a comparison of the normalized difference and the tasseled cap vegetation indices: a case study of...
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A COMPARISON OF THE NORMALIZED DIFFERENCE AND THE TASSELED CAP VEGETATION INDICES: A CASE STUDY OF USING SATELLITE REMOTE SENSING IMAGERY FOR ASSESSMENT OF ENVIRONMENTAL IMPACT OF A
HYDROELECTRIC POWER PROJECT ON THE RIVER DANUBE
By
JOSEPH L. AUFMUTH
A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2001
Copyright 2001
by
Joseph L. Aufmuth
For all of the endless days and nights spent waiting; For all of those “Ground Hog Days”; For being interested in what I was doing; For believing in us; and most of all, For being my wife and friend of 20 years, this is dedicated to Marcia and our children, Kitty, Annie, Claudius, Marge, Jack, Andy and Robbie.
ACKNOWLEDGMENTS
In accomplishing this six-year project there were many individuals, familiar and
professional, who contributed support, insight and understanding. The support and
prayers of immediate and extended family reinforced the blessings given to me by God.
To individual Hungarian researchers and new friends, Lajos Horvath, Gyorgy
Büttner, Ferenc Szilagyi, and Zoltan Somogyi, as well as the following Hungarian
agencies, the North-Transdanubian Environmental Protection Authority, FOMI Remote
Sensing Center, and the Hungarian Forest Research Institute, I am deeply grateful for
your generous assistance in making available local field interpretations, research data and
satellite imagery.
For their tireless support and encouragement towards the completion of this
document, I would like to acknowledge and thank the University of Florida’s George A.
Smathers Libraries, the Government Documents Library team, and specifically,
Government Documents department chair, Jan Swanbeck.
I will remember forever the faculty, staff and students of the geomatics program
for instilling an appreciation of the elements of the survey and mapping profession and its
relationship to GIS and remote rensing. I am further indebted to my friend, and fellow
graduate student Mark Lee for helping guide my “real world” perspective on spiritual,
mental and physical health during times of trial.
The patience and guidance of committee members Dr. Bon Dewitt and Dr.
Grenville Barnes is greatly appreciated. Their commitment to excellence was an
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inspiration toward further developing my research topic, and interpreting and presenting
the data. My deepest thanks and heartfelt appreciation are reserved for committee
member and chair Dr. Scot Smith. He continually supported my academic development
and research, and most of all provided his valued friendship.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS ................................................................................................. iv
LIST OF TABLES........................................................................................................... viii
LIST OF FIGURES ............................................................................................................ x
ABSTRACT..................................................................................................................... xiii
CHAPTERS 1 INTRODUCTION ............................................................................................................1
Background and Rationale for Study.............................................................................. 1 Study Area Description................................................................................................... 5 Problem Statement and Objectives ................................................................................. 6
2 LITERATURE REVIEW ...............................................................................................10
Spectral and Spatial Resolution of the Satellite Sensor ................................................ 11 Image Rectification....................................................................................................... 12 Image to Image Normalization ..................................................................................... 13 Atmospheric Correction................................................................................................ 13 Seasonal Variability of Vegetation Canopy.................................................................. 14 Human Activities .......................................................................................................... 14 Change Detection Analysis........................................................................................... 15 Map Algebra ................................................................................................................. 15 NDVI and Tasseled Cap Vegetation Indices ................................................................ 16 Previous GBS Environmental Studies .......................................................................... 18
3 MATERIALS AND METHODS....................................................................................21
Imagery ......................................................................................................................... 24 Software and Hardware................................................................................................. 24 Image Pre-processing.................................................................................................... 24
Radiometric Correction............................................................................................. 25 Atmospheric Correction............................................................................................ 26 Geometric Correction................................................................................................ 27
Image Post-Processing .................................................................................................. 30
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Image Classification.................................................................................................. 31 Vegetation and Moisture Indices .............................................................................. 33
Statistical Analysis........................................................................................................ 34 Country Zones........................................................................................................... 34 Buffer zones .............................................................................................................. 35 Regional zones .......................................................................................................... 35 Map Algebra ............................................................................................................. 36 Statistics .................................................................................................................... 37
Precipitation Data.......................................................................................................... 39 4 RESULTS AND DISCUSSION.....................................................................................44
Image Classification...................................................................................................... 44 Anecdotal Accuracy Assessment.................................................................................. 46 Analysis of Land Cover Changes ................................................................................. 47 Vegetation and Moisture Index Images ........................................................................ 50
NDVI and GVI Images ............................................................................................. 51 WI Images................................................................................................................. 54
Observations and Comparisons..................................................................................... 56 Precipitation Comparisons ........................................................................................ 56 Control NDVI and GVI Observations and Comparisons ......................................... 57 Control WI Observations .......................................................................................... 59 Control GVI and WI Trends ..................................................................................... 60 Control and Study Area Forest Index Value Comparison ........................................ 61 Control and Water Buffer Distance Comparison...................................................... 62 Control Comparison Summary ................................................................................. 64 Study Area Indices by Buffer Distance .................................................................... 64 Study Area Indices By Land Cover .......................................................................... 68 Study Area Indices by Region .................................................................................. 69 Study Area Indices by Country................................................................................. 71
5 CONCLUSIONS.............................................................................................................73
LIST OF REFERENCES...................................................................................................76
BIOGRAPHICAL SKETCH .............................................................................................82
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LIST OF TABLES
Table Page 1 Tasseled Cap Multiplicative Matrix....................................................................................17
