Geology topics

Lei Wang

Publications and source records attributed to Lei Wang.

7 recordsLinked to original sources

USGS2021

Assessing habitat change and migration of barrier islands

Barrier islands are dynamic environments that experience gradual change from waves, tides, and currents, and rapid change from extreme storms. These islands are expected to change drastically over the coming century due to accelerated sea-level rise and changes in frequency and intensity of storm events. The dynamic nature of barrier islands coupled with the importance of these environments make it critical for natural resource managers to understand how habitats on barrier islands are changing or may change over time to determine when and where management actions may be needed. In this study, we applied a habitat change assessment framework, which included exploring areal coverage and distribution changes and change component analysis. Change component analysis, which breaks differences into net gain/loss and allocation difference (i.e., habitat oscillation), has not previously been used to study barrier island habitat evolution. Here, we demonstrate the approach using habitat predictions from a geomorphic modeling effort on Dauphin Island, Alabama (USA). We explored differences of habitat predictions for potential island configurations with and without a beach and dune restoration action under future conditions related to sea level and storminess. We found a potential linkage between landward migration of barrier islands and exchange, an output of change component analysis. The hypothesis may be tested to explore whether this linkage applies over space and time and whether the approach is applicable to monitoring landward migration of coastal wetlands. Collectively, our results highlight the utility of change component analysis for monitoring and quantifying barrier island habitat change and migration.

Alabama↗

USGS2019

Modeling barrier island habitats using landscape position information

Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.

Remote Sensing↗

USGS2019

Advancing barrier island habitat mapping using landscape position information

Barrier islands are dynamic ecosystems that change gradually from coastal processes, including currents and tides, and rapidly from episodic events, such as storms. These islands provide many important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism. Habitat maps, developed by scientists, provide a critical tool for monitoring changes to these dynamic ecosystems. Barrier island monitoring often requires custom habitat maps due to several factors, including island size and the classification of unique geomorphology-based habitats, such as beach, dune, and barrier flats. In this study, we reviewed barrier-island-specific habitat mapping efforts and highlighted common habitat class types, source data, and mapping approaches. We also developed a framework for mapping geomorphology-based barrier island habitats using a rule-based, geographic object-based image analysis approach, which included the use of field data, tide data, high-resolution orthophotography, and lidar data. This framework integrates several barrier island mapping advancements with regard to the use of landscape position information for automated dune extraction and the use of Monte Carlo analyses for the treatment of elevation uncertainty for elevation-dependent habitats. Specifically, we used the uncertainty analyses to refine automated dune delineation based on elevation relative to extreme storm water levels and to increase the accuracy of intertidal and supratidal/upland habitat delineation. We found that dune extraction results were enhanced when elevation relative to storm water levels and visual interpretation were also applied. This framework could also be applied to beach–dune systems found along a mainland.

Alabama↗

USGS2018

Efficient delineation of nested depression hierarchy in digital elevation models for hydrological analysis using level-set method

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill‐merge‐spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level‐set method based on graph theory. The proposed level‐set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level‐set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine‐resolution Light Detection and Ranging‐derived DEMs show that the raster‐based level‐set algorithm is much more efficient (~150 times faster) than the vector‐based contour tree method. The proposed level‐set algorithm has great potential for being applied to large‐scale ecohydrological analysis and watershed modeling.

Journal of the American Water Resources Associatio↗

USGS2017

The impact of lidar elevation uncertainty on mapping intertidal habitats on barrier islands

While airborne lidar data have revolutionized the spatial resolution that elevations can be realized, data limitations are often magnified in coastal settings. Researchers have found that airborne lidar can have a vertical error as high as 60 cm in densely vegetated intertidal areas. The uncertainty of digital elevation models is often left unaddressed; however, in low-relief environments, such as barrier islands, centimeter differences in elevation can affect exposure to physically demanding abiotic conditions, which greatly influence ecosystem structure and function. In this study, we used airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information to delineate low-lying lands and the intertidal wetlands on Dauphin Island, a barrier island along the coast of Alabama, USA. We compared three different elevation error treatments, which included leaving error untreated and treatments that used Monte Carlo simulations to incorporate elevation vertical uncertainty using general information from lidar metadata and site-specific Real-Time Kinematic Global Position System data, respectively. To aid researchers in instances where limited information is available for error propagation, we conducted a sensitivity test to assess the effect of minor changes to error and bias. Treatment of error with site-specific observations produced the fewest omission errors, although the treatment using the lidar metadata had the most well-balanced results. The percent coverage of intertidal wetlands was increased by up to 80% when treating the vertical error of the digital elevation models. Based on the results from the sensitivity analysis, it could be reasonable to use error and positive bias values from literature for similar environments, conditions, and lidar acquisition characteristics in the event that collection of site-specific data is not feasible and information in the lidar metadata is insufficient. The methodology presented in this study should increase efficiency and enhance results for habitat mapping and analyses in dynamic, low-relief coastal environments.

