Regional mapping of human settlements in southeastern China with multisensor remotely sensed data

Abstract

Mapping human settlements from remotely sensed data at regional and global scales has attracted increasingly attention but remains a challenge. The thresholding technique is a common approach for settlement mapping based on the DMSP-OLS data. However, this approach often omits the areas with small proportional settlements such as towns and villages and overestimates urban extents, resulting in information loss of spatial patterns. This paper explored an integrated approach based on a combined use of multiple remotely sensed data to map settlements in southeastern China. Human settlements for selected sites were mapped from Landsat ETM+ images with a hybrid approach and they were used as reference data. The DMSP-OLS and Terra MODIS NDVI data were combined to develop a settlement index image. This index image was used to map a pixel-based settlement image with expert rules. A regression model was established to estimate fractional settlements at the regional scale, which the DMSP-OLS and MODIS NDVI data were used as independent variables and the settlement data derived from ETM+ images were used as a dependent variable. This research indicated that a combination of DMSP-OLS and NDVI variables provided a better estimation performance than single DMSP-OLS or NDVI variable, and the integrated approach for settlement mapping at the regional scale was promising. Compared to the results from the traditional thresholding technique, the estimated fractional settlement image in this paper greatly improved the spatial patterns of settlement distribution and accuracy of settlement areas. This paper provided a rapid and accurate approach to estimate fractional settlements from coarse spatial resolution images at the regional scale by combining a limited number of medium spatial resolution images. This research is especially valuable for timely updating settlement databases at regional and global scales with limited time, labor, and cost.