A Comparison of Urbanization Effects on Stream Benthic Macroinvertebrates and Water Chemistry in an Urban and an Urbanizing Basin in Southern Ontario, Canada

2010 ◽  
Vol 45 (3) ◽  
pp. 327-341 ◽  
Author(s):  
Normand L. Bazinet ◽  
Beth M. Gilbert ◽  
Angela M. Wallace
Keyword(s):  
Land Cover ◽  
Urban Development ◽  
Correspondence Analysis ◽  
Lake Ontario ◽  
Urban Land ◽  
Biotic Index ◽  
Southern Ontario ◽  
Urban Land Cover ◽  
Linear Relationships ◽  
Lake Simcoe

Abstract Benthic invertebrate communities were compared in several watersheds within an urban basin and an urbanizing basin in southern Ontario, Canada. The urban watersheds of the Lake Ontario basin and the urbanizing watersheds within the Lake Simcoe basin share similar geologies, soils, and climates, but differ in the stage of urban development within these two basins. Correspondence analysis showed that invertebrate populations formed distinct groups split between these two basins owing to intense urban development in the Lake Ontario watersheds versus the agricultural nature of the Lake Simcoe basin. Canonical correspondence analysis ordinations indicated that the major environmental gradients were related to urban land cover (imperviousness), chloride, nitrates and stream order factors. Urban land cover and chloride were most strongly associated with the first axis. The typical logarithmic relationship between urban land cover and benthos found in other studies was not evident in this study. Rather, 9 of the 12 metrics tested had significant linear relationships with urban land cover. The Hilsenhoff Family Biotic Index and percent Oligochaeta metrics showed the strongest positive linear relationships with urban land cover. Pollution sensitive groups (Ephemeroptera, Plecoptera, and Trichoptera) along with richness and diversity measures decreased with increasing urbanization.

scholarly journals Comparison of the impacts of urban development and climate change in exposing European cities to pluvial flooding

2017 ◽  
Author(s):  
Per Skougaard Kaspersen ◽  
Nanna Høegh Ravn ◽  
Karsten Arnbjerg-Nielsen ◽  
Henrik Madsen ◽  
Martin Drews
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Urban Development ◽  
Extreme Precipitation ◽  
Urban Areas ◽  
Urban Land ◽  
European Cities ◽  
Urban Land Cover ◽  
Pluvial Flooding ◽  
Rcp 8.5

Abstract. The economic and human consequences of extreme precipitation and the related flooding of urban areas have increased rapidly over the past decades. Some of the key factors that affect the risks to urban areas include climate change, the densification of assets within cities and the general expansion of urban areas. In this paper, we examine and compare quantitatively the impact of climate change and recent urban development patterns on the exposure of four European cities to pluvial flooding. In particular, we investigate the degree to which pluvial floods of varying severity and in different geographical locations are influenced to the same extent by changes in urban land cover and climate change. We have selected the European cities of Odense, Vienna, Strasbourg and Nice for analyses to represent, different climatic conditions, trends in urban development and topographical characteristics. We develop and apply a combined remote-sensing and flood-modelling approach to simulate the extent of pluvial flooding for a range of extreme precipitation events for historical (1984) and present-day (2014) urban land cover and for two climate-change scenarios (RCP 4.5 and RCP 8.5). Changes in urban land cover are estimated using Landsat satellite imagery for the period 1984–2014. We combine the remote-sensing analyses with regionally downscaled estimates of precipitation extremes of current and expected future climate to enable 2D overland flow simulations and flood-hazard assessments. The individual and combined impacts of urban development and climate change are quantified by examining the variations in flooding between the different simulations along with the corresponding uncertainties. For all four cities, we find an increase in flood exposure corresponding to an observed absolute growth in impervious surfaces of 7–12 % during the past thirty years of urban development. Similarly, we find that climate change increases exposure to pluvial flooding under both the RCP 4.5 and RCP 8.5 scenarios. The relative importance of urban development and climate change on flood exposure varies considerably between the cities. For Odense, the impact of urban development is comparable to that of climate change under an RCP 8.5 scenario (2081–2100), while for Vienna and Strasbourg it is comparable to the impacts of an RCP 4.5 scenario. For Nice, climate change dominates urban development as the primary driver of changes in exposure to flooding. The variation between geographical locations is caused by differences in soil infiltration properties, historical trends in urban development and the projected regional impacts of climate change on extreme precipitation.

Subpixel Urban Land Cover Estimation

2008 ◽  
Vol 74 (10) ◽  
pp. 1213-1222 ◽  
Author(s):  
Jeffrey T. Walton
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Urban Land Cover

scholarly journals IMPLEMENTATION OF THE MARKOV RANDOM FIELD FOR URBAN LAND COVER CLASSIFICATION OF UAV VHIR DATA

2016 ◽  
Vol 3 (2) ◽  
pp. 127
Author(s):  
Jati Pratomo ◽  
Triyoga Widiastomo
Keyword(s):  
Land Cover ◽  
Random Field ◽  
Markov Random Field ◽  
Optimal Parameter ◽  
Land Cover Classification ◽  
Urban Land ◽  
Urban Land Cover ◽  
Markov Random ◽  
Atmospheric Variations ◽  
The Impact

The usage of Unmanned Aerial Vehicle (UAV) has grown rapidly in various fields, such as urban planning, search and rescue, and surveillance. Capturing images from UAV has many advantages compared with satellite imagery. For instance, higher spatial resolution and less impact from atmospheric variations can be obtained. However, there are difficulties in classifying urban features, due to the complexity of the urban land covers. The usage of Maximum Likelihood Classification (MLC) has limitations since it is based on the assumption of the normal distribution of pixel values, where, in fact, urban features are not normally distributed. There are advantages in using the Markov Random Field (MRF) for urban land cover classification as it assumes that neighboring pixels have a higher probability to be classified in the same class rather than a different class. This research aimed to determine the impact of the smoothness (λ) and the updating temperature (Tupd) on the accuracy result (κ) in MRF. We used a UAV VHIR sized 587 square meters, with six-centimetre resolution, taken in Bogor Regency, Indonesia. The result showed that the kappa value (κ) increases proportionally with the smoothness (λ) until it reaches the maximum (κ), then the value drops. The usage of higher (Tupd) has resulted in better (κ) although it also led to a higher Standard Deviations (SD). Using the most optimal parameter, MRF resulted in slightly higher (κ) compared with MLC.

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