2 Tasseled Cap Additive Matrix. ...........................................................................................17
3 Previous Rescaled NDVI Values (Smith, et al, 2000). ......................................................21
4 Meteorological Conditions in the Study Area at Time of Satellite Overpass.....................25
5 1988 Rectification Parameters. ...........................................................................................28
6 Visual Interpretation Standard. ...........................................................................................32
7 Digital Numbers by Image Band for a Sample pixel..........................................................34
8 Software vs. Hand Calculated Tasseled Cap Values. .........................................................34
9 An Example of 1988 Histogram Weighted GVI Means (WTMN) by Zone. ....................38
10 Regional Monthly Precipitation Values Surrounding Image Dates.................................43
11 Average Monthly Precipitation Prior to Image Date. ......................................................43
12 Study Area Land Cover Classes (ha and percent cover), 1988-1997. .............................45
13 Szigetköz Region Land Cover Classifications.................................................................45
14 Land Cover Classification Comparisons. .........................................................................47
15 Annual Precipitation for the Water-Years 1989 to 1997. ................................................56
16 Mean Weighted GVI and NDVI Values. .........................................................................59
17 Histogram Weighted Mean Study Area Forest Indices to Control Indices Correlations.61
18 Vegetation Index to Wetness Index Correlations by Year...............................................67
19 Index Correlations Across Regions. ................................................................................71
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20 Correlations Within Year and Across Regions. ...............................................................71
21 Mean Weighted Index Values..........................................................................................72
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LIST OF FIGURES
Figure Page 1 Hungarian-Slovakian Danube River Border and Study Area Location..............................2
2 GBS System and the Surrounding Region..........................................................................4
3 Flow Rates of the Old Danube at Rajka, m3/s (Smith et al., 2000). ...................................7
4 Water Levels at Rajka 1992 to 1998 (Smith et al., 2000)...................................................8
5 Control and Study Area Locations......................................................................................22
6 Northern Controls 1988, 1992, 1993, and 1997..................................................................23
7 South-Central Controls 1988, 1992, 1993, and 1997..........................................................23
8 Southern Controls 1988, 1992, 1993 and 1997...................................................................23
9 1997 Pre-Haze Reduction Cloud Cover and Shadow. .......................................................26
10 1997 Post Haze Reduction Image. ...................................................................................27
11 1988 False Color Composite (TM Bands 4,3,2). .............................................................29
12 1992 False Color Composite (TM Bands 4, 3, 2). ...........................................................29
13 1993 False Color Composite (TM Bands 4, 3, 2). ...........................................................30
14 1997 False Color Composite (TM Bands 4, 3, 2). ...........................................................30
15 Study Area Land Cover Classifications. ..........................................................................33
16 Country Statistical Zones. ................................................................................................35
17 Water Buffer Statistical Zones..........................................................................................35
18 Regional Statistical Analysis Zones.................................................................................36
19 1988 Map Algebra and Zonal Image Creation. ...............................................................37
x
20 Zonal Statistics Image Example, 1988.............................................................................37
21 MSN Excel's Linear Correlation Formula. ......................................................................39
22 Precipitation Recording Stations......................................................................................40
23 1988 Monthly Precipitation Totals. .................................................................................40
24 1992 Monthly Precipitation Totals. .................................................................................41
25 1993 Monthly Precipitation Totals. .................................................................................41
26 Water Year Precipitation Totals.......................................................................................42
27 Pre- and Post-Danube Diversion, 1992 and 1993, Respectively. .....................................44
28 Percent Land Cover Composition by Year. .....................................................................46
29 1993 Weir Construction...................................................................................................49
30 1997 Post Weir Construction. ..........................................................................................49
31 Study Area and Control Example Image Locations. .......................................................50
32 Sample Study Area NDVI Images 1988, 1992, 1993 and 1997. .....................................51
33 Sample Study Area GVI Images 1988, 1992, 1993 and 1997. ........................................52
34 Southern Control Area NDVI Images 1988, 1992, 1993, 1997.......................................52
35 Southern Control Area GVI Images 1988, 1992, 1993, 1997. ........................................53
36 Sample Study Area WI Images 1988, 1992, 1993 and 1997. ..........................................54
37 Southern Control Area WI Images 1988, 1992, 1993, 1997. ..........................................55
38 Mean Control Area NDVI per Year.................................................................................58
39 Mean Control Area GVI per Year. ..................................................................................58
40 Yearly Mean Control WI Levels......................................................................................60
41 Mean NDVI, GVI and WI Comparison...........................................................................60
42 Yearly Control GVI Means..............................................................................................62
43 Mean Control and Weighted Mean Forest WI Values.....................................................62
44 Mean Control, Weighted Mean Forested and Buffer Distance Forested GVI Values.....63
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45 Mean Control, Weighted Mean Forest and Buffer WI Values. .......................................64
46 GVI Means by Buffer Distance from Water....................................................................65
47 WI Means By Buffer Distance From Water. ...................................................................65
48 1988 Mean GVI vs. WI By Buffer Distance To Water. ..................................................66
49 1992 Mean GVI vs. WI By Buffer Distance To Water. ..................................................66
50 1993 Mean GVI vs. WI By Buffer Distance To Water. ..................................................67
51 1997 Mean GVI vs. WI By Buffer Distance To Water. ..................................................67
52 Mean Water Class Index Values......................................................................................68
53 Mean Forest Class Index Values......................................................................................68
54 Mean Grass Class Index Values. .....................................................................................69
55 Mean Exposed Class Index Values..................................................................................69
56 Upper Region Mean Index Values...................................................................................70
57 Middle Region Weighted Mean Index Values.................................................................70
58 Lower Region Weighted Mean Index Values..................................................................70
59 Index Correlations between Countries.............................................................................72
xii
Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science
A COMPARISON OF THE NORMALIZED DIFFERENCE AND THE TASSELED CAP VEGETATION INDICES: A CASE STUDY OF USING SATELLITE REMOTE SENSING IMAGERY FOR ASSESSMENT OF ENVIRONMENTAL IMPACT OF A
HYDROELECTRIC POWER PROJECT ON THE RIVER DANUBE
By
Joseph L. Aufmuth
May 2001 Chairman: Dr. Scot E. Smith Major Department: Civil and Coastal Engineering
In the fall of 1992, water from a section of the Danube River border between
Hungary and Slovakia was diverted to an adjacent hydroelectric power system known as
the Gabcikovo Barrage System (GBS). Originally, hydropower production in the
common reach was a joint goal of the two countries. In 1989, Hungary voided the
original 1977 agreement, sighting potential adverse environmental effects. Slovakia
finished the project alone and the conflict was brought before the International Court of
Justice.
