An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme
An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme
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DOI：
10.1007/s10661-017-6399-z
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发表时间：
2017
影响因子：
3
通讯作者：
L. Valderrama-Landeros;F. Flores-de-Santiago;J. Kovacs;F. Flores-Verdugo
中科院分区：
文献类型：
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作者：
L. Valderrama-Landeros;F. Flores-de-Santiago;J. Kovacs;F. Flores-Verdugo
优化红树林的分类准确性对于保护从业者来说至关重要。鉴于这些森林湿地实地工作的后勤困难，使用卫星遥感技术进行红树林测绘是迄今为止最常用的分类方法。然而，现在卫星传感器有多种选择，这导致对红树林位置和范围的估计存在很大差异，特别是对退化系统的关注。本研究的目的是评估使用不同遥感数据源（即 Landsat-8、SPOT-5、Sentinel-2 和 WorldView-2）对墨西哥太平洋沿岸系统进行红树林分类的准确性。具体来说，我们检查了一片受压的半干旱红树林，该森林提供了各种条件，例如死亡区域、退化林分、健康红树林和非常茂密的红树林岛。结果表明，Landsat-8（每像素 30 m）的总体准确度最低，为 64%，而 WorldView-2（每像素 1.6 m）的总体准确度最高，为 93%。此外，SPOT-5 和 Sentinel-2 分类（每像素 10 m）非常相似，准确度分别为 75% 和 78%。与 WorldView-2 相比，其他传感器高估了 Lagunularia racemosa 的范围，而低估了 Rhizophora mangle 的范围。在考虑此类传感器时，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型红树林岛的地图尤其重要。
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 ofLaguncularia racemosaand underestimated the extent ofRhizophora 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.