Search Results (31)

The forests and grasslands in the U.S. are vulnerable to global warming and extreme weather events. Current satellites do not provide historical vegetation density images over the long term (more than 50 years), which has restricted the documentation of key ecological processes and their resultant responses over decades due to the absence of large-scale and long-term monitoring studies. We performed point-by-point regression and collected data from 391 tree-ring plots to reconstruct the annual normalized difference vegetation index (NDVI) time-series maps for the contiguous U.S. from 1850 to 2010. Among three machine learning approaches for regressions—Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)—we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. From the Little Ice Age to the present, the NDVI increased by 6.73% across the contiguous U.S., except during some extreme events such as the Dust Bowl drought, during which the averaged NDVI decreased, particularly in New Mexico. The NDVI trend was positive in the Northern Forest, Tropical Humid Forest, Northern West Forest Mountains, Marin West Coast Forests, and Mediterranean California, while other ecoregions showed a negative trend. At the state level, Washington and Louisiana had significantly positive correlations with temperature (p < 0.05). Washington had a significantly negative correlation with precipitation (p < 0.05), whereas Oklahoma had a significantly positive correlation (p < 0.05) with precipitation. This study provides insights into the spatial distribution of paleo-vegetation and its climate drivers. This study is the first to attempt a national-scale reconstruction of the NDVI over such a long period (151 years) using tree rings and machine learning. Full article

Tropical cyclones (TCs), often characterized by high wind speeds and heavy rainfall, cause widespread devastation, affecting millions of people and leading to economic losses worldwide. TC-specific research in Georgia is scarce, likely due to the minimal geographical extent of its coast and the infrequency of direct landfalls. Research on Georgia TCs does not account for storms that make landfall in other southeastern states (e.g., Florida) and continue north, northeast, or northwest into Georgia. This study used the North Atlantic Basin hurricane database (HURDAT2) to quantify the spatiotemporal patterns of direct and indirect landfalling of Georgia tropical cyclones (>16 ms⁻¹) from 1851 to 2021. TC-induced rainfall was also quantified using rainfall data (nClimGrid-Daily and nClimGrid) from 1951 to 2021 to estimate the proportion of Georgia’s total annual and monthly rainfall attributed to TCs. A multi-methodological approach, incorporating statistics and mapping, is employed to assess the trends of Georgia’s tropical cyclones and the associated rainfall. The study analyzed 113 TCs and found that, on average, less than one TC annually ($\overline{\mathrm{x}}$ = 0.66) traverses the state. September averaged the highest percentage (25%) of TC-induced rainfall, followed by October (14%), and August (13%). This pattern aligns with the TC season, with the highest frequency of TCs occurring in September (n = 35), followed by August (n = 25), and October (n = 18). We found that 10% of tropical storms make landfall on the coastline, while the remaining 91% enter Georgia by making landfall in Florida (92%), Louisiana (7%), or South Carolina (1%) first. A threat of TCs during the peak of the season emphasizes the importance of heightened awareness, increased planning practices, and resource allocation during these periods to protect Georgia’s history and natural beauty, and its residents. Full article

Tidal freshwater forests are usually located at or above the level of mean high water. Some Louisiana coastal forests are below mean high water, especially bald cypress (Taxodium distichum (L.) Rich.) forests because flooding has increased due to the combined effects of global sea level rise and local subsidence. In addition, constructed channels from the coast inland act as conduits for saltwater. As a result, saltwater intrusion affects the productivity of Louisiana’s coastal bald cypress forests. To study the long-term effects of hydrology and salinity on the health of these systems, we fitted dendrometer bands on selected trees to record basal area increment as a measure of growth in permanent forest productivity plots established within six bald cypress stands. Three stands were in freshwater sites with low salinity rooting zone groundwater (0.1–1.3 ppt), while the other three had higher salinity rooting zone groundwater (0.2–4.9 ppt). Water level was logged continuously, and salinity was measured monthly to quarterly on the surface and in groundwater wells. Higher groundwater salinity levels were related to decreased bald cypress radial growth, while higher freshwater flooding increased radial growth. With these data, coastal managers can model rates of bald cypress forest change as a function of salinity and flooding. Full article

