This document describes a project to derive geomorphological regions in Cotopaxi Province, Ecuador using GIS. Spatial data from various sources was analyzed using ArcGIS. An unsupervised classification was performed based on elevation and slope data to identify regions of similar geomorphology. The project area around Quilotoa volcano was used to assess the accuracy of the classification by correlating the derived geomorphological regions with soil and precipitation data. The results identified some limitations of the classification approach but also areas of correlation between geomorphology, soils, and precipitation patterns.

1. Deriving the Geomorphological Regions
In Cotopaxi Province, Ecuador.
April 2004
W. Greg Burton 0348090
Suzanne Vijfhuizen 0019224

2. Introduction
Course Description
As part of the GIS-course, Geografische Informatie Systemen: Theorie en Praktijk, a case
study was conducted to demonstrate knowledge of GIS processes using ESRI®
ArcGIS™ with a study project being assigned to evaluate the geomorphology within the
Cotopaxi province, Ecuador using these techniques (Verkuijlen & Seijmonsbergen 2004).
Project Area
Ecuador is located on the northwest coast of South America between approximately 1o
north latitude and 5o
South latitude; and lies between Peru to the south and Colombia to
the north and east. Ecuadorian elevations range from 0 meters at sea level to 6267 meters
atop Mt. Chimborazo. Residing on the equator, the climate of Ecuador varies from
Tropical Rainforest in the Amazon Basin east of the Andes Mountains to Tropical
Monsoon on the western side of the Andes (EcuadorExploer 2004).
The geology of Ecuador comprises three main regions: the coastal plain, the two Andean
ranges, and the upper Amazon Basin. These three regions represent the fore-arc, volcanic
arc, and back-arc regions of subduction of the Nazca Plate (Pacific Ocean) beneath the
South America Plate (Alvarado, et al 2003).
Cotopaxi Province straddles the crests of the Cordillera Occidental (western Andes) and
the Cordillera Oriental (eastern Andes); and is a major source of the drainage basins that
empty into the Pacific Ocean in the west and the Amazon Basin to the east. It is here in
the Andes that one can find specialized elevation-related climates of Temperate Humid,
Temperate Dry, and even an Arid climate found between the crests of the Cordillera
Occidental (western Andes) and the Cordillera Oriental (eastern Andes).
This area of the Andes contains many of the active volcanoes of the world including:
Cayambe, Reventador, Gaugua Pichincha, Antisana, Sumaco, Tungurahua, Sangay,
Chacana, and Cotopaxi. Cotopaxi, at 5911 meters is the tallest active volcano in the
world (University of North Dakota 2004). In fact, the highway between Quito to the north
and the city of Latacunga, located in the subduction valley between the west and east
Andes, is referred to as “volcano alley”. The project area is centered on the Quilotoa
volcano, which last erupted 800 years ago. The Quilotoa volcano caldera contains a
3500-meter level, brilliantly emerald-colored, alkaline lake.
Project Description
The geomorphology evaluation for the Cotopaxi Province was based on an unsupervised
classification using ArcGIS Spatial Analyst™ and morphometric criteria. Lack of time,
the size of the Cotopaxi province, and lack of data made a supervised classification, using
an enhanced ERDAS imagery problematic.
2

3. The project solution was to define the geomorphological regions within the Cotopaxi
Province, and then establish a focus area around the Quilotoa Caldera located in the
central highlands of the province to assemble the derived spatial data available. Using
this focus area, this study could determine the accuracy of the unsupervised classification
for geomorphologic regions involved.
The classification of the geomorphologic regions is based on morphometric data:
elevation and slope angle. The resulting derived raster data map was then integrated with
the other derived spatial data, including a 3D-image environment, using ESRI
ArcScene™, and evaluated using knowledge of geomorphological surfaces processes,
knowledge of the areas and landforms found in the Andes of central Ecuador, and derived
spatial data for precipitation and soils.
Project Spatial Data
The spatial data for the project was obtained from EcoCienca: Quito, Ecuador. Other data
evaluated was obtained from Dr. Marc Souris at the Asian institute of Technology,
Thailand, which provides for a small Ecuadorian dataset online. These datasets provided
the basis for our work, and was processed using ESRI® ArcGIS™ 8.3 software.
The basic idea of working with a GIS package and base data is to manipulate, analyze,
and visualize data to obtain new data and new provocative ways of evaluating
environmental issues (Burroughs 1998)(Gregory 2004). The processes involved in this
case study involved integrating the data obtained, visualizing the data, establishing a
workable study area, extracting and deriving data to meet the criteria established by the
geomorphological criteria, and then comparing available datasets defined within the
sample area to establish corollary accuracy of these geomorphological criteria.
Suzanne Vijfhuizen, Greg Burton
April 30, 2004.
3

