preliminary analysis of satellite imagery for the delineation of caribou land cover utilization

8/14/2019 Preliminary Analysis of Satellite Imagery for the Delineation of Caribou Land Cover Utilization.

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Introduction and Study Area Description:

The use of satellite imagery for the classification and delineation of range utilization by

ungulates has been increasing. One issue that arises when using publically available satellite

imagery is the ability to delineate fine scale land cover features deemed important to a specific

species of ungulate. In this exercise, a preliminary evaluation was conducted for Landsat 7

satellite imagery with respect to feature identification and data modification that would facilitate

the efficient identification of these existing features. A comparison was made to the CanVec

dataset (a pre-classified dataset available from the Canadian Government) to determine if large

landscape features could be identified.

For this exercise, an Orthorectified Landsat 7 ETM+ image was acquired from the Geogratis

website (www.geogratis.ca). The image was from Path 04 Row 026 taken on August 05, 2001.

These images were acquired from a near polar orbit at an altitude of 705 km and an inclination of

98.2 degrees. Six multispectral bands at 30 meters resolution, one panchromatic band at 15

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meters and two, a high-gain and low-gain band, at a resolution of 60 meters. The image has

undergone geometric and radiometric correction and is stated as having less than 10 percent

cloud cover. Image has been projected using NAD 83 UTM 21. Complete details on algorithms

and control data used during corrections can be found on the Geogratis website.

A subset of the image was created using ArcGis 9.2. A 30 x 30 km subset was extracted havingthe following extent:

The selected area is located in the Central Newfoundland Forest Ecoregion, North-central

subregion as shown in Figure 1 (Parks Division 2000).

The study area is characterized by a complex of coniferous forests and wetlands (Parks 2000,

Damman 1964). Wetlands are represented by mire complexes, as defined by Rydin and Jeglum

(2006 p. 330), often as mixture of bogs and fens. Raised bogs are a common feature in this area.

Forests are predominately coniferous and represented by Black Spruce (Picea marianna ) and

Balsam Fir (Abies balsamea). Fire plays an important role in the occurrence of specific forest

types allowing for the establishment of Black Spruce forest in areas previously dominated by

Balsam Fir as well as the establishment of localized stands of White Birch (Betula papyrifera),

Trembling Aspen (Populus tremuloides) and Pin Cherry (Prunus pensylcanica) (Damman

1964). Alder (Alnus rugosa) is also abundant along the edges of waterways and waterbodies or

the transition zones between mires and forests.

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Figure 1 Newfoundland Study Area

Based on data from Environment Canada (2004) this region experiences a more continental

climate than other areas of the island with an average yearly temperature of 3.5 degrees and 1200

mm of annual precipitation, approximately 30 percent which falls as snow. Warmest

temperatures are in July, average 16.2 degrees and the coldest month is February, average -9.1

degrees. The region experiences 140 -160 growing days with green up beginning around mid-

May. Evapotranspiration rates range from 450 500 mm leading to a moisture surplus of 380

630 mm per year (Damman 1964).

In 2004, the study area contained 1440 km of linear features comprised of roadways,

transmission corridors and an old railway bed (Forestry 2004). The majority of these roads are

unpaved forest access roads used for pulpwood harvesting activities. Forest harvesting has

occurred in this area on a regular basis since the 1980s and has resulted in a mosaic of cutovers

in various stages of regeneration (Forestry 2000). Three large forest fires (over 200 ha) have

been recorded in the study area occurring in 1964 (393 ha, location 49.0 -56.07), 1986 (1399 ha,

location 49.031 -56.095) and 1999 (3675 ha, location 49.3 -56.23) (Canadian Forest Service

2002).

Topography of the area is characterized by rolling terrain with elevation ranging from 100 470

meters and a mean of 285 meters (Wildlife Division 2007). Extreme values are represented by

river valleys and rock outcrops. Forests are restricted to higher terrain or areas where the terrain

rises above the surrounding mires.

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Landsat 7 ETM+ Bands and Visualization:

Landsat spectral bands have been used individually or in combination, as composite images, for

land cover classification of forested and other vegetated areas throughout the world (Jakubauskas

and Price 1994, Boyd and Danson 2005, Wulder et al. 2004, Ramsey et al. 2004, Syed and

Abdulla 2002). Several band combinations (composites) have been used to aid in the visilizationof distinct surface features. Two of the most common are a true colour composite combining the

red, blue and green bands and the false colour near infrared composite made up of bands red,

green and the near infrared band (Richards and Jia 2006 pp. 69 and 146, Mather 2004 p. 158,

Canada centre for Remote Sensing 2007 pp. 45-46, US Army Corps of Engineers 2003 p. 5.2). In

this exercise, it was found the 3, 4 and 5 band combination displayed a better separation

Figure 2 Red, Green and Blue Composite of 30x30 km Study Area, Bands 3,2,1.

