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Spatial Ecology of Mangrove Forests: A Remote Sensing Perspective

Chapter

Abstract

Over the past few decades, a diverse range of remote sensing data have been acquired over mangrove areas in different modes and with varying spatial resolutions and temporal frequencies, with these used to advance our understanding of mangrove ecosystems and their response to natural and human-induced change. Detailed information on the floristic composition, structure, biomass and growth stage of mangroves and changes in these attributes over time and at different scales of observation has been obtained and the knowledge gained has been to better inform on, for example, carbon dynamics, floral and faunal diversity, connectivity with adjacent environments, and responses to changing hydrological regimes and climate. Significant opportunities also exist for more effective use of these data for actively managing mangroves and the services they provide and ensuring that they are not overexploited and their integrity within the coastal environment is maintained. The benefits of including these data in mangrove characterization, mapping and monitoring programs are demonstrated using case studies from a wide range of locations, including in Australia, Southeast Asia and central America, and instruments such as radar, lidar and optical sensors. Local to global efforts aimed at monitoring mangrove dynamics using remote sensing data are also increasing, with these leading to more informed decisions in relation to conservation, management and sustainable use.

The authors would like to acknowledge Jorg Hacker of Airborne Research Australia (ARA) for providing LIDAR data for the Gulf of Carpentaria and the Japanese Space Exploration Agency (JAXA) for access to Japanese L-band SAR data.

Keywords

Mangrove Forests Japanese Space Exploration Agency (JAXA) Canopy Height Model (CHMs) Système Pour l’Observation De La Terre (SPOT) Geoscience Laser Altimeter System (GLAS) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

The authors would like to thank Jorg Hacker of Airborne Research Australia for the provision of the LIDAR mosaic for the Leichardt River.

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Centre for Ecosystem Science, School of Biological, Earth and Environmental ScienceThe University of New South WalesKensingtonAustralia
  2. 2.Department of Plant and Soil SciencesUniversity of DelawareNewarkMexico
  3. 3.GeomaticsNational Commission for the Knowledge and Use of Biodiversity (CONABIO)Mexico CityMexico
  4. 4.Remote Sensing Research Centre, School of Geography, Planning and Environmental ManagementThe University of QueenslandBrisbaneAustralia
  5. 5.Geography and Earth SciencesAberystwyth UniversityAberystwythUK
  6. 6.German Remote Sensing Data CentreDFD of the German Aerospace Centre, DLRWesslingGermany

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