This study aimed to detect and understand remotely sensed urban wetland dynamics as a sensitive indicator of the combined effects of human disturbances and climate impacts in the course of global change. To address this objective, the study developed technical approaches to detect and interpret wetland changes across spatial scales in complex urban landscapes. Using a series of Satellite Pour l’Observation de la Terre (SPOT) images covering 1992–2010, the study was conducted in the Kansas City metropolitan area of the USA, which has experienced significant urban sprawl in recent decades. As a fine-tuning of the traditional supervised image classification, a knowledge-based classification algorithm was developed to identify fine-scale, hidden wetlands that cannot be appropriately detected based on their spectral differentiability. The analyses of wetland change were implemented at the metropolitan, watershed, and sub-watershed scales as well as being based on the size of surface water bodies in order to reveal real pictures of urban wetland change trends in relation to major driving factors. The results of the study indicated that the knowledge-based classification approach improved the detection capability and accuracy of urban wetlands by fine-tuning the traditional classification results. The cross-scale analysis of detected land covers revealed that wetland dynamics varied in trend and magnitude from metropolitan, watersheds, to sub-watershed scales. The study found that increased precipitation swelled wetlands, which inflated the findings of remotely sensed wetland cover and related trend interpretation. During an 18 year study period, human development activities in the study area resulted in a large increase in impervious surfaces, which was mainly at the expense of farmland/grassland areas and some small wetlands in all urban watersheds. In contrast, increased precipitation in the region swelled large wetlands in particular. This mixed picture of urban wetland dynamics, associated with the analysis of underlying driving factors, provides a new baseline for relevant urban planning, management, and research in a global change perspective.

Urban wetlands play important roles in providing several ecosystem services that support the urban environment. As such, scientists have studied them to understand the urban processes that lead to their continued decline. However, little attention has been given to the drivers of land-use change that may affect this fragile ecosystem in the future. Understanding this could serve as a critical step towards urban wetland management and sustainability. In this study, we utilized an integrated approach that combined Similarity Weighted Instance-based Machine Learning and Markov chain, both embedded in the IDRISI Land Change Modeler to simulate change in the landscape of three watersheds in the Kansas City Metropolitan area. The purpose was to assess the possible future impacts of urban expansion-induced landscape change on wetlands within the study area, using a retrospective approach. To achieve this, classified SPOT satellite data covering the three watersheds were used to generate historical land cover maps of the study area between 1992 and 2010 to analyze changes to the landscape. In addition, the study identified several drivers of land change associated with the historical change process in the study area, and accounted for their role in the modeling process. On this basis, the study made the prediction of urban landscape transformation to the end date of 2014. The prediction result was verified with a more accurate map that was derived from independently classifying a 2014 SPOT image of the study area. Results from this study show that impervious surfaces, which were used as an index of urban expansion, may increase by approximately the same magnitude experienced historically, which may result in a small but significant loss of wetlands and other land cover classes within the study area.

Several studies have shown human impacts on urban wetlands. These impacts are mostly studied at broad scales, which may generalize and aggregate important information needed for landscape quantification or terrain analysis. This situation can weakly or inappropriately address the structure of wetland landscapes, thus affecting the assessment of the quantities and qualities of terrestrial wetland habitats. To address these issues for urban wetland dynamics, this study proposes the use of landscape and terrain indices to characterize the landscape structure of urban wetlands at a fine scale in order to assess its usefulness in contributing to wildlife sustainability. To achieve this goal, secondary terrain attribute data are integrated with an object-based satellite image classification at the wetland and watershed level. The result reveals a general swell in wetland coverage at the watershed level. Further analysis shows the size and shape complexities, and edge irregularities are increased significantly at the patch level but slightly at the watershed level. Terrain analysis further reveals a potential increase in wetness and decrease in stream power vulnerability for most of the major wetlands under study. These results suggest that terrain and landscape indices are effective in characterizing the structure of urban wetlands that supports socio-ecological sustainability.

With the increase in global urbanization, satellite imagery and other types of geospatial data have been extensively used in urban landscape change research, which includes environmental modeling in order to assess the change impact on urban watersheds. For urban hydrological modeling, as a focus of this study, several related research questions are raised: (1) How sensitive are runoff simulation to land use and land cover change patterning? (2) How will input data quality impact the simulation outcome? (3) How effective is integrating and synthesizing various forms of geospatial data for runoff modeling? These issues were not fully or adequately addressed in previous related studies. With the aim of answering these questions as research objectives, we conducted a spatial land use and land cover (LULC) change analysis and an urban runoff simulation in the Blue River watershed in the Kansas City metropolitan area between 2003 and 2017. In this study, approaches were developed to incorporate the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model with remote sensing, geographic information systems (GIS), and radar rainfall data. The impact of data quality on the model simulation outcome was also analyzed. The results indicate that there are no significant differences between simulated runoff responses in the two study years (2003 and 2017) due to spatial and temporal heterogeneity of urbanization processes in the region. While the metropolitan area has been experiencing remarkable urban development in the past few decades, the gain in built-up land in the study watershed during the study period is insignificant. On the other hand, the gain in vegetated land caused by forestation activities is offset by a decrease in farmland and grassland. The results show that increasing spatial data resolution does not necessarily or noticeably improve the HEC-HMS model performance or outcomes. Under these conditions, using Next Generation Weather Radar (NEXRAD) rainfall data in the simulation provides a satisfactory fit in hydrographs’ shapes, peak discharge amounts and time after calibration efforts, while they may overestimate the amount of rainfall as compared with gauge data. This study shows that the developed approach of synthesizing satellite, GIS, and radar rainfall data in hydrological modeling is effective and useful for incorporating urban landscape and precipitation change data in dynamic flood risk assessment at a watershed level.

Prairies or grasslands together with areas designated as agricultural lands are one of the largest types of land cover and land use that exist today. While prairies provide habitat to a wide variety of animals and organisms, and agricultural lands support human populations, these lands, especially those in the immediate vicinities of large urban areas, are giving way to urbanization at alarming rates. In particular, prairies are often viewed as wastelands because their benefit to the effective functioning of the urban ecosystem is often not fully understood. On the other hand, many agricultural lands are being converted for several urban uses because of the high economic returns from their sale. In this study, we classified SPOT (Satellite Pour l’Observation de la Terre) satellite data of the study area using the supervised maximum likelihood classification approach in order to investigate the loss of prairies and agricultural lands due to urban expansion in six sub-watersheds in the Kansas City metropolitan area of the States of Kansas and Missouri in the U.S. Based on the classified maps, we computed the magnitude and rate of urban expansion, and the proportion of loss in prairies and agricultural lands that was a result of urban expansion. Results from the 22-year study revealed that in all six sub-watersheds, agricultural lands and grassland were depleted at alarming rates with no sustainable effort to conserve them. These results provide baseline information that can support a data-driven and sustainable path for urban expansion in the examined sub-watersheds.