This study focused on a part of the Szigetköz (Hungarian side) and Csalloköz
(Slovakian side) regions along the Danube that are located between two flood dikes. This
area contains a unique wetland system with numerous river branches immediately
downstream from the diversion. Four single remotely sensed late summer Landsat
satellite images from 1988, 1992, 1993, and 1997 were used to detect, measure and
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compare changes in vegetation cover and condition with environmental moisture
throughout the study area and three regional forested controls. The 1988 and 1992
images represented pre-diversion conditions, and the 1993 and 1997 images represented
post-diversion conditions.
Integrated supervised and unsupervised satellite image classification techniques
produced 4 study area land cover classes; water, forest, grass, and exposed. Average
regional monthly and annual rainfall records were collected. For each study area and
control image, satellite derived Normalized Difference Vegetation Index (NDVI) and
Tasseled Cap greenness (GVI) were used to indicate plant condition and Tasseled Cap
wetness (WI) was used to quantify environmental moisture. Three separate zonal images,
country, water buffer distance, and region, were created for study area analysis. Map
algebra was used to combine the 3 separate zonal images with each of the 4 land cover
class images, which resulted in four new, 144 zone images. The mean NDVI, GVI and
WI value per unique analysis zone were calculated and weighted by the number of pixels
per zone, or histogram. Mean weighted index values for the forested study area and
water buffer zones were compared to forested control index values and average rainfall.
Additional mean histogram weighted zonal NDVI, GVI and WI relationships were
investigated for the study area.
Results showed study area and control WI patterns were similar and followed
rainfall patterns. Higher average rainfall corresponded with lower WI values. WI values
for the study area were higher than controls, except for 1988, but exhibited the same
trends as controls. Control NDVI and GVI values were higher than or equal to study area
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values. For study and control areas NDVI and GVI values, and NDVI and WI values
were highly correlated.
This study concluded that a number of important, but limited, environmental
changes are detectable from the satellite imagery and so the imagery provides a suitable
means to monitor future changes in the region.
xv
CHAPTER 1 INTRODUCTION
A hydroelectric dam located in Slovakia, near the Danube River border with
Hungary, was completed in 1993. It’s commissioning resulted in domestic and
international concern over its potential environmental impacts. Since the project diverts
water from the two countries’ shared river border, much of the Hungarian concern was
over downstream impacts to a large forested wetland system, the Szigetköz.
This thesis examined the validity of some of those concerns using Landsat TM
imagery as its basis. The study was part of a larger effort to understand the dam’s
environmental impact in its entirety. This particular research effort focused on forested
wetland composition changes and vegetation response related to moisture conditions in
the area surrounding the Danube.
Background and Rationale for Study
As a result of the 1920 Trianon Treaty, the Danube forms the international
boundary between Hungary and Slovakia (formerly Czechoslovakia) (Figure 1). There
are 33 hydroelectric dams on the Danube from its headwaters in Germany to its delta in
Romania (ICPDR-PCU, 2000), but none previously resided in the portion of the river
shared by Hungary and Slovakia.
1
2
Figure 1 Hungarian-Slovakian Danube River Border and Study Area Location.
In October of 1992, along a 58 kilometer (km) portion of the Danube River
between Rajka in Hungary and Sap in present day Slovakia, Czechoslovakia diverted
85% of the water from the river into a 27 km long concrete hydroelectric power channel.
This was one of only a few instances in modern history when an upstream country
unilaterally diverted a major river representing a natural border between countries (Smith
et al., 2000, Kurland et al., 1992). Other major ongoing international water disputes
involving dams exist between Iraq, Iran, Turkey, and Syria over the Tigris and Euphrates
River (Trade and Environment Database, 2000), and between Laos, Thailand, China,
Cambodia, Vietnam, and Myanmar, over the Mekong River (Samson and Charrier,
1997). However, the situation in Hungary and Slovakia caught international attention
when it ended up as the first environmental lawsuit before the International Court of
Justice ( aka the World Court) (International Court of Justice, 1997). As water becomes a
scarcer commodity these types of conflicts are very likely to escalate in the future (Smith
and Al-Rawaby, 1990).
3
Under provisions of a treaty agreed to in 1977, the construction and operation of a
two dam system for hydropower production, the Gabcikovo-Nagymaros Barrage System
(GNBS), was a joint agreement between Hungary and Czechoslovakia. One major dam
was built at Gabcikovo in eastern Czechoslovakia and the other would have been built
downstream at Nagymaros, Hungary. The entire project, presently known as the
Gabcikovo Barrage System (GBS), had four major objectives: (i) hydroelectric power
production (ii) improved navigation on a reach of the Danube (iii) reliable upstream
water supply and, (iv) economic development. In this region, flood dikes protect the
adjacent lands from the Danube on the Hungarian and Slovakian sides. The dikes
enclose a temperate forested flood plane called the Szigetköz on the Hungarian side and
the Csalloköz (Zitny Ostrov in Slovakian) on the Slovakian side. A map of the GBS and
surrounding region is shown in Figure 2.
