Valderrama-Landeros L, Flores-de-Santiago F, Kovacs J M, Flores-Verdugo F
Subcoordinación de Percepción Remota, Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad (CONABIO), 4903 Liga Periférico-Insurgentes Sur, 14010, Tlalpan, CDMX, Mexico.
Instituto de Ciencias del Mar y Limnología, Unidad Académica Procesos Oceánicos y Costeros, Universidad Nacional Autónoma de México, A.P. 70-305, Av. Universidad 3000, Ciudad Universitaria, 04510, Coyoacán, CDMX, Mexico.
Environ Monit Assess. 2017 Dec 14;190(1):23. doi: 10.1007/s10661-017-6399-z.
Optimizing the classification accuracy of a mangrove forest is of utmost importance for conservation practitioners. Mangrove forest mapping using satellite-based remote sensing techniques is by far the most common method of classification currently used given the logistical difficulties of field endeavors in these forested wetlands. However, there is now an abundance of options from which to choose in regards to satellite sensors, which has led to substantially different estimations of mangrove forest location and extent with particular concern for degraded systems. The objective of this study was to assess the accuracy of mangrove forest classification using different remotely sensed data sources (i.e., Landsat-8, SPOT-5, Sentinel-2, and WorldView-2) for a system located along the Pacific coast of Mexico. Specifically, we examined a stressed semiarid mangrove forest which offers a variety of conditions such as dead areas, degraded stands, healthy mangroves, and very dense mangrove island formations. The results indicated that Landsat-8 (30 m per pixel) had the lowest overall accuracy at 64% and that WorldView-2 (1.6 m per pixel) had the highest at 93%. Moreover, the SPOT-5 and the Sentinel-2 classifications (10 m per pixel) were very similar having accuracies of 75 and 78%, respectively. In comparison to WorldView-2, the other sensors overestimated the extent of Laguncularia racemosa and underestimated the extent of Rhizophora mangle. When considering such type of sensors, the higher spatial resolution can be particularly important in mapping small mangrove islands that often occur in degraded mangrove systems.
对于保护从业者来说，优化红树林森林分类的准确性至关重要。鉴于在这些森林湿地进行实地调查存在后勤困难，利用卫星遥感技术绘制红树林森林地图是目前最常用的分类方法。然而，现在在卫星传感器方面有大量选择，这导致对红树林森林位置和范围的估计存在很大差异，尤其是对于退化系统。本研究的目的是评估使用不同遥感数据源（即 Landsat - 8、SPOT - 5、哨兵 - 2 和 WorldView - 2）对墨西哥太平洋沿岸一个系统的红树林森林分类的准确性。具体而言，我们研究了一个压力较大的半干旱红树林森林，它具有各种条件，如死亡区域、退化林分、健康的红树林以及非常密集的红树林岛屿形成。结果表明，Landsat - 8（每像素 30 米）的总体准确率最低，为 64%，而 WorldView - 2（每像素 1.6 米）的总体准确率最高时为 93%。此外，SPOT - 5 和哨兵 - 2 的分类（每像素 10 米）非常相似，准确率分别为 75%和 78%。与 WorldView - 2 相比，其他传感器高估了拉贡木（Laguncularia racemosa）的范围，低估了红树（Rhizophora mangle）的范围。在考虑这类传感器时，较高的空间分辨率对于绘制退化红树林系统中经常出现的小红树林岛屿可能特别重要。
