IDRISI Andes Image Classification Report
Introduction
Satellite imagery provides vast amounts of digital data. In LANDSAT 5 Thematic
Mapper images, the data is in the form of electromagnetic reflectance values at different wavelengths. The possible reflectance values for each pixel in each band range from 0 to 255.
[1]
LANDSAT 5 Thematic Mapper records seven bands of information: blue (band 1;
0.45—0.52 µm), green (band 2; 0.52—0.60 µm), red (band 3; 0.63—0.69 µm), near infrared (band 4; 0.76—0.90 µm), middle infrared (band 5; 1.55—1.75 µm), thermal infrared (band 6; 10.40—12.50 µm) and another middle infrared (band 7; 2.08—2.35
µm).
[2]
Reflectance is affected by season and the reflective surface among other things.
Image classification relies on identifying a ‘unique’ sets (reflectance values for all wavelengths) of reflectance values for a specific land use known as a spectral signature. This is not entirely possible as there is not necessarily a unique set for every different land use.
[1]
While the eye provides the best interpretation, computer algorithms are quicker and provide more consistent classification. The human eye is often used to guide digital classification
[1]
or in conjunction with digital classification as in the case of the reclassified images in this report.
Data
The data for this report are the seven bands of LANDSAT 5 Thematic Mapper for
Howe Hill, Worcester, Massachusetts, United States of America taken 10 September
1987
[3]
– at which time the deciduous trees still had leaves. The date also indicates that the images were taken during the harvest period so the reflectance of agricultural land is likely to have considerable variation.
Figure 1: Google Image of Howe Hill, Worcester, Massachusetts, United States of
America. This site is used in the tutorials for both IDRISI32 and IDRISI Andes though this image covers a larger area than the tutorials use.
Figure 2: IDRISI Andes composite image of LANDSAT 5 Thematic Mapper bands
4, 3, and 1 showing Howe Hill, Worcester, Massachusetts, United States of America.
The site used in this report and the IDRISI tutorial (both the IDRISI32 and IDRISI
Andes versions) is a 2.16 km (width) by 2.58 km (length) section of Howe Hill,
Worcester, Massachusetts, United States of America (an area of 5.5728 km
2
).
[3]
Howe Hill is relatively undeveloped. Most of it is covered by forest (either coniferous or deciduous). Figure 1 suggests that a clean distinction between the different forest types may not be possible. The other features of Howe Hill are several water bodies, some agricultural land (mostly but not exclusively in the eastern half of the image), some housing (to the north and north-east) and an old airstrip (to the south-west). Several roads run through the site. The roads are clearly visible in the GoogleEarth image (Fig. 1) (presumably owing to its having been taken in a season in which the deciduous trees had shed their leaves) but not in the LANDSAT 5
Thematic Mapper image (Fig. 2).
Methods

All the methods used in this report are hard classifiers, which means that they assign each pixel to a fixed class.
[1]
A Principle Component’s Analysis (PCA) was run in IDRISI Andes (set to seven components and full output) to determine the combination of bands carrying the most information as an alternative to using all bands in classification. The three bands carrying most of the information were bands 4, 3 and 1. This agrees with the IDRISI
Andes Manual
[1]
which indicates that the most information for many environments is contained in the near infrared (band 4) and red (band 3) bands.
The six land use categories identified in the IDRISI Andes tutorial
[1]
(deep water, shallow water, agriculture, urban area, coniferous forest and deciduous forest) were used for this report.
Eight classification algorithms were applied to reflectance data of the LANDSAT 5
Thematic Mapper bands. The following three unsupervised classification algorithms were used in IDRISI Andes: CLUSTER, ISOCLUST (sometimes considered semisupervised) and KMEANS. The following five supervised classification algorithms were used in IDRISI Andes: FISHER, KNN, MAXLIKE, MINDIST and PIPED.
Unsupervised Classification.
CLUSTER
The CLUSTER algorithm makes use of a technique analysing histogram peaks. The
The ‘broad’ classification looks for distinct peaks while the ‘fine’ classification recognises significant overlaps between peaks. In IDRISI Andes, CLUSTER is specifically modified to work with three band 8-bit composite images.
[1]
The IDRISI Andes CLUSTER algorithm was set to fine classification and a user selected number of classes with all the other settings at the default position.