4. Table of Contents
Introduction 2
• Course Description 2
• Project Area 2
• Project Description 2
• Project Spatial Data 3
Table of Contents 4
Methodology: Creating the Geomorphological Map 5
• Spatial Database Overview and Integration 5
• Choosing the Project Area 5
• Deriving Spatial Data 5
Methodology: Project Area Correlation 12
• Derived Spatial Data 12
• Correlating Geomorphology with Soils and Precipitation 12
• Visualizing Topography 19
Results and Discussion 21
• Project Constraints 21
• Morphometric Constraints Revisited 21
• Soil and Precipitation Correlation 22
Conclusion 23
References 24
• Bibliography 24
• List of Figures 26
• List of Tables 26
• List of Charts 27
Map Appendix 28
1. Masked Elevation Grid Cotopaxi Province 28
2. Masked Slope Grid Cotopaxi Province 29
3. Quilotoa Geomorphology & Soils 30
4

5. Methodology: Creating the Geomorphological Map
Spatial Database Overview and Integration
The spatial data used for this project came from dr. R. Coppus working at EcoCienca,
Quito, Ecuador. The database contained various shapefiles, coverages, and images
(rectified and unrectified) representing the Cotopaxi Province of Ecuador. These spatial
data files represented cities, small villages, soils, point-vegetation files, point-
precipitation, contour interval polylines, and mosaiced ERDAS and tiff images of
differing time intervals. Other datasets explored was the dataset from the Asian Institute
of Technology (Dr. Marc Souris), which contained shapefiles representing Ecuadorian
provinces, cities, volcanoes, erosion, and unmosaiced DEMs with a spatial resolution of
50 meters.
These datasets were viewed in ESRI® ArcGIS 8.3™ and ESRI® 3D Analyst™ and
ArcScene™. For this project, as the data provided was represented by shapefiles,
coverages, and images of various formats and projections, these spatial data were re-
projected into a standard UTM zone 17 South projection using ERSI ArcCatalog™. The
unmosaiced DEMs provided by the Asian institute of Technology were converted
reprojected, and mosaiced using ESRI® ArcToolbox™. The ERDAS image provided
was viewed in ERDAS Imagine™. As none of the datasets provided contained Metadata,
a rudimentary metadata was created for those datasets used for this project. Thumbnails
were also created for all provided and extracted datasets used for this project.
Choosing the Project Area
Due to the time limits of the project, the nature of some of the data provided (mosaiced,
unenhanced ERDAS image, lack of metadata and attribute information); it was decided
that data provided for the project by EcoCienca was used, as this data was from a known
source associated with the University of Amsterdam. Using this dataset it was decided
that an unsupervised classification of the geomorphology of the Cotopaxi province could
be attempted, with accuracy assessed correlating this classification technique with other
data provided. However, due to apparent limitations of ArcScene in drawing the larger
TIN dataset derived for the Cotopaxi Province contour-interval polylines shapefile, it was
thought that a smaller project area could be chosen from which an accuracy correlations
could be determined more quickly. Therefore, a project area around Lake Quilotoa was
chosen as the test area, as it contained apparent volcanic, glacial, alluvial, and mass
movement features. Additionally, soil characteristic upon which a correlation could be
attempted could be well defined for this project.
Deriving Spatial Data
For creating a map with main geomorphologic features of the landscape only
morphometric data was used. The choice for an unsupervised classification was made due
5

6. to short time available for the project. This part deals with the individual steps that have
to be made to derive the resulting map as in figure 1.
Figure 1: Geomorphological Map of Cotopaxi Province, Ecuador.
To find classes for geomorphology purely based on morphometry elevation and slope
angle are needed. Combining these data makes it possible to classify an area into the next
classes, based on processes: alluvial, aeolian, volcanic, mass movement, glacial or stable
areas. The final legend includes, next to processes, also current landforms that result from
prevailing (comparable) processes.
6

