Abstract
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.
Highlights
From the beginning of the 20th century, coastal areas have drawn increasing attention from scientists and governments because of the growing population in those regions and the vital ecosystem resources they provide
The results offer valuable insights into land loss mechanisms and predictions, which will help decision-makers and local communities to reduce potential risks from future land cover changes, and efficiently manage the land restoration and population relocation in coastal Louisiana
The conditional probability analysis indicates that the majority of land loss happened in regions where the elevation was lower than 10 m and the distance to water was closer than 250 m in the fifteen years
Summary
From the beginning of the 20th century, coastal areas have drawn increasing attention from scientists and governments because of the growing population in those regions and the vital ecosystem resources they provide. Louisiana’s wetlands comprise nearly 40% of the U.S.’s continental wetlands, which serve as habitats for many species and buffer zones for coastal communities from the frequent natural hazards [5,6,7]. This study analyzed the land cover changes in Louisiana Coastal Zone (LCZ) and developed a regional land-loss model through statistics and machine-learning algorithms. The results offer valuable insights into land loss mechanisms and predictions, which will help decision-makers and local communities to reduce potential risks from future land cover changes, and efficiently manage the land restoration and population relocation in coastal Louisiana. The knowledge gained from coastal Louisiana could inform other vulnerable coastal regions globally to reduce threats and potential damages from land loss.
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