The changes on the Louisiana coast due to land loss can be understood as a process, and the social construction of these processes is highly complex. Due to this complexity, we will examine these social patterns of interpretation as well as individual experiences of coastal land loss in Louisiana within a neopragmatic meta-theoretical framework using several methods, data, researcher perspectives, forms of representation, and theories, with a special focus on the construction of coastal land loss by the media. For this purpose, comments below a YouTube video on a hurricane event on Grand Isle, Louisiana, as well as on-site interviews with people affected by coastal land loss, were qualitatively analyzed. The results were interpreted with the help of various theories such as the theory of three landscapes, Dahrendorf’s conflict theory, Bourdieu’s theory of social capital, and Luhmann’s autopoietic systems theory. The research reveals patterns of interpretation, categorization, and evaluation of processes from an internal and external perspective that are highly morally charged. Full article

Chinese tallow is a non-native invasive tree expanding in range and abundance throughout the southern United States. Several biogeographical studies mapping tallow distribution and examining key underlying environmental factors relied on the U.S. Forest Service Forest Inventory and Analysis (FIA) data, representing forestlands at scales of ~2400 ha. However, given that most invasive trees, like tallow, are cosmopolitan and dynamic in nature, FIA data fails to capture the extent and severity of the invasion especially outside areas classified as forestlands. To develop tallow maps that more adequately depict its distribution at finer spatial scales and to capture observations in non-forestlands, we combined verified citizen science observations with FIA data. Further, we described spatiotemporal patterns and compared citizen science to FIA and other previously published distribution maps. From our work, although tallow is prevalent in the south, Louisiana, Texas, and Mississippi were the most invaded states. Tallow was associated with flatwoods and prairie grasslands of the Gulf Coast. Annual extreme minimum temperatures of less than −12.2 °C (10 °F) represented the northern limit of naturalized tallow populations. Tallow’s northward and inland expansion was captured in citizen science and FIA data, indicating a tallow spread rate ranging from 5 to 20 km annually over the last decade. Systematic sampling, such as FIA, and citizen science data both have their own unique pitfalls. However, the use of citizen science data can complement invasive plant distribution mapping, especially when combined with data from established systematic monitoring networks. This approach provides for a more complete understanding of invasive tree extent and spatiotemporal dynamics across large landscapes. Full article

Despite increased hurricane intensity, the U.S. Gulf of Mexico coast has experienced dramatic coastal population increase of 24.5% from 2000 to 2016. However, in areas of coastal Louisiana with dramatic wetland loss, parishes have experienced population declines and lower rates of population growth. Therefore, understanding the magnitude of the effect of wetland loss as a main driver of population loss in coastal Louisiana is critical. Using regression analysis, this study finds that wetland loss has a significant and persistent negative effect on population growth in coastal Louisiana. This effect resulted in a reduction in the population growth rate in coastal parishes over time. A counterfactual simulation was conducted to estimate the potential population size in the absence of wetland loss from 1990 to 2021. On average, the effect of 1 hectare of wetland lost causes a reduction of approximately 1000 persons. This indicates that for the year 2021, the population was approximately 18% lower than the population that would have existed in the absence of wetland loss. This research underscores the role of wetlands in providing direct and indirect benefits to people in coastal Louisiana that are ultimately reflected in its population levels. Full article

The oyster industry is a significant component of United States aquaculture and is vulnerable to various food frauds. In addition to species substitution, mislabeling of oyster geographical origin is performed for economic gains. The geographical origin misrepresentations are performed to claim a famed region of origin known for its unique flavor profile. DNA barcoding is the gold standard method for identifying seafood species but has limited resolution to the species level. This pilot study was conducted to characterize and compare the oyster gill microbiome as an alternative approach for tracking oysters’ origin. Commercially available raw east coast oysters (Crassostrea virginica) from two distinct geographical locations were purchased. Genomic DNA isolated from the gills was processed for microbiome analysis. The data revealed distinct microbiome signatures among the two sample sets. Oysters from Louisiana showed the presence of eighteen unique bacterial genera, whereas Maryland oysters showed a higher abundance of twelve genera. Findings from this study demonstrate the applicability of microbiome analysis as an emerging alternative approach for identifying geographical origin misrepresentations. Full article