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of features. All composites for this exercise were created using the Idrisi Andes software

package (http://www.clarklabs.org/). The images obtained from these combinations are displayed

in figures 2 and 3.

A third image (figure 4) was created using a technique called pan-sharpening (Canada Centre for

remote Sensing 2007 p.64, Fox et al. 2002, Eastman 2006 pp. 34-35 and 92). The differencebetween the standard (30m pixel) and the pan-sharpened (15m pixel) images are shown in

figures 5 and 6. For comparison a high resolution image obtained from Google Earth

(http://earth.google.com/) is displayed in figure 7. The improvement in resolution for the pan-

sharpened image is readily apparent leading to a more distinct separation of land cover features

(Pohl and Van Gendersen 1998).

Figure 3 False Colour NIR, Bands 3,4,5.

Clouds and haze always present a problem when obtaining satellite imagery. In this case, haze is

apparent in the southern section of the image. Methods for the removal of haze have been

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developed (Richards and Jia 2006 p. 35). Also apparent in all images are the location of

waterbodies, more so in the near infrared composite (NIR) and the pan-sharpened enhancement.

This occurs for two reasons, in the NIR image the band 5 is absorbed in the first several

centimeters of water leading to a lower digital number (DN) thus a darker image; whereas in the

pan-sharpened image a gain in resolution and texture allows for better discrimination (Lillesand

and Kiefer1994 p.202, Eastman 2006 p.35). The Town of Badger can be seen just above the

south east corner of all images; although it is much more difficult to distinguish in the pan-

sharpened image when displayed at this scale. This is a

Figure 4 Pan-Sharpened RGB Composite, Bands 3,2,1,8.

result of the increased resolution of the pan-sharpened image which causes a reduction in the

spatial extent of features that fall below the 30m pixel size of the original image. Roads in the

pan-sharpened image are also more difficult to see for this reason.

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When one compares the standard and sharpened RGB composite, a striking difference in colour

is noted. The colour shift is a result of the fusing of the panchromatic and RGB bands (Zhang

2002). This issue has been recognized and methods for it correction have and continue to be

developed by ongoing research.

The heterogeneity of the study area can be seen in all images. NIR composites enhance thisheterogeneity through the variability in the reflectance of different vegetation types and stages

Figure 5 Standard RGB Composite, 30m Pixels.

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Figure 6 Pan-Sharpened RGB Composite, 15m Pixels.

and band 4 has become a standard for use in most vegetation studies (Price et al. 2002, Ramsey

et al. 2004, Luque 2000). The ability of this composite to distinguish between cutovers in

various stages of regeneration, as well as remaining forests and mires can be seen by comparing

figure 7 and figure 8. Although the traditional composite for NIR is bands 4, 3 and 2, for visual

presentation a composite of bands 5, 4 and 3 appear to provide a better separation of features.

(Figure 9).

Bands and Spectral Reflectance:

The spectral band used and the interaction of discreet land cover features with those bands

(Lillesand and Kiefer 1994 pp. 585-586, Wulder 2002, Myers and Patil 2006 pp. 4-9) influence

classification of land cover features. With this information, classification can be conducted

using unsupervised or supervised methods (Lillesand and Kiefer 1994 pp. 586-589). Thisconcept has been expanded to include hierarchical classification, which is influenced by the

image type used, and inherent features of the target landscape (Anderson et al. 1976, Cheng et al.

2004).

For this exercise, a supervised classification will be used to construct the spectral signatures for

three landscape features, Vegetation Cover (wooded area), Saturated Soils (mire complexes) and

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Waterbodies. Supervised classification requires access to training data which can be used for

the extraction of spectral values associated with selected classes (Lillesand and Kiefer 1994 p.

587, Richards and Jia 2006 p. 296). Training data was obtained from the Geogratis website,

specifically the CanVec dataset. The dataset was compiled from the best available data sources

covering Canadian territory (CanVec 2007a). Three features (entities) were selected from this

dataset; 1240009 Vegetation with >35% tree cover over 2m in height, 1320049 Water Saturated

Soils and 1480009 Hydrography (CanVec 2007b).

Areas representing the three selected feature types were digitized using Idrisi Andes. Using the

tools available in the software a spectral graph was produced for bands 1,2,3,4,5 and 7, with the

lower resolution , bands 6a and 6b, being omitted (Figure 10). Immediately apparent in this

graph is the overlap of digital numbers (DN) for the three features represented. Although some

overlap in spectral signatures is usually present, bands should be selected to minimize the degree

of overlap. If this selection were based on the mean value for each feature in a specific band

then bands 3,5 and 7 appear to be the best for our classification exercise.