Alabama↗

USGS2017

Barrier island habitat map and vegetation survey—Dauphin Island, Alabama, 2015

Barrier islands are dynamic environments due to their position at the land-sea interface. Storms, waves, tides, currents, and relative sea-level rise are powerful forces that shape barrier island geomorphology and habitats (for example, beach, dune, marsh, and forest). Hurricane Katrina in 2005 and the Deep Water Horizon oil spill in 2010 are two major events that have affected habitats and natural resources on Dauphin Island, Alabama. The latter event prompted a collaborative effort between the U.S. Geological Survey, the U.S. Army Corps of Engineers, and the State of Alabama funded by the National Fish and Wildlife Foundation to investigate viable, sustainable restoration options that protect and restore the natural resources of Dauphin Island, Alabama. In order to understand the feasibility and sustainability of various restoration scenarios, it is important to understand current conditions on Dauphin Island. To further this understanding, a detailed 19-class habitat map for Dauphin Island was produced from 1-foot aerial infrared photography collected on December 4, 2015, and lidar data collected in January 2015. We also conducted a ground survey of habitat types, vegetation community structure, and elevations in November and December 2015. These products provide baseline data regarding the ecological and general geomorphological attributes of the area, which can be compared with observations from other dates for tracking changes over time.

Alabama↗

USGS2016

Rare earth element deposits in China

China is the world’s leading rare earth element (REE) producer and hosts a variety of deposit types. Carbonatite- related REE deposits, the most significant deposit type, include two giant deposits presently being mined in China, Bayan Obo and Maoniuping, the first and third largest deposits of this type in the world, respectively. The carbonatite-related deposits host the majority of China’s REE resource and are the primary supplier of the world’s light REE. The REE-bearing clay deposits, or ion adsorption-type deposits, are second in importance and are the main source in China for heavy REE resources. Other REE resources include those within monazite or xenotime placers, beach placers, alkaline granites, pegmatites, and hydrothermal veins, as well as some additional deposit types in which REE are recovered as by-products. Carbonatite-related REE deposits in China occur along craton margins, both in rifts (e.g., Bayan Obo) and in reactivated transpressional margins (e.g., Maoniuping). They comprise those along the northern, eastern, and southern margins of the North China block, and along the western margin of the Yangtze block. Major structural features along the craton margins provide first-order controls for REE-related Proterozoic to Cenozoic carbonatite alkaline complexes; these are emplaced in continental margin rifts or strike-slip faults. The ion adsorption-type REE deposits, mainly situated in the South China block, are genetically linked to the weathering of granite and, less commonly, volcanic rocks and lamprophyres. Indosinian (early Mesozoic) and Yanshanian (late Mesozoic) granites are the most important parent rocks for these REE deposits, although Caledonian (early Paleozoic) granites are also of local importance. The primary REE enrichment is hosted in various mineral phases in the igneous rocks and, during the weathering process, the REE are released and adsorbed by clay minerals in the weathering profile. Currently, these REE-rich clays are primarily mined from open-pit operations in southern China. The complex geologic evolution of China’s Precambrian blocks, particularly the long-term subduction of ocean crust below the North and South China blocks, enabled recycling of REE-rich pelagic sediments into mantle lithosphere. This resulted in the REE-enriched nature of the mantle below the Precambrian cratons, which were reactivated and thus essentially decratonized during various tectonic episodes throughout the Proterozoic and Phanerozoic. Deep fault zones within and along the edges of the blocks, including continental rifts and strike-slip faults, provided pathways for upwelling of mantle material.

Book chapter↗