Estimation of impervious surface area is important to the study of urban environments and social development, but surface characteristics, as well as the temporal, spectral, and spatial resolutions of remote sensing images, influence the estimation accuracy. To investigate the effects of regional environmental characteristics on the estimation of impervious surface area, we divided China into seven sub-regions based on climate, soil type, feature complexity, and vegetation phenology: arid and semi-arid areas, Huang-Huai-Hai winter wheat production areas, typical temperate regions, the Pearl River Delta, the middle and lower reaches of the Yangtze River, typical tropical and subtropical regions, and the Qinghai Tibet Plateau. Impervious surface area was estimated from Landsat 8 images of five typical cities, including Yinchuan, Shijiazhuang, Shenyang, Ningbo, and Kunming. Using the linear spectral unmixing method, impervious and permeable surface areas were determined at the pixel-scale based on end-member proportions. We calculated the producer’s accuracy, user’s accuracy, and overall accuracy to assess the estimation accuracy, and compared the accuracies among images acquired from different seasons and locations. In tropical and subtropical regions, vegetation canopies can confound the identification of impervious surfaces and, thus, images acquired in winter, early spring, and autumn are most suitable; estimations in the Pearl River Delta, the middle and lower reaches of the Yangtze River are influenced by soil, vegetation phenology, vegetation canopy, and water, and images acquired in spring, summer, and autumn provide the best results; in typical temperate areas, images acquired from spring to autumn are most effective for estimations; in winter wheat-growing areas, images acquired throughout the year are suitable; and in arid and semi-arid areas, summer and early autumn, during which vegetation is abundant, are the optimal seasons for estimations. Knowledge of optimal time frames, multi-source data, and intelligent algorithms should be integrated to reduce spectral confusion and improve the estimation of impervious surface area from Landsat 8 OLI imagery.

Regional transportation development (RTD) is an important stressor of urban agglomeration ecosystems. Groundwater recharge potential may be adversely affected when natural soil is replaced by impervious materials. To systematically identify the urban agglomeration ecological risk (UAER) of RTD in the southern Fujian Golden Triangle, water regulation was used as an assessment endpoint, and RTD was considered the stressor. We used the Soil Conservation Service Curve Number method (SCS-CN) to analyze the internal relationship between the assessment endpoint and the stressor factors. Then, a multi-level risk characterization method was used to show the evolutionary process of risk, and to provide a scientific basis for the management of UAER. Based on the current RTD plan, the UAER assessment shows that there is a 0.90 probability of that the risk distribution results will occur by 2030. The intensity of stress from arterial roads on the urban agglomeration ecosystem is greater than that of the railway system. By considering the development of the railway system as a factor in the stress of RTD, this study of UAER assessment differs from past studies of urban ecological risk assessment, since the latter considers only highways. We also propose a systematic method of risk assessment simulation-prediction.