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Spectral mixture analysis (SMA) has been widely applied for estimating fractional land-cover types from remote sensing pixels. SMA typically assumes each spectral band has equal contribution to the...

Along with rapid urbanization, the prevalence of urban impervious surfaces, a major biophysical component of urbanized areas, has increased concurrently. As a key indicator of environmental quality and urbanization intensity, the accurate estimation of impervious surfaces is essential. To address this problem, numerous automated estimation approaches have been developed in the past several decades. Among these approaches, spectral mixture analysis SMA is an especially powerful and widely used technique. Although SMA has proved valuable in impervious surface estimation, the issues of seasonal sensitivity and spectral confusion have not been successfully addressed. In particular, impervious surface estimation is likely to be sensitive to seasonal variations, largely due to the shadowing effects of vegetation canopy during summer and confusion between impervious surfaces and soil during winter. In this study, we developed two temporal mixture analysis methods: phenology-based temporal mixture analysis PTMA and phenology-based multi-endmember temporal mixture analysis PMETMA, to quantify impervious surface areal fractions using multi-temporal MODIS NDVI data. Specifically, 1 year-continuous MODIS NDVI series were employed to address seasonal sensitivity and spectral confusion issues. Furthermore, the estimated results were compared to TMAs that applied only to summer and winter data. The results indicate that both PTMA and PMETMA perform well for estimating the percentage of impervious surface areas. Moreover, a comparative analysis indicates that PMETMA performs slightly better than PTMA root mean square error RMSE of 7.27%, SE of 3.25%, and MAE of 4.03% and much better than summer TMA and winter TMA, with a RMSE of 7.54%, an SE of 2.13%, an MAE of 3.36%, and an R2 of 0.7623.

Small-area population estimates for a non-census year are essential for supporting a wide variety of planning processes. Many demographic or geographic-information-based models have been developed for generating small-area population estimates. Little research, however, attempted to integrate these two types of models to achieve a better estimation. This study explores the feasibility of incorporating geographic information system (GIS), remote-sensing and demographic data into the housing-unit (HU) method, a popular demographic model, to estimate small-area population in Grafton, WI, USA. In particular, two major components of the HU method, HU counts and persons per household (PPH), are obtained by modelling their relationships with demographic and geographic factors using a sequence of ordinary least-squares (OLS) regression models. Analysis of results indicates that spatial factors derived from remote sensing and GIS datasets, together with demographic information, can significantly improve the accuracy of small-area population estimation.

Small-area population estimation is an important task that has received considerable attention from the remote-sensing community in the past four decades. The wealth of related studies reveals that the notion of living space had been considered a key linkage between population and remote-sensing measurements. Unfortunately, a formal definition for this important variable has proved difficult, due, in part, to the relatively coarse spatial resolution of the remote-sensing data used for population estimation. The advent of airborne Light Detection And Ranging (LiDAR) sensors for measuring elevation at fine spatial resolutions has provided new opportunities for considering the three-dimensional nature of living space in urban environments and for improving small-area population estimations. In this study, we assess the potential of fine-spatial-resolution LiDAR measurements (1 m) coupled with automated techniques for building extraction and land-use classification. The study seeks to provide an answer to the question: what level of information extracted from fine-spatial-resolution LiDAR and aerial photographs can be realistically translated into improved small-area population estimation? This question is addressed through a comparative study of up to seven linear models with building count, building area and/or building volume as explanatory variables at one of two land-use levels: single-family dwelling, multi-family dwelling and other types, versus residential and other types. Results show that, while building volume fits more naturally the population figures, it also represents the most challenging variable to measure by automated means. Because of this, a simple model expressed in terms of residential-building counts results in more reliable population estimates.

Impervious surface distribution and its temporal changes are considered key urbanization indicators and are utilized for analysing urban growth and influences of urbanization on natural environments. Recently, urban impervious surface information was extracted from medium/coarse resolution remote sensing imagery (e.g. Landsat ETM+ and AVHRR) through spectral analytical methods (e.g. spectral mixture analysis (SMA), regression tree, etc.). Few studies, however, have attempted to generate impervious surface information from high resolution remotely sensed imagery (e.g. IKONOS and Quickbird). High resolution images provide detailed information about urban features and are, therefore, more valuable for urban analysis. The improved spatial resolution, however, also brings new challenges when existing spectral analytical methods are applied. In particular, a higher spatial resolution leads to reduced boundary effects and increased within-class variability. Taking Grafton, Wisconsin, USA as a study site, this paper analyses the spectral characteristics of IKONOS imagery and explores the applicability of SMA for impervious surface estimation. Results suggest that with improved spatial resolution, IKONOS imagery contains 40-50% of mixed urban pixels for the study area, and the within-class variability is a severe problem for spectral analysis. To address this problem, this paper proposes two approaches, interior end-member set selection and spectral normalization, for SMA. Analysis of results indicates that these approaches can reasonably reduce the problems associated with boundary effects and within-class variability, therefore generating better impervious surface estimates.

Knowledge of the size and distribution of human population is essential for understanding and responding to many social, political, economical and environmental problems (Liu 2003). In the United S...