In May 1989, a controversy arose after Hungary, stating concerns over economic
viability and potential environmental impacts, suspended project participation. In June,
citing the need for further studies, the Hungarians stated “Having studied the expected
impacts of the construction in accordance with the original plan, the Committee [ad hoc,
set up for this purpose] of the Academy [Hungarian Academy of Sciences (HAS)] came
to the conclusion that we do not have adequate knowledge of the consequences of
environmental risks. . . . Thorough and time consuming studies are necessary" (ICJ,
1997).
4
Figure 2 GBS System and the Surrounding Region.
On May 19, 1992 Hungary terminated the 1977 Treaty. Czechoslovakia
continued constructing a variation of the original plan, not requiring Hungarian
participation and, in 1996, Slovakia, now independent from the Czech Republic,
completed the project.
The Hungarian and Slovakian governments jointly brought the case before the
ICJ. The Hungarian government's position was that
�� Insufficient engineering and environmental studies were performed before the
construction of the GBS;
�� The source of drinking water for Budapest is located in the gravel plateau of the
Szigetköz region;
�� A valuable forested wetland in the river's floodplain might be adversely impacted;
5
�� A potential ground water level decrease might cause a decline in local agricultural
production;
�� International law and policy issues regarding trans-boundary biodiversity were in
doubt (Dobson, 1992, Chelminski, 1993).
The Slovakian government's position was that
�� Adequate assessment of the project's environmental side effects took place.
�� The potential environmental impacts were outweighed by the benefits afforded by
flood protection, hydroelectric power production and improved navigation.
On September 25, 1997, the ICJ concluded that, while it was illegal for Hungary
to break the formal 1977 agreement to jointly build the GNBS, it was also illegal for
Czechoslovakia to divert the Danube unilaterally in 1992 and that the 1977 Treaty was
transferable from Czechoslovakia to Slovakia. The judgment obligates both parties to
take all necessary measures to ensure the achievement of the objectives of the September
16, 1977 Treaty and, according to the Court “the Parties together should look afresh at
the effects on the environment of the operation of the Gabcikovo power plant”
(International Court of Justice, 1997). A headline in the New York Times summed up
the decision: “World Court Leaves Fight Over Danube Unresolved” (Perlez, 1997).
Study Area Description
The study area shown in Figure 2, falls into the common Hungarian and
Slovakian stretch of the Danube located between Rajka and Sap. The area includes the
floodplain situated between the flood protection dikes in this river stretch. The study area
belongs to the 232,700 hectares (ha) Little Lowland Unit. The Little Lowland contains
6
two sub-areas, the 52,700 ha Hungarian Szigetköz Region and the 180,000 ha Slovakian
Csalloköz Region. The total 10,100 ha study area represents 4.3 percent of the Little
Lowland Unit. Of the Total, 5,200 ha of the study area cover approximately 10 percent of
the Hungarian Szigetköz and the remaining 4,900 ha cover approximately 3 percent of
the Slovakian Csalloköz.
One of the most important environmental factors in this region is the groundwater
level. The forestry and the agriculture of the Szigetköz’s Little Lowland Unit are based
on a very thin soil layer. A deep gravel bed, which has high water conductivity, exists
under this soil layer and forms one of Europe's largest freshwater reservoirs.
The study area delineation was based on it being directly downstream of the
diversion, having important environmental and commercial floodplain forests and
occurring between flood control dikes along the entire length. The area is a mixture of
natural wooded wildlife habitat and forestry tree plantations that were first established at
the end of the 1800s. Commercial forestry operations consisting of thinning, clear
cutting and selective harvesting are conducted throughout the region in both Hungary and
Slovakia.
Problem Statement and Objectives
Electrical demand governs continued operation of the dam for hydro-electric
power. Peak and off-peak electrical demand results in repeated cycles of river diversion
or flow. The continued Danube river diversion results in fluctuations in hydro-period, or
changes in the amount and duration of water in the forested wetland system. The hydro-
period fluctuations are hypothesized to cause quantifiable changes in vegetation condition
within the forested Szigetköz and Csalloköz regions and consequently, changes in land
7
cover composition. In fact, reduced and erratic water flow downstream of the diversion
has been observed during and after diversion to the GBS power channel, (Figure 3 and
Figure 4).
Hungary’s North-Transdanubian Environmental Inspectorate (NTDEI) provided
Figure 3 and Figure 4. The tabular data used to create the figures was not available for
this research. Trends prior to 1992 are not represented. The NTDEI provided an
interpretation of their data and figures, which concluded: 1) After the diversion in late
1992, flow rates dropped until 1995 when lateral channel weirs were constructed to
increase water supply (Figure 3); 2) In late 1992, a 4 m post diversion drop in water level
occurred at Rajka (Figure 4); and 3) Due to a 1995 agreement between Hungary and
Slovakia, less water was diverted from the Danube and water levels at Rajka increased
~300-400 m3/s (Figure 4).
1000
2000
3000
1992 1993 1994 1995 1996 1997
Diversion Water supply To Laterals
Figure 3 Flow Rates of the Old Danube at Rajka, m3/s (Smith et al., 2000).
8
Diversion 11/92
Figure 4 Water Levels at Rajka 1992 to 1998 (Smith et al., 2000).
In order to quantify forest vegetation responses solely due to water diversion,
naturally occurring environmental moisture conditions, such as precipitation or drought,
should be identified in forested control areas away from the diversion. Control area
vegetation response associated with naturally occurring moisture conditions should also
be measured. Simultaneous measurement of study area moisture condition and
vegetation response and comparison with control measurements would help identify
moisture related affects strictly due to the diversion. Monitoring studies such as these are
vital to implementing the ICJ decision.
