An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme.

Abstract

Optimizing the classification accuracy of amangrove forest is of utmost importance for conservationpractitioners. 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 (30m per pixel)had the lowest overall accuracyat 64% and thatWorldView-2 (1.6m per pixel)hadthe highest at 93%. Moreover, theSPOT-5 and theSentinel-2 classifications (10m per pixel)were very similar havingaccuracies of 75 and 78%, respectively. In comparisontoWorldView-2,theother sensors overestimatedthe extentof Laguncularia racemosa and underestimatedthe extent of Rhizophora mangle. When considering such typeof sensors, the higher spatial resolutioncan be particularly important in mapping small mangrove islands that often occur in degraded mangrove systems.