Experimentation indicated that the use of all seven LANDSAT 5 Thematic Mapper bands was less effective than the use of three bands. Accordingly bands 4, 3 and 1 were used to classify the image. I selected 12 and 16 classes for this report though my experimentation extended to 20 classes. The CLUSTER outputs were then reclassified using the RECLASS algorithm to show the aforementioned six land use categories. The reclassified images were analysed using the AREA algorithm in
IDRISI Andes. Both reclassified images were also compared pixel by pixel with each other and the images from all the other classification algorithms used.
ISOCLUST
In IDRISI Andes, after entering the number of bands to be used in the classification, a histogram is created to provide the user with a guide to choosing a meaningful number of classes (by selecting breaks in the histogram). After the user has determined the desired number of classes, ISOCLUST places classes then assigns pixels to the nearest class. When this is completed, a new mean location is computed and pixels. The placing of pixels in classes and calculation of new mean locations is repeated until the output does not change significantly. ISOCLUST makes use of
CLUSTER to locate the seed classes and a maximum likelihood procedure for the iterations.
[1]

The number of iterations for each run of the IDRISI Andes ISOCLUST algorithm was set at one less than the number of classes chosen. The other settings were left at the default. Experimentation indicated that the use of all seven LANDSAT 5 Thematic
Mapper bands was less effective than the use of three bands. Accordingly bands 4, 3 and 1 were used to classify the image. I selected 10 (9 iterations) and 16 (15 iterations) classes for this report but my experimentation was more extensive. The
ISOCLUST outputs were then reclassified using the RECLASS algorithm to show the aforementioned six land use categories. The reclassified images were analysed using the AREA algorithm in IDRISI Andes. Both reclassified images were also compared pixel by pixel with each other and the images from all the other classification algorithms used.
KMEANS
The KMEANS algorithm uses a K-means clustering technique. The algorithm places
K means (centroids) then assigns pixels to the nearest mean. Euclidean distance is used to calculate which class a pixel belongs to. The means are then updated and the pixels are reassigned. The process is repeated until the means are fixed. The algorithm minimises the sum of squared errors. The algorithm is highly dependent on the initial centroids. More classes than are desired are advised to get as good a configuration of initial centroids as possible.
[1]
The IDRISI Andes Manual
[1]
indicates that the maximum number of classes is 256.
Using the Howe Hill data, KMEANS found a maximum of 18 classes even with the maximum set at 20 classes.
The selection of the number of classes to be generated was the only setting altered in the IDRISI Andes KMEANS algorithm. Experimentation indicated that the use of all seven LANDSAT 5 Thematic Mapper bands was more effective than the use of three bands. Accordingly all bands were used to classify the image. I selected 11 and 16 classes for this report but my experimentation went up to 20 classes. The KMEANS outputs were then reclassified using the RECLASS algorithm to show the aforementioned six land use categories. The reclassified images were analysed using the AREA algorithm in IDRISI Andes. Both reclassified images were also compared pixel by pixel with each other and the images from all the other classification algorithms used.
Supervised Classification
Signature Files
I used a composite image of bands 2, 5 and 7 to do the on-screen digitising needed to create spectral signatures for each land use for the supervised classification. The digitising was done using the default settings. The resulting vector file was run through MAKESIG in IDRISI Andes. MAKESIG indicates if more pixels are required in a classification category and more digitising can be added to the vector file to reach the required number of pixels (which is ten times the number of bands used to create the signature files
[1]
). The classification of the six identified land use classes was: 1 = Deep Water, 2 = Shallow Water, 3 = Agriculture, 4 = Urban Area, 5 =
Coniferous Forest and 6 = Deciduous Forest. All seven LANDSAT 5 Thematic
Mapper bands were used to create the signature files. All the other settings were left on the default.

FISHER
The FISHER algorithm is a form of linear discriminant analysis. Linear functions are used to classify each pixel. These functions minimise intraclass variance and maximise interclass variance.
[1]
The FISHER algorithm in IDRISI Andes was applied using all seven bands and the default settings. The resulting images were analysed using the AREA algorithm in
IDRISI Andes and also compared pixel by pixel with the images from all the other classification algorithms used.
KNN
The KNN algorithm uses k-nearest neighbours to determine which class a pixel belongs to. It can be used to do either hard or soft classification. The dominant class of the k-nearest neighbours is the one to which a pixel is assigned in the hard classification. Euclidean distance is used to calculate which class a pixel belongs to.
