Search for: All records

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. 

Using interferometric synthetic aperture radar (InSAR) to observe slow ground deformation can be challenging due to many sources of error, with tropospheric phase delay and unwrapping errors being the most significant. While analytical methods, weather models, and data exist to mitigate tropospheric error, most of these techniques are unsuitable for all InSAR applications (e.g., complex tropospheric mixing in the tropics) or are deficient in spatial or temporal resolution. Likewise, there are methods for removing the unwrapping error, but they cannot resolve the true phase when there is a high prevalence (>40%) of unwrapping error in a set of interferograms. Applying tropospheric delay removal techniques is unnecessary for C-band Sentinel-1 InSAR time series studies, and the effect of unwrapping error can be minimized if the full dataset is utilized. We demonstrate that using interferograms with long temporal baselines (800 days to 1600 days) but very short perpendicular baselines (<5 m) (LTSPB) can lower the velocity detection threshold to 2 mm y−1 to 3 mm y−1 for long-term coherent permanent scatterers. The LTSPB interferograms can measure slow deformation rates because the expected differential phases are larger than those of small baselines and potentially exceed the typical noise amplitude while also reducing the sensitivity of the time series estimation to the noise sources. The method takes advantage of the Sentinel-1 mission length (2016 to present), which, for most regions, can yield up to 300 interferograms that meet the LTSPB baseline criteria. We demonstrate that low velocity detection can be achieved by comparing the expected LTSPB differential phase measurements to synthetic tests and tropospheric delay from the Global Navigation Satellite System. We then characterize the slow (~3 mm/y) ground deformation of the Socorro Magma Body, New Mexico, and the Tampa Bay Area using LTSPB InSAR analysis. The method we describe has implications for simplifying the InSAR time series processing chain and enhancing the velocity detection threshold. 

The Indian River Lagoon is a 250-km-long Estuary of National Significance located along the east-central Florida coast of the USA. NOAA tidal data generated at a station located in the central reaches of the estuary indicate sea level rise has accelerated over the duration of record to an average of 9.6 ± 1.6 mm year−1 (2003–2022). It is expected to continue accelerating over the duration of this century. This investigation simulated submergence of the estuary using the on-line geospatial tool Future Shorelines to evaluate the effects of sea level rise on a suite of natural and built attributes that either contribute to (i.e., boat ramps, spoil islands, seagrass) or degrade (septic and wastewater treatment systems) ecosystem services. The simulations are based upon the median NOAA high sea level rise trajectory in target years 2050, 2070, and 2100. By 2050, 23% of the public motorized boat ramps and 87% of the spoil islands that provide recreation and conservation services will be largely to completely inundated. Seven percent of the known or likely septic systems in the watershed will be submerged by 2050. Sea level rise does not compromise any of the eleven wastewater treatment plants considered in this study over the next 25 years. Seagrass distribution is expected to decline 34% by 2050 due to a reduction in substate area above the light-dependent median depth limit. By 2100, all ramps, spoil islands, over 27,000 (22%) septic systems, and six wastewater treatment plants will be inundated. By then, the average water depth will exceed the median depth limit for seagrass throughout most of the estuary. Ecosystem service mitigation strategies are presented for the attributes considered. The development of the submergence simulation tool and discussion of mitigation options benefited by collaboration with project partners responsible for resource management in the study domain. This coproduction ensured the simulation outputs and mitigation options were realistic and actionable. The risks to estuarine ecosystem services induced by urbanization and sea level rise are reported worldwide and the methodological approach of this study offers a novel means of developing or enhancing mitigation strategies. 

Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event. 

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 ICESat‐2 and GEDI missions were launched in 2018, becoming the new generation of space‐borne laser altimeters. These missions provide unprecedented global geodetic elevations, opening great opportunities for water level monitoring. The potential of these altimeters has been demonstrated in open‐water environments such as lakes, rivers, and reservoirs. However, detailed evaluations in vegetated environments, such as wetlands, floodplains, and other areas not constrained by water canal networks, are essential for continued improvement and further hydrological application. We developed a systematic accuracy assessment of ICESat‐2 ATL08, and GEDI L2A products to monitor spatial‐temporal water level and depth dynamics over the South Florida Everglades wetlands. The evaluation was performed on data acquired between 2020 and 2021, using gauge‐based water level and depth estimates as references. The results showed an RMSE of 0.17 m (water level) and 0.15 m (water depth) for ICESat‐2 and 0.75 m (water level) and 0.37 m (water depth) for GEDI. The analysis suggested that nighttime acquisitions were more accurate for both missions than daytime ones. The low‐power beams achieved slightly higher accuracies than those of the high‐power beams over the evaluated wetlands. Water level retrieval was more problematic in densely vegetated areas; however, we derived a correction model based on the leaf area index that improved the accuracy by up to 75% for water depth retrievals from GEDI. Furthermore, the analysis provides new insights to understand the potential of the altimeters in monitoring the spatial‐temporal dynamics of water levels in the evaluated wetlands. 

Extreme rainfall, induced by severe weather events, such as hurricanes, impacts wetlands because rapid water-depth increases can lead to flora and fauna mortality. This study developed an innovative algorithm to detect significant water-depth increases (SWDI, defined as water-depth increases above a threshold) in wetlands, using Sentinel-1 SAR backscatter. We used Hurricane Irma as an example that made landfall in the south Florida Everglades wetlands in September 2017 and produced tremendous rainfall. The algorithm detects SWDI for during- and post-event SAR acquisition dates, using pre-event water-depth as a baseline. The algorithm calculates Normalized Difference Backscatter Index (NDBI), using pre-, during-, and post-event backscatter, at a 20-m SAR resolution, as an indicator of the likelihood of SWDI, and detects SWDI using all NDBI values in a 400-m resolution pixel. The algorithm successfully detected large SWDI areas for the during-event date and progressive expansion of non-SWDI areas (water-depth differences less than the threshold) for five post-event dates in the following two months. The algorithm achieved good performance in both ‘herbaceous dominant’ and ‘trees embedded within herbaceous matrix’ land covers, with an overall accuracy of 81%. This study provides a solution for accurate mapping of SWDI and can be used in global wetlands, vulnerable to extreme rainfall.