scholarly journals Geomorphic classification of coastal land loss between 1932 and 1990 in the Mississippi River delta plain, southeastern Louisiana

2000 ◽  
Author(s):  
Shea Penland ◽  
Lynda Wayne ◽  
L.D. Britsch ◽  
S. Jeffress Williams ◽  
Andrew D. Beall ◽  
...  
Keyword(s):  
Mississippi River ◽  
River Delta ◽  
Mississippi River Delta ◽  
Land Loss ◽  
Delta Plain ◽  
Coastal Land Loss ◽  
Coastal Land ◽  
Southeastern Louisiana

scholarly journals Land loss by pond expansion on the Mississippi River Delta Plain

2017 ◽  
Vol 44 (8) ◽  
pp. 3635-3642 ◽  
Author(s):  
Alejandra C. Ortiz ◽  
Samapriya Roy ◽  
Douglas A. Edmonds
Keyword(s):  
Mississippi River ◽  
River Delta ◽  
Mississippi River Delta ◽  
Land Loss ◽  
Delta Plain

scholarly journals Scale effects on land loss modeling in the Mississippi River Delta

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Nina S.-N. Lam ◽  
Heng Cai ◽  
Lei Zou ◽  
Kam-biu Liu
Keyword(s):  
Mississippi River ◽  
Neighborhood Effects ◽  
Census Data ◽  
Scale Effects ◽  
System Modeling ◽  
River Delta ◽  
Neighborhood Size ◽  
Mississippi River Delta ◽  
Land Loss ◽  
Loss Modeling

<p><strong>Abstract.</strong> In complex natural-human system modeling, often times a first step is to examine the relationships between a dependent variable and a number of independent variables at their locations. The neighborhood effect, also known as a scale effect, has seldom been considered in the analysis. Previous research has shown that scale effects affect the reliability of analysis results, and rigorous scientific studies should take an extra step to examine the scale effects for more accurate analysis and modeling. However, detecting the neighborhood effects of various variables and then incorporating them into a holistic modeling system posts a serious challenge because of the fundamental difference in properties between variables from the human component (e.g., census data) and variables from the natural component (e.g., landscape properties). Moreover, uncertainties involved in data, data scale, algorithms, and scale of analysis make the findings and the interpretations of the findings unreliable. A major issue of modeling neighborhood effects is the determination of appropriate neighborhood size, also known as the spatial context scale or the operational scale. It has been shown in the literature that neighborhood effects vary with the neighborhood size used to compute the effects. Thus, research on how to determine the neighborhood size that best captures the scale of operation of a phenomenon is very much needed so that we can have more confidence in the modeling results.</p><p>This study examines the use of variogram in detecting the appropriate neighborhood size of the variables involved in land loss modeling in the Mississippi River Delta. The goal is to find out the best combination of variables and their neighborhood sizes that best explain the variation of land loss patterns in the Deltaic region. The Mississippi River Delta has been suffering substantial land loss during the past several decades. Land loss has been a subject of intense research by many researchers from multiple disciplines, aiming at mitigating the land loss process and its potential damages. However, a majority of land loss projections were derived solely from the natural processes, such as sea level rise, regional subsidence, and reduced sediment flows. Very few studies have incorporated human-induced factors such as land fragmentation, urbanization, energy industrialization, and marine transportation. Even fewer have studied the scale effects. A study that captures and quantifies both natural and human factors as well as their neighborhood effects would help uncover the complex mechanism of land loss and provide a more accurate spatiotemporal projection of land loss patterns and probability.</p><p>The analysis procedures are as follows. (1) First, the study area is rasterized into 1-km by 1-km grids. (2) A set of natural and human variables related to land loss in the deltaic region are collected. (3) Variogram analysis of each variable is conducted to identify the spatial neighborhood size of each variable, and a neighborhood variable for each independent variable is created. (4) Elastic Net regression analysis is applied to test and select the significant variables that affect land loss. Regression results between the model with and the model without neighborhood variables are compared. Through this study, we should be able to derive a more accurate land loss model for detailed analysis and future projections.</p>

scholarly journals Understanding the Mississippi River Delta as a Coupled Natural-Human System: Research Methods, Challenges, and Prospects

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1054 ◽  
Author(s):  
Nina Lam ◽  
Y. Xu ◽  
Kam-biu Liu ◽  
David Dismukes ◽  
Margaret Reams ◽  
...  
Keyword(s):  
Mississippi River ◽  
Population Change ◽  
River Delta ◽  
Mississippi River Delta ◽  
Land Loss ◽  
Modeling Framework ◽  
Study Region ◽  
Human System ◽  
Lower Mississippi River ◽  
Coastal Resilience

A pressing question facing the Mississippi River Delta (MRD), like many deltaic communities around the world, is: Will the system be sustainable in the future given the threats of sea level rise, land loss, natural disasters, and depleting natural resources? An integrated coastal modeling framework that incorporates both the natural and human components of these communities, and their interactions with both pulse and press stressors, is needed to help improve our understanding of coastal resilience. However, studying the coastal communities using a coupled natural-human system (CNH) approach is difficult. This paper presents a CNH modeling framework to analyze coastal resilience. We first describe such a CNH modeling framework through a case study of the Lower Mississippi River Delta in coastal Louisiana, USA. Persistent land loss and associated population decrease in the study region, a result of interplays between human and natural factors, are a serious threat to the sustainability of the region. Then, the paper describes the methods and findings of three studies on how community resilience of the MRD system is measured, how land loss is modeled using an artificial neural network-cellular automata approach, and how a system dynamic modeling approach is used to simulate population change in the region. The paper concludes by highlighting lessons learned from these studies and suggesting the path forward for analysis of coupled natural-human systems.

MARSH COLLAPSE BY POND EXPANSION ON THE MISSISSIPPI RIVER DELTA PLAIN

2017 ◽  
Author(s):  
Alejandra Ortiz ◽  
◽  
Samapriya Roy ◽  
Douglas A. Edmonds
Keyword(s):  
Mississippi River ◽  
River Delta ◽  
Mississippi River Delta ◽  
Delta Plain
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