[1]
The KNN algorithm in IDRISI Andes was applied as a hard classifier using all seven bands and the default settings. The resulting images were analysed using the AREA algorithm in IDRISI Andes and also compared pixel by pixel with the images from all the other classification algorithms used.
MAXLIKE
The MAXLIKE algorithm uses a maximum likelihood procedure derived from
Bayesian probability theory. MAXLIKE uses the mean, variances and covariance data from the signatures to calculate which class each pixel belongs to.
[1]
The MAXLIKE algorithm in IDRISI Andes was applied using all seven bands and the default settings. The resulting images were analysed using the AREA algorithm in
IDRISI Andes and also compared pixel by pixel with the images from all the other classification algorithms used.
MINDIST
The MINDIST algorithm in IDRISI Andes identifies each class by the mean position it has on each band. Pixels are classified in the class with the mean nearest to the pixel value. This means that MINDIST is less likely to classify pixels belonging to highly variable classes classes accurately.
[1]
The MINDIST algorithm in IDRISI Andes was applied using all seven bands and the default settings. The resulting images were analysed using the AREA algorithm in
IDRISI Andes and also compared pixel by pixel with the images from all the other classification algorithms used.
PIPED
The PIPED algorithm identifies each class by the range of values it is expected to have on each band. The ranges for multispectral image data form box-like polygons known as parallelpipeds which enclose the expected values. Any pixel not within a parallelpiped is left unclassified.
[1]
The PIPED algorithm in IDRISI Andes was applied using all seven bands and the default settings. The resulting images were analysed using the AREA algorithm in

IDRISI Andes and also compared pixel by pixel with the images from all the other classification algorithms used.
Results and Discussion
Table 1: Percentage of pixels classified to each land use for each of the hard classifiers used in this report. The percentages were calculated from the outputs of the AREA algorithm in IDRISI Andes. The unsupervised classification algorithms were reclassified before the area analysis was carried out. Where the land use category of ‘Shallow Water’ was not classified, a single value for water is given under
‘Total Water.’ The PIPED algorithm did not classify 38% of the pixels.
Hard Classifier
CLUSTER
(12 classes)
Deep
Water
-
Shallow
Water
-
Total
Water
7.8
Agriculture
4.0
Urban
Area
8.5
Coniferous
Forest
13.4
Deciduous
Forest
66.4
7.1 0.4 7.5 4.9 8.5 13.6 65.5 CLUSTER
(16 classes)
ISOCLUST
(10 classes)
ISOCLUST
(16 classes)
KMEANS
(11 classes)
KMEANS
(16 classes)
FISHER
-
4.6
5.8
5.5
4.7
-
3.8
3.0
2.9
4.0
6.3
8.4
8.8
8.4
8.7
6.5
10.8
8.7
4.2
8.2
5.0
8.5
9.3
9.6
6.7
17.6
14.4
12.6
16.6
14.5
64.6
57.9
60.7
61.1
62.0
KNN
MAXLIKE
4.4
3.0
4.4
5.2
8.8
8.2
7.0
15.7
8.0
15.9
14.9
12.5
61.3
47.6
MINDIST
PIPED
4.5
2.1
4.3
4.4
8.8
6.5
6.4
5.5
7.7
7.1
14.5
10.2
62.6
32.7
Figure 3: CLUSTER image of Howe Hill, Worcester, Massachusetts, United States of America produced in IDRISI Andes using LANDSAT 5 Thematic Mapper bands 4,
3 and 1, fine classification restricted to 12 classes and default settings elsewhere.
Figure 4: Reclassified CLUSTER image of Howe Hill, Worcester, Massachusetts,
United States of America produced in IDRISI Andes. The CLUSTER settings were fine classification restricted to 12 classes and default settings elsewhere using
LANDSAT 5 Thematic Mapper bands 4, 3 and 1. The RECLASS settings were the defaults. The reclassification was as follows: Deep Water (1) was CLUSTER class
4; Agriculture (3) was CLUSTER classes 8 and 11; Urban Area (4) was CLUSTER classes 6, 7 and 12; Coniferous Forest (5) was CLUSTER classes 3, 9 and 10 and
Deciduous Forest (6) was CLUSTER classes 1, 2 and 5. No CLUSTER class could be classified as Shallow Water (2) – it was combined with Deep Water (1) and
Coniferous Forest (5).

