Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan

Abstract

a b s t r a c tAs a proxy measure of the human ecological footprint, impervious surface area (ISA) has recently become a key concept in the field of urban remote sensing, with a focus on estimation of the ISA at a city-scale by using Landsat-style satellite images. However, ISA estimation is also in demand in disciplines such as the environmental assessment and policy making at a national scale. This paper proposes a new method for estimating the ISA fraction in Japan based on a temporal mixture analysis (TMA) technique. The required inputs for the proposed method are rearranged MODIS NDVI time-series datasets at the temporal stable zone (i.e., the first to the sixth largest NDVI values in a year). Three ISA distribution maps obtained from Landsat-5 TM data were used as reference maps to evaluate the performance of the proposed method. The results showed that the proposed TMA-based method achieved a large reduction in the effects of endmember variability compared with the previous methods (e.g., SMA and NSMA), and thus the new method has promising accuracy for estimating ISA in Japan. The overall root mean square error (RMSE) of the proposed method was 8.7%, with a coefficient of determination of 0.86, and there was no obvious underestimation or overestimation for the whole ISA range. Ó

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

“…ISA was derived year by year using a temporal mixture analysis of the annual NDVI time series (proposed by Yang et al (2012) [58]), resulting in a temporal resolution of one year and a spatial resolution of 250 m. The fundamental assumption of this process is that the NDVI series can be regarded as linear mixtures of temporal profiles of endmember NDVI series. In detail, the NDVI series of each pixel were rearranged from high to low values and six maximums were extracted; then principal component analysis (PCA) was conducted on the six maximum NDVI series and three endmembers (i.e., forest, crops, and impervious surface) were determined; and finally, the NDVI series of each pixel were linearly unmixed according to the endmember NDVI series and the endmember fractions (including ISA) were obtained.…”

Incorporating Endmember Variability into Linear Unmixing of Coarse Resolution Imagery: Mapping Large-Scale Impervious Surface Abundance Using a Hierarchically Object-Based Spectral Mixture Analysis

“…First, the spectral contrast between subpixel urban impervious surface and water seems to be increased at the km scale. Generally, pure water pixels and water-contaminated pixels are masked out in the pre-or post-processing step through unsupervised classification or ancillary data to avoid the overestimation of dark impervious surface [7,64]. In contrast, with the use of WCS endmembers at the 1-km resolution, urban impervious surfaces have slightly higher signatures due to the mixture with high albedo materials in urbanized areas, such as concrete and metal.…”

“…[4][5][6]. This process results in the increase of impervious surface coverage [7,8], such as roads, parking lots, buildings, drive ways, and sidewalks. With different climate and socioeconomic influences, the relationship between landscape changes in the urbanization process and global environmental change is still not very explicit [1,9].…”

Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data

“…Two types of variables-vegetation abundance and nighttime light data-are often used. Because of the inverse correlation between ISA and vegetation indices, MODIS normalized difference vegetation index (NDVI) has been used to map ISA in a large area [21][22][23][24]. However, vegetation distribution in an urban landscape is influenced by many factors such as terrain, climate, population, economic conditions, and cultures; thus, using these data alone for large-scale ISA mapping may generate high inaccuracy [25].…”