Coastal forests along the southeastern Gulf of Mexico are known to be diminishing at an alarming rate. The live-oak dominant chenier forests of southeast Louisiana are amongst those exhibiting the steepest declines. The remnant stands have experienced numerous hurricanes and intense storm events in recent years, calling into question the current status and immediate future of this imperiled natural resource. Despite their noted ecological and physiographic importance, there is a lack within national geographic data repositories of accurate representations of forest loss and wetland extent for this region. Supervised machine learning algorithms in the Google Earth Engine were used to classify and process high-resolution National Agricultural Image Product (NAIP) datasets to create accurate (>90%) tree cover maps of the Louisiana Chenier Plains in Cameron and Vermilion Parishes. Data from three different years (2003, 2007, and 2019) were used to map 2302 km² along the southwestern coast of Louisiana. According to the analyses, there was a 35.73% loss of forest cover in this region between 2003 and 2019. A majority of the land-use change was from tree cover to saltmarsh, with losses in pastoral land also documented. We found variable rates of loss with respect to elevation. Forest cover losses corresponded strongly to rises in mean sea level. These findings deliver a baseline understanding of the rate of forest loss in this region, highlighting the reduction and potentially the eventual extirpation of this imperiled ecosystem. Full article

Atmospheric cold fronts can periodically generate storm surges and affect sediment transport in the Northern Gulf of Mexico (NGOM). In this paper, we evaluate water circulation spatiotemporal patterns induced by six atmospheric cold front events in the Wax Lake Delta (WLD) in coastal Louisiana using the 3-D hydrodynamic model ECOM-si. Model simulations show that channelized and inter-distributary water flow is significantly impacted by cold fronts. Water volume transport throughout the deltaic channel network is not just constrained to the main channels but also occurs laterally across channels accounting for about a quarter of the total flow. Results show that a significant landward flow occurs across the delta prior to the frontal passage, resulting in a positive storm surge on the coast. The along-channel current velocity dominates while cross-channel water transport occurs at the southwest lobe during the post-frontal stage. Depending on local weather conditions, the cold-front-induced flushing event lasts for 1.7 to 7 days and can flush 32–76% of the total water mass out of the system, a greater range of variability than previous reports. The magnitude of water flushed out of the system is not necessarily dependent on the duration of the frontal events. An energy partitioning analysis shows that the relative importance of subtidal energy (10–45% of the total) and tidal energy (20–70%) varies substantially from station to station and is linked to the weather impact. It is important to note that within the WLD region, the weather-induced subtidal energy (46–66% of the total) is much greater than the diurnal tidal energy (13–25% of the total). The wind associated with cold fronts in winter is the main factor controlling water circulation in the WLD and is a major driver in the spatial configuration of the channel network and delta progradation rates. Full article

Coastal areas serve as a vital interface between the land and sea or ocean and host about 40% of the world’s population, providing significant social, economic, and ecological functions. Meanwhile, the sea-level rise caused by climate change, along with coastal erosion and accretion, alters coastal landscapes profoundly, threatening coastal sustainability. For instance, the Mississippi River Delta in Louisiana is one of the most vulnerable coastal areas. It faces severe long-term land loss that has disrupted the regional ecosystem balance during the past few decades. There is an urgent need to understand the land loss mechanism in coastal Louisiana and identify areas prone to land loss in the future. This study modeled the current and predicted the future land loss and identified natural–human variables in the Louisiana Coastal Zone (LCZ) using remote sensing and machine-learning approaches. First, we analyzed the temporal and spatial land loss patterns from 2001 to 2016 in the study area. Second, logistic regression, extreme gradient boosting (XGBoost), and random forest models with 15 human and natural variables were carried out during each five-year and the fifteen-year period to delineate the short- and long-term land loss mechanisms. Finally, we simulated the land-loss probability in 2031 using the optimal model. The results indicate that land loss patterns in different parts change through time at an overall decelerating speed. The oil and gas well density and subsidence rate were the most significant land loss drivers during 2001–2016. The simulation shows that a total area of 180 km² of land has over a 50% probability of turning to water from 2016 to 2031. This research offers valuable information for decision-makers and local communities to prepare for future land cover changes, reduce potential risks, and efficiently manage the land restoration in coastal Louisiana. Full article

