期刊
REMOTE SENSING OF ENVIRONMENT
卷 114, 期 8, 页码 1733-1746出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2010.03.003
关键词
Urban areas; Urbanization; Cities; Global monitoring; Land cover; Environment; Classification; Decision trees; Machine learning
资金
- NASA [NNX08AE61A]
- NASA [102885, NNX08AE61A] Funding Source: Federal RePORTER
Although cities, towns and settlements cover only a tiny fraction (<1%) of the world's surface, urban areas are the nexus of human activity with more than 50% of the population and 70-90% of economic activity. As such, material and energy consumption, air pollution, and expanding impervious surface are all concentrated in urban areas, with important environmental implications at local, regional and potentially global scales. New ways to measure and monitor the built environment over large areas are thus critical to answering a wide range of environmental research questions related to the role of urbanization in climate, biogeochemistry and hydrological cycles. This paper presents a new dataset depicting global urban land at 500-m spatial resolution based on MODIS data (available at http://sage.wisc.edu/urbanenvironment.html). The methodological approach exploits temporal and spectral information in one year of MODIS observations, classified using a global training database and an ensemble decision-tree classification algorithm. To overcome confusion between urban and built-up lands and other land cover types, a stratification based on climate, vegetation, and urban topology was developed that allowed region-specific processing. Using reference data from a sample of 140 cities stratified by region, population size, and level of economic development, results show a mean overall accuracy of 93% (k = 0.65) at the pixel level and a high level of agreement at the city scale (R-2 = 0.90). (C) 2010 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据