文章基本信息

标题：Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines
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作者：Iizuka, Kotaro ; Johnson, Brian A. ; Onishi, Akio 等
期刊名称：Land
印刷版ISSN：2073-445X
出版年度：2017
卷号：6
期号：2
页码：1-21
出版社：MDPI, Open Access Journal
摘要：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.
关键词：landuse; change; open data; landscape; remote sensing; GIS; Markov Chain