Louisiana has lost over 4800 km² of coastal land since 1932, and a large-scale effort to restore coastal Louisiana is underway, guided by Louisiana’s Comprehensive Master Plan for a Sustainable Coast. This paper reviews science-based planning processes to address uncertainties in management decisions, and determine the most effective combination of restoration and flood risk reduction projects to reduce land loss, maintain and restore coastal environments, and sustain communities. The large-scale effort to restore coastal Louisiana is made more challenging by uncertainties in sediment in the Mississippi River, rising sea levels, subsidence, storms, oil and gas activities, flood-control levees, and navigation infrastructure. To inform decision making, CPRA uses structured approaches to incorporate science at all stages of restoration project planning and implementation to: (1) identify alternative management actions, (2) select the management action based on the best available science, and (3) assess performance of the implemented management decisions. Applied science and synthesis initiatives are critical for solving scientific and technical uncertainties in the successive stages of program and project management, from planning, implementation, operations, to monitoring and assessment. The processes developed and lessons learned from planning and implementing restoration in coastal Louisiana are relevant to other vulnerable coastal regions around the globe. Full article

Levee construction aboveground and hydrocarbon removal from belowground in coastal wetlands can create hydrologic changes that increase plant stress through flooding. But the significance of the subsidence they cause individually or in combination is contested. This study untangled them to demonstrate elevational limits of salt marshes by studying dredged and natural waterways in two salt marshes in Louisiana, USA. The areas had a homogenous plant cover before drilling for oil and gas extraction peaked in the 1960s, and now are a mixed network of natural waterways and dredged canals used to drill wells with an average drill date of 1965.8 ± 2.7 (µ ± 1 SEM; n = 18) and well depth of 4661.0 m ± 56.6 (µ ± 1 SEM; n = 18). Aerial imagery was used to document how canals widened to become 2 to 4 times larger than their original construction width at the high production site and 50% larger at the low production site, whereas increases at the nearby natural channels were much less. Light detection and ranging (LIDAR) measurements at the high production site from 2002 showed that the marsh surface near wells subsided by 34 cm compared to undredged sites. Elevation in marshes at producing and dry wells were equal at the low production site, but high production well locations were even lower than at dry wells. An elevation vs. percent open water curve developed from these data overlapped with an independent analysis of a brackish marsh. A relative subsidence rate between 7.4 to 10.4 mm y⁻¹ transformed these salt marshes to an open water habitat within a few decades. The local creation of accommodation space through hydrocarbon removal and leveed wetlands is a parsimonious explanation for the spatial and temporal land loss rates on this deltaic coast over the last 80 years of oil and gas exploration. Substantial losses from the accelerating rates of sea level rise are indicated to occur before 2050. Full article

This study examines the characteristics of the members in the most popular Geographic Information Systems (GIS) Professional (GISP) certification program in the United States as well as the spatial patterns of the certified GISPs. The results show that the majority of GISPs (97.3%) are located in urban areas. About 75% of the GISPs are male. Among all the GISPs, 3971 GISPs (43.3%) play a managerial role, while 4983 individuals (54.5%) assume a non-administrative role. Among the GISPs with a non-administrative role, 348 GISPs (7%) fall within the GIS Developer group, 3392 GISPs (68%) belong to the GIS Analyst group, and 1243 GISPs (25%) play other roles. Additionally, in our analysis of spatial patterns, we identified two hotspots and two coldspots. The first hotspot is centered around Idaho and Wyoming, while the second hotspot includes Virginia, Washington DC, and Maryland. One coldspot is along Iowa, Missouri, Arkansas, and Louisiana in the central U.S., while the other coldspot includes states such as Connecticut, New Jersey, and New York on the east coast. The information presented in this study can help GIS educators and practitioners develop a better understanding of the current state of this certification program in the U.S and shed light on how to further improve the GISP certification program. Full article

