Open AccessRemote Sensing Approach for Environmental Monitoring: Application of Sudbury, Ontario, Canada
Author(s) -
Rifaat Abdalla,
Abdulahad Malik,
Jonathan Sparlin,
Susanna Li
Publication year - 2017
Publication title -
environmental management and sustainable development
Language(s) - English
Resource type - Journals
ISSN - 2164-7682
DOI - 10.5296/emsd.v6i2.11610
Subject(s) - urban heat island , environmental science , vegetation (pathology) , land use , sustainability , hydrology (agriculture) , phosphorus , water quality , physical geography , remote sensing , geography , geology , meteorology , ecology , materials science , medicine , geotechnical engineering , pathology , metallurgy , biology
ENVI and ArcGIS software along with Landsat TM data were used to evaluate sustainability and environmental conservation efforts in Sudbury region, Ontario, Canada. The study adopted three phase analysis. First, the change in landscape from 1984 to 2007 was studied. In the second part the study area was analyzed for urban heat island phenomenon by comparing thermal changes in relation to vegetation changes. The last part dealt with observing change in water quality parameter. Findings of the study revealed that significant change has taken place in Sudbury during the study period. Post classification comparison method has quantified the change and presented the results in the form of a change matrix, also an increase in the reclaimed land and dense vegetation in 2007 was observed while a significant decrease in the built up and barren land was also evident. Thermal analysis results showed overall higher temperatures in 1984 while the thermal signatures of 2007 images showed characteristic of urban heat island where urban core of Sudbury had high temperatures while the rural and vegetative areas had low temperatures. The water quality analysis showed an increase in the levels of phosphorus and dissolved organic carbon (DOC) concentrations in lakes around Sudbury with the exception of Kelly Lake. The error analysis shown regression-derived phosphorus distribution maps were unreliable in this application, due to significant average error.