Given the scale and rate of mangrove loss globally, it is increasingly important to map and monitor mangrove forest health in a timely fashion. This study aims to identify the conditions of mangroves in a coastal lagoon south of the city of Mazatlán, Mexico, using proximal hyperspectral remote sensing techniques. The dominant mangrove species in this area includes the red (Rhizophora mangle), the black (Avicennia germinans) and the white (Laguncularia racemosa) mangrove. Moreover, large patches of poor condition black and red mangrove and healthy dwarf black mangrove are commonly found. Mangrove leaves were collected from this forest representing all of the aforementioned species and conditions. The leaves were then transported to a laboratory for spectral measurements using an ASD plot, principal components analysis and stepwise discriminant analyses were then used to select wavebands deemed most appropriate for further mangrove classification. Specifically, the wavebands at 520, 560, 650, 710, 760, 2100 and 2230 nm were selected, which correspond to chlorophyll absorption, red edge, starch, cellulose, nitrogen and protein regions of the spectrum. The classification and validation indicate that these wavebands are capable of identifying mangrove species and mangrove conditions common to this degraded forest with an overall accuracy and Khat coefficient higher than 90% and 0.9, respectively. Although lower in accuracy, the classifications of the stressed (poor condition and dwarf) mangroves were found to be satisfactory with accuracies higher than 80%. The results of this study indicate that it could be possible to apply laboratory hyperspectral data for classifying mangroves, not only at the species level, but also according to their health conditions.

Changes in chlorophyll a (chl a), leaf area, and leaf length need to be considered when developing ecological assessments of mangrove forests where distinct seasons occur. The purpose of this study was to assess such changes between the dry and rainy seasons in a variety of mangrove classes. Six different classes were examined, consisting of 3 species (white mangrove Laguncularia racemosa, red mangrove Rhizophora mangle, and black mangrove Avicennia germinans) that were either healthy or in poor condition. In total, 360 leaf samples were taken from the upper and lower canopy for chl a content. Additionally, leaf area index (LAI) was recorded at the same locations. For all the poor-condition classes, we observed an increase in the chl a content during the rainy season in both the upper and lower canopies. Moreover, dwarf black mangrove was the only poor-condition class which did not show an increase in leaf length during the rainy season. The healthy white mangrove showed no seasonal difference in chl a in the upper canopy, but the lower canopy did have higher chl a content during the dry season as well as a lower LAI. The healthy red mangrove also did not show any seasonal difference in chl a content, but the upper canopy had a higher chl a content. For the healthy black mangrove, no seasonal differences were found in chl a content, LAI, or leaf morphology in both upper and lower canopies. Consequently, for future endeavors we recommend that seasonal changes in the upper canopy be considered, especially when examining stands in poor condition. KEY WORDS: Mangrove · Seasonal changes · Chlorophyll a · Leaf area index · LAIResale or republication not permitted without written consent of the publisher

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.

We provide a baseline account as to the type of mangrove that is typical for Guinea, Africa using field based and remotely sensed data. Specifically, the mangroves of the estuarine islands of Mabala and Yélitono were classified using satellite and airborne optical remote sensing data. Mangroves were mapped according to four classes: tall red (Rhizophora racemosa), medium red (R. racemosa), dwarf red (R. mangle and R. harisonii), and black mangrove (Avicennia germinans). Producer's and user's accuracies for the mapping of mangrove from nonmangrove areas were both 98%. When separating amongst the mangrove classes most of the confusion resulted from the medium red mangrove class. Of the 10,442 ha of mangrove mapped, approximately 30% were classified as riverine, dominated by tall R. racemosa. The remaining mangrove areas were dominated by dwarf mangrove of either Rhizophora or A. germinans. Biophysical parameter data collected from 56 transects varied considerably amongst the classes. For the tallest mangrove class, the mean values of height, DBH, estimated LAI, stem density and basal area recorded were 13 m, 15.1 cm, 4.3, 838 stems/ha, and 25.9 m 2 /ha, respectively. In contrast, for A. germinans, values of 3 m, 4.6 cm, 1.5, 2,877 stems/ha, and 6.0 m 2 /ha were calculated, respectively.