7. As can be seen in table 1 several combinations of both elevation and slope derive the
same geomorphology. The geomorphologic parameters designated simulate the specific
environment needed for these processes (Summerfield, 1996) but still are some arbitrary
and in the field these geomorphology-classes will often turn out to be a combination of
several processes.
Height
(meters)
Height-
code
Slope
(degrees)
Slope-
code
Geomorphology
0 - 400 1
0 – 15 1 Alluvial
15 – 25 2 Stable area
25 – 35 3 Stable area
35 - 86 4 Mass movement
400 - 800 2
0 – 15 1 Alluvial
15 – 25 2 Stable area
25 – 35 3 Stable area
35 - 86 4 Mass movement
800 - 1200 3
0 – 15 1 Alluvial
15 – 25 2 Stable area
25 – 35 3 Stable area
35 - 86 4 Mass movement
1200 –
1600
4
0 – 15 1 Alluvial
15 – 25 2 Stable area
25 – 35 3 Stable area
35 - 86 4 Mass movement
1600 –
2000
5
0 – 15 1 Alluvial
15 – 25 2 Alluvial
25 – 35 3 Mass movement / alluvial
35 - 86 4 Mass movement
2000 –
2400
6
0 – 15 1 Alluvial / Aeolian
15 – 25 2 Alluvial
25 – 35 3 Mass movement / alluvial
35 - 86 4 Mass movement
2400 –
2800
7
0 – 15 1 Alluvial / Aeolian
15 – 25 2 Alluvial
25 – 35 3 Mass movement / alluvial
35 - 86 4 Mass movement
2800 - 3200 8
0 – 15 1 Alluvial / Aeolian
15 – 25 2 Alluvial
25 – 35 3 Mass movement / alluvial
35 - 86 4 Mass movement
3200 –
3600
9
0 – 15 1 Alluvial / Aeolian
15 – 25 2 Glacial
25 – 35 3 Mass movement / alluvial
35 - 86 4 Mass movement
7

8. 3600 –
4000
10
0 – 15 1 Alluvial / Aeolian
15 – 25 2 Glacial
25 – 35 3 Mass movement/ alluvial
35 - 86 4 Mass movement
4000 –
4400
11
0 – 15 1 Alluvial / Aeolian
15 – 25 2 Glacial
25 – 35 3 Volcanic
35 - 86 4 Mass movement / volcanic
4400 –
4800
12
0 – 15 1 Alluvial / Aeolian
15 – 25 2 Glacial
25 – 35 3 Volcanic
35 - 86 4 Mass movement / volcanic
4800 –
5200
13
0 – 15 1 Alluvial / Aeolian
15 – 25 2 Glacial
25 – 35 3 Volcanic
35 - 86 4 Mass movement / volcanic
5200 - 5600 14
0 – 15 1 Aeolian
15 – 25 2 Glacial
25 – 35 3 Volcanic
35 - 86 4 Mass movement / volcanic
5600 –
6000
15
0 – 15 1 Aeolian
15 – 25 2 Glacial
25 – 35 3 Volcanic
35 - 86 4 Mass movement / volcanic
Table 1: Morphometric Standards for Cotopaxi Geomorphology.
With the ArcGIS Spatial Analyst™ raster calculator conversions were made to combine
the elevation map and the slope map. The calculations are according to table 1.
For creating an elevation and slope map some standard calculations are used. The
elevation map could be extracted directly from the original data and the slope map was
calculated from the elevation map.
For the elevation map a TIN was made from the available contour map. The creation of a
TIN file is a predefined calculation in the ESRI® 3D Analyst™. This TIN file however
is very large and therefore not useful for processing. From this TIN file a grid file can be
created with ESRI® Arc Toolbox™, just for decreasing the size of the file. After
reclassification (ArcGIS Spatial Analyst™ ) the grid file is used for deriving a slope map
with the slope-function (ArcGIS Surface Analyses™) in Spatial Analyst™ or 3D-
Analyst™.
With both the slope map and the grid file a reclassification was made to derive few
classes in height and slope, instead of individual values. The classes for elevation (from
the grid file) are steps of 400 meters, with a range from 0 – 6000, Map Appendix: 1.
Masked Elevation Grid Cotopaxi Province. The slopes were divided in classes of 0 – 15,
8

