Modeling Intersecting Processes of Wetland Shrinkage and Urban Expansion by a Time-Varying Methodology

Abstract

Continuous urban expansion worldwide has resulted in significant wetland degradation and loss. A limited number of studies have addressed the coupling of wetland and urban dynamics, but this relationship remains unclear. In this study, a time-varying methodology of predicting wetland distribution was developed to support decision-making. The novelty of the methodology is its ability to dynamically simulate wetland shrinkage together with urban expansion and reveal conflicts and potential tradeoffs under different scenarios. The developed methodology consists of three modules: a historical change detection of wetland and urban areas module, a spatial urban sprawl simulation and forecasting module that can accommodate different development priorities, and a wetland distribution module with time-varying logistic regression. The methodology was applied and tested in the Tonghu Wetland as a case study. The wetland and urban extents presented a spatially intersecting shift, where wetlands lost more than 40% of their area from 1977 to 2017, while urban areas expanded by 10-fold, threatening wetlands. The increase in the relative importance metric of the time-varying regression model indicated an enhanced influence of urban expansion on the wetland. An accuracy assessment validated a robust statistical result and a good visual fit between spatially distributed wetland occurrence probabilities and the actual distribution of wetland. Incorporating the new variable of urban expansion improved modeling performance and, particularly, realized a greater ability to predict potential wetland loss than provided by the traditional method. Future wetland loss probabilities were visualized under different scenarios. The historical trend scenario predicted continuously expanding urban growth and wetland shrinkage to 2030. However, a specific urban development strategy scenario was designed interactively to control the potential wetland loss. Consideration of such scenarios can facilitate identifying tradeoffs to support wetland conservation.

Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

“…In recent years, some spatial models have combined remote sensing (RS) and geographic information systems (GISs) to simulate and predict future scenarios of LULC [54], for instance, Markov chain [55,56], cellular automata-Markov (CA-Markov) [57], logistic regression [58,59], cellular automata model [60,61], SLEUTH model [62,63], and artificial neural network model [64,65]. Many studies in the literature have also separately applied the Fractal [66,67], CA-Markov [68][69][70][71] and ANN [72][73][74], logistic regression [75,76], and agent-based [77,78] models, which are popular in prediction studies for LULC. Among these, Markov chain analysis (MCA), cellular automata (CA), cellular automata-Markov model (CA-Markov), artificial neural network (ANN), binary logistic regression, and the fractal model can be considered the most common [79].…”

Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

“…As urban expansion causes degradation of wetlands [45,46], the research considering the distance from wetlands investigated these relationships [31,47,48]. Likewise, it is necessary to examine the effects of distance from the airport due to the negative impacts of its noise and emissions on residential areas [49].…”