We have established a stable regional geodetic reference frame using long-history (13.5 years on average) observations from 55 continuously operated Global Navigation Satellite System (GNSS) stations adjacent to the Gulf of Mexico (GOM). The regional reference frame, designated as GOM20, is aligned in origin and scale with the International GNSS Reference Frame 2014 (IGS14). The primary product from this study is the seven-parameters for transforming the Earth-Centered-Earth-Fixed (ECEF) Cartesian coordinates from IGS14 to GOM20. The frame stability of GOM20 is approximately 0.3 mm/year in the horizontal directions and 0.5 mm/year in the vertical direction. The regional reference frame can be confidently used for the time window from the 1990s to 2030 without causing positional errors larger than the accuracy of 24-h static GNSS measurements. Applications of GOM20 in delineating rapid urban subsidence, coastal subsidence and faulting, and sea-level rise are demonstrated in this article. According to this study, subsidence faster than 2 cm/year is ongoing in several major cities in central Mexico, with the most rapid subsidence reaching to 27 cm/year in Mexico City; a large portion of the Texas and Louisiana coasts are subsiding at 3 to 6.5 mm/year; the average sea-level-rise rate (with respect to GOM20) along the Gulf coast is 2.6 mm/year with a 95% confidence interval of ±1 mm/year during the past five decades. GOM20 provides a consistent platform to integrate ground deformational observations from different remote sensing techniques (e.g., GPS, InSAR, LiDAR, UAV-Photogrammetry) and ground surveys (e.g., tide gauge, leveling surveying) into a unified geodetic reference frame and enables multidisciplinary and cross-disciplinary research. Full article

Recently, different algorithms have been developed to assess near-surface particulate organic matter (POC) concentration over coastal waters. In this study, we gathered an extensive in situ dataset representing various contrasted bio-optical coastal environments at low, medium, and high latitudes, with various bulk particulate matter chemical compositions (mineral-dominated, 50% of the data set, mixed, 40%, or organic-dominated, 10%). The dataset includes 606 coincident measurements of POC concentration and remote-sensing reflectance, R_rs, with POC concentrations covering three orders of magnitude. Twelve existing algorithms have then been tested on this data set, and a new one was proposed. The results show that the performance of historical algorithms depends on the type of water, with an overall low performance observed for mineral-dominated waters. Furthermore, none of the tested algorithms provided satisfactory results over the whole POC range. A novel approach was thus developed based on a maximum band ratio of R_rs (red/blue, red/yellow or red/green ratio). Based on the standard statistical metric for the evaluation of inverse models, the new algorithm presents the best performance. The root-mean square deviation for log-transformed data (RMSD_log) is 0.25. The mean absolute percentage difference (MAPD) is 37.48%. The mean bias (MB) and median ratio (MR) values are 0.54 μg L⁻¹ and 1.02, respectively. This algorithm replicates quite well the distribution of in situ data. The new algorithm was also tested on a matchup dataset gathering 154 coincident MERIS (MEdium Resolution Imaging Spectrometer) R_rs and in situ POC concentration sampled along the French coast. The matchup analysis showed that the performance of the new algorithm is satisfactory (RMSD_log = 0.24, MAPD = 34.16%, MR = 0.92). A regional illustration of the model performance for the Louisiana continental shelf shows that monthly mean POC concentrations derived from MERIS with the new algorithm are consistent with those derived from the 2016 algorithm of Le et al. which was specifically developed for this region. Full article