Mapping the Extent of Mangrove Ecosystem Degradation by Integrating an Ecological Conceptual Model with Satellite Data

Abstract

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.

MDPrePost-Net: A Spatial-Spectral-Temporal Fully Convolutional Network for Mapping of Mangrove Degradation Affected by Hurricane Irma 2017 Using Sentinel-2 Data

“…In September 2017, southwest Florida was hit by one such storm, classed as a Category 3 hurricane, named Hurricane Irma [20]. Studies have reported on the Hurricane Irma-induced mangrove degradation in southwest Florida [20][21][22].…”

Ecosystem Services Assessment for the Conservation of Mangroves in French Guiana Using Fuzzy Cognitive Mapping

“…For most stakeholders (excepted one scientist), climate change was considered as a broad variable. However, it is difficult to summarize Climate change interactions with mangroves in a single variable as this corresponds to multiple factors of environmental changes (e.g., sea level rise, drought, change in salinity) that can have different-if not reverse-impacts on mangroves, depending on the context (Alongi, 2015;Lee et al, 2021). For example, in French Guiana, climate change intensifies the swell regime, which accelerates mud bank movements that should impact coastal mangroves.…”

Monitoring Post-Disaster Mangrove Forest Recoveries in Lawaan-Balangiga, Eastern Samar Using Time Series Analysis of Moisture and Vegetation Indices

“…Another useful index in characterizing mangrove intactness or degradation is the Normalized Difference Moisture Index (NDMI) (Lee et al, 2021). It is calculated as a ratio between the near-infrared (NIR) and short-wave infrared (SWIR) bands (see Table 1, Equation 4) and can be applied to determine vegetation water content (U.S. Geological Survey, 2021c).…”