Figure 5: CLUSTER image of Howe Hill, Worcester, Massachusetts, United States of America produced in IDRISI Andes using LANDSAT 5 Thematic Mapper bands 4,
3 and 1, fine classification restricted to 16 classes and default settings elsewhere.
Figure 6: Reclassified CLUSTER image of Howe Hill, Worcester, Massachusetts,
United States of America produced in IDRISI Andes. The CLUSTER settings were fine classification restricted to 16 classes and default settings elsewhere using
LANDSAT 5 Thematic Mapper bands 4, 3 and 1. The RECLASS settings were the defaults. The reclassification was as follows: Deep Water (1) was CLUSTER class
4; Shallow Water (2) was CLUSTER class 16; Agriculture (3) was CLUSTER classes 8, 12 and 15; Urban Area (4) was CLUSTER classes 6, 7, 11 and 14;
Coniferous Forest (5) was CLUSTER classes 3, 9, 10 and 13 and Deciduous Forest
(6) was CLUSTER classes 1, 2 and 5.
The raw outputs from the CLUSTER algorithm are shown in Figures 3 and 5 while the reclassified images are Figures 4 and 6. The raw outputs are included to allow assessment of the reclassification.
The CLUSTER algorithm classed ‘Shallow Water’ with either ‘Deep Water’ or
‘Coniferous Forest’ for 12 classes. I elected to include the 12 class images (Figs 3 and 4) in this report to demonstrate the difficulties of identifying ‘Shallow Water’ with the CLUSTER algorithm. At 16 classes, some ‘Shallow Water’ was identified but most was still classified as either ‘Coniferous Forest’ or ‘Deep Water’ – only
0.4% of the pixels in the image were classified as ‘Shallow Water’ (Table 1). Going up to 20 classes did not increase the identification of ‘Shallow Water’ or noticeably improve the classification enough to justify the increased amount of time required to assess the reclassification.
‘Agriculture’ and ‘Urban Area’ were not cleanly separated. At both 12 and 16 classes, the reclassified image exaggerated the cover of the ‘Urban Area’ and diminished the cover of ‘Agriculture’ (Table 1). ‘Agriculture’ and ‘Deciduous Forest’ were not cleanly separated in some fields, e.g. the field adjacent to the main ‘Urban
Area.’
Figure 7: ISOCLUST image of Howe Hill, Worcester, Massachusetts, United States of America produced in IDRISI Andes using LANDSAT 5 Thematic Mapper bands 4,
3 and 1 with 10 classes, 9 iterations and default settings elsewhere.
Figure 8: Reclassified ISOCLUST image of Howe Hill, Worcester, Massachusetts,
United States of America produced in IDRISI Andes. The ISOCLUST settings were
10 classes, 9 iterations and default settings elsewhere using LANDSAT 5 Thematic
Mapper bands 4, 3 and 1. The RECLASS settings were the defaults. The reclassification was as follows: Deep Water (1) was ISOCLUST class 4; Agriculture
(3) was ISOCLUST class 8; Urban Area (4) was ISOCLUST class 6; Coniferous
Forest (5) was ISOCLUST classes 3 and 10 and Deciduous Forest (6) was
ISOCLUST classes 1, 2, 5, 7 and 9. No ISOCLUST class could be classified as
Shallow Water (2) – it was combined with Deep Water (1) and Coniferous Forest (5).
Figure 9: ISOCLUST image of Howe Hill, Worcester, Massachusetts, United States of America produced in IDRISI Andes using LANDSAT 5 Thematic Mapper bands 4,
3 and 1 with 16 classes, 15 iterations and default settings elsewhere.
Figure 10: Reclassified ISOCLUST image of Howe Hill, Worcester, Massachusetts,
United States of America produced in IDRISI Andes. The ISOCLUST settings were

16 classes, 15 iterations and default settings elsewhere using LANDSAT 5 Thematic
Mapper bands 4, 3 and 1. The RECLASS settings were the defaults. The reclassification was as follows: Deep Water (1) was ISOCLUST class 4; Shallow
Water (2) was ISOCLUST class 16; Agriculture (3) was ISOCLUST classes 8 and
12; Urban Area (4) was ISOCLUST classes 6, 11 and 14; Coniferous Forest (5) was
ISOCLUST classes 9, 10 and 13 and Deciduous Forest (6) was ISOCLUST classes 1,
2, 3, 5, 7 and 15.