9. 15 – 25, 25 – 35 and 35 - 86 degrees, see Map Appenidx 2: Masked slope grid Cotopaxi
Province.
A mathematical property of the TIN-function is that it creates triangles to form a 3D
image. Because our contour image was clipped from a larger file some lines had open
ends. The open ends are combined in one triangle when using the TIN function. The
border of the TIN map is therefore not realistic and with all the resulting maps a mask
had to be put over it to give a correct view without border-bias.
The reclassification is necessary to be able to have only few legend units for the
geomorphologic map. The total amount of possible legend units is calculated by
multiplying the legend units of both elevation and slope map. In this case already 60
possible units are generated.
The legend for the elevation map was chosen arbitrary, 15 units with equal intervals. For
the slope map was chosen to have non-equal intervals. The decision was based on
knowledge of the processes that occur in the area rather than distribution or other
information.
The 0 – 15 degrees interval is supposed to link processes that are not forced by gravity.
This refers to alluvial processes, aeolian processes and with higher elevation also to
glacial and volcanic processes. The reason why later two are not found in table 1, as a
combination of high elevation and low slope angle, is due to a (assumed) secondary
influence on the landscape.
For the 25 – 35 degrees interval it is most important that this class makes the difference
between accumulation and transport because 30 degrees is the rest-angle for loose
material. All steeper slopes have processes that are the result of gravity. The 35 – 86
degrees interval is therefore dominated by mass movement processes.
The 15 – 25 degrees interval was the result of the 3 choices described above. This class
can represent several processes: however aeolian processes are excluded because it is
assumed that they occur only on flat surfaces. Also mass movement does not appear in
this class, however can be combined with alluvial processes.
From an elevation of 3200 meters glacial processes take place. This refers to
landforms/remains of prevailing processes/glaciations as well as to active processes.
From the same height alluvial processes still take place because it is assumed that water
and ice coexist.
ArcGIS Spatial Analyst™ calculates the total number of raster-cells each
geomorphological region contains within the Cotopaxi Province. Based on the
information that each cell is 45 meters on a side, the total area for each geomorphological
region type can be calculated (Table 2).
Table 2 indicates that the alluvial and alluvial processes dominated in the unsupervised
classification method used for this study.
9

10. Cell-Value Geomorphology Raster-cell Count Area (sq-meters)
2 Alluvial 616133.00 1247669325
3 Alluvial/Aeolian 1049800.00 2125845000
4 Glacial 348054.00 704809350
5 Mass Movement 276019.00 558938475
6 Alluvial/Mass Movement 399665.00 809321625
7 Stable 228803.00 463326075
8 Volcanic 42737.00 86542425
9 Volcanic Mass Movement 9254.00 18739350
10 Aeolian 10.00 20250
Table 2: Geomorphological Regions within Cotopaxi Province
Except for the raster calculation it would have been another option to combine elevation
and slope with a union-Overlay (Arc Toolbox™). For this method polygon-coverages are
required that can be generated again in Arc Toolbox™. However this method generates
lots of polygons and thereby a large file. The result would be over 20 000 polygons to
classify.
Subsequently, This larger image was clipped to represent the study area established for
the project (Figure 2). This image was created using the raster calculator and the polygon
shapefile created for the Quilotoa study area in ESRI® ArcCatalog™ and ESRI®
ArcEditor™. Additionally, a custom-clipping tool was created and experimented with for
this project. This tool was based on a script download from The ESRI scripts pages
located on the website: http//arcscripts.esri.com/details.asp?dbid=13043. This tool,
customized through the ArcGIS interface, can be used to clip ESRI grids, and tiff and
ERDAS images.
10

11. Figure 2: Geomorphologic Map of the Quilotoa Study Area.
Table 3 displays the results of the relative areas calculated for the different
geomorphological regions around lake Quilotoa.
Cell-Value Geomorphology Raster cell-Count Area (sq-meters)
2 Alluvial 1892 3831300
3 Alluvial/Aeolian 22362 45283050
4 Glacial 14891 30154275
5 Mass Movement 4573 9260325
6 Alluvial/Mass Movement 11034 22343850
Table 3: Geomorphological Regions Lake Quilotoa
11

12. Methodology: Project Area Correlation
Derived Spatial Data
Once the area around Lake Quilotoa was chosen, spatial data representing streams,
contours-intervals, rainfall, lakes, and soils were clipped using ArcGIS Geoprocessor.
The soil shapefile underwent substantial modification, updating its attribute table to
reflect current USDA Soil Taxonomy. Other shapefiles were created to represent the
project areas under consideration were created using ESRI® ArcEditor™. Additionally,
the precipitation point file provided by Universidad de Amsterdam – IBED, in Quito was
interpolated into a raster image, creating continuous surface rainfall data.
Correlating Geomorphology with Soils and Precipitation
Geomorphology is the study of surfaces processes and landforms, with the processes
responsible for the earth’s features being weathering, mass wasting, alluvial, glacial,
aeolian, volcanism and tectonics (Easterbrook 1999). The processes that result in soils
formation are integrated with geomorphology (parent material, climate, topography,
biota, and time), as soil-forming factors are a subset to these processes (USDA 1999).
The Quilotoa Soil Map in Figure 3 indicates that the area soils are composed of Andepts
(Andisols), which are derived from weathered volcanic rock.
12

