A comparison of nutrient export at two agricultural catchments: insight into the effect of increasing urban land cover in southern Ontario

2014 ◽  
Vol 28 (14) ◽  
pp. 4328-4339 ◽  
Author(s):  
Shanel M. Raney ◽  
M. Catherine Eimers
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Nutrient Export ◽  
Southern Ontario ◽  
Agricultural Catchments ◽  
Urban Land Cover ◽  
Insight Into

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.

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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