Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Abstract

Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based on a fuzzy rough set and evidential reasoning (FRSER), for land cover classification in an urban fringe area. The approach is implemented on Landsat Operation Land Imager data covering the urban fringe area of Wuhan city, China. A fuzzy rough set is first used to define a decision table from multispectral imagery and ground reference data. Then the fuzzy rough information system is interpreted using the Dempster-Shafer theory, based on an evidential reasoning system. A final land cover classification with uncertainty is achieved by evidential reasoning. The results are compared with the traditional maximum likelihood classifier (MLC) and some rough set-based classifiers including classical rough set classifier (RS), fuzzy rough set classifier (FRS), and variable precision fuzzy rough set classifier (VPFRS). The better overall accuracy, user's and producer's accuracies, and the kappa coefficient, in comparison with the other classifiers, suggest that the proposed approach can effectively discriminate land cover types in urban fringe areas with high inter-class similarities and intra-class heterogeneity. It is also capable of handling the uncertainty in data processing, and the final land cover map comes with a degree of uncertainty. The proposed approach that can efficiently integrate the merits of both the fuzzy rough set and DS theory provides an efficient method for urban fringe land cover classification.

Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters

“…This ultimately results in a considerable reduction in the open and green areas of that region. Related studies have reported that the increasing level of urbanization was leading to landscape fragmentation in Wuhan [54,55]. This resembles the findings in previous studies [4,5] that the rapid human activity in Dhaka, Bangladesh has leaded to numerous unconnected small patches of vegetation and cultivated land, greater isolation as well as higher percentage of edge areas in patches.…”

“…This process is similar that which has occurred in Ho Chi Minh [2], Dhaka [1,[3][4][5][6]10], Delhi [7], Hanoi [8] and Ansan [9]. Numerous bare land areas emerging along with urbanization processes have cast irreversible impacts on the urban environment, such as air pollution and soil loss [51][52][53][54][55][56][57]. In the plum rain season, flash flooding hazards have become a serious issue and have caused human death and damages to urban infrastructure in the city [52], similar to that in Dhaka [3].…”

“…Urbanization process of the city was monitored and analyzed from the maps, since urban growth is crucial for local environmental and ecological management. The city of Wuhan has undergone one of the most rapid economic, industrial and urban development in its history, similar to other cities in China, India, Bangladesh, Vietnam and other developing counties [1][2][3][4][5][6][7][8][9][10][51][52][53][54][55][56][57]. The study revealed that the urban expansion in Wuhan has been rapid, with the majority of built-up and bare land acquired by converting areas that were previously agricultural land, forest and water bodies.…”

Is Big Good or Bad?: Testing the Performance of Urban Growth Cellular Automata Simulation at Different Spatial Extents

“…It is important to study land use changes in the rural-urban fringe to understand the location of the transition area between urban and non-urban land use types [1,2]. This is also an area with prominent conflicts between farmland protection and urban expansion [3,4].…”

A Novel Classification Technique of Landsat-8 OLI Image-Based Data Visualization: The Application of Andrews’ Plots and Fuzzy Evidential Reasoning

“…This method reduces classification error compared to Markov random field (MRF)-based contextual classification. Recently, Yang et al [21] proposed a classification approach based on DS theory and fuzzy rough sets. This approach can effectively delineate land cover types in urban fringe areas when uncertainties occur in the processing.…”