13. Figure 3: Soil Map of the Quilotoa Study Area.
As the major surface land feature of the Cotopaxi region is active volcanic landforms one
would surmise that the major soil order indicative of these volcanic processes would be
young Andisol soils of recent volcanic origin. Further, since the major climatic types for
the highlands region is Temperature Humid, Temperate Dry, and Arid one could predict
preponderance of alluvial processes and soils moisture regimes that reflect these climates,
drainage, and elevations, with the predominant soil parent material being volcanic rock.
13

14. Figure 4: Quilotoa Precipitation (transparency)/Soil Map
As can be seen in Figure 4, the precipitation trend in the Quilotoa area is decreasing from
southwest to northeast, which would reflect the tropical monsoonal, orographic flow that
predominates in the area. This phenomena is reflected in the Andisol soil moisture
regimes reflected in the soil Great Group nomenclature: the predominant soil Great
Group to the west are the Dystrandepts, which are characterized by high weathering and
leaching, reflective of a humid precipitation regime. To the east the soils are dominated
by the Eutrandepts, which are Andisols that are subjected to a more ustic aridic climate
and thus are not as weathered and register a higher base saturation (less leaching). In the
center of the map are located the soil Great Group vitrandepts and exposed unweathered
rock. Vitrandepts are young, unweathered soils, having a higher base saturation (less
leaching), and contain much volcanic glass. Since the center of the Quilotoa area is also
dominated by exposed volcanic rock, it can be assumed that the exposed volcanic rock
and vitrandepts might be indicative of the recent eruption of Quilotoa, just 800 years
previously.
14

15. A Further confirmation of these phenomena can be the existence of the soil Great Group
Ustpsamments, which appears just to the northwest of the Quilotoa caldera. As these
Ustpsamments are sandy volcanic soils with an ustic moisture regime, one might
postulate, due to the adjacency to the caldera and drainage system, that these young
volcanic sandy soils might represent a weathering papilli field. This would have to be
checked with fieldwork or a supervised ERDAS classification. Below are two charts,
Chart 1 and Chart 2, prepared in Microsoft Excel, indicating the percentage of soil Great
Groups and geomorphology for the Quilotoa study area.
Quilotoa Soils
Vitrandepts
35%
Eutrandepts
11%
Exposed Rock
44%
Dystrandepts
8%
Ustpsamments
2%
Cryandepts
0% Vitrandepts
Eutrandepts
Exposed Rock
Dystrandepts
Ustpsamments
Cryandepts
Chart 1: Quilotoa Soils
Quilotoa Geomorphology
Alluvial
3%
Alluvial/Aeolian
42%
Alluvial/Mass
Movement
20%
Glacial
27%
Mass Movement
8%
Alluvial
Alluvial/Aeolian
Alluvial/Mass Movement
Glacial
Mass Movement
Chart 2: Quilotoa Geomorphology
15

16. By comparing the percentage land units between soils and derived geomorphology, it is
readily seen that the unclassified derived geomorphology for the Quilotoa region misses
the impact that volcanism has had on the region. The soil layer derived from the
EcoCienca, spatial data clearly shows that the predominate units, vitrandepts and exposed
volcanic rock, clearly implicate recent volcanic processes on the land; but the derived
geomorphological classification scheme misses volcanism entirely. This is most likely a
reflection of the classification scheme being based on current surface climatic
configurations (elevation/slope), and not the recent underlying endogenous processes that
result in manifestations of volcanism.
Another problematic finding is the derived presence of glacial geomorphological
processes (27%). Whereas it is unlikely that the area was not subject to continental
glaciations due to its latitude, with only one small soil unit reflecting a cryic temperature
regime (cryandepts), any sign of glaciations was mostly likely destroyed by the five
eruptions of Quilotoa within the last 40,000 years, the last one occurring only 800 years
ago. Despite these findings, the calculated presence of alluvial, alluvial/aeolian,
alluvial/mass movement, and mass movement are clearly indicated by the young soils,
and the presence of the drainage basin systems that resides on both sides of the Andean
divide.
To provide a more quantitative method for evaluating soils versus geomorphology, the
raster image that defined geomorphologic regions within the Quilotoa study area was
converted to a vector shapefile in ESRI® Spatial Analyst™, and then using the ArcGIS
Geoprocessor, these two vector files were Intersected, with the soil types of each
geomorphological category being joined into one record (See Map Quilotoa
Geomorphology & Soils in Map Appendix). The total percentage for each soil type in its
geomorphological region was then charted in Microsoft Excel to study whether the
prevailing surface process and soil categories were related. Chart 3 displays the
percentages of soils types versus the prevailing surface process derived for this landscape
unit.
Alluvial Soil Types
Exposed Rock
55%
Vitrandepts
21%
Dystrandepts
9%
Eutrandepts
15% Exposed Rock
Vitrandepts
Dystrandepts
Eutrandepts
Chart 3: Alluvial Soil Types
16

