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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. [ABSTRACT FROM AUTHOR]

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method's limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation. [ABSTRACT FROM AUTHOR]

Mediterranean hurricanes, or medicanes, are tropical-like cyclones forming once or twice per year over the waters of the Mediterranean Sea. These mesocyclones pose a serious threat to coastal infrastructure and lives because of their strong winds and intense rainfall. Infrasound technology has already been employed to investigate the acoustic signatures of severe weather events, and this study aims at characterizing, for the first time, the infrasound detections that can be related to medicanes. This work also contributes to infrasound source discrimination efforts in the context of the Comprehensive Nuclear-Test-Ban Treaty. We use data from the infrasound station IS48 of the International Monitoring System in Tunisia to investigate the infrasound signatures of mesocyclones using a multi-channel correlation algorithm. We discuss the detections using meteorological fields to assess the presence of stratospheric waveguides favoring propagation. We corroborate the detections by considering other datasets, such as satellite observations, a surface lightning detection network, and products mapping the simulated intensity of the swell. High- and low-frequency detections are evidenced for three medicanes at distances ranging between 250 and 1100 km from the station. Several cases of non-detection are also discussed. While deep convective systems, and mostly lightning within them, seem to be the main source of detections above 1 Hz, hotspots of swell (microbarom) related to the medicanes are evidenced between 0.1 and 0.5 Hz. In the latter case, simulations of microbarom detections are consistent with the observations. Multi-source situations are highlighted, stressing the need for more resilient detection-estimation algorithms. Cloud-to-ground lightning seems not to explain all high-frequency detections, suggesting that additional sources of electrical or dynamical origin may be at play that are related to deep convective systems. [ABSTRACT FROM AUTHOR]

Floods are often associated with hurricanes making landfall. When tropical cyclones/hurricanes make landfall, they are usually accompanied by heavy rainfall and storm surges that inundate coastal areas. The worst natural disaster in the United States, in terms of loss of life and property damage, was caused by hurricane storm surges and their associated coastal flooding. To monitor coastal flooding in the areas affected by hurricanes, we used data from sensors aboard the operational Polar-orbiting and Geostationary Operational Environmental Satellites. This study aims to apply a downscaling model to recent severe coastal flooding events caused by hurricanes. To demonstrate how high-resolution 3D flood mapping can be made from moderate-resolution operational satellite observations, the downscaling model was applied to the catastrophic coastal flooding in Florida due to Hurricane Ian and in New Orleans due to Hurricanes Ida and Laura. The floodwater fraction data derived from the SNPP/NOAA-20 VIIRS (Visible Infrared Imaging Radiometer Suite) observations at the original 375 m resolution were input into the downscaling model to obtain 3D flooding information at 30 m resolution, including flooding extent, water surface level and water depth. Compared to a 2D flood extent map at the VIIRS' original 375 m resolution, the downscaled 30 m floodwater depth maps, even when shown as 2D images, can provide more details about floodwater distribution, while 3D visualizations can demonstrate floodwater depth more clearly in relative to the terrain and provide a more direct perception of the inundation situations caused by hurricanes. The use of 3D visualization can help users clearly see floodwaters occurring over various types of terrain conditions, thus identifying a hazardous flood from non-hazardous flood types. Furthermore, 3D maps displaying floodwater depth may provide additional information for rescue efforts and damage assessments. The downscaling model can help enhance the capabilities of moderate-to-coarse resolution sensors, such as those used in operational weather satellites, flood detection and monitoring. [ABSTRACT FROM AUTHOR]

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation.