Using Landsat Thematic Mapper (TM) imagery, this study compared control and
study area vegetation indices, Normalized Difference Vegetation Index (NDVI) and
Tassel Cap Greenness (GVI), with a moisture index, Tasseled Cap Wetness (WI), for pre-
and post-dam periods. Specifically, the three research objectives were: 1) Detect and
compare pre and post diversion land cover composition; 2) Detect control and study area
9
plant condition and hydro-period change using NDVI, GVI and WI as estimators of
vegetation condition and environmental moisture respectively; and 3) Compare and
potentially correlate control and study area vegetation and moisture indices and 4)
Identify trends in GVI and WI.
The research questions asked were as follows:
�� Are WI values, as a measure of surface moisture, similar to precipitation patterns?
�� Are study area forest NDVI, GVI and WI patterns similar to Control areas?
�� Are GVI and NDVI correlated?
�� Are there correlations between WI and NDVI or GVI?
�� Are effects detectable by 1) Distance to Water, 2) Land Cover Class, 3) Region, or 4)
Country.
CHAPTER 2 LITERATURE REVIEW
Vegetation change, specifically loss of tree canopy due to changing
environmental conditions, is a global concern (Forseth, 1997, Fisher and Levine 1999,
Petch and Kolajka, 1993, Pineda, 1992, Anonymous, 1993, Erlich and Wilson, 1991,
Lauver and Whistler, 1993). The change can be a result of natural disasters, infestations
or societal alterations of the environment. As environmental conditions (surface water,
hydrology, soil moisture, nutrients, weather, etc.) change, vegetation can become stressed
(Lichtenthaler, 1996). Sufficient and constant stress resulting in extreme plant cellular
structure and chlorophyll change can eventually lead to plant mortality (Forseth, 1997,
Kay, 1991).
Measurable reflected sunlight energy (reflectance) of a plant, both visible and
near infrared wavelengths is based upon its chlorophyll content and cellular structure
(Lillesand and Kiefer, 1994). In response to environmental conditions, change in the
plant’s physiology (i.e. cellular structure and chlorophyll content) affects the measurable
reflectance values. Consequently, as surrounding environmental conditions change, the
reflected sunlight energy from the earth’s surface changes. Since remote sensing
satellites record reflectance values, remote sensing is an ideal tool for detecting and
quantifying environmental and vegetation health change (Muchoney and Hacck, 1994,
van Leeuwen and Huete, 1996, Todd and Hoffer, 1998, Todd et al., 1998, Gao et al.,
2000, Serrano et al., 2000).
10
11
Several factors influence the quantification of plant health and land surface
condition using satellite imagery. The DNs from bands of a satellite image represent an
instantaneous reflectance of ground features and environmental conditions. The values
contributed by plants are a result of cyclic environmental interactions that occur
throughout its life span. Trying to relate any one instantaneous measurement with the
current vegetation condition is a difficult task. A plant's elastic response to
environmental change has been well documented (Lichtenthaler 1996). An elastic
response is a mechanism of evolution, which allows genetic survival during times of
stress. A slow response allows compensation for slight changes that, if environmental
conditions persist, culminates in a response such as senescence, or ultimately, death. A
single observation may be the culmination of a series of daily, monthly, or yearly events
and trends. Correlation of these trends with environmental conditions requires numerous
and consistent field observations and data sets. Measuring plant response and vegetation
change directly related to environmental conditions are governed by a satellite sensor’s
spectral and spatial resolution, percent cloud cover, image rectification, image to image
normalization, seasonal variability of vegetation canopy, and human activities.
Spectral and Spatial Resolution of the Satellite Sensor
When this study was started, the highest combined spectral and spatial resolution
available to civilians was Landsat Thematic Mapper (TM5) satellite imagery. TM5 has 7
spectral bands and a nominal pixel size of 28.5m x 28.5m. The TM image is typically re-
sampled to 30m x 30m and sometimes 25m x 25m (Jensen 1996). The spectral
sensitivity of TM’s charge-coupled device (CCD) helps detect environmental and
12
vegetation reflectance. The CCD’s spatial resolution influences the recorded digital
number (DN). The DN of the 30m x 30m pixel represents a mixed reflectance of sub-
pixel elements contained in the corresponding 30m x 30m ground area. For example, a
small house (70ft x 26ft or 22m x 8m) and the surrounding landscape with varying
canopy sizes all contribute different reflectance values to the individual 30m x 30m pixel.
Urban examples such as this one or other highly heterogeneous systems produce mixed
reflectance values (Jensen, 1981). Therefore, it is desirable to study large homogeneous
vegetation and land use areas to avoid mixing of sub-pixel elements. However, changes
to be studied frequently occur along habitat and land use edges where reflectance values
can be influenced by the surrounding pixels (Todd and Hoffer, 1998).
Image Rectification
While it is possible to interpret, analyze and classify remotely sensed data without
image rectification, assigning a known coordinate system to the image aids in relating
recorded field data coordinates to image coordinates and performing accuracy
assessments. In order to perform change detection analysis, images must be co-registered
to each other, or have a common coordinate system. Digital number values are affected
by the method of rectification, i.e. nearest neighbor, bilinear interpolation or cubic
convolution (Smith, et. al 1995). The success of change detection analysis is directly
related to the positional correlation between multi-date images and it is reflected in the
root mean square error (RMSE), of the two images. RMSE is a measure of residual
errors, or deviations, produced during column/row to map X,Y coordinate transformation.
Rectification is critical in “change detection” studies, especially when the image dates to
13
be compared vary in location by a pixel or more and the change occurs along a narrow
edge (Jensen, 1996).
Image to Image Normalization
Equally important in assessing vegetation changes is normalizing, or histogram
matching of multi-date imagery. Temporally, digital numbers between image dates can
vary at the same location due to sun angle, atmospheric conditions and satellite
radiometric quality (Jensen, 1996). By matching histograms of very bright or very dark
areas in one image to the same areas in another image, multi-date image variability can
be adjusted (Chavez, and MacKinnon, 1994.).
