Frequently occurring flood disasters caused by extreme climate and urbanization processes have become the most common natural hazard and pose a great threat to human society. Therefore, urban flood risk assessment is of great significance for disaster mitigation and prevention. In this paper, the analytic hierarchy process (AHP) was applied to quantify the spatiotemporal variations in flood risk in Wuhan during 2000–2018. A comprehensive flood risk assessment index system was constructed from the hazard, sensitivity, and vulnerability components with seven indices. The results showed that the central urban area, especially the area in the west bank of the Yangtze river, had high risk due to its high flood sensitivity that was determined by land use type and high vulnerability with dense population and per unit GDP. Specifically, the Jianghan, Qiaokou, Jiangan, and Wuchang districts had the highest flood risk, more than 60% of whose area was in medium or above-medium risk regions. During 2000–2018, the flood risk overall showed an increasing trend, with Hongshan district increasing the most, and the year of 2010 was identified as a turning point for rapid risk increase. In addition, the comparison between the risk maps and actual historical inundation point records showed good agreement, indicating that the assessment framework and method proposed in this study can be useful to assist flood mitigation and management, and relevant policy recommendations were proposed based on the assessment results.

Land types play an important guiding role in human survival and production. Clarifying the division of land types is the basis for ensuring the sustainable and coordinated development of social-economic-natural complex ecosystems. To date, the land type classification system has not formed a unified standard, and the existing classification fails to highlight the natural background elements of land. Therefore, it is important to construct a classification system that can reflect natural background elements. Additionally, land type classification is often based on land resource surveys. Updating the land type is generally difficult and slow, mainly due to a lack of appropriate information. Hence, it is necessary to develop an automatic land type renewal method using multisource information. This study proposes the ecology-geomorphology cognition (Eco-geoC) approach for land type classification. The approach is realized by the segmentation of land units using remote sensing images, geographic information, vegetation, soil, DEM, and geoscience knowledge. This approach is an extension of the object-based image analysis method. The spatial objects segmented from different attribute data are integrated, and finally, a comprehensive land mapping unit representing a certain degree of geographical homogeneity and land use potential is generated. The results show that the Eco-geoC approach is an integrated approach with objectification cognition on remote sensing images and multisource information using geo-knowledge. The Eco-geoC approach is tested in the Altay region. From coarse to fine scales, the study area is divided into two kinds of natural belts, 27 land systems and 78 land units, and a 1:500,000 land-type map, which shows a good coupling relationship between the physiognomy, vegetation, and soil in the Altay region, is compiled. The results of this study show that the use of the Eco-geoC approach for land type classification is significant and has potential for land assessment and planning. This approach can provide a scientific basis for the restoration of the regional ecology and the comprehensive management and adjustment of land resources and the environment.