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Mangrove forests play a critical role in coastal ecosystems by buffering shorelines against the destructive forces of storms and storm surges, but in doing so, they often endure significant structural damage, including defoliation, tree snapping, and branch loss. Using decade-long remote sensing Landsat 8 data, we calculated the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to assess patterns and trends within the decade-long time series for each index in mangrove forests of southwestern Florida Everglades. Before calculating NDVI and NDMI, we cloud-filtered and calculated the monthly spectral means of the study region from March 2013 to March 2023. Using both NDVI and NDMI, we found seasonal variations in the value of both indices, in which the value increased during the wet season and decreased during the dry season of the Everglades. We also detected the impact of Hurricane Irma on mangroves in 2017 due to a sudden drop in the indices’ values right after the storm. The time series showed a slow recovery of indices values compared to pre-storm values. Using an exponential recovery model, we calculated that most mangrove areas recovered within two to four years. However, some small mangrove areas show no recovery, which we attribute to saltwater ponding and areas without freshwater flow and hydrological connectivity. 

Early to intermediate ontogenetic stages of trees are important in forest regeneration. However, these critical life stages are often overlooked due to survey intensity and impracticality and/or disinterest in characterizing early life stage cohorts. This problem is particularly pervasive in mangrove forests where visibility of smaller stature trees may be limited by tidal flooding and younger cohorts are particularly vulnerable to changing hydrologic and biogeochemical conditions driven by climate change. Lacking data on early life stages in mangrove forests makes it difficult to predict ecosystem degradation and inform habitat resilience and restoration in one of the earth's most valuable blue carbon ecosystems. We identify challenges to collecting empirical data on early to intermediate age classes in mangroves and provide solutions to characterizing these cohorts. We emphasize the importance of gathering these data for improved understanding of forest regeneration dynamics and provide multi-scalar solutions to quantify vegetation structure of mangrove forest. 

In September 2017, Hurricane Irma made landfall in South Florida, causing a great deal of damage to mangrove forests along the southwest coast. A combination of hurricane strength winds and high storm surge across the area resulted in canopy defoliation, broken branches, and downed trees. Evaluating changes in mangrove forest structure is significant, as a loss or change in mangrove forest structure can lead to loss in the ecosystems services that they provide. In this study, we used lidar remote sensing technology and field data to assess damage to the South Florida mangrove forests from Hurricane Irma. Lidar data provided an opportunity to investigate changes in mangrove forests using 3D high-resolution data to assess hurricane-induced changes at different tree structure levels. Using lidar data in conjunction with field observations, we were able to model aboveground necromass (AGN; standing dead trees) on a regional scale across the Shark River and Harney River within Everglades National Park. AGN estimates were higher in the mouth and downstream section of Shark River and higher in the downstream section of the Harney River, with higher impact observed in Shark River. Mean AGN estimates were 46 Mg/ha in Shark River and 38 Mg/ha in Harney River and an average loss of 29% in biomass, showing a significant damage when compared to other areas impacted by Hurricane Irma and previous disturbances in our study region. 

Abstract The BlueFlux field campaign, supported by NASA’s Carbon Monitoring System, will develop prototype blue carbon products to inform coastal carbon management. While blue carbon has been suggested as a nature-based climate solution (NBS) to remove carbon dioxide (CO 2 ) from the atmosphere, these ecosystems also release additional greenhouse gases (GHGs) such as methane (CH 4 ) and are sensitive to disturbances including hurricanes and sea-level rise. To understand blue carbon as an NBS, BlueFlux is conducting multi-scale measurements of CO 2 and CH 4 fluxes across coastal landscapes, combined with long-term carbon burial, in Southern Florida using chambers, flux towers, and aircraft combined with remote-sensing observations for regional upscaling. During the first deployment in April 2022, CO 2 uptake and CH 4 emissions across the Everglades National Park averaged −4.9 ± 4.7 μ mol CO 2 m −2 s −1 and 19.8 ± 41.1 nmol CH 4 m −2 s −1 , respectively. When scaled to the region, mangrove CH 4 emissions offset the mangrove CO 2 uptake by about 5% (assuming a 100 year CH 4 global warming potential of 28), leading to total net uptake of 31.8 Tg CO 2 -eq y −1 . Subsequent field campaigns will measure diurnal and seasonal changes in emissions and integrate measurements of long-term carbon burial to develop comprehensive annual and long-term GHG budgets to inform blue carbon as a climate solution. 

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically. 

Abstract Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant resilience traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (>10 m). Mangroves on well-drained sites (83%) resprouted new leaves within one year after the storm. By contrast, in poorly-drained inland sites, we detected one of the largest mangrove diebacks on record (10,760 ha), triggered by Irma. We found evidence that the combination of low elevation (median = 9.4 cm asl), storm surge water levels (>1.4 m above the ground surface), and hydrologic isolation drove coastal forest vulnerability and were independent of tree height or wind exposure. Our results indicated that storm surge and ponding caused dieback, not wind. Tidal restoration and hydrologic management in these vulnerable, low-lying coastal areas can reduce mangrove mortality and improve resilience to future cyclones. 

Coastal ecosystems display consistent patterns of trade-offs between resistance and resilience to tropical cyclones. 

Abstract Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. We present a research framework designed to compare tropical cyclone effects within and across ecosystems that: a) uses a disaggregating approach that measures the responses of individual ecosystem components, b) links the response of ecosystem components at fine temporal scales to meteorology and antecedent conditions, and c) examines responses of ecosystem using a resistance–resilience perspective by quantifying the magnitude of change and recovery time. We demonstrate the utility of the framework using three examples of ecosystem response: gross primary productivity, stream biogeochemical export, and organismal abundances. Finally, we present the case for a network of sentinel sites with consistent monitoring to measure and compare ecosystem responses to cyclones across the United States, which could help improve coastal ecosystem resilience.