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.

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.

This study presents the result of an investigation into the dynamics of urban growth and urban land cover change in the Dallas-Fort Worth area of the United States using landscape metric and Landsat satellite data over a 30-year period. The goal was to assess the impact of urban growth on the land cover of the area in order to provide baseline information that can support a sustainable urban growth of the area in the future. To achieve this goal, archived Landsat satellite data were classified on a broad scale for four epochs (1988, 1998, 2008 and 2018) and a change detection of the land cover of the area was conducted using the four datasets. To understand the dynamics of the spatial pattern of the change that is taking place in the area, landscape metrics were incorporated into the analysis. The results showed that over the 30-year period, built-up lands doubled its original size, mostly from expansion and infills. Additionally, there was a small decadal increase in entropy values, which indicated that the area is still experiencing sprawl, but on a small scale. These results provided baseline data that can be incorporated into future urban growth plans for the area.

Preserving riparian vegetation is important for maintaining water quality and riparian functions. Streamside protection ordinances have been widely established in many rapidly urbanizing areas, however, there has been a lack of assessment of the effectiveness of such ordinances. A study was conducted to determine the effectiveness of riparian vegetation preservation with and without ordinance protection. SPOT imagery was used to classify landscape changes over time (1992 through 2012) across multiple jurisdictions and pre- and post-ordinance implementation periods. Results indicated the spatial and temporal patterns of vegetation change differed by administrative areas and ordinance boundaries. The rate of tree loss and gains in developed lands in ordinance-protected areas generally increased following implementation of ordinances but at a lower rate than in non-ordinance areas. These findings suggest spatial and temporal monitoring of riparian ordinance implementation across adjacent jurisdictions is important to ensure the full effects of the ordinance protection on stream systems. Such monitoring and assessments can be used by local decision makers to adapt existing ordinances or in the development of new ordinances.