Liu, Weibo

This study investigates the spatial disparities in flood risk and social vulnerability across 66,543 census tracts in the Conterminous United States (CONUS), emphasizing urban–rural differences. Utilizing the American Community Survey (ACS) 2016–2020 data, we focused on 16 social factors representing socioeconomic status, household composition, racial and ethnic minority status, and housing and transportation access. Principal Component Analysis (PCA) reduced these variables into five principal components: Socioeconomic Disadvantage, Elderly and Disability, Housing Density and Vehicle Access, Youth and Mobile Housing, and Group Quarters and Unemployment. An additive model created a comprehensive Social Vulnerability Index (SVI). Statistical analysis, including the Mann–Whitney U test, indicated significant differences in flood risk and social vulnerability between urban and rural areas. Spatial cluster analysis using Local Indicators of Spatial Association (LISA) revealed significant high flood risk and social vulnerability clusters, particularly in urban regions along the Gulf Coast, Atlantic Seaboard, and Mississippi River. Global and local regression models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), highlighted social vulnerability’s spatial variability and localized impacts on flood risk. The results showed substantial regional disparities, with urban areas exhibiting higher flood risks and social vulnerability, especially in southeastern urban centers.

Previously published in Geographies 2023, 3(1), 161-177 (DOI: https://doi.org/10.3390/geographies3010010)
Inundation dynamics coupled with seasonal information is critical to study the wetland environment. Analyses based on remotely sensed data are the most effective means to monitor and investigate wetland inundation dynamics. For the first time, this study deployed an automated thresholding method to quantify and compare the annual inundation characteristics in dry and wet seasons in the Everglades, using Landsat imagery in Google Earth Engine (GEE). This research presents the long-term time series maps from 2002 to 2021, with a comprehensive spatiotemporal depiction of inundation.
In this paper, we bridged the research gap of space-time analysis for multi-season inundation dynamics, which is urgently needed for the Everglades wetland. Within a GIS-based framework, we integrated statistical models, such as Mann–Kendall and Sen’s Slope tests, to track the evolutionary trend of seasonal inundation dynamics. The spatiotemporal analyses highlight the significant differences in wet and dry seasons through time and space. The stationary or permanent inundation is more likely to be distributed along the coastal regions (Gulf of Mexico and Florida Bay) of the Everglades, presenting a warning regarding their vulnerability to sea level rise.

Tidal flat refers to the sediment-rich environment along the seashore, which is alternatively exposed or inundated during tidal cycles. It is widely recognized as not only the sentinel of coastal environment change, but also the safeguard for beachfront communities. It is necessary to comprehensively understand the wellness of tidal flat environments, especially for the United States (US), which has the eighth longest coastline throughout the world. Aiming at the dynamics of tidal flats, this dissertation firstly proposed a monitoring framework from three levels, including the pixel, object, and lifecycle. In addition, eleven events were defined to describe the dynamic activities throughout the lifecycles, which were captured, represented, and analyzed by utilizing graph theory. The Everglades in the southeastern corner of Florida Peninsula was selected to test this approach, which verifies an effective way to track, represent, and analyze the dynamic activities of tidal flats. Secondly, this dissertation mapped the distributions of tidal flats in the conterminous US, which provides a reliable dataset on a large spatiotemporal scale for future use. A random forest classification model was proposed, which uses 30 predictor variables to describe the spectral change patterns between the satellite images acquired in subsequent time steps. On the other hand, a total of 58,735 ground truth samples were collected under five classes, including permanent water, tidal flats, barren grounds, vegetated lands, and artificial surfaces. These sample points were randomly divided into two parts: 80% of them were used to train the random forest model, and the rest 20% were used to validate the results.

Global population is increasing at an alarming rate with rapid urbanization of the earth’s land surface. Currently, more than half of the world’s population lives in urban areas and this number is projected to increase to 66% by 2050. Urban expansion in coastal zones is more complex due to the rapid urbanization and higher population growth. In the United States (US), more than 39% of the total population now lives in coastal counties. Although urbanization offers some advantages such as economic development, unplanned urbanization can adversely affect our environment. Additionally, coastal communities in the US are frequently impacted by disasters. Climate change such as sea level rise could intensify these coastal disasters and impact more lives and properties. Therefore, using Geographic Information Systems (GIS) and remote sensing, this study examines these pressing environmental challenges with the coastal US as the Study area. We first quantified the historical spatiotemporal patterns and major explanatory factors of urban expansion in the Miami Metropolitan Area during 1992 - 2016 at different spatial scales. Additionally, different urban expansion dynamics such as expansion rate, pattern, types, intensity, and landscape metrics were analyzed. Multi-level spatiotemporal analyses suggest that urban growth varied both spatially and temporally across the study area. We then measured the community resilience to coastal disasters by constructing a composite index. Additionally, spatial relationships between resilience components and disaster impacts were investigated. Results suggest that northeastern coastal communities in the US are more resilient to disasters compared to the southeastern communities. Furthermore, community resilience varies across the space and resilience components used in this study can explain disaster damages. Finally, this research also simulates and predicts three future urban growth scenarios including business as usual, planned growth, and sustainable growth in the study area. Then current and future exposures to flooding were estimated by considering different sea level rise scenarios. Results suggest that future urban areas will be developed significantly in the flood risk areas if development is not restricted in the high-risk flooding zone. Findings from this study could be useful for area-specific disaster management policy guidelines and formation of land use policy and planning.