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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.

Given the alarming global rates of mangrove forest loss it is important that resource managers have access to updated information regarding both the extent and condition of their mangrove forests. Mexican mangroves in particular have been identified as experiencing an exceptional high annual rate of loss. However, conflicting studies, using remote sensing techniques, of the current state of many of these forests may be hindering all efforts to conserve and manage what remains. Focusing on one such system, the Teacapán-Agua Brava-Las Haciendas estuarine-mangrove complex of the Mexican Pacific, an attempt was made to develop a rapid method of mapping the current condition of the mangroves based on estimated LAI. Specifically, using an AccuPAR LP-80 Ceptometer, 300 indirect in situ LAI measurements were taken at various sites within the black mangrove (Avicennia germinans) dominated forests of the northern section of this system. From this sample, 225 measurements were then used to develop linear regression models based on their relationship with corresponding values derived from QuickBird very high resolution optical satellite data. Specifically, regression analyses of the in situ LAI with both the normalized difference vegetation index (NDVI) and the simple ration (SR) vegetation index revealed significant positive relationships [LAI versus NDVI (R (2) = 0.63); LAI versus SR (R (2) = 0.68)]. Moreover, using the remaining sample, further examination of standard errors and of an F test of the residual variances indicated little difference between the two models. Based on the NDVI model, a map of estimated mangrove LAI was then created. Excluding the dead mangrove areas (i.e. LAI = 0), which represented 40% of the total 30.4 km(2) of mangrove area identified in the scene, a mean estimated LAI value of 2.71 was recorded. By grouping the healthy fringe mangrove with the healthy riverine mangrove and by grouping the dwarf mangrove together with the poor condition mangrove, mean estimated LAI values of 4.66 and 2.39 were calculated, respectively. Given that the former healthy group only represents 8% of the total mangrove area examined, it is concluded that this mangrove system, considered one of the most important of the Pacific coast of the Americas, is currently experiencing a considerable state of degradation. Furthermore, based on the results of this investigation it is suggested that this approach could provide resource managers and scientists alike with a very rapid and effective method for monitoring the state of remaining mangrove forests of the Mexican Pacific and, possibly, other areas of the tropics.