Abstract

Natural wetland ecosystems provide not only important habitats for many wildlife species, but also food for migratory and resident animals. In Shanghai, the Chongming Dongtan International Wetland, located at the mouth of the Yangtze River, plays an important role in maintaining both ecosystem health and ecological security of the island. Meanwhile it provides an especially important stopover and overwintering site for migratory birds, being located in the middle of the East Asian-Australasian Flyway. However, with the increase in development intensity and human activities, this wetland suffers from increasing environmental pressure. On the other hand, biological succession in the mudflat wetland makes Chongming Dongtan a rapidly developing and rare ecosystem in the world. Therefore, studying the wetland spatio-temporal change is an important precondition for analyzing the relationship between wetland evolution processes and human activities. This paper presents a novel method for analyzing land-use/cover changes (LUCC) on Chongming Dongtan wetland using multispectral satellite images. Our method mainly takes advantages of a machine learning algorithm, named the Kernel Extreme Learning Machine (K-ELM), which is applied to distinguish between different objects and extract their information from images. In the K-ELM, the kernel trick makes it more stable and accurate. The comparison between K-ELM and three other conventional classification methods indicates that the proposed K-ELM has the highest overall accuracy, especially for distinguishing between Spartina alternflora, Scirpus mariqueter, and Phragmites australis. Meanwhile, its efficiency is remarkable as well. Then a total of eight Landsat TM series images acquired from 1986 to 2013 were used for the LUCC analysis with K-ELM. According to the classification result, the change detection and spatio-temporal quantitative analysis were performed. The specific analysis of different objects are significant for learning about the historical changes to Chongming Dongtan and obtaining the evaluation rules. Generally, the rapid speed of Chongming Dongtan’s urbanization brought about great influence with respect to natural resources and the environment. Integrating the results into the ecological analysis and ecological regional planning of Dongtan could provide a reliable scientific basis for rational planning, development, and the ecological balance and regional sustainability of the wetland area.

Highlights

  • Wetlands play an important role in ecosystem management, ranging from the local to the global scale [1]

  • For the east part, which is mainly occupied by Phragmites australis, Scirpus mariqueter, and bare flat in practice, Kernel Extreme Learning Machine (K-extreme learning machine (ELM)) could distinguish them significantly, while Maximum likelihood classification (MLC) and support vector machines (SVMs) misclassify some areas as grassland and Spartina alterniflora, rReesmpoetecStievnse.l2y0.1T8,h1e0,ExLFMORcPoEuElRdRnEoVtIEeWffectively recognize Scirpus mariqueter as well

  • In this study a novel method, which mainly takes advantages of the kernel extreme learning machine (K-ELM) algorithm, was applied to distinguish between different objects and extract their distribution information over the Dongtan wetland located on Chongming Island, Shanghai, China

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Summary

Introduction

Wetlands play an important role in ecosystem management, ranging from the local to the global scale [1]. They are defined as the boundary area of terrestrial and aquatic ecosystems, having features of both [2]. Wetlands generate a variety of benefits to society and nature, such as providing fertile soils for agriculture, food, and habitat for shorebirds, generating oxygen, adjusting climate, improving water quality, etc. They have been regarded as one of the most valuable resources in the world [3]. The study of wetland conservation has generated considerable interest among researchers and achieved significant progress [5]

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