1. D e p a r t m e n t o f G e o g r a p h i c a l a n d L i f e S c i e n c e s
2014
An Analysis of
Vegetation Cover using
Landsat Imagery in
South West of Williams
Lake, British Columbia,
Canada
Katherine Williams and James Bambridge
Advanced GIS and Remote Sensing

2. 1
An Analysis of Vegetation Cover using Landsat Imagery in British Columbia, Canada, south
west of Williams Lake
Introduction:
Canada’s forests cover 417.6 million hectares, which is nearly 50% of the total land cover of the
country (Wulder, Werner, & Gillis, 2004), it also accounts for 10% of the world’s forests (Wulder,
Werner, & Gillis, 2004; Natural Resources Canada, 2013). This then means that the Canadian forests
play a major part on the world’s environment by filtering air and water, reducing erosion and
biodiversity (Wulder, Werner, & Gillis, 2004); it also plays a role in Canada’s economic, social and
spiritual well-being (National Status, 2005).
The World’s Logging:
The global export of roundwood has increased since 1990 from 73 million tonnes to 129 million
tonnes in 2006, an increase of 77% (Dumont, & Wright, 2006). Over this period of time the rate of
tropical hardwoods has only increased slightly, this has been due to the substitution of tropical
hardwood for temperate hardwood, majority of which are from the northern hemisphere (Dumont,
& Wright, 2006). The main importers of hardwood, those that have driven the change in
exportation, are countries that do not have sufficient forests to meet their domestic needs, in
particular China, Japan, South Korea and Spain (Dumont, & Wright, 2006).
Canada:
Canada’s export of roundwood increased by 438% between 1995 and 2005, from 1,187 tonnes of
roundwood to 5,197 tonnes (Dumont, & Wright, 2006). This then meant by 2006 Canada is the fifth
largest log exporter in the world (Dumont, & Wright, 2006), and by 2010 it was the second largest
exporter of forest products (e.g. soft lumber, newsprint, and wood pulp) (Natural Resources Canada,
2013); with the majority of their exports going to the Pacific Rim countries (Dumont, & Wright,
2006). Annually less than 1% of Canada’s forests are harvested, with 0.6 million hectares harvested
in 2009 (Natural Resources Canada, 2013). This on average contributes around $24 billion to the
country’s GDP (Natural Resources Canada, 2013); it also directly employs over 373,000 people,
supports the country’s multibillion dollar recreation and tourism industries (Wulder, Werner, &
Gillis, 2004), and is responsible from 30% of the manufacturing investment Canada receives
(National Status, 2005). However, despite forestry being one of the largest industries in Canada the
country is a net importer of logs, primarily from the United States, and has been for the past 10
years by a ratio of 2:1 (Dumont, & Wright, 2006). This can be put down to the fact that there is very
little trade between the provinces (Dumont, & Wright, 2006), which then drives up the importation