In this paper, we present a method for retrieving sea surface wind speed (SSWS) from Fengyun-3D (FY-3D) microwave radiation imager (MWRI) data. In contrast to the conventional point-to-point (P2P) retrieval methods, we propose a field-to-field (F2F) SSWS retrieval method based on the basic framework of a Convolutional Neural Network (CNN). Considering the spatial continuity and consistency characteristics of wind fields within a certain range, we construct the model based on the basic framework of CNN, which is suitable for retrieving various wind speed intervals, and then synchronously obtaining the smooth and continuous wind field. The retrieval results show that: (1) Comparing the retrieval results with the label data, the root-mean-square error ( R M S E ) of wind speed is about 0.26 m/s, the F2F-NN model is highly efficient in training and has a strong fitting ability to label data. Comparing the retrieval results with the buoys (NDBC and TAO) data, the R M S E of F2F-NN wind speed is less than 0.91 m/s, the retrieval accuracy is better than the wind field products involved in the comparison. (2) In the hurricane ( S a m ) area, the F2F-NN model greatly improves the accuracy of wind speed in the FY-3D wind field. Comparing five wind field products with the Stepped-Frequency Microwave Radiometer (SFMR) data, the overall accuracy of the F2F-NN wind data is the highest. Comparing the five wind field products with the International Best Track Archive for Climate Stewardship (IBTrACS) data, the F2F-NN wind field is superior to the other products in terms of maximum wind speed and maximum wind speed radius. The structure of the wind field retrieved by F2F-NN is complete and accurate, and the wind speed changes smoothly and continuously. [ABSTRACT FROM AUTHOR]

In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-ocean videos of conditions in the core of a major hurricane and reporting near-surface winds as high as 40 m/s. Here, we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in Hurricane Sam, using the following datasets for direct and indirect comparisons: an NDBC buoy in the path of the storm, radiometer tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers and SAR (RadarSat2), and HWRF model winds. The SD winds show excellent consistency with the satellite observations and a remarkable ability to detect the strength of the winds at the SD location. We use the HWRF model and satellite data to perform cross-comparisons of the SD with the buoy, which sampled different relative locations within the storm. Finally, we review the collective consistency among these measurements by describing the uncertainty of each wind dataset and discussing potential sources of systematic errors, such as the impact of extreme conditions on the SD measurements and uncertainties in the methodology. [ABSTRACT FROM AUTHOR]

This study examines the impacts of assimilating ocean-surface winds derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS) on improving the short-range numerical simulations and forecasts of landfalling hurricanes using the NCEP operational Hurricane Weather Research and Forecasting (HWRF) model. A series of data assimilation experiments are performed using HWRF and a Gridpoint Statistical Interpolation (GSI)-based hybrid 3-dimensional ensemble-variational (3DEnVar) data assimilation system. The influence of CYGNSS data on hurricane forecasts is compared with that of Advanced Scatterometer (ASCAT) wind products that have already been assimilated into the HWRF forecast system in a series of assimilation experiments. The effects of different versions of CYGNSS data (V2.1 vs. V3.0) on hurricane forecasts are evaluated. The results indicate that CYGNSS ocean-surface wind can lead to improved numerical simulations and forecasts of hurricane track and intensity, asymmetric wind structure, and precipitation. The impacts of CYGNSS on hurricane forecasts are comparable and complementary to the operational use of ASCAT satellite data products. The dependence of the relative impacts of different versions of CYGNSS data on optimal thinning distances is evident. [ABSTRACT FROM AUTHOR]

Coastal ecosystems are greatly endangered due to anthropogenic development and climate change. Multiple disturbances may erode the ability of a system to recover from stress if there is little time between disturbance events. We evaluated the ability of the saltmarshes in Barataria Bay, Louisiana, USA, to recover from two successive disturbances, the DeepWater Horizon oil spill in 2010 and Hurricane Isaac in 2012. We measured recovery using vegetation indices and land cover change metrics. We found that after the hurricane, land loss along oiled shorelines was 17.8%, while along oil-free shorelines, it was 13.6% within the first 7 m. At a distance of 7-14 m, land loss from oiled regions was 11.6%, but only 6.3% in oil-free regions. We found no differences in vulnerability to land loss between narrow and wide shorelines; however, vegetation in narrow sites was significantly more stressed, potentially leading to future land loss. Treated oiled regions also lost more land due to the hurricane than untreated regions. These results suggest that ecosystem recovery after the two disturbances is compromised, as the observed high rates of land loss may prevent salt marsh from establishing in the same areas where it existed prior to the oil spill.