Two different radiometric normalization methods are widely accepted: "dark and
bright objects" (DBO) and pseudo invariant features (PIF) (Jensen 1981, Lillesand and
Kiefer, 1994). Both methods are based on statistical invariance of certain scene elements
and use linear functions for scene normalization. The DBO method uses statistics of
features having time independent reflectance. The PIF method relies on man made in-
scene elements present in urban areas (Schott et al., 1988). Other methods have been
developed by image processing software developers. ERDAS Imagine offers a histogram
matching algorithm (Erdas Field Guide 4th Ed.)
Atmospheric Correction
Cloud cover affects not only the ability to sense what is beneath the clouds using
certain bands, but can also increase the DN values of adjacent pixels. Clouds scatter light
thereby increasing brightness measured from ground features. Shorter wavelengths, such
as TM 5 bands 1, 2, and 3 (blue, green, and red) scatter more than longer wavelengths
14
(Jensen, 1996, Lillesand and Kiefer, 1994). Consequently, adjacent pixels can be
affected because clouds often have gradations of vapor moisture leading to the cloud
center. Additionally, cloud shadow may be cast further from the adjacent pixels, thereby
darkening the brightness component other pixels. Song et al. (2001) recommended dark
object subtraction with or without further atmospheric correction or relative atmospheric
correction for classification and change detection applications.
Seasonal Variability of Vegetation Canopy
When performing change detection analysis, the seasonal variability of canopy
cover can be accounted for by performing the analysis at a consistent time period across
multiple years (Schiever and Congalton, 1995). By performing canopy analysis during
full leaf out, change should be apparent. However, if climatic conditions have been
severe (i.e. extreme drought, flood, cold, or heat) and not measured or normalized,
natural climatic events may be confused with artificially induced environmental canopy
change (Lambin, 1996). A plant species diversity study in California concluded weather
variables account for the bulk of the diversity patterns in the models used and that mean
weather variables are generally more important than seasonality or irregularity
(Richerson and Lum, 1980). Campbell et al., (2001) reported that available moisture
during dry summers was the environmental variable that limits forests the most.
Human Activities
Lastly, human activities can directly impact the ability to assess environmental
and vegetation changes. According to Anderson (1991), the natural soil-topography-
15
vegetation wetness system can be disturbed by human intervention in the form of canopy
cover changes and artificial drainage. In areas where trees have died, forestry practices
often cull dead trees for paper/pulp or firewood. Forestry practices may also reintroduce
new trees to replace those removed (Sader, 1995). Other effects on change detection
studies may result from efforts to correct the original environmental alterations that
caused the vegetation stress, such as adding weirs to retain water.
Change Detection Analysis
There are several approaches to measuring environmental and tree canopy
change. One involves image classification techniques (unsupervised, supervised, cluster
busting, accuracy assessment, etc.) and others involve band ratio or indexing techniques
such as normalized vegetation index (NDVI) or Tasseled Cap (Bauer, et al 1994). With
either technique, the final step is a cross year comparison of classified or index results
and a quantification of change (Green et al.,, 1994). Many studies utilizing satellite
imagery for monitoring vegetation change have been conducted and the literature
synthesized (Van Niel, 1995).
Map Algebra
Map algebra (Tomlin, 1983) refers to calculating new spatial data from the
interaction of two or more existing layers. This study used map algebra to calculate
vegetation and moisture indices from multiple bands of satellite imagery and create
geographical zones for statistical analysis. Four cartographic modeling operations,
focalfunction, incrementalfunction, local function, and zonal function, were described by
16
Tomlin (1990). Of the four operations, the zonalfunction uses a secondary layer to create
zones for statistical analysis of a primary, or first layer. This study applied the
zonalfunction using the index values as primary layers and the geographic zones as the
secondary layer.
NDVI and Tasseled Cap Vegetation Indices
NDVI (Rouse et al., 1974) is a ratio of reflectance bands and has been found to be
an accurate and reliable means of detecting vegetation health or vigor (Kidwell 1990) and
was reported by Tucker (1979) to respond to green biomass changes. The NDVI ratio is
expressed as NDVI = (IR-R)/(IR+R); where IR is a near infrared band (band 4 in TM5)
and R is the visible red band (band 3 in TM5) (Rouse et al., 1974). Thus, the equation
this study used to calculate NDVI for the TM imagery is NDVI = (TM4-
TM3)/(TM4+TM3). NDVI values range between -1 and 1. In theory, healthy, or
vigorous plants have turgid cells and high chlorophyll. Because of the turgid cell
structure, more IR (band 4) is reflected and the high chlorophyll content absorbs more
red, i.e. less band 3 reflectance (Jensen, 1996, Lillesand and Kiefer, 1994).
The “Tasseled Cap” transformation (Kauth and Thomas, 1976) has been found to
be an accurate and reliable means of detecting vegetation health (“greenness”),
environmental condition (“brightness” and “wetness”) and atmospheric condition (haze)
(Crist and Cicone, 1984, Crist and Kauth, 1986, van Leeuwen and Huete, 1996, Todd and
Hoffer, 1998). Tasseled Cap wetness has been used to stratify TM image ratios and
accounted for 78% of the variation in canopies (Kushla and Ripple, 1998). Results of
Serrano et al., (2000) indicated the landscape scale sensitivity of WI to variations in
17
canopy relative water content. The Tasseled Cap transformation multiplies a band’s pixel
DN by a band’s specific constant and adds an index specific factor to the sum of the
index for all bands (Crist and Cicone, 1984, Crist and Kauth, 1986). Multiplicative and
additive Tasseled Cap constants are presented in Table 1 and Table 2 (Crist et al., 1986,
ERDAS Field Guide).