3. 2
of logs by provinces with smaller forestry industries. Due to this economic importance of Canada’s
forests, it is important to maintain and manage them correctly (National Status, 2005).
Forestry Management:
Within forestry management the concept of sustainability arose, and became increasingly
recognised during the 1980s (Wang, 2004). Sustainable forestry management generally refers to ‘the
ways and processes of managing forests resources to meet society’s varied needs, today and
tomorrow, without compromising the ecological capacity and the renewal potential of the forest
resource base’ (Wang, 2004: 206). Conventional forest practices are criticized for being timber-
centric, and failing to account for environmental, social and cultural benefits of the forest (Wang,
2004). However, conventional practices cannot be wholly blamed for forest loss, as in 2009 15.2
million hectares of forest in Canada were affected by insect defoliation, and 3.2 million hectares
were lost due to forest fires in 2010 (Natural Resources Canada, 2013).
Timber companies and environmental groups have agreed an aim to protect two thirds of Canada’s
forests from unsustainable logging (Black, 2010); these protected areas will give protection from
destruction of habitats and overexploitation or natural resources (Andrew, Wulder, & Coops, 2011).
However, this does not protect all of the forests, and so sustainable forest management will
continue to be an issue for Canada and the world in the twenty-first century (Wulder, Werner, &
Gillis, 2004).
British Columbia:
Forestry in British Columbia dates back to the 1850s when the forests were perceived to be endless
(Pedersen, 1995). It was not until 1912 when concerns were first issued which brought about the
establishment of the Forest Branch, which was then replaced by the present day Ministry of Forests
(Pedersen, 1995). Today, two-thirds of its forests are harvested, which amounts to 60 million
hectares, of which 30% is from the coast and the rest from the interior (Pedersen, 1995; No Author,
2014). The forests in British Columbia are made up of three types: Temperature, spruce, and
montane forests (Slaymaker, 2000). This industry contributed $15.4 billion to the provinces GDP in
2011 (Natural Resource Canada, 2013).
Over 90% of this land is publicly owned (Forest Practices Board, no date), which means that it comes
under the province’s Forest and Range Practices Act. This act includes requirements conservation of
soils, to reforest logged areas, and to protect riparian areas, fish and fish habitats, watersheds,
biodiversity and wildlife (No Author, 2014). Reforestation of logged areas should happen soon after
the area has been cleared, and must be reforested with native species suited to the local ecological

4. 3
conditions to maintain and protect natural diversity (No Author, 2014). This equated to about 200
million seedlings planted each year in areas that have been logged as well as damaged by forest fires
and insect infestations (No Author, 2014). However, the forest area in British Columbia that is now
inadequately stocked, also known as ‘not satisfactorily restocked’ (NSR), are now three times greater
in area then it was 25 years ago when the federal government began efforts to address the lack of
reforestation (Britniff, 2011). NSR’s threaten to increase due to the dominance of British Columbia in
Canada’s logging export industry (Dumont, & Wright, 2006), and so increases demand for adequate
forests to harvest.
Despite the economic drive for timber, there is a greater awareness of the issue of sustainability,
with most British Columbians now accept that biodiversity, wildlife, recreation and spiritual beliefs
deserve equal consideration when planning forest uses (Pedersen, 1995); as well as growing concern
over climate change. This raises two questions; are the forests being harvest at sustainable levels?
And, to what extent has British Columbia’s forest been affected?
Methodology:
The area south of Williams Lake has been chosen as the case study site for the province (Figure 4);
this is due to two factors. Firstly, as shown in Figure 1, the majority of the area is consists of Logging
Concession Areas, which gives consent for logging within publicly owned land, which means that
logging occurs in this area. Secondly, as seen from Figures 2 and 3 between 2000 and 2012 the loss
of trees is greater than the gain, this shows that NSR areas in the site seem to be increasing. This
area represents many of the issues that relate to forestry in Canada, and so an appropriate site to
use to show the change in Canada’s forests.
To discover this, maps were created using remote sensing to visualise the land use change over time.
This was based on Landsat TM image from 1989, and the Landsat-5 ETM from 2009 to produce false
colour images of the chosen site; to show the change in the area over time. Remote sensing can
have some factors to take into account, one of which is shadows that hinder interpretation as it can
hide the true terrain below (Lillesand, Kiefer, & Chipman, 2008). This is the same for clouds, which
meant that Landsat images with 10% or less coverage were used to reduce the risk of covering the
terrain below (Weiers, et al., 2004). Another factor to take into account is temporal aspects; the
aspect here is vegetation, as it varies during the year the Landsat images should be collected at the
same time of year to a avoid misinterpretation (Weiers, et al., 2004; Lillesand, Kiefer, & Chipman,
2008). All this creates noise, which is any unwanted disturbance in the image (Lillesand, Kiefer, &
Chipman, 2008); if this noise is not reduced then is can either degrade or mask the true radiometric
information content of the image (Lillesand, Kiefer, & Chipman, 2008).

