Advancement in Remote Sensing allows rapid mangrove mapping without the need for data-intensive methodologies, complex classifiers, and skill-dependent classification techniques. This study proposes a new index, the Mangrove Vegetation Index (MVI), to rapidly and accurately map mangroves extent from remotely-sensed imageries. The MVI utilizes three Sentinel-2 bands green, Near Infrared (NIR) and Shortwave Infrared (SWIR) in the form |NIR-Green|/|SWIR-Green| to discriminate the distinct greenness and moisture of mangroves from terrestrial vegetation and other land cover. Spectral band analysis shows that the |NIR-Green| part of MVI captures the differences of greenness between mangrove forests and terrestrial vegetation. The |SWIR-Green| part of the index expresses the distinct moisture of mangroves without the need for additional intertidal data and water indices. The MVI value increases with the increasing probability of a pixel being classified as mangroves. Eleven mangrove forests in the Philippines and one mangrove park in Japan were then mapped using MVI. Accuracy assessment was done using field inventory data and high-resolution drone orthophotos. MVI have successfully separated the mangroves from other cover especially terrestrial vegetation, with an overall index accuracy of 92%. The MVI was applied to Landsat 8 images using the equivalent bands to test the universality of the index. Comparable MVI mangrove maps were produced between Sentinel-2 and Landsat images, with an optimal minimum threshold of 4.5 and 4.6, respectively. MVI can effectively highlight the greenness and moisture information in mangroves as reflected by its moderate to high correlation value (r = 0.63 and 0.84, α = 0.05) with the Sentinel-derived chlorophyll-a (C_a) and canopy water (C_w) biophysical products. This study developed and implemented two automated platforms: an offline IDL-based ‘MVI Mapper’ and an online Google Earth Engine-based MVI mapping interface. The MVI implemented in Google Earth Engine was used in generating the latest mangrove extent map of the Philippines. Additionally, the application of MVI were tested to four additional mangrove forests in Southeast Asia: Thailand, Vietnam, Indonesia and Cambodia; and to selected mangroves forests in South America, Africa and Australia.

遥感技术的进步允许快速进行红树林制图，而无需数据密集型方法，复杂的分类器和与技能相关的分类技术。这项研究提出了一个新的指标，即红树林植被指数（MVI），可以从遥感图像中快速准确地绘制红树林范围。MVI以| NIR-Green | / || SWIR-Green |的形式利用三个Sentinel-2波段绿色，近红外（NIR）和短波红外（SWIR）。区分红树林和陆地植被和其他土地覆盖物的绿色与湿气。光谱分析表明| NIR-Green | MVI的一部分记录了红树林和陆地植被之间的绿色差异。|| SWIR-Green | 该指数的一部分表示红树林的独特水分，而无需其他潮间带数据和水分指数。MVI值随着像素被分类为红树林的可能性的增加而增加。然后使用MVI绘制了菲律宾的11个红树林和日本的1个红树林公园的地图。使用现场库存数据和高分辨率无人机正射影像进行准确性评估。MVI已成功地将红树林与其他覆盖物特别是陆地植被分离开来，总体指数准确性为92％。使用等效带将MVI应用于Landsat 8图像，以测试索引的通用性。在Sentinel-2和Landsat影像之间产生了可比较的MVI红树林图，最佳最小阈值分别为4.5和4.6。_a）和冠层水（C _w）生物物理产品。这项研究开发并实现了两个自动化平台：一个基于IDL的离线“ MVI Mapper”和一个基于Google Earth Engine的在线MVI映射界面。在Google Earth Engine中实现的MVI用于生成菲律宾的最新红树林范围地图。此外，还对东南亚地区的另外四种红树林进行了测试，测试了MVI的应用：泰国，越南，印度尼西亚和柬埔寨；以及南美，非洲和澳大利亚的部分红树林。