The Town of Gumby would like to know how much room their solid waste landfill has left without exceeding the elevations that the Ontario Ministry of the Environment has put in place for them (Buma, 2019). In addition, the Town of Gumby would like to know where in the landfill more room for waste is, where there is no more room, and where it has exceeded the Ministry of the Environment’s regulations (Buma, 2019). A cut/fill analysis was completed concluding that the Gumby landfill will be at it’s maximum allowable capacity to be full by the year 2035, assuming, the population remains the same, the rate of waste entering the landfill stays the same, the waste cannot be further compacted and that the areas that are already too full are redistributed. Three maps were created one showing the existing landfill surface, another showing the maximum allowed landfill surface and the third showing the remaining depth of landfill.
Map Analysis
Additional Calculations
It is estimated that the Gumby landfill will be full by 2035. The followings are the calculations made to estimate the date that the landfill will be full. The following, Figure 15, is a graph of how much each person contribute to the landfill each year over the past 14 years. From 2001 to 2008 the amount waste that that ends up in the Gumby landfill is declining. After 2008 it appears that there is a plateau. This plateau was used to predict how full the landfill would be in the future.
The following are the steps that were taken to determine when the landfill would be full.
What is known
- Conversion: 0.6 tonnes per m3.
- Room left in landfill: 176959 m3.
- Volume exceeding Ministry of the Environment’s regulations: 4372.916667 m3.
- On average 2.5 people live in one house.
- There are 10200 homes filling the Gumby landfill.
- The rate of waste entering the landfill 0.3136 tonnes/capita/year.
*The trend line shown in Figure 15 above will be assumed a constant of 0.3136 tonnes/capita/year due to the miniscule slope of the line.
Assumptions
- The areas that are exceeding the Ministry of the Environment’s regulations will be redistributed to the areas where there is still room.
- The rate will not change.
- The population will remain the same.
- Recycling remains the same.
- The solid waste cannot be further compacted.
- What is in the landfill will not be reprocessed by the new sheepsfoot roller.
The Maths
Solid waste from the Town of Gumby entering the landfill each year:
m^3 entering the landfill each year = rate of waste entering the landfill * Number of people per house * Number of Houses.
m^3 entering the landfill each year = 0.3136 tonnes/capita/year * 2.5 people * 10200 Homes
m^3 entering the landfill each year = 7996.8 m^3 per year
Space left in landfill after the areas that exceed the Ministry of the Environment’s regulations have been redistributed:
Post redistribution: room left in landfill = Room left in landfill - Volume exceeding Ministry of the Environment’s regulations
Post redistribution: room left in landfill = 176959 m^3 - 4372.916667 m^3
Post redistribution: room left in landfill = 172586.083333 m^3
Years until landfill is full:
Years until landfill is full = Post redistribution: room left in landfill / m^3 entering the landfill each year
Years until landfill is full = 172586.083333 m^3 per year/ 7996.8 m^3
Years until landfill is full = 21.5818931739 years
Answer
The Gumby landfill will be full in 2035.
Conclusions and Recommendations
In conclusion, the Gumby landfill will be full in year of 2035. There is currently 176959 m3 of free space for more waste however, there is also 4372.916667 m3 of areas that exceeds the Ministry of the Environment’s regulations.
It is my recommendation that these areas that exceeds the Ministry of the Environment’s regulations be landscaped so that they don’t exceed these regulations. There is more than enough room to do this in the area that aren’t exceeding the Ministry of the Environment’s regulations. Even after landscaping these areas, it will take the Town of Gumby 21.58 years from 2014 to fill the landfill completely at its current rate.
Bibliography
Buma, M. (2019). GISC9322D1 - Raster Based Analysis of Terrain Surfaces . Niagara-on-the-Lake: Niagara College.
Introduction
Classification is the process of taking a multispectral image and dividing it up into classes. Classes are categories which are created automatically or are user defined. Classes simplify and aid in image interpretation including calculating change detection. There are two types of classification methods: Supervised and Unsupervised (Harris Geospatial Solutions, 2018). Supervised classification is information based and the classes are user defined (Hanna, Supervised Classification, 2014). Unsupervised classification is automatic (algorithmic) and the classes are based off spectral reflectance (Hanna, Unsupervised Classification, 2014). See Final Maps section for an example map of both kinds of classification method and see the Methodology section to learn how to run both classification strategies.
Map Analysis
Classification Comparison
Classification is the process of identifying regions of multispectral images by features on the ground, such as water, trees, grass, etc. There are two variations: supervised, and unsupervised classification (Esri, 2018).
Unsupervised classification is automatic (algorithmic) and is very fast. Supervised classification takes longer and requires guidance from the user. Choosing which method is best suited for a particular application takes some experience (Esri, 2018). There are advantages and disadvantages to both techniques, which need to be understood, which are discussed below.
