Next Article in Journal
Could Black Be the New Gold? Design-Driven Challenges in New Sustainable Luxury Materials for Jewelry
Next Article in Special Issue
Groundwater Level Dynamics in Bengaluru City, India
Previous Article in Journal
Cultivated Land Use Benefits Under State and Collective Agrarian Property Regimes in China
Previous Article in Special Issue
Adaptive Governance and Market Heterogeneity: An Institutional Analysis of an Urban Food System in Sub-Saharan Africa
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessFeature PaperArticle
Sustainability 2018, 10(1), 10; https://doi.org/10.3390/su10010010

Analyzing Land Cover Change and Urban Growth Trajectories of the Mega-Urban Region of Dhaka Using Remotely Sensed Data and an Ensemble Classifier

Department of Geography, University of Florida; Gainesville, FL 32611, USA
*
Author to whom correspondence should be addressed.
Received: 29 November 2017 / Revised: 19 December 2017 / Accepted: 19 December 2017 / Published: 21 December 2017
(This article belongs to the Special Issue Sustainability in an Urbanizing World: The Role of People)
View Full-Text   |   Download PDF [15370 KB, uploaded 21 December 2017]   |  

Abstract

Accurate information on, and human interpretation of, urban land cover using satellite-derived sensor imagery is critical given the intricate nature and niches of socioeconomic, demographic, and environmental factors occurring at multiple temporal and spatial scales. Detailed knowledge of urban land and their changing pattern over time periods associated with ecological risk is, however, required for the best use of critical land and its environmental resources. Interest in this topic has increased recently, driven by a surge in the use of open-source computing software, satellite-derived imagery, and improved classification algorithms. Using the machine learning algorithm Random Forest, combined with multi-date Landsat imagery, we classified eight periods of land cover maps with up-to-date spatial and temporal information of urban land between the period of 1972 and 2015 for the mega-urban region of greater Dhaka in Bangladesh. Random Forest—a non-parametric ensemble classifier—has shown a quantum increase in satellite-derived image classification accuracy due to its outperformance over traditional approaches, e.g., Maximum Likelihood. Employing Random Forest as an image classification approach for this study with independent cross-validation techniques, we obtained high classification accuracy, user and producer accuracy. Our overall classification accuracy ranges were between 85% and 97% with kappa values between 0.81 and 0.94. The area statistics derived from the thematic land cover map show that the built-up area in the 43-year study period expanded quickly, from 35 km2 in 1972 to 378 km2 in 2015, with a net increase rate of approximately 980% and an average annual growth rate of 6%. This growth rate, however, was higher in peripheral areas, with a 2903% increase and an annual expansion rate of 8%, compared to a 460% increase with an annual growth rate of 4% in the core city area (Dhaka City Corporation). This huge urban expansion took place in the north, northwest, and southwest regions of Dhaka, transforming areas that were previously agricultural land, vegetation cover, wetland, and water bodies. The main factors driving the city towards northern corridors include flood-free higher land, the availability of a transportation network, and the agglomeration of manufacturing-based employment centers. The resulting thematic map and spatial information produced from this study therefore serve to facilitate a detailed understanding of urban growth dynamics and land cover change patterns in the mega-urban region of Dhaka, Bangladesh. View Full-Text
Keywords: ensemble classifier; random forest; remote sensing; urban growth; land cover change; greater Dhaka ensemble classifier; random forest; remote sensing; urban growth; land cover change; greater Dhaka
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Hassan, M.M.; Southworth, J. Analyzing Land Cover Change and Urban Growth Trajectories of the Mega-Urban Region of Dhaka Using Remotely Sensed Data and an Ensemble Classifier. Sustainability 2018, 10, 10.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top