Mapping the extent of mangrove ecosystem degradation by integrating an ecological conceptual model with satellite data

Anthropogenic and natural disturbances can cause degradation of ecosystems, reducing their capacity to sustain biodiversity and provide ecosystem services. Understanding the extent of ecosystem degradation is critical for estimating risks to ecosystems, yet there are few existing methods to map degradation at the ecosystem scale and none using freely available satellite data for mangrove ecosystems. In this study, we developed a quantitative classification model of mangrove ecosystem degradation using freely available earth observation data. Crucially, a conceptual model of mangrove ecosystem degradation was established to identify suitable remote sensing variables that support the quantitative classification model, bridging the gap between satellite-derived variables and ecosystem degradation with explicit ecological links. We applied our degradation model to two case-studies, the mangroves of Rakhine State, Myanmar, which are severely threatened by anthropogenic disturbances, and Shark River within the Everglades National Park, USA, which is periodically disturbed by severe tropical storms. Our model suggested that 40% (597 km2) of the extent of mangroves in Rakhine showed evidence of degradation. In the Everglades, the model suggested that the extent of degraded mangrove forest increased from 5.1% to 97.4% following the Category 4 Hurricane Irma in 2017. Quantitative accuracy assessments indicated the model achieved overall accuracies of 77.6% and 79.1% for the Rakhine and the Everglades, respectively. We highlight that using an ecological conceptual model as the basis for building quantitative classification models to estimate the extent of ecosystem degradation ensures the ecological relevance of the classification models. Our developed method enables researchers to move beyond only mapping ecosystem distribution to condition and degradation as well. These results can help support ecosystem risk assessments, natural capital accounting, and restoration planning and provide quantitative estimates of ecosystem degradation for new global biodiversity targets.</jats:p

Deforestation in the Ayeyarwady Delta and the conservation implications of an internationally-engaged Myanmar

10.1016/j.gloenvcha.2013.10.007Global Environmental Change241321-333GECH

Spatial Ecology of Mangrove Forests:A Remote Sensing Perspective

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