5. 4
Three methods of classification were used via ERDAS: unsupervised, supervised and image
difference (detection), however, problems can arise from either type of classification. Unsupervised
is used to examine results by creating a classification based on unknown pixels arranged in a number
of bands (Lillesand, Kiefer, & Chipman, 2008). This then, in effect, creates an automatic smoothing of
the classification based on the spatial variation in the image data (Atkinson, 2004), but the
smoothing is controlled by the spatial information in the data and not by the investigator (Atkinson,
2004). This can mean that some different features in the image can be classified into the same
colour band, and so can be misinterpreted.
Most studies, prefer to use supervised classification (Keuchel, et al., 2003; Atkinson, 2004; Abd El-
Kawy, et al., 2011), as it’s more useful due to classifying known land covers. Supervised classification
creates a matrix of the interpreted land cover categories (Lillesand, Kiefer, & Chipman, 2008), which
starts with the training stage where different classes of land cover are defined from training samples
(Keuchel, et al., 2003). In the next stage, the classification stage, each pixel is set to a specific land
cover class of which it most closely resembles (Lillesand, Kiefer, & Chipman, 2008).This creates a
multidimensional matrix of the area, resulting in a digital matrix map (Lillesand, Kiefer, & Chipman,
2008). The main advantage of using supervised over unsupervised, is that the user has control over
the classes in the training stage. The computer then uses these predefined areas to calculate the
extent to which the classes are use. But to create a complete image with all categorises correctly
represented a smaller area of forest was used for the supervised classification. However poor
training stage classification within supervised can create poor results; ‘garbage in, garbage out.’
The final classification method used was Image difference, where it uses the two images, from 1989
and 2009 and classifies it as either an increase or a decrease.

6. 5
= Logging concession areas.
“Logging concession” refers to an area allocated by a government for logging in a public forest…. “Concession” is
used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage
and extract timber from public forests (Global Forest Watch, 2014).
Figure 1 (Global Forest Watch, 2014)
Global Forest Watch – Logging Concession Areas

7. 6
= Tree loss from the 2000 to 2012.
“Loss” indicates the removal or mortality of tree canopy cover and can be due to a variety of factors, including
mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation (Global
Forest Watch, 2014).
Figure 2 (Global Forest Watch, 2014)
Global Forest Watch – Trees Loss Extent

8. 7
= Tree gain from 2000 to 2012.
Tree cover gain was defined as the inverse of loss, or the establishment of tree canopy in an area that previously
had no tree cover (Global Forest Watch, 2014).
Figure 3 (Global Forest Watch, 2014)
Global Forest Watch – Trees Gain Extent

9. 8
Landsat Study Area from USGS
Path 48
Row 24
Lat: 51° 41’ 54” N, Lon: 123°10’ 38” W.
This is the study area that was used for this investigation. All landsat data used is from this site.
Figure 4 (USGS, 2014)

10. 9
Figure 5 above, shows two image sets, the grey scale image is the unsupervised classification and the colour is the landsat
image from 1989 (see Unsupervised Classification 3
rd
October map). Lakes are shown as the yellow highlighted circle while
the red circles are shaded areas. By using the unsupervised classification on ERDAS, the software categories these two
different topographic areas in the same class, this is a problem when interpreting image data sets.
Figure 6 above, shows two image sets, the grey scale image is the unsupervised classification and the colour is the landsat
image from 1989 (see Unsupervised Classification 3
rd
October map). Bare rocks are shown as the yellow highlighted circle
while the red circles are bare vegetation. By using the unsupervised classification on ERDAS, the software categories these
two different topographic areas in the same class, this is a problem when interpreting image data sets.

11. 10
Figure 7 shows the supervised classification and the colour image is the landsat image from 1989 (see Supervised Classification
3
rd
October map). The yellow highlighted area is healthy vegetation; the landsat 1989 image shows a much larger area than
what the supervised classification has created.
Figure 8 shows the supervised classification and the colour image is the landsat image from 1989 (see Supervised Classification
3
rd
October map). The three circles of yellow, orange and red show three different points of slightly different vegetation cover.
From the landsat image it is possible to see that these three areas show various shades of green, with some obvious clearing
of vegetation within the red circle. However the supervised classification classes these three areas slightly differently.

12. 11
Analysis:
Conclusion:
Word count: 1550

13. 12
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