A data driven technique applying GIS, and remote sensing to rank locations for waste disposal site expansion
2019
Richter, Amy | Ng, Kelvin Tsun Wai | Karimi, Nima
Landfilling is the most common method for final treatment of municipal solid waste worldwide. Canadians generated 973 kg/cap of waste in 2016, and 73% of that was sent to landfills or incinerators. This study proposes a novel method which combines remote sensing and vector data to rank the suitability of current landfill sites and their area of influence for expansion in Saskatchewan, Canada; where there are currently more than 500 active landfills. This study found that using average normalized data, 55.3% of the land in the study area was suitable or moderately suitable for landfill expansion while 45% of the area was unsuitable for landfill expansion. Polygon 32, an area dominated by agriculture and pasture land, is the most suitable for landfill expansion based on the mean normalized rank and was ranked 9th (out of 39) in terms of standard deviation. Polygon 27 is the least suitable for landfill expansion, having the largest mean normalized rank, and was ranked 38th (out of 39) in terms of standard deviation. This method is advantageous compared to other decision-making tools which rely on expert opinion. This method relies solely on remote sensing and vector data; but is flexible enough that weighting of data sets can be applied by policy makers if so desired. Results show that using remote sensing data and vector data together are capable of capturing distinctly different aspects of the study area, and that vector data can be used as a proxy for imagery where cloud cover is present.
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