Mediterranean hurricanes, or medicanes, are tropical-like cyclones forming once or twice per year over the waters of the Mediterranean Sea. These mesocyclones pose a serious threat to coastal infrastructure and lives because of their strong winds and intense rainfall. Infrasound technology has already been employed to investigate the acoustic signatures of severe weather events, and this study aims at characterizing, for the first time, the infrasound detections that can be related to medicanes. This work also contributes to infrasound source discrimination efforts in the context of the Comprehensive Nuclear-Test-Ban Treaty. We use data from the infrasound station IS48 of the International Monitoring System in Tunisia to investigate the infrasound signatures of mesocyclones using a multi-channel correlation algorithm. We discuss the detections using meteorological fields to assess the presence of stratospheric waveguides favoring propagation. We corroborate the detections by considering other datasets, such as satellite observations, a surface lightning detection network, and products mapping the simulated intensity of the swell. High- and low-frequency detections are evidenced for three medicanes at distances ranging between 250 and 1100 km from the station. Several cases of non-detection are also discussed. While deep convective systems, and mostly lightning within them, seem to be the main source of detections above 1 Hz, hotspots of swell (microbarom) related to the medicanes are evidenced between 0.1 and 0.5 Hz. In the latter case, simulations of microbarom detections are consistent with the observations. Multi-source situations are highlighted, stressing the need for more resilient detection-estimation algorithms. Cloud-to-ground lightning seems not to explain all high-frequency detections, suggesting that additional sources of electrical or dynamical origin may be at play that are related to deep convective systems.

Floods are often associated with hurricanes making landfall. When tropical cyclones/hurricanes make landfall, they are usually accompanied by heavy rainfall and storm surges that inundate coastal areas. The worst natural disaster in the United States, in terms of loss of life and property damage, was caused by hurricane storm surges and their associated coastal flooding. To monitor coastal flooding in the areas affected by hurricanes, we used data from sensors aboard the operational Polar-orbiting and Geostationary Operational Environmental Satellites. This study aims to apply a downscaling model to recent severe coastal flooding events caused by hurricanes. To demonstrate how high-resolution 3D flood mapping can be made from moderate-resolution operational satellite observations, the downscaling model was applied to the catastrophic coastal flooding in Florida due to Hurricane Ian and in New Orleans due to Hurricanes Ida and Laura. The floodwater fraction data derived from the SNPP/NOAA-20 VIIRS (Visible Infrared Imaging Radiometer Suite) observations at the original 375 m resolution were input into the downscaling model to obtain 3D flooding information at 30 m resolution, including flooding extent, water surface level and water depth. Compared to a 2D flood extent map at the VIIRS’ original 375 m resolution, the downscaled 30 m floodwater depth maps, even when shown as 2D images, can provide more details about floodwater distribution, while 3D visualizations can demonstrate floodwater depth more clearly in relative to the terrain and provide a more direct perception of the inundation situations caused by hurricanes. The use of 3D visualization can help users clearly see floodwaters occurring over various types of terrain conditions, thus identifying a hazardous flood from non-hazardous flood types. Furthermore, 3D maps displaying floodwater depth may provide additional information for rescue efforts and damage assessments. The downscaling model can help enhance the capabilities of moderate-to-coarse resolution sensors, such as those used in operational weather satellites, flood detection and monitoring.