The raw outputs of the ISOCLUST algorithm are shown in Figures 7 and 9 while the reclassified images are Figures 8 and 10. The raw outputs are included to allow assessment of the reclassification.
ISOCLUST did not identify ‘Shallow Water’ in under 16 classes. Below 16 classes,
‘Shallow Water’ is mostly classified as ‘Coniferous Forest’ though some is also classified as ‘Deep Water’ (Fig. 8). I elected to include the 10 class images (Figs 7 and 8) in this report to demonstrate the difficulties of identifying ‘Shallow Water’ with the ISOCLUST algorithm. However, the identification of ‘Shallow Water’ at 16 classes is acceptable (3.8% of pixels are assigned to ‘Shallow Water’ (Table 1)). As with CLUSTER, increasing the number of classes beyond 16 provided no noticeable improvements.
‘Agriculture’ and ‘Urban Area’ were poorly distinguished from each other in
ISOCLUST. In some fields, ‘Agriculture’ was poorly separated from ‘Deciduous
Forest.’ A reclassification of 10 classes produces a reasonable assessment of the area covered by ‘Urban Area’ but an underassessment of the area covered by ‘Agriculture’
(Fig. 8). The reclassification of 16 classes appears to exaggerate the cover of both
‘Agriculture’ and ‘Urban Area’ (Fig. 10)
Figure 11: KMEANS image of Howe Hill, Worcester, Massachusetts, United States of America produced in IDRISI Andes using all seven LANDSAT 5 Thematic
Mapper bands with 11 classes and default settings elsewhere.
Figure 12: Reclassified KMEANS image of Howe Hill, Worcester, Massachusetts,
United States of America produced in IDRISI Andes. The KMEANS settings were
11 classes and default settings elsewhere using all seven LANDSAT 5 Thematic
Mapper bands. The RECLASS settings were the defaults. The reclassification was as follows: Deep Water (1) was KMEANS class 4; Shallow Water (2) was KMEANS class 5; Agriculture (3) was KMEANS classes 8 and 9; Urban Area (4) was
KMEANS classes 2 and10; Coniferous Forest (5) was KMEANS classes 3 and 11 and Deciduous Forest (6) was KMEANS classes 1, 6 and 7.
Figure 13: KMEANS image of Howe Hill, Worcester, Massachusetts, United States of America produced in IDRISI Andes using all seven LANDSAT 5 Thematic
Mapper bands with 16 classes and default settings elsewhere.
Figure 14: Reclassified KMEANS image of Howe Hill, Worcester, Massachusetts,
United States of America produced in IDRISI Andes. The KMEANS settings were
16 classes and default settings elsewhere using all seven LANDSAT 5 Thematic
Mapper bands. The RECLASS settings were the defaults. The reclassification was as follows: Deep Water (1) was KMEANS class 12; Shallow Water (2) was KMEANS class 13; Agriculture (3) was KMEANS classes 4 and 8; Urban Area (4) was
KMEANS classes 3, 9 and 11; Coniferous Forest (5) was KMEANS classes 1, 2, 5 and 6 and Deciduous Forest (6) was KMEANS classes 7, 10 and 14—16.

The raw outputs from the KMEANS algorithm are shown in Figures 11 and 13 while the reclassified images are Figures 12 and 14. The raw outputs are included to allow assessment of the reclassification.
The reclassified 11 class image (Fig. 12) provides a fair first approximation of the land cover categories in a relatively short time interval which is why I included it. It appears to exaggerate the cover of the categories of ‘Agriculture’ and ‘Urban Area.’ I consider the reclassified 16 class image (Fig. 14) superior but it also exaggerates the
‘Urban Area’ though the 'Agriculture’ is underestimated. The KMEANS algorithm is second only to the MAXLIKE algorithm in the exaggeration of the cover of ‘Urban
Area’ (Table 1).
Figure 15: FISHER image of Howe Hill, Worcester, Massachusetts, United States of
America produced in IDRISI Andes using default settings.
The FISHER algorithm inflates the ‘Agriculture’ and ‘Urban Area’ land use categories (Fig. 15). It is visually appealing because it does not place many ‘Urban
Area’ pixels in ‘Agriculture’ at points which the GoogleEarth image (Fig. 1) shows to have no buildings.
Figure 16: KNN image of Howe Hill, Worcester, Massachusetts, United States of
America produced in IDRISI Andes using default settings.
