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RSS FeedsRemote Sensing, Vol. 14, Pages 6197: Death and Regeneration of an Amazonian Mangrove Forest by Anthropic and Natural Forces (Remote Sensing)

 
 

7 december 2022 11:30:16

 
Remote Sensing, Vol. 14, Pages 6197: Death and Regeneration of an Amazonian Mangrove Forest by Anthropic and Natural Forces (Remote Sensing)
 


The Amazon Macrotidal Mangrove Coast contains the most extensive and continuous mangrove belt globally, occupying an area of ~6500 km2 and accounting for 4.2% of global mangroves. The tallest and densest mangrove forests in the Amazon occur on the Bragança Peninsula. However, road construction that occurred in 1973 caused significant mangrove degradation in the area. A spatial-temporal analysis (1986–2019) based on optical, Synthetic Aperture Radar (SAR), drone images, and altimetric data obtained by photogrammetry and validated by a topographic survey were carried out to understand how the construction of a road led to the death of mangroves. The topographic data suggested that this road altered the hydrodynamical flow, damming tidal waters. This process killed at least 4.3 km2 of mangrove trees. Nevertheless, due to natural mangrove recolonization, the area exhibiting degraded mangrove health decreased to ~2.8 km2 in 2003 and ~0.73 km2 in 2019. Climatic extreme events such as “El Niño” and “La Niña” had ephemeral control over the mangrove degradation/regeneration. In contrast, the relative sea-level rise during the last several decades caused long-term mangrove recolonization, expanding mangrove areas from lower to higher tidal flats. Permanently flooded depressions in the study area, created by the altered hydrodynamical flow due to the road, are unlikely to be recolonized by mangroves unless connections are re-established between these depressions with drainage on the Caeté estuary through pipes or bridges to prevent water accumulation between the road and depressions. To minimize impacts on mangroves, this road should have initially been designed to cross mangrove areas on the highest tidal flats and to skirt the channel headwaters to avoid interruption of regular tidal flow.


 
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Remote Sensing, Vol. 14, Pages 6192: A Comparison of Four Methods for Automatic Delineation of Tree Stands from Grids of LiDAR Metrics (Remote Sensing)
Remote Sensing, Vol. 14, Pages 6198: Predicting Leaf Phenology in Forest Tree Species Using UAVs and Satellite Images: A Case Study for European Beech (Fagus sylvatica L.) (Remote Sensing)
 
 
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