFI成功提取。尽管仍有一些不确定性和局限性，但《多边基金》在准确绘制世界各地的红树林以及其他沿海和水生植被地图方面提供了巨大的好处。
Mangrove forests are tropical trees and shrubs that grow in sheltered intertidal zones. Accurate mapping of mangrove forests is a great challenge for remote sensing because mangroves are periodically submerged by tidal floods. Traditionally, multi-tides images were needed to remove the influence of water; however, such images are often unavailable due to rainy climates and uncertain local tidal conditions. Therefore, extracting mangrove forests from a single-tide imagery is of great importance. In this study, reflectance of red-edge bands in Sentinel-2 imagery were utilized to establish a new vegetation index that is sensitive to submerged mangrove forests. Specifically, red and short-wave near infrared bands were used to build a linear baseline; the average reflectance value of four red-edge bands above the baseline is defined as the Mangrove Forest Index (MFI). To evaluate MFI, capabilities of detecting mangrove forests were quantitatively assessed between MFI and four widely used vegetation indices (VIs). Additionally, the practical roles of MFI were validated by applying it to three mangrove forest sites globally. Results showed that: (1) theoretically, Jensen–Shannon divergence demonstrated that a submerged mangrove forest and water pixels have the largest distance in MFI compared to other VIs. In addition, the boxplot showed that all submerged mangrove forests could be separated from the water background in the MFI image. Furthermore, in the MFI image, to separate mangrove forests and water, the threshold is a constant that is equal to zero. (2) Practically, after applying the MFI to three global sites, 99–102% of submerged mangrove forests were successfully extracted by MFI. Although there are still some uncertainties and limitations, the MFI offers great benefits in accurately mapping mangrove forests as well as other coastal and aquatic vegetation worldwide.