To determine if the observed overlap poses a significant problem for classification, the SEPSIG

module of the Idrisi program was run. Selecting Transform Divergence from the submenu all 6

bands were analyzed using the default multiplier of 2000. Output from the module is listed

below with a graphical representation in Figure 10:

Separability between VEGETATION COVER and SATURATED SOIL on bands :

1 2 3 4 5 6 1999.70

Separability between VEGETATION COVER and WATERBODIES on bands :

1 2 3 4 5 6 2000.00

Separability between SATURATED SOIL and WATERBODIES on bands :

1 2 3 4 5 6 2000.00

Average Separability Over All Pairwise Combinations of Signatures :

1 2 3 4 5 6 1999.90

Command line used to create these results:SEPSIG x C:\Unit 6\GeoTiff\Geogratis Tiff\TS_30x30.sgf*6*2*2000*

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Figure 7 Image of Zoom Area Obtained from Google Earth.

Figure 8 Sub-Scene of Bands 4,3,2 Showing Land Cover Differences.

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Figure 9 Bands 5,4 and 3 False Colour NIR.

Figure 10 Spectral Graphs for Three Selected Land Cover Features.

Based on the associated help file for this operation, a value above 1600 indicates a good

possibility for separating the features given the derived spectral signatures. The slightly lower

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separability between vegetation cover and saturated soil can be attributed to the occurrence of

small clumps of forest and shrub in mires, saturated soil intermixed with wooded areas,

especially at the lower end of the 35% cover depicted by the CanVec data, the presence of

cutovers which have regrown with common saturated soil species not depicted by the CanVec

data, and the occurrence of small fens and bogs contained within contiguous forest areas. An in-

depth description of separability measures can be found in chapter 10 of Richards and Jia (2006).

Ancillary Data and Visualization:

The addition of ancillary data to landsat imagery can provide viewers a better understanding of

the topography and spatial location of specific features (Congalton and Green 1999, Curtis et al.

2002 Ch. 6, Canada Centre for remote Sensing 2007 pp. 165, 175, 178). To illustrate the utility

of including ancillary data with landsat imagery, for assistance in land cover classification of the

study, data from a variety of sources has been combined will the pan-sharpened RGB image

created in a previous exercise. All data included in the new image and source information is

listed in Table 1.

In addition to the data conversion listed in the table, it was necessary to exaggerate terrain height

to provide for a better visualization of the relationship between features and topography. This is

especially apparent in the location of forest access roads, which avoid water saturated soil and

are abundant on higher terrain where forest growth is better (Figure 11).

Table 1 Ancillary Data Used to Enhance Study Area Visualization.

Data Used Format Source

Elevation 12H01 and 12A16 DEM www.geobase.ca

Elevation 12H01 and 12A16 TIN Created from Geobase DEMsRoads Shapefile Dept. Natural Resources,

Newfoundland and Labrador,

Canada

Landsat 7 +ETM File Path 4 Row 26 TIFF www.geogratis.ca

Pansharpened RGB Image TIFF Created from GeogratisLandsat image, Bands 1,2,3,8

Rivers Coverage Dept. Natural Resources,

Newfoundland and Labrador,Canada

Streams Coverage Dept. Natural Resources,

Newfoundland and Labrador,Canada

Lakes Coverage Dept. Natural Resources,

Newfoundland and Labrador,

Canada

Images such as these provide the ability to identify specific land cover features and a means to

verify the accuracy of any classification techniques utilized. This information can then be

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incorporated into refinements of the classification process leading to a reduction in error. It

remains to be seen if the resolution obtained through pan-sharpening will be sufficient to classify

land cover features in areas utilized by caribou.

Figure 11 Pan-sharpened RGB image and Ancillary Data Composite.

Conclusion:

In closing, another feature that should be highlighted is the ability to create animations of the

study area and to zoom into specific areas of interest (Bratt and Booth 2002, pp. 196, 225-242).

These abilities, coupled with the ability to view the area form any perspective provide a

mechanism for users to visually evaluate concerns or questions from a multi-stakeholder

viewpoint (i.e. wetland conservation groups, tourism visibility issues, etc.).

This work will continue with the evaluation of alternate spatial datasets or combinations thereof.

It is postulated that not one publically available dataset will be sufficient for the classification of

all caribou related land cover features. Preliminary habitat assessment work seems to indicate

that caribou are interacting with small-scale land cover features that may be below the resolution

of currently available datasets. The success of using publically available spatial datasets will

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therefore be dictated by the inherent resolution of the dataset selected and the ability to modify

datasets to enhance classification of features.

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preliminary analysis of satellite imagery for the delineation of caribou land cover utilization

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