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

Kotaro Iizuka, Brian A. Johnson, Akio Onishi, Damasa B. Magcale-Macandog, Isao Endo and Milben Bragais
Additional contact information
Kotaro Iizuka: Center for Southeast Asian Studies (CSEAS), Kyoto University, 46, Yoshida Shimoadachicho, Sakyo-ku Kyoto-shi, Kyoto 606-8501, Japan
Brian A. Johnson: Institute for Global Environmental Strategies (IGES), 2108-11 Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan
Akio Onishi: Faculty of Environmental Studies, Tokyo City University, 3-3-1 Ushikubo-nishi, Tsuzuki-ku, Yokohama, Kanagawa 224-8551, Japan
Damasa B. Magcale-Macandog: Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna 4031, Philippines
Isao Endo: Institute for Global Environmental Strategies (IGES), 2108-11 Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan
Milben Bragais: Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna 4031, Philippines

Land, 2017, vol. 6, issue 2, 1-21

Abstract: 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.

Keywords: landuse; change; open data; landscape; remote sensing; GIS; Markov Chain (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:6:y:2017:i:2:p:26-:d:95830

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