2012
DOI: 10.1007/s00267-012-9955-x
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Statistical and Spatial Analysis of Land Cover Impact on Selected Metro Vancouver, British Columbia Watersheds

Abstract: The Greater Vancouver area has undergone significant land use and land cover (LULC) change over the past several decades, often adversely affecting stream health and water quality, particularly in those areas that have undergone the most urbanization. In this study 30 years of historical LULC and water quality data were examined using GIS and statistical analysis to better understand these impacts and to help build a broader understanding of cause and effect relationships of changing LULC, especially since urb… Show more

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Cited by 11 publications
(3 citation statements)
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“…it appears as signi cant variable in the second best model. The relationship between nitrate concentration and agriculture is well known (see, for instance,Monteagudo et al, 2012;Shupe, 2013), and it was also observed in a previous study in 23 of these streams(Amuchástegui et al 2016). …”
supporting
confidence: 67%
“…it appears as signi cant variable in the second best model. The relationship between nitrate concentration and agriculture is well known (see, for instance,Monteagudo et al, 2012;Shupe, 2013), and it was also observed in a previous study in 23 of these streams(Amuchástegui et al 2016). …”
supporting
confidence: 67%
“…In a similar study, Schilling et al [9] using statistical analysis of annual discharge and precipitation in the Mississippi River, indicate that expanding soybean acreage in the basin is the main factor that caused the increasing discharge of the Upper Mississippi River Basin. Moreover, LULC change affects not only the quantity of water but also the quality of water [10,11]. …”
Section: Introductionmentioning
confidence: 99%
“…Specifically, a partition of different land cover types based on the analytic description of the region of Vancouver obtained by the research of [74] was made. The selected region encompasses all the geological features specific to the area, serving as a general criterion to ensure the robustness and effectiveness of the proposed feature extraction process across various datasets.…”
Section: Dipolementioning
confidence: 99%