Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal

Yikalo H Araya,Pedro Cabral

doi:10.3390/rs2061549

Abstract

The expansion of cities entails the abandonment of forest and agricultural lands, and these lands’ conversion into urban areas, which results in substantial impacts on ecosystems. Monitoring these changes and planning urban development can be successfully achieved using multitemporal remotely sensed data, spatial metrics, and modeling. In this paper, urban land use change analysis and modeling was carried out for the Concelhos of Setúbal and Sesimbra in Portugal. An existing land cover map for the year 1990, together with two derived land cover maps from multispectral satellite images for the years 2000 and 2006, were utilized using an object-oriented classification approach. Classification accuracy assessment revealed satisfactory results that fulfilled minimum standard accuracy levels. Urban land use dynamics, in terms of both patterns and quantities, were studied using selected landscape metrics and the Shannon Entropy index. Results show that urban areas increased by 91.11% between 1990 and 2006. In contrast, the change was only 6.34% between 2000 and 2006. The entropy value was 0.73 for both municipalities in 1990, indicating a high rate of urban sprawl in the area. In 2006, this value, for both Sesimbra and Setúbal, reached almost 0.90. This is demonstrative of a tendency toward intensive urban sprawl. Urban land use change for the year 2020 was modeled using a Cellular Automata based approach. The predictive power of the model was successfully validated using Kappa variations. Projected land cover changes show a growing tendency in urban land use, which might threaten areas that are currently reserved for natural parks and agricultural lands.

Highlights

The history of urban growth indicates that urban areas are one of most dynamic places on the
In [33], it is noted that a minimum accuracy value of 85% is required for effective and reliable land cover change analysis and modeling
The classification achieved in this study produces an overall accuracy that fulfils the minimum accuracy threshold

Summary

Introduction

The history of urban growth indicates that urban areas are one of most dynamic places on theEarth‘s surface. The history of urban growth indicates that urban areas are one of most dynamic places on the. The trend of urban growth is towards the urban-rural-fringe, where there is less built-up area, and access to irrigation and other water management systems. In the last few decades, a substantial growth of urban areas has occurred worldwide. Population increase is one of the most obvious agents responsible for this growth. In 1900, only 14% of the world‘s population lived in urban areas, but by 2000, this figure had increased to 47% [2]. The report predicts that by 2030, the percentage of the population residing in urban areas will be 60%. The major environmental concerns that have to be analyzed and monitored carefully, for effective land use management, are those driven by urban growth

Methods

Results

Conclusion