In this paper, we present a method for retrieving sea surface wind speed (SSWS) from Fengyun-3D (FY-3D) microwave radiation imager (MWRI) data. In contrast to the conventional point-to-point (P2P) retrieval methods, we propose a field-to-field (F2F) SSWS retrieval method based on the basic framework of a Convolutional Neural Network (CNN). Considering the spatial continuity and consistency characteristics of wind fields within a certain range, we construct the model based on the basic framework of CNN, which is suitable for retrieving various wind speed intervals, and then synchronously obtaining the smooth and continuous wind field. The retrieval results show that: (1) Comparing the retrieval results with the label data, the root-mean-square error (RMSE) of wind speed is about 0.26 m/s, the F2F-NN model is highly efficient in training and has a strong fitting ability to label data. Comparing the retrieval results with the buoys (NDBC and TAO) data, the RMSE of F2F-NN wind speed is less than 0.91 m/s, the retrieval accuracy is better than the wind field products involved in the comparison. (2) In the hurricane (Sam) area, the F2F-NN model greatly improves the accuracy of wind speed in the FY-3D wind field. Comparing five wind field products with the Stepped-Frequency Microwave Radiometer (SFMR) data, the overall accuracy of the F2F-NN wind data is the highest. Comparing the five wind field products with the International Best Track Archive for Climate Stewardship (IBTrACS) data, the F2F-NN wind field is superior to the other products in terms of maximum wind speed and maximum wind speed radius. The structure of the wind field retrieved by F2F-NN is complete and accurate, and the wind speed changes smoothly and continuously.

In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-ocean videos of conditions in the core of a major hurricane and reporting near-surface winds as high as 40 m/s. Here, we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in Hurricane Sam, using the following datasets for direct and indirect comparisons: an NDBC buoy in the path of the storm, radiometer tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers and SAR (RadarSat2), and HWRF model winds. The SD winds show excellent consistency with the satellite observations and a remarkable ability to detect the strength of the winds at the SD location. We use the HWRF model and satellite data to perform cross-comparisons of the SD with the buoy, which sampled different relative locations within the storm. Finally, we review the collective consistency among these measurements by describing the uncertainty of each wind dataset and discussing potential sources of systematic errors, such as the impact of extreme conditions on the SD measurements and uncertainties in the methodology.

This study examines the impacts of assimilating ocean-surface winds derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS) on improving the short-range numerical simulations and forecasts of landfalling hurricanes using the NCEP operational Hurricane Weather Research and Forecasting (HWRF) model. A series of data assimilation experiments are performed using HWRF and a Gridpoint Statistical Interpolation (GSI)-based hybrid 3-dimensional ensemble-variational (3DEnVar) data assimilation system. The influence of CYGNSS data on hurricane forecasts is compared with that of Advanced Scatterometer (ASCAT) wind products that have already been assimilated into the HWRF forecast system in a series of assimilation experiments. The effects of different versions of CYGNSS data (V2.1 vs. V3.0) on hurricane forecasts are evaluated. The results indicate that CYGNSS ocean-surface wind can lead to improved numerical simulations and forecasts of hurricane track and intensity, asymmetric wind structure, and precipitation. The impacts of CYGNSS on hurricane forecasts are comparable and complementary to the operational use of ASCAT satellite data products. The dependence of the relative impacts of different versions of CYGNSS data on optimal thinning distances is evident.

In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.

This work quantifies the magnitude, spatial structure, and temporal evolution of the cold wake left by North Atlantic hurricanes. To this end we composited the sea surface temperature anomalies (SSTA) induced by hurricane observations from 2002 to 2018 derived from the international best track archive for climate stewardship (IBTrACS). Cold wake characteristics were distinguished by a set of hurricane and oceanic properties: Hurricane translation speed and intensity, and the characteristics of the upper ocean stratification represented by two barrier layer metrics: Barrier layer thickness (BLT) and barrier layer potential energy (BLPE). The contribution of the above properties to the amplitude of the cold wake was analyzed individually and in combination. The mean magnitude of the hurricane-induced cooling was of 1.7 °C when all hurricanes without any distinction were considered, and the largest cooling was found for slow-moving, strong hurricanes passing over thinner barrier layers, with a cooling above 3.5 °C with respect to pre-storm sea surface temperature (SST) conditions. On average the cold wake needed about 60 days to disappear and experienced a strong decay in the first 20 days, when the magnitude of the cold wake had decreased by 80%. Differences between the cold wakes yielded by mostly infrared and merged infrared and microwave remote sensed SST data were also evaluated, with an overall relative underestimation of the hurricane-induced cooling of about 0.4 °C for infrared-mostly data.

