Next Article in Journal / Special Issue
Factors Influencing Perceptions and Use of Urban Nature: Surveys of Park Visitors in Delhi
Previous Article in Journal / Special Issue
Analysis of Urban Green Spaces Based on Sentinel-2A: Case Studies from Slovakia
Article Menu
Issue 2 (June) cover image

Export Article

Open AccessArticle

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

Center for Southeast Asian Studies (CSEAS), Kyoto University, 46, Yoshida Shimoadachicho, Sakyo-ku Kyoto-shi, Kyoto 606-8501, Japan
Institute for Global Environmental Strategies (IGES), 2108-11 Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan
Faculty of Environmental Studies, Tokyo City University, 3-3-1 Ushikubo-nishi, Tsuzuki-ku, Yokohama, Kanagawa 224-8551, Japan
Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna 4031, Philippines
Author to whom correspondence should be addressed.
Academic Editors: Andrew Millington, Harini Nagendra and Monika Kopecka
Received: 6 March 2017 / Revised: 10 April 2017 / Accepted: 11 April 2017 / Published: 14 April 2017
(This article belongs to the Special Issue Urban Land Systems: An Ecosystems Perspective)
Full-Text   |   PDF [9861 KB, uploaded 18 April 2017]   |  


This study uses a spatially-explicit land-use/land-cover (LULC) modeling approach to model and map the future (2016–2030) LULC of the area surrounding the Laguna de Bay of Philippines under three different scenarios: ‘business-as-usual’, ‘compact development’, and ‘high sprawl’ scenarios. The Laguna de Bay is the largest lake in the Philippines and an important natural resource for the population in/around Metro Manila. The LULC around the lake is rapidly changing due to urban sprawl, so local and national government agencies situated in the area need an understanding of the future (likely) LULC changes and their associated hydrological impacts. The spatial modeling approach involved three main steps: (1) mapping the locations of past LULC changes; (2) identifying the drivers of these past changes; and (3) identifying where and when future LULC changes are likely to occur. Utilizing various publically-available spatial datasets representing potential drivers of LULC changes, a LULC change model was calibrated using the Multilayer Perceptron (MLP) neural network algorithm. After calibrating the model, future LULC changes were modeled and mapped up to the year 2030. Our modeling results showed that the ‘built-up’ LULC class is likely to experience the greatest increase in land area due to losses in ‘crop/grass’ (and to a lesser degree ‘tree’) LULC, and this is attributed to continued urban sprawl. View Full-Text
Keywords: landuse; change; open data; landscape; remote sensing; GIS; Markov Chain landuse; change; open data; landscape; remote sensing; GIS; Markov Chain

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).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Iizuka, K.; Johnson, B.A.; Onishi, A.; Magcale-Macandog, D.B.; Endo, I.; Bragais, M. Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines. Land 2017, 6, 26.

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



[Return to top]
Land EISSN 2073-445X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top