17. As Chart 3 indicates that the predominate “soil” type is exposed rock, perhaps this
contraindicates the alluvial process, but perhaps this phenomena is more related to recent
volcanic activity and erosion than any soil building component of alluvial processes.
Alluvial/Aeolian Soils
Exposed Rock
31%
Vitrandepts
50%
Dystrandepts
8%
Eutrandepts
10%
Ustpsamments
1%
Cryandepts
0% Exposed Rock
Vitrandepts
Dystrandepts
Eutrandepts
Ustpsamments
Cryandepts
Chart 4: Alluvial/Aeolian Soil Types
Chart 4 may be more indicative weathering related to soil building as these soils are in
more moderate slope areas, with 69% of the landscape unit containing developing soils.
Although the role of aeolian processes in unclear within this unit, soil is developing in
situ.
Alluvial/Mass Movement Soils
Vitrandepts
23%
Exposed Rock
59%
Eutrandepts
9%
Dystrandepts
6%
Ustpsamments
3% Cryandepts
0%
Vitrandepts
Exposed Rock
Eutrandepts
Dystrandepts
Ustpsamments
Cryandepts
Chart 5: Alluvial/Mass Movement Soil Types
Chart 5 makes a correlation between Alluvial/Mass Movement processes as exposed rock
accounts for 59% of the landscape units “soils”.
17

18. Mass Movement Soils
Vitrandepts
19%
Exposed Rock
77%
Dystrandepts
1%
Eutrandepts
2%
Ustpsamments
1%
Vitrandepts
Exposed Rock
Dystrandepts
Eutrandepts
Ustpsamments
Chart 6: Mass Movement Soil Types
As could be predicted, the Mass Movement process unit contains more “soils” correlated
with mass movement, i.e. exposed rock (77%), and is consistent with Chart 5.
Glacial Soils
Exposed Rock
42%
Vitrandepts
28%
Eutrandepts
17%
Dystrandepts
10%
Ustpsamments
3%
Cryandepts
0%
Exposed Rock
Vitrandepts
Eutrandepts
Dystrandepts
Ustpsamments
Cryandepts
Chart 7: Glacial Soil Types
As indicated previously, there is no substantiative case for glacial landforms given the
latitude and geologically recent processes (volcanism). However, it is interesting that
exposed rock accounts for 42% of the “soils” in this process unit.
18

19. Visualizing Topography
The next three figures: Figure 5, Figure 6, and Figure 7 depict 3D images of elevation,
geomorphology, and soils for the Quilotoa study area.
Figure 5: Quilotoa 3D Elevation
19
N
N

20. Figure 6: Quilotoa 3D Geomorphology
Figure 7: Quilotoa 3D Soils
These images were rendered in ESRI® ArcScene™, using the TIN model and shapefiles
derived for the Quilotoa study area. When comparing the topography of the 3D image of
Quilotoa Elevation with Quilotoa Geomorphology, one can readily see that the derived
geomorphology for alluvial, alluvial mass movement, and mass movement are well
predicted using the unsupervised classification scheme derived for the study. Although, at
present, there is not enough information about the area to determine whether wind
provides any substantial geomorphological process to the study area, it is apparent that
the alluvial aeolian processes land classification corresponds to areas of soil development
in situ.
One can easily see that the derived geomorphology matches that found within the deeply
incised canyon that contains Rio Zimbagua and Rio Toachi where one would expect to
find alluvial and mass movement processes.
20
N