Rapid assessment of natural disasters is essential for disaster analysis and spatially explicit strategic decisions of post-disaster reconstruction but requires timely available data. The recent daily data of the National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provide new opportunities to detect and evaluate natural disasters. Here, we introduce an application of NPP-VIIRS DNB daily data for rapidly assessing the damage of a severe typhoon that struck the urban agglomerations along the western Taiwan Straits in China. Our research explored the methods of rapid identification and extraction of the areas based on changes in nighttime light (NTL) after the typhoon disaster by using a statistical radiation-normalization method. We analyzed the correlations of NTL image derivatives with human population, population density, and gross domestic product (GDP). The strong correlations were found between NTL image light density and population density (R2 = 0.83) and between the total nighttime light intensity and GDP (R2 = 0.96) at the prefecture level. In addition, we examined the interrelationships between changes in NTL images and the areas affected by the typhoon and proposed a method to predict the affected population. Finally, the affected area and the affected population in the study area could be rapidly retrieved based on the proposed remote sensing method. The overall accuracy was 83.2% for the detection of the affected population after disaster and the recovery rate of the affected area was 86.9% in the third week after the typhoon. This research demonstrates that the NTL image-based change detection method is simple and effective, and further explains that the NPP-VIIRS DNB daily data are useful for rapidly assessing affected areas and affected populations after typhoon disasters, and for timely quantifying the degree of recovery at a large spatial scale.

In this letter, we discuss some observations of the Soil Moisture Active Passive (SMAP) mission’s high-resolution synthetic aperture radar (SAR) for extreme winds and tropical cyclones. We find that the L-band cross-polarized backscatter is far more sensitive to wind speed at extreme winds than the co-polarized backscatter and it is essential to observations of extreme winds with L-band SAR. We introduce a cyclone wind speed retrieval algorithm and apply it to the limited SMAP SAR dataset of cyclones. We show that the SMAP SAR instrument is capable of measuring extreme winds up to the category 5 (70 m/s) wind speed regime providing unique capabilities as compared to traditional scatterometers with C and Ku-band radars.

Differences in synthetic aperture radar (SAR)-retrieved hurricane wind speeds from co-polarization and cross-polarization measurements are found to be correlated with rain rate. A quality index is proposed for the SAR-retrieved wind speed product to recognize heavy rain- affected areas by taking account of the different imaging mechanisms of the radar backscattering from the ocean surface via cross-polarization and co-polarization observations. A procedure is proposed to rectify wind retrievals in the rain-contaminated areas within the hurricane core, based on the theoretical physical profile for hurricanes. The effectiveness of the proposed methodology for heavy rain area recognition and wind speed reconstruction in the rain-affected areas is validated against step frequency microwave radiometer measurements from hurricane reconnaissance missions and the hurricane surface wind analysis product (HWIND). The quality flags provide confidence levels of hurricane surface winds from SAR, which together with the proposed method to correct wind retrievals in rain-contaminated areas, can contribute to improved operational applications of SAR-derived winds under hurricane conditions.

It has been shown that hurricanes generate seismic noise, called microseisms, through the creation and non-linear interaction of ocean surface waves. Here we model microseisms generated by the spatially inhomogeneous waves of a hurricane using the non-linear wave equation where a second-order acoustic field is created by first-order ocean surface wave motion. We treat range-dependent waveguide environments to account for microseisms that propagate from the deep ocean to a receiver on land. We compare estimates based on the ocean surface wave field measured in hurricane Bonnie in 1998 with seismic measurements made roughly 1000 km away in Florida.