Table 1 Tasseled Cap Multiplicative Matrix. Tasseled
Cap Index
TM Band1
TM Band2
TM Band3
TM Band4
TM Band5
TM Band6
TM Band7
Brightness 0.2909 0.2493 0.4806 0.5568 0.4438 0.0000 0.1706Greenness -0.2728 -0.2174 -0.5508 0.7221 0.0733 0.0000 -0.1648Wetness 0.1446 0.1761 0.3322 0.3396 -0.6210 0.0000 -0.4186Haze 0.8461 -0.0731 -0.4640 -0.0032 -0.0492 0.0000 0.0119Other1 0.0549 -0.0232 0.0339 -0.1937 0.4162 0.0000 -0.7823Other2 0.1186 -0.8069 0.4094 0.0571 -0.0228 0.0000 -0.0220
Table 2 Tasseled Cap Additive Matrix. Brightness Greenness Wetness Haze Other1 Other2
Scale 10.3695 0.7310 -3.3828 0.7879 -2.4750 -0.0336
The matrix formulas for Greenness and Wetness are
Greenness = (-0.2728(Band1) + -0.2174(Band2) + -0.5508(Band3) +
0.7221(Band4) + 0.0733(Band5) + 0.0(Band6) + -0.1648(Band7)) + -0.7310
Wetness = (0.1446(Band1) + 0.1761(Band2) + 0.3322(Band3) + 0.3396(Band4)
+ -0.6210(Band5) + 0.0(Band6) + -0.4186(Band7)) + -3.3828
18
The single most equivalent spectral index study reported to date is Todd and
Hoffer (1998). Their mathematically modeled study compared NDVI, and Tasseled Cap
GVI, WI, and brightness (BI) values calculated from composite reflectance (soil and
green vegetation) value estimates. Vegetation and soil spectral reflectance curves from
Hoffer (1978) and Bartolucci (1977) were used to develop the estimates. Simulated DN
values were calculated to 8-bit data, thereby simulating TM data. In the study, Todd and
Hoffer used estimates of percent vegetation cover (100%, 80%, 50% and 20%), and soil
types (silt, sand and clay) at two moisture levels to compare NDVI and GVI to BI and
WI. They also compared NDVI and GVI to percent change in green vegetation cover.
GVI varied little at 80% cover for both moisture levels. NDVI values were higher for the
more moist soils. Todd and Hoffer concluded that GVI was less affected by variation in
soil type and moisture when predicting green vegetation cover. They also found
substantial increases in NDVI with increases in WI. The study’s discussion of results
suggests high wetness values are produced by absorption of mid-infrared (TM bands 5
and 7) reflectance in healthy green vegetation. The study concludes the index results are
representative of homogenous canopies and heterogeneous plant canopy application is
untested. This further demonstrates the need for quantifying the affect on vegetation
indices of a landscape feature’s scale, distribution, condition and diversity, as well as
environmental condition.
Previous GBS Environmental Studies
Scientific literature describing the environmental impacts of the GBS diversion in
scientific journals is sparse. Newspaper and magazine articles, papers filed in the World
19
Court case and lengthy discussions on both sides of the controversy abound in informal
forums such as the Internet due to the highly politically charged nature of the subject.
Scientific research is still in progress and unpublished in peer reviewed international
scientific literature, except Smith et al., 2000. Several reports on the topic have been
published by the Hungarian Academy of Sciences (CEC Working Group, 1993; HAS
Working Group, 1993, HAS Working Group, 1994).
The only publication written in English by Slovakian scientists and available to
previous studies was “Dams in Slovakia” by Abaffy et al., (1995). This book has a
detailed description of the GBS with detailed schematics and specifications. Cleminski
(1993) published an article describing the GBS in general terms as well as other dams
along the Danube. A 1999 private consulting publication supporting the Slovakian
claims appeared on the web, “Visit to The Area of The Gabcikovo Hydropower Project”
(Mucha et al., 1999). Petch and Kolejka (1988) presented a regional skeleton of
ecological stability of Slovakia based on Miklos (1988), which portrays the area as the
most threatened linear feature and an ecologically important landscape area outside the
main territorial system.
Two previous analyses of regional satellite data for the study area have been
performed. Both focused on generalized analysis of the area’s vegetation composition
and non-weighted mean NDVI for the study area and some controls. The first research
project, conducted at the Hungarian Center for Remote Sensing (FOMI), used 1992, 1993
and 1994 TM imagery to map vegetation composition in the region between the Danube
dikes (Smith et al., 1996). The August 1992 imagery represented the region’s pre-
diversion status and August 1993/1994 the post diversion status. Cloud cover obscured
20
the 1994 image and vegetation was not classified. The second project, conducted at the
University of Florida, expanded the 1996 study to include September1988 and September
1997 TM imagery (Smith et al., 2000).
CHAPTER 3 MATERIALS AND METHODS
The methods used in this thesis surpasses the image processing and analysis
methods employed during an initial Joint US-Hungarian Fund project conducted shortly
after the Gabcikovo Dam was commissioned (Smith et al., 1996). A subsequent
publication concerning two additional satellite image dates (Smith et al., 2000) was also
used. Pre and post processing of Smith’s image dates, 1988 and 1997, was a prerequisite
to this thesis. Both previous studies focused on generalized analysis of the area’s
vegetation composition and non-weighted-mean, rescaled NDVI values (0 to 255) for the
study area and selected controls, Table 3.
Table 3 Previous Rescaled NDVI Values (Smith, et al, 2000). Zone/Year 25 ma 50 ma 100 ma 200 ma All foresta
( a ) Upper zone 1988 169.5 178.2 182.2 182.2 181.0 1992 191.4 195.4 198.3 199.8 200.7 1993 161.9 169.4 175.0 177.7 179.2 1997 181.3 186.7 189.4 190.1 188.8
( b ) Middle zone 1988 166.0 176.7 181.5 185.1 183.0 1992 194.0 197.9 200.8 202.2 202.9 1993 159.4 166.7 172.5 175.3 177.0 1997 183.2 189.6 191.9 192.7 191.7
( c ) Lower zone 1988 159.8 173.1 184.5 189.0 185.4 1992 193.0 196.9 199.9 201.0 202.4 1993 160.0 168.6 174.6 176.4 177.6 1997 183.6 189.7 192.7 192.6 191.5
a Denotes distance from image classified left and right banks of the 1992 Danube River.