The KNN algorithm is not entirely able to separate the land use categories
‘Agriculture’ and ‘Urban Area’ but otherwise provides a fair representation of the area (Fig. 16).
Figure 17: MAXLIKE image of Howe Hill, Worcester, Massachusetts, United States of America produced in IDRISI Andes using default settings.
While maximum likelihood classification is perhaps the most frequently used supervised classification, [1] it was neither easier to use nor more effective at classifying the land use categories than the other supervised classifications (with the exception of the PIPED algorithm which is definitely less effective than the
MAXLIKE algorithm).
The MAXLIKE algorithm drastically inflated the cover of the ‘Agriculture’ and
‘Urban Area’ land use categories (Fig. 17, Table 1).
Figure 18: MINDIST image of Howe Hill, Worcester, Massachusetts, United States of America produced in IDRISI Andes using default settings.
As with the KNN algorithm, the MINDIST algorithm was not entirely successful at separating the ‘Agriculture’ from the ‘Urban Area’ but was otherwise a fair representation of Howe Hill (Fig. 18). The MINDIST and KNN outputs had a Kappa
Index of Agreement of 0.9599.
Figure 19: PIPED image of Howe Hill, Worcester, Massachusetts, United States of
America produced in IDRISI Andes using default settings.

The PIPED algorithm does not classify all the pixels. The unclassified pixels are black in Figure 19. The algorithm left 38% of the pixels unclassified (Table 1) which meant that it underestimated the cover of most of the land use classes. Interestingly enough, the only class with a pixel percentage comparable with the other supervised classifications was ‘Shallow Water’ (Table 1).
Conclusions
The land use categories ‘Agriculture’ and ‘Urban Area’ were difficult to separate from each other. Both were also confused with ‘Deciduous Forest’ in some pixels.
The land use categories ‘Deep Water,’ ‘Shallow Water’ and ‘Coniferous Forest’ were not readily separated by all the algorithms – as may be seen by comparing the southern shore of the southern most water body in the different IDRISI Andes images with the GoogleEarth image. A ‘shoreline’ (generally classified as either
‘Agriculture’ or ‘Urban Area’) would be expected adjacent to the water rather than behind trees. I will not try to say which land use category was the most difficult to extract overall as where a pixel is misclassified both the correct land use category and the misapplied category are not accurately extracted.
The agricultural land west of the water bodies that were classified as ‘Shallow Water’ by the supervised hard classifiers appears to have been harvested since its reflectance tended to be confused with the ‘Urban Area’ and was distinctly different from the agricultural land adjacent to the main ‘Urban Area.’ The latter tended to be confused with ‘Deciduous Forest’ suggesting that it had not been harvested.
For all the classifications, some of the apparently inaccurately classified urban pixels may be accurate as there are a number of roads which appear to have been largely obscured by the deciduous forest in the satellite imagery. The south-east end of the western water body is probably accurately classified as urban area since it appears to be a dam wall.
In general, producing a land use classification using the supervised classifications was quicker and probably better than the unsupervised classifications. The best unsupervised classification algorithm was the KMEANS and the worst one was the
CLUSTER. I was unable to decide if I preferred the FISHER or the KNN algorithms for the supervised classification. I preferred the FISHER algorithm’s agriculture where the GoogleEarth image suggested the classification is appropriate but I preferred the KNN algorithm’s urban area where the GoogleEarth image suggested the classification was appropriate (both did have pixels classified inaccurately in both the agriculture and urban area categories). The PIPED algorithm was the worst supervised classification algorithm as it did not classify all the pixels.
Comparison of the GoogleEarth image and the IDRISI Andes outputs indicates that the landscape has not changed much since 1987.
References
1. Eastman JR. 2006. IDRISI Andes Tutorial. Worcester, MA: Clark Labs, Clark
University. 284 p. Provided as a PDF with the IDRISI Andes software package.

2. Landsat Project. Landsat 5 History [Internet]. Landsat Project, United States
Geological Survey, United States Department of the Interior, United States
Government; Updated 2006 Jun. 7 [cited 2007 Sept. 17]. Available from: http://landsat.usgs.gov/project_facts/history/landsat_5.php
.
2. Eastman JR. 2006. IDRISI Andes Guide to GIS and Image Processing. Worcester,
MA: Clark Labs, Clark University. 328 p. Provided as a PDF with the IDRISI Andes software package.