Subcoordinación de Percepción Remota, Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad (CONABIO), 4903 Liga Periférico-Insurgentes Sur, Tlalpan, Cd. México, 14010, Mexico.
Instituto de Ciencias del Mar y Limnología, Unidad Académica Mazatlán, Universidad Nacional Autónoma de México, Mazatlán, Sinaloa, 82100, Mexico.
J Environ Manage. 2021 Feb 1;279:111617. doi: 10.1016/j.jenvman.2020.111617. Epub 2020 Nov 10.
Continuum monitoring of mangrove ecosystems is required to maintain and improve upon national mangrove conservation strategies. In particular, mangrove canopy assessments using remote sensing methods can be undertaken rapidly and, if freely available, optimize costs. Although such spaceborne data have been used for such purposes, their application to map mangroves at the species level has been limited by the capacity to provide continuous data. The objective of this study was to assess mangrove seasonal patterns using seven multispectral vegetation indices based on a Sentinel-2 (S2) time series (July 2018 to October 2019) to assess phenological trajectories of various semiarid mangrove classes in the Google Earth Engine platform using Fourier analysis for an area located in Western Mexico. The results indicate that the months from November through December and from May through July were critical in mangrove species discrimination using the EVI2, NDVI, and VARI series. The Random Forest classification accuracy for the S2 image was calculated at 79% during the optimal acquisition period (June 25, 2019), whereas only 55% accuracy was calculated for the non-optimal image acquired date (March 2, 2019). Although mangroves are considered evergreen forests, the phenological pattern of various mangrove canopies, based on these indices, were shown to be very similar to the surrounding land-based semiarid deciduous forest. Consequently, it is believed that the rainfall pattern is likely to be the key environmental factor driving mangrove phenology in this semiarid coastal system and thus the degree of success in mangrove remote sensing classification endeavors. Identifying the optimal dates when canopy spectral conditions are ideal in achieving mangrove species discrimination could be of utmost importance when purchasing more expensive very-high spatial resolution satellite images or collecting spatial data from UAVs.
需要对红树林生态系统进行连续监测，以维护和改进国家红树林保护战略。特别是，使用遥感方法进行红树林冠层评估可以快速进行，如果可以免费获得，则可以优化成本。尽管已经使用了这种天基数据来达到这些目的，但由于提供连续数据的能力有限，其在物种水平上绘制红树林地图的应用受到限制。本研究的目的是使用基于 Sentinel-2（S2）时间序列（2018 年 7 月至 2019 年 10 月）的七种多光谱植被指数评估红树林的季节性模式，使用傅里叶分析在 Google Earth Engine 平台上评估各种半干旱红树林类别的物候轨迹在墨西哥西部的一个地区。结果表明，使用 EVI2、NDVI 和 VARI 系列，11 月至 12 月和 5 月至 7 月是红树林物种区分的关键月份。在最佳采集期（2019 年 6 月 25 日），S2 图像的随机森林分类精度计算为 79%，而在非最佳采集日期（2019 年 3 月 2 日）获取的图像的精度计算为 55%。尽管红树林被认为是常绿林，但基于这些指数的各种红树林冠层的物候模式与周围基于陆地的半干旱落叶林非常相似。因此，人们认为降雨模式可能是驱动半干旱沿海系统中红树林物候的关键环境因素，从而也是红树林遥感分类工作成功的程度。在购买更昂贵的高空间分辨率卫星图像或从无人机收集空间数据时，确定树冠光谱条件理想以实现红树林物种区分的最佳日期可能至关重要。