Previous research has documented the usefulness of Lidar data to derive a variety of topographic products (e.g., DEM, DTM, canopy and forest structure, and urban infrastructure). Lidar has been used to map coastal environments and geomorphology; however, there is no comprehensive model to derive coastal geomorphology. Therefore, the purpose of this project was to build on existing research and develop an automated modeling approach to classify coastal geomorphology across barrier islands. The model was developed and tested at four sites in North Carolina including two undeveloped and two developed islands. Barrier island geomorphology is shaped by natural coastal processes, such as storms and longshore sediment transport, as well as human influences, such as beach nourishment and urban development. The model was developed to classify ten geomorphic features over four time-steps from 1998 to 2014. Model results were compared to compute change through time and derived the rate and direction of feature movement. Tropical storms and hurricanes had the most influence in geomorphic change and movement. On the developed islands, there was less influence of storms due to the inability of features to move because of coastal infrastructure. From 2005 to 2010, beach nourishment was the dominant influence on developed beaches because this activity ameliorated the natural tendency for an island to erode. Understanding how natural and anthropogenic processes influence barrier island geomorphology is critical to predicting an island’s future response to changing environmental factors such as sea-level rise. The development of an automated model enables it to be replicated in other locations where policy makers and coastal managers may use this information to make development and conservation decisions.

Frequent hurricane landfalls along the northern Gulf of Mexico, in addition to causing immediate damage to vegetation, also have long term effects on coastal ecosystem structure and function. This study investigated the utility of using time series enhanced vegetation index (EVI) imagery composited in MODIS product MOD13Q1 for assessing hurricane damage to vegetation and its recovery. Vegetation in four US coastal states disturbed by five hurricanes between 2002 and 2008 were explored by change imagery derived from pre- and post-hurricane EVI data. Interpretation of the EVI changes within months and between years distinguished a clear disturbance pattern caused by Hurricanes Katrina and Rita in 2005, and a recovering trend of the vegetation between 2005 and 2008, particularly within the 100 km coastal zone. However, for Hurricanes Gustav, Ike, and Lili, the disturbance pattern which varied by the change imagery were not noticeable in some images due to lighter vegetation damage. The EVI pre- and post-hurricane differences between two adjacent years and around one month after hurricane disturbance provided the most likely damage area and patterns. The study also revealed that as hurricanes damaged vegetation in some coastal areas, strong precipitation associated with these storms may benefit growth of vegetation in other areas. Overall, the study illustrated that the MODIS product could be employed to detect severe hurricane damage to vegetation, monitor vegetation recovery dynamics, and assess benefits of hurricanes to vegetation.

Coastal ecosystems are greatly endangered due to anthropogenic development and climate change. Multiple disturbances may erode the ability of a system to recover from stress if there is little time between disturbance events. We evaluated the ability of the saltmarshes in Barataria Bay, Louisiana, USA, to recover from two successive disturbances, the DeepWater Horizon oil spill in 2010 and Hurricane Isaac in 2012. We measured recovery using vegetation indices and land cover change metrics. We found that after the hurricane, land loss along oiled shorelines was 17.8%, while along oil-free shorelines, it was 13.6% within the first 7 m. At a distance of 7–14 m, land loss from oiled regions was 11.6%, but only 6.3% in oil-free regions. We found no differences in vulnerability to land loss between narrow and wide shorelines; however, vegetation in narrow sites was significantly more stressed, potentially leading to future land loss. Treated oiled regions also lost more land due to the hurricane than untreated regions. These results suggest that ecosystem recovery after the two disturbances is compromised, as the observed high rates of land loss may prevent salt marsh from establishing in the same areas where it existed prior to the oil spill.