21. Results and Discussion
Project Constraints
The original goal of this study was to develop an erosion hazards map for the Cotopaxi
Province of Ecuador. This hazards map could have been based on a soil database for the
area, landuse, vegetation map, precipitation and climate data, and geomorphology.
However due to short time available, processing speed, and the incomplete dataset
(spatial data was missing, ERDAS image needed enhancement, thematic data incomplete)
it was decided that an attempt would be made to derive a geomorphological map based
on morphometric constraints, correlated with available and derived data within a
geomorphological region that allowed a timely, somewhat complete review of the
resources made available.
A technical problem while creating a vegetation map was the resolution of the satellite
image. This resolution is lower than the resolution of the created geomorphological map.
Combining information from both maps is therefore very hard because it results in a low
resolution map. This low resolution would be transferred to all other created maps and
will thereby reduce the information value of the maps.
Morphometric Constraints Revisited
The geomorphological map as in figure 1 has only small areas with volcanic properties.
The Andes however is an active volcanic area. It could be a better classification if some
areas that are currently classified as glacial would become volcanic. The easiest solution
to this problem seems to take a higher elevation for the base of glacial morphology. The
remaining areas can than be classified as volcanic.
In retrospect, is problematic that volcanic processes can be confined to any elevation and
slope category; because volcanism might not be predicted based on geographic criteria
that influences local climate and thereby those geomorphological processes influence by
local climate. Perhaps geomorphological processes could be categorized by type
(geographic, endogenous) and then combined yielding more accurate results. Further,
since geomorphological processes can occur simultaneously, and on differing time-scales
could integrating several raster layers of differing morphometric constraints yield more
reliable results? Unfortunately, these are questions that cannot be addressed in the time
frame for this study.
21

22. Soil and Precipitation Correlation
Fortunately, data was available to could be used to correlate this morphometric study and
give some insight into the efficacy of the derivation of geomorphology via these
morphometric constraints. This correlation with soils and precipitation highlight the
deficiency of predicting volcanic regions reliably, but did indicate that other
geomorphological land units could be predicted with somewhat reliable results.
22

23. Conclusion
Though the derived unsupervised classification was found to be not always accurate,
especially within the volcanic and glaciation processes units, other derived processes
were found to correspond to the right geomorphology, especially those areas derived as
alluvial, alluvial mass movement, and mass movement. When considering
Alluvial/Aeolian it is not clear what role wind erosion plays in the Quilotoa Study area,
but at 69% soil, this landscape unit has more developing soil in situ than the other
geomorphologic layer soils.
When considering geomorphology one must always keep in mind that this science is
about competing complex processes of building up and wearing down; processes that
occur over extremely long geologic time scales; and with different geomorphological
processes occurring on the same landscape unit simultaneously.
Within the current context, we are dealing with a very young landscape where derived
concepts of surface processes that are only restricted to slope and elevation may not be
able to account for time scales that too short for these competing simultaneous processes
to express themselves completely in this limited derived setting.
23

24. References
Bibliography
Alvarado, A., Eguez, A., Yepes, H., Dart, R., Machette, M., 2003, Map of Quaternary
Faults and Folds of Ecuador and Its Offshore Regions, National Polytechnical School
Quito, Ecuador and the U.S. Geological Survey, Denver, Colorado,
http://pubs.usgs.gov/of/2003/ofr-03-289/.
IBID, 2003, Alvarado, A., Costa, C., Eguez, A., Yepes, H., Dart, R., Machette, M., 2003,
Database and Map of Quaternary faults and folds of Ecuador and its offshore regions,
National Polytechnical School Quito, Ecuador and the U.S. Geological Survey, Denver,
Colorado, http://pubs.usgs.gov/of/2003/ofr-03-289/.
Booth, B., 2000, Using ArcGIS 3D Analyst, ESRI Press, Redlands, California.
Booth, B. & Mitchell, A., 2001, Getting Started With ArcGIS, ESRI Press, Redlands,
California.
Burroughs, P.A. and McDonnell, A., 1998, Principles of Geographical
Information Systems, Oxford: Oxford University Press.
Coppus, R., 2004, working at EcoCienca: Quito, Ecuador: Personal Communication.
EcuadorExploer, 2004, Ecuador Map Center,
http://www.ecuadorexplorer.com/html/map_center.html
Easterbrook, D., 2nd Edition, 1999, Surface Processes and Landforms, Prentice-
Hall, New Jersey.
Fritz, S., 2003, Reconstruction of Late Glacial cirque and valley glaciers and modelling
changes of the Equilibrium Line Altitude with GIS software, Universiteit van Amsterdam,
Amsterdam, Netherlands.
Gregory, I., 2004, Fundamentals of GIS: What is a GIS, University of Portsmouth,
United Kingdom, http://ahds.ac.uk/documents/gis_fundamentals.ppt
McCoy, J. & Johnston, K., 2002, Using ArcGIS Spatial Analyst, ESRI Press, Redlands,
California.
Perry-Castaneda Library Map Collection, Ecuador Maps, University of Texas at Austin,
http://www.lib.utexas.edu/maps/ecuador.html.
Shaner, J. & Wrightsell, J., 2000, Editing in ArcMap, ESRI Press, Redlands, California.
24

