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Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index datasets are integrated as high-level features under an object-based framework. We applied the HOTex method to the GLS-2010 Landsat images to produce a global map of human built-up and settlement extent. As shown by visual assessments, our method could effectively map urban extent and generate consistent results using images with inconsistent acquisition time and vegetation phenology. Using scene-level cross validation for results in Europe, we assessed the performance of HOTex and achieved a kappa coefficient of 0.91, compared to 0.74 from a baseline method using per-pixel classification using spectral information.

Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 2000-2010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approachAn independent validation dataset was also developed using Google Earth™ imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 ± 7.84 km 2 .

[1] As an important component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection, eight state-of-the-art land cover/use data sets have been compiled and made consistent with the ISLSCP Initiative II land/water mask in support of global modeling efforts. These data sets contain new and improved global data sets at coarse resolutions (1/4, 1/2 and 1°) describing historical, recent and present land cover conditions and are a testament to the tremendous progress made in this area over the past decade. In addition to the historical data, data describing the subcell heterogeneity in land cover are also provided, both in terms of subcell proportions of land cover classes and vegetation continuous fields such as % tree, grass and bare cover. Here we present the various ISLSCPII land cover data sets and compare the principal satellite-derived data sets and the effect of their respective aggregation methods. We find that despite some notable disagreements among similar classes, the satellite-based data sets agree remarkably well over large portions of the Earth's surface (over 50% for all resolutions). We also find that the methods of aggregation, whether done by a strictly dominant type, or using more information on subcell tree cover, can have an important impact on the final output and need to be considered by the user. Finally, by integrating the vegetation continuous fields data into our analyses we are able to show that the principal differences in terms of discrete land cover classes are in fact transition zones between similar classes. Citation: Brown de Colstoun, E. C., R. S. DeFries, and J. R. G. Townshend (2006), Evaluation of ISLSCP Initiative II satellite-based land cover data sets and assessment of progress in land cover data for global modeling,