Frequent hurricane landfalls along the northern Gulf of Mexico, in addition to causing immediate damage to vegetation, also have long term effects on coastal ecosystem structure and function. This study investigated the utility of using time series enhanced vegetation index (EVI) imagery composited in MODIS product MOD13Q1 for assessing hurricane damage to vegetation and its recovery. Vegetation in four US coastal states disturbed by five hurricanes between 2002 and 2008 were explored by change imagery derived from pre- and post-hurricane EVI data. Interpretation of the EVI changes within months and between years distinguished a clear disturbance pattern caused by Hurricanes Katrina and Rita in 2005, and a recovering trend of the vegetation between 2005 and 2008, particularly within the 100 km coastal zone. However, for Hurricanes Gustav, Ike, and Lili, the disturbance pattern which varied by the change imagery were not noticeable in some images due to lighter vegetation damage. The EVI pre- and post-hurricane differences between two adjacent years and around one month after hurricane disturbance provided the most likely damage area and patterns. The study also revealed that as hurricanes damaged vegetation in some coastal areas, strong precipitation associated with these storms may benefit growth of vegetation in other areas. Overall, the study illustrated that the MODIS product could be employed to detect severe hurricane damage to vegetation, monitor vegetation recovery dynamics, and assess benefits of hurricanes to vegetation. [ABSTRACT FROM AUTHOR]

Named storms can cause substantial impacts on the habitat and reproductive output of threatened species, such as marine turtles. To determine the impacts of named storms on marine turtles and inform management, it is necessary to determine the exposure of marine turtle nesting grounds to recent storm activities. To address this, remote sensing information of named storm tracks coupled with nesting ground data were used to investigate the temporal and spatial overlap between nesting grounds for four species of marine turtles in the continental United States of America. All species of marine turtles were exposed to named storms, with variation in exposure driven by the spatial distribution of each population's nesting ground, the temporal overlap between the storms and reproductive events, and nest placement on the beach. Loggerhead turtles were the most exposed species to named storms, with the northern management unit having significantly higher exposure levels than all other loggerhead management units. Kemp's ridley turtles, in contrast, were found to be the least exposed species to named storms. This study establishes a valuable current baseline against which to measure and compare future impacts that result as climate change progresses and storms become more frequent and intense. Importantly, cumulative and synergetic effects from other climatic processes and anthropogenic stressors should be considered in future analysis. [ABSTRACT FROM AUTHOR]

This work quantifies the magnitude, spatial structure, and temporal evolution of the cold wake left by North Atlantic hurricanes. To this end we composited the sea surface temperature anomalies (SSTA) induced by hurricane observations from 2002 to 2018 derived from the international best track archive for climate stewardship (IBTrACS). Cold wake characteristics were distinguished by a set of hurricane and oceanic properties: Hurricane translation speed and intensity, and the characteristics of the upper ocean stratification represented by two barrier layer metrics: Barrier layer thickness (BLT) and barrier layer potential energy (BLPE). The contribution of the above properties to the amplitude of the cold wake was analyzed individually and in combination. The mean magnitude of the hurricane-induced cooling was of 1.7 °C when all hurricanes without any distinction were considered, and the largest cooling was found for slow-moving, strong hurricanes passing over thinner barrier layers, with a cooling above 3.5 °C with respect to pre-storm sea surface temperature (SST) conditions. On average the cold wake needed about 60 days to disappear and experienced a strong decay in the first 20 days, when the magnitude of the cold wake had decreased by 80%. Differences between the cold wakes yielded by mostly infrared and merged infrared and microwave remote sensed SST data were also evaluated, with an overall relative underestimation of the hurricane-induced cooling of about 0.4 °C for infrared-mostly data. [ABSTRACT FROM AUTHOR]