Supervised classification is when an image is broken down into user defined classes. To run a supervised classification, the image must have recognisable and distinctive class possibilities (i.e. water, forest, grasslands). The classes then, must be chosen before the classification is complete. After the classes have been chosen, training sites for each class must be selected and created. These training sites must only contain known pixels which belong to the class being created. Many training sites are required per class so that ERDAS has a wide swath of pixels and ranges to work with. This will make the classification more accurate. After all the training sites have been created, the spectral signatures of each class’s training site must be merged together. This creates a unique spectral signature for each class. The training site polygons can be saved as an AIO if needed. These spectral signatures will classify the rest of the image. In the image was classified for this report, Supervised Classification (K-means) Niagara – Welland Canal, the classes where selected prior to the classification. This particular classification turned out quite it appears that most features where classified correctly and the ones that weren’t had pixels of the correct class speckled across their area. On class that was particularly difficult to classify in this image was water. The reason water was so difficult to classify in this image was that there were so many kinds of water bodies and colours of water. There was the Welland Canal, swimming pools, golf course water features, the lagoons at Niagara College to name a few and to make matters worse some of the water along the canal for whatever reason was white like a cloud or ice even though it is clear that this image was taken in the summer, form the grass and number of outdoor pools that were visible. The figure below shows the original image with the different colours of water highlighted with red circles, the boxes on the right exemplify the diversity in colours water has in the image.
It is important to remember that the naming of these classes is done by human assessment and that ERDAS doesn’t know what “grass” or “water” is, it only classifies a spectral range. It is humans that interpret these classes and infer that a particular class appears to be mostly made up of “grass”, for example, on the ground as its primary feature. No prior knowledge of the image is required to run an unsupervised classification nor do there have to be any distinctive areas that could be classified but either way, but post interpretation is necessary (Hanna, Unsupervised Classification, 2014). A good time to use unsupervised classification is if an image has less noticeable (to the human eye) variability (i.e. the whole image is shrubland).
Bibliography
Hanna, S. (2014). Supervised Classification. Retrieved from Humbolt State University: http://gsp.humboldt.edu/olm_2015/courses/gsp_216_online/lesson6-1/supervised.html
Hanna, S. (2014). Unsupervised Classification. Retrieved from Humboldt State University: http://gsp.humboldt.edu/olm_2015/courses/gsp_216_online/lesson6-1/unsupervised.html
Harris Geospatial Solutions. (2018). Harris Geospatial Solutions. Retrieved from Harris Geospatial Solutions: https://www.harrisgeospatial.com/docs/Mahalanobis.html
Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote Sensing and Image Interpretation (7th ed.). Hoboken: Wiley.
Wilkinson, M. (2019). GISC9217D1 Unsupervised and Supervised Classification. Niagara-on-the-Lake: Niagara College.
Introduction
The goal of this project was to learn how to use polynomial correction and orthorectification to correctly place air-photos in their exact geographic location and to mosaic the images together to create one seamless image. A comparison of both methods was also done concluding that orthomosaic method was superior.
Map Analysis
Conclusion
In conclusion, three air-photos where geometrically corrected, orthorectified, and a mosaic was made of each correction type. The orthomosaic appeared to be the superior method since it had smaller errors and less visible misalignments.
It is my recommendation that if there is DEM data available, orthorectification is the best option when choosing between Geometric correction and orthorectification.
Introduction
The Jiang’s Fabulous Fictitious Fern is an endangered species. Reintroducing it to the Niagara College Niagara-on-the-Lake (NOTL) Campus will likely ensure its survival (Smith, 2019). The Niagara College NOTL Campus has recently become a suitable location for this plant to grow (Smith, 2019). However, due to the scarcity and fragility of the fern, each must be planted in optimum locations (Smith, 2019).
The following is a suitability analysis of the Niagara College NOTL campus and surrounding area. This analysis includes a Weighted Multicriteria Evaluation (MCE) and a Fuzzy Overlay Analysis. Both analyses suggest where the ferns will grow best. We found that the Fuzzy Overlay Analysis provided the best location results.
Maps and Analysis
Conculsion
Summary
I found that Fuzzy Overlay had superior results to the Weighted Overlay.
The analysis started with point, breakline, and soil type data. The point and breakline data were originally divided into north and south segments, thus needed to be merged together. Then, these merged layers were converted into a TIN which was then made into a raster. This raster was then made into an aspect raster, slope raster, and hillshade raster. The soil type data started out in the wrong location but was corrected by being georeferenced. These four layers; soil, aspect, hillshade, and slope were then reclassified. Once these layers were assigned new values, a Multicriteria Evaluation (MCE) was run. Next, the previous four layers; soil, aspect, hillshade, and slope were assigned a fuzzy membership. Finally, a Fuzzy Overlay analysis was run on the fuzzy membership layers.
Recommendations
My recommendation is that the Jiang’s Fabulous Fictitious Fern be planted as suggested by the Fuzzy Overlay Map and be planted in the dark green sections. I also suggest, that the ferns be planted in the Best Growing Conditions areas that are close to the road, see Figure 9. Carrying 110 plants into the escarpment forest would be extremely hard work, if they could be driven by car or truck most of the way, it would make the job a lot easier.
Bibliography
ESRI. (2016a). How Aspect Works. Retrieved from ArcGIS for Desktop: http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-aspectworks.htm
ESRI. (2016b). Hillshade Function. Retrieved from ArcGIS for Desktop: http://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/hillshadefunction.htm
ESRI. (2016c). How Slope Works. Retrieved from ArcGIS for Desktop: http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-slope-works.htm
ESRI. (2016d). How Fuzzy Membership Works. Retrieved from ArcGIS for Desktop: http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-fuzzymembership-works.htm
Smith, I. (2019). GISC9318D2 - Introduction to ArcGIS Spatial Analyst Extension. Niagara-on-the-Lake: Niagara College.