25. Summerfield, M.A., 1996, Global Geomorphology, an introduction to the study of
landforms, Addison-Wesley Pub Company.
Tucker, C., 2002, Using ArcToolbox, ESRI Press, Redlands, California.
Turishaven, 2004, Quilotoa Volcano,
http://ww.turishaven.com/paginas/quiltoa_volcano.htm
USDA Soil Survey Staff, 1999, Soil Taxonomy, 2nd
Edition, NRCS Agricultural
Handbook 436, United States Department of Agriculture, Washington, D.C. 20402.
USGS, 2004, Ecuador Volcanoes and Volcanics,
http://vulcan.wr.usgs.gov/Volcanoes/Ecuador/Maps/map_ecuador_volcanoes.html
University of North Dakota, 2004, Volcanoes in South and Central America, Ecuador,
http://volcano.und.edu/vwdocs/volc_images/south_america/south_america.html
Verkuijlen & Seijmonsbergen, 2004, Personal Communication, Universiteit van
Amsterdam, Amsterdam.
Vienneau, A., 2001, Using ArcCatalog, ESRI Press, Redlands, California.
25

26. List of Figures
Front Page: Marcano, J.E., 2004,Cotopaxi Volcano, El Planet Tierra,
http://jmarcano.topcities.com/graficos/fotos/cotopaxi.jpg
Figure 1: Vijfhuizen, S. & Burton G., 2004, The Geomorphologic Map of Cotopaxi
Province, ArcGIS project quilotoa_geomorphology.mxd, UvA.
Figure 2: Vijfhuizen, S. & Burton G., 2004, The Geomorphologic Map of Quilotoa Study
Area, ArcGIS project quilotoa_geomorphology.mxd, UvA.
Figure 3: Vijfhuizen, S. & Burton, G., Soil Map of the Quilotoa Study Area, ArcGIS
project quilotoa_geomorphology2.mxd
Figure 4: Vijfhuizen, S. & Burton, G., 2004, Quilotoa Precipitation (transparency)/Soil
Map, ArcGIS project quilotoa_geomorphology2.mxd
Figure 5: Vijfhuizen, S. & Burton, G., 2004, Quilotoa 3D Elevation, ArcGIS project
quilotoa_view01.sxd
Figure 6: Vijfhuizen, S. & Burton, G., 2004, Quilotoa 3D Geomorphology, ArcGIS
project quilotoa_view01.sxd
Figure 7: Vijfhuizen, S. & Burton, G., 2004, Quilotoa 3D Soils, ArcGIS project
quilotoa_view01.sxd
List of Tables
Table 1: Vijfhuizen, S., 2004, Morphometric Standards for Geomorphology.
Table 2: Burton, G., 2004, Geomorphological Regions within Cotopaxi Province. Source:
Microsoft Excel spreadsheet file: cotopaxi_geomorphology.xls.
Table 3: Burton, G., 2004, Quilotoa Regions within Cotopaxi Province. Source:
Microsoft Excel spreadsheet file: quilotoa_geomorphology.xls.
26

27. List of Charts
Chart 1: Burton, G., 2004,Quilotoa Soils, Source Microsoft Excel, soils_table1.xls.
Chart 2: Burton, G., 2004, Quilotoa geomorphology, Source: Microsoft Excel,
geomorphology_table1.xls.
Chart 3: Burton, G., 2004, Alluvial Soil Types, Source: Microsoft Excel,
alluvial_soils1.xls.
Chart 4: Burton, G., 2004, Alluvial/Aeolian Soil Types, Source: Microsoft Excel,
alluvial_aeolian_soils1.xl.
Chart 5: Burton, G., 2004, Alluvial/Mass Movement Soil Types, Source: Microsoft Excel,
alluvial_massmovemnt_soils1.xl.
Chart 6: Burton, G., 2004, Mass Movement Soil Types, Source: Microsoft Excel,
massmovemnt_aeolian_soils1.xl.
Chart 7: Burton, G., 2004, Glacial Soil Types, Source: Microsoft Excel, glacial_soils1.xl.
27

28. Map Appendix
1. Vijfhuizen, S., Burton, G., Masked Elevation Grid Cotopaxi Province, ArcGIS project
cotopaxi_1.mxd.
28

29. 2. Vijfhuizen, S., Burton, G., Masked Slope Grid Cotopaxi Province, ArcGIS project
reclass_slope.mxd.
29

30. 3. Vijfhuizen, S., Burton, G., Quilotoa Geomorphology & Soils, ArcGIS project
Quilotoa_geomorphology2.mxd.
30