Rapid assessment of natural disasters is essential for disaster analysis and spatially explicit strategic decisions of post-disaster reconstruction but requires timely available data. The recent daily data of the National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provide new opportunities to detect and evaluate natural disasters. Here, we introduce an application of NPP-VIIRS DNB daily data for rapidly assessing the damage of a severe typhoon that struck the urban agglomerations along the western Taiwan Straits in China. Our research explored the methods of rapid identification and extraction of the areas based on changes in nighttime light (NTL) after the typhoon disaster by using a statistical radiation-normalization method. We analyzed the correlations of NTL image derivatives with human population, population density, and gross domestic product (GDP). The strong correlations were found between NTL image light density and population density (R2 = 0.83) and between the total nighttime light intensity and GDP (R2 = 0.96) at the prefecture level. In addition, we examined the interrelationships between changes in NTL images and the areas affected by the typhoon and proposed a method to predict the affected population. Finally, the affected area and the affected population in the study area could be rapidly retrieved based on the proposed remote sensing method. The overall accuracy was 83.2% for the detection of the affected population after disaster and the recovery rate of the affected area was 86.9% in the third week after the typhoon. This research demonstrates that the NTL image-based change detection method is simple and effective, and further explains that the NPP-VIIRS DNB daily data are useful for rapidly assessing affected areas and affected populations after typhoon disasters, and for timely quantifying the degree of recovery at a large spatial scale. [ABSTRACT FROM AUTHOR]

In this letter, we discuss some observations of the Soil Moisture Active Passive (SMAP) mission's high-resolution synthetic aperture radar (SAR) for extreme winds and tropical cyclones. We find that the L-band cross-polarized backscatter is far more sensitive to wind speed at extreme winds than the co-polarized backscatter and it is essential to observations of extreme winds with L-band SAR. We introduce a cyclone wind speed retrieval algorithm and apply it to the limited SMAP SAR dataset of cyclones. We show that the SMAP SAR instrument is capable of measuring extreme winds up to the category 5 (70 m/s) wind speed regime providing unique capabilities as compared to traditional scatterometers with C and Ku-band radars. [ABSTRACT FROM AUTHOR]

Differences in synthetic aperture radar (SAR)-retrieved hurricane wind speeds from co-polarization and cross-polarization measurements are found to be correlated with rain rate. A quality index is proposed for the SAR-retrieved wind speed product to recognize heavy rain- affected areas by taking account of the different imaging mechanisms of the radar backscattering from the ocean surface via cross-polarization and co-polarization observations. A procedure is proposed to rectify wind retrievals in the rain-contaminated areas within the hurricane core, based on the theoretical physical profile for hurricanes. The effectiveness of the proposed methodology for heavy rain area recognition and wind speed reconstruction in the rain-affected areas is validated against step frequency microwave radiometer measurements from hurricane reconnaissance missions and the hurricane surface wind analysis product (HWIND). The quality flags provide confidence levels of hurricane surface winds from SAR, which together with the proposed method to correct wind retrievals in rain-contaminated areas, can contribute to improved operational applications of SAR-derived winds under hurricane conditions. [ABSTRACT FROM AUTHOR]

Previous research has documented the usefulness of Lidar data to derive a variety of topographic products (e.g., DEM, DTM, canopy and forest structure, and urban infrastructure). Lidar has been used to map coastal environments and geomorphology; however, there is no comprehensive model to derive coastal geomorphology. Therefore, the purpose of this project was to build on existing research and develop an automated modeling approach to classify coastal geomorphology across barrier islands. The model was developed and tested at four sites in North Carolina including two undeveloped and two developed islands. Barrier island geomorphology is shaped by natural coastal processes, such as storms and longshore sediment transport, as well as human influences, such as beach nourishment and urban development. The model was developed to classify ten geomorphic features over four time-steps from 1998 to 2014. Model results were compared to compute change through time and derived the rate and direction of feature movement. Tropical storms and hurricanes had the most influence in geomorphic change and movement. On the developed islands, there was less influence of storms due to the inability of features to move because of coastal infrastructure. From 2005 to 2010, beach nourishment was the dominant influence on developed beaches because this activity ameliorated the natural tendency for an island to erode. Understanding how natural and anthropogenic processes influence barrier island geomorphology is critical to predicting an island's future response to changing environmental factors such as sea-level rise. The development of an automated model enables it to be replicated in other locations where policy makers and coastal managers may use this information to make development and conservation decisions. [ABSTRACT FROM AUTHOR]