21
22
The methods of this thesis differ from the previous studies in that: 1) two
additional indices, GVI and WI, were evaluated; 2) in order to detect subtle changes,
floating point index values were calculated and not rescaled; 3) WI values were
compared to precipitation trends; 4) mean index values were weighted by histogram and
compared. Also, three new control areas were chosen because previous studies’ control
boundaries were unavailable. Controls were chosen to measure regional forest vegetation
index values and non-diversion related moisture index values. Figure 5 shows the
location of the study area and the new controls, Northern (N), South-Central (SC) and
Southern (S). Figure 6 to Figure 8 depict control area conditions. In general, controls
exhibited visual characteristics similar to forested portions of the study area and controls
appeared similar across all image dates. Controls were presumed to be isolated from
potential river diversion related effects.
Figure 5 Control and Study Area Locations.
23
1988 1992 1993 1997
Figure 6 Northern Controls 1988, 1992, 1993, and 1997.
1988 1992 1993 1997
Figure 7 South-Central Controls 1988, 1992, 1993, and 1997.
1988 1992
1993 1997
Figure 8 Southern Controls 1988, 1992, 1993 and 1997.
The methods were developed to preprocess and classify satellite images according
to 4 land cover types (Water, Forest, Grass and Exposed), calculate NDVI, GVI and WI
indexes for all image years, create a zonal statistics image, compare precipitation patterns
and WI values, and lastly, compare mean-histogram-weighted NDVI, GVI and WI study
zonal values to control zonal values.
24
Imagery
TM scenes taken during the same seasonal time period were used to compare
vegetation regimes between years at approximately the same growth stage. A time series
of five TM images taken in August and September between 1988-1997 were obtained.
The 1988 and 1992 images were taken prior to diversion of the Danube, whereas the
other three images were taken post diversion. Due to excessive cloud cover and haze
over the northern portion of the 1994 study area image, it was not used in the analysis.
Similarly, cloud cover was limited to isolated regions in the northern portion of the 1997
image; however the cloud cover did not prohibit the use of the image.
Software and Hardware
Image processing was conducted using algorithms supplied with ERDAS’
Imagine image processing software (v. 8.3.1). Imagine’s inability to perform raster zonal
summaries on continuous floating-point data, required the use of additional analysis and
graphic display software. For post processing map algebra functions, Environmental
Science Research Institute’s (ESRI) Arc/Info (v 7.2.1) GRID software was chosen. Due
to the number of images and variables analyzed, processing models and analysis
automation programs were written. Microsoft EXCEL 2000 was used to calculate linear
index correlations.
Image Pre-processing
Standard image pre-processing techniques were applied to all images. Satellite
images taken at different dates were normalized to account for different acquisition
25
parameters (e.g., sun angle and sensor response) and environmental parameters (e.g.,
atmospheric attenuation). Some atmospheric conditions at the time of satellite overpass
were recorded (Table 4).
Table 4 Meteorological Conditions in the Study Area at Time of Satellite Overpass.
Year Average air temperature
(oC)
Vapor content
(%)
Water temperature
(oC)
Cloud cover (%)
1988 --a --a --a 0 1992 24.8 67 22.2 0 1993 19.8 55 19.1 0 1997 --a --a --a 6
aNot available.
After applying radiometric (sensor) and atmospheric corrections, a geometric correction
was applied, fitting the images to a common coordinate system.
Radiometric Correction
Because satellite sensor response varies over time, an invariant feature on the
ground one year can have a different DN the following year. Image to image radiometric
normalization was necessary to utilize the NDVI index derived from the red and near-
infrared bands of the imagery (Mather, 1987; Swain, and Davis 1978) as well as the
Tasseled Cap indices derived from the six reflective bands of TM.
FOMI applied the DBO normalization method to the 1992 and 1993 images.
Radiometric correction of the 1988 and 1997 imagery used ERDAS’ Imagine histogram
matching algorithm. The histogram matching algorithm, which was developed and
implemented after the FOMI study, matches high and low areas of one image’s histogram
to highs and lows of another image (ERDAS Field Guide, 4th Ed). Atmospheric
26
corrections were applied prior to 1997’s radiometric correction. Both 1988 and 1997
images were histogram matched to the 1992 TM image.
Atmospheric Correction
Due to excessive cloud cover and haze over the northern portion of the 1994 study
area image, it was ultimately not used in the analysis. Even after FOMI applied haze
reduction algorithms, the central portion of the study area remained obscured. Similarly,
the 1997 image contained 6 percent cloud cover. However, the cloud cover was isolated
to limited regions in the northern portion of the image. Figure 9 shows the cloud cover
prior to haze reduction. Imagine’s haze reduction algorithm was applied. Figure 10
shows the post haze reduction conditions. Areas of interest (AOI) were formed around
the remaining, visually apparent clouds and cloud shadows. The AOIs were used to
eliminate these pixels from the analysis.
Figure 9 1997 Pre-Haze Reduction Cloud Cover and Shadow.
27
Figure 10 1997 Post-Haze Reduction Image.
Geometric Correction
The geometrically corrected TM images for 1992, 1993,1994, and 1997, were
provided by FOMI. Several 29 km by 24 km subset images of the TM scene covering
both the Hungarian and Slovakian sides of the Danube in the vicinity of Gabcikovo were
originally selected. FOMI determined the parameