Abstract
Abstract. This study proposes the concept of urban wet-landscapes (loosely-defined wetlands) as against dry-landscapes (mainly impervious surfaces). The study is to examine whether the dynamics of urban wet-landscapes is a sensitive indicator of the coupled effects of the two major driving forces of urban landscape change – human built-up impact and climate (precipitation) variation. Using a series of satellite images, the study was conducted in the Kansas City metropolitan area of the United States. A rule-based classification algorithm was developed to identify fine-scale, hidden wetlands that could not be appropriately detected based on their spectral differentiability by a traditional image classification. The spatial analyses of wetland changes were implemented at the scales of metropolitan, watershed, and sub-watershed as well as based on the size of surface water bodies in order to reveal urban wetland change trends in relation to the driving forces. The study identified that wet-landscape dynamics varied in trend and magnitude from the metropolitan, watersheds, to sub-watersheds. The study also found that increased precipitation in the region in the past decades swelled larger wetlands in particular while smaller wetlands decreased mainly due to human development activities. These findings suggest that wet-landscapes, as against the dry-landscapes, can be a more effective indicator of the coupled effects of human impact and climate change.
Highlights
To understand urban landscape transformation and associated consequences, remote sensing techniques have been widely adopted
The related change detections predominantly focused on urban impervious surface dynamics associated with human developments as driving forces (e.g. Arnold and Gibbons, 1996; Da Costa and Cintra, 1999; Chen et al, 2000; Masek et al, 2000; Ryznar and Wagner, 2001; Lo and Yang, 2002; Yang, 2002; Ji et al, 2006; Herold, et al, 2008; Thapa and Murayama, 2009; Weng, 2012)
The knowledge-based approach noticeably improved detecting capabilities of many wetland features at fine scales that could not be revealed by the traditional approach
Summary
To understand urban landscape transformation and associated consequences, remote sensing techniques have been widely adopted. The related change detections predominantly focused on urban impervious surface dynamics associated with human developments as driving forces While remote sensing-detected impervious surface change can depict humaninduced urban land cover changes in general, this indicator, here referred to as “dry-landscapes”, is less effective to reflect urban landscape changes that have been shaped jointly by climate impacts (e.g. precipitation variation). To address this issue, this study investigates the sensitivity of “wet-landscapes” (loosely defined wetlands or surface waters) to the coupled effect of human disturbances and climate variation. The study has set its objectives as follows: (1) developing a knowledge-based image classification approach to detect urban wetlands at fine scales that cannot be mapped appropriately with traditional classification methods, (2) identifying urban wetland change patterns at the metropolitan, watershed, and sub-watershed levels, and (3) analysing precipitation impacts on the wetlands of different size groups
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