Competition between Hardwood Hammocks and Mangroves

Sternberg, Leonel da Silveira Lobo; Teh, Su Yean; Ewe, Sharon M. L.; Miralles‐Wilhelm, Fernando; Jiang, Jiang

doi:10.1007/s10021-007-9050-y

Cited by 52 publications

(56 citation statements)

References 26 publications

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“…Therefore, salinity is considerably lower and nutrient input is constant [54]. Representative species of this type of mangrove include R. mangle, A. germinans, and L. racemosa, associated with other evergreen and semi-evergreen tropical trees intolerant to salinity [54,56].…”

Section: Study Areamentioning

confidence: 99%

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

et al. 2015

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There is a need to develop indicators of mangrove condition using remotely sensed data. However, remote estimation of leaf and canopy biochemical properties and vegetation condition remains challenging. In this paper, we (i) tested the performance of selected hyperspectral and broad band indices to predict chlorophyll concentration (CC) on mangrove leaves and (ii) showed the potential of Landsat 8 for estimation of mangrove CC at the landscape level. Relative leaf CC and leaf spectral response were measured at 12 Elementary Sampling Units (ESU) distributed along the northwest coast of the Yucatan Peninsula, Mexico. Linear regression models and coefficients of determination were computed to measure the association between CC and spectral response. At leaf level, the narrow band indices with the largest correlation with CC were Vogelmann indices and the MTCI (R 2 > 0.5). Indices with spectral bands around the red edge (705-753 nm) were more OPEN ACCESSRemote Sens. 2015, 7 14531 sensitive to mangrove leaf CC. At the ESU level Landsat 8 NDVI green, which uses the green band in its formulation explained most of the variation in CC (R 2 > 0.8). Accuracy assessment between estimated CC and observed CC using the leave-one-out cross-validation (LOOCV) method yielded a root mean squared error (RMSE) = 15 μg·cm −2 , and R 2 = 0.703. CC maps showing the spatiotemporal variation of CC at landscape scale were created using the linear model. Our results indicate that Landsat 8 NDVI green can be employed to estimate CC in large mangrove areas where ground networks cannot be applied, and mapping techniques based on satellite data, are necessary. Furthermore, using upcoming technologies that will include two bands around the red edge such as Sentinel 2 will improve mangrove monitoring at higher spatial and temporal resolutions.

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Section: Study Areamentioning

confidence: 99%

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

et al. 2015

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“…The model TUNA has since been enhanced to allow the assessment of the role of mangrove for coastal protection [4]. The impact of storm surges induced by tsunami or wind-waves on coastal vegetation has also been investigated [5,6]. The propagation of tsunami in deep oceans may be simulated by the depth-averaged two-dimensional shallow water equations (SWE), following the proposal of the Intergovernmental Oceanography Commission (IOC).…”

Section: Tuna: Tsunami Utilities and Applicationmentioning

confidence: 99%

Tsunami risk mapping simulation for Malaysia

Teh

Koh

Moh

et al. 2011

WIT Transactions on the Built Environment

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The 26 December 2004 Andaman mega tsunami killed about a quarter of a million people worldwide. Since then several significant tsunamis have recurred in this region, including the most recent 25 October 2010 Mentawai tsunami. These tsunamis grimly remind us of the devastating destruction that a tsunami might inflict on the affected coastal communities. There is evidence that tsunamis of similar or higher magnitudes might occur again in the near future in this region. Of particular concern to Malaysia are tsunamigenic earthquakes occurring along the northern part of the Sunda Trench. Further, the Manila Trench in the South China Sea has been identified as another source of potential tsunamigenic earthquakes that might trigger large tsunamis. To protect coastal communities that might be affected by future tsunamis, an effective early warning system must be properly installed and maintained to provide adequate time for residents to be evacuated from risk zones. Affected communities must be prepared and educated in advance regarding tsunami risk zones, evacuation routes as well as an effective evacuation procedure that must be taken during a tsunami occurrence. For these purposes, tsunami risk zones must be identified and classified according to the levels of risk simulated. This paper presents an analysis of tsunami simulations for the South China Sea and the Andaman Sea for the purpose of developing a tsunami risk zone classification map for Malaysia based upon simulated maximum wave heights.

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“…Hardwood hammocks generally occupy areas of higher elevation, where salinities are low, while mangroves can tolerate higher salinity levels but also have the ability to grow in freshwater (Sternberg & Swart, 1987;Odum & Mclvor, 1990). There exists an equilibrium state at which both vegetation types are present, where the salinity of the vadose layer is at an intermediate level of lethality for freshwater hammock species (Sternberg et al, 2007). Teh et al (2008) suggest that because of climate change, sea level rise and storm surges are potential perturbations which may cause the vegetation to shift from hardwood hammocks to mangroves as an increase in salinities of the vadose zone induced by these events might eradicate the salinity-intolerant hardwood hammocks at higher elevations and promote landward migration of mangroves.…”

Section: Mangrove and Hardwood Hammock Ecosystemsmentioning

confidence: 99%

Impacts of Climate Change on Vegetation Distribution No. 2 - Climate Change Induced Vegetation Shifts in the New World

Hufnágel¹,

Garamvölgyi²

2014

Appl Ecol Env Res

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Abstract. After giving an overview of climate change induced vegetation shifts in the Palearctic region in our previous paper, in this article we review literature available in Web of Science on North and South America. We study different geographical regions such as Canada, Alaska, California, Southwestern, Eastern and Southeastern USA, the Great Lakes region, the Great Plains, intermontane basins and plateaus, Rocky Mountains and the Cascades as well as Central and South America. We summarize main results of relevant field studies, experiments and model simulations. Predicted environmental changes include temperature increases, altering precipitation patterns, droughts, permafrost thaw and ground subsidence in arctic regions, enhanced El Niño Southern Oscillation, sea level rise, increasing salinity of the vadose zone, snowpack declines and various disturbances. All vegetation types are affected by these changes, to the most important phenomena belong e.g. reduction of arctic and alpine communities, decreasing area of taiga, shrub encroachment in tundra areas, northward expansion of the tree line, reduction in wetland areas, invasion, altering forest regeneration patterns, decrease in dominance of conifer species, increased cover of salt-tolerant plant species in tidal marshes, expansion of grassland, compositional and structural changes of grasslands and forests, drying up of bogs, landward migration of mangroves, savannification of forests, expansion of chaparral as well as upward migration of species in the mountains.

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Competition between Hardwood Hammocks and Mangroves

Cited by 52 publications

References 26 publications

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

Tsunami risk mapping simulation for Malaysia

Impacts of Climate Change on Vegetation Distribution No. 2 - Climate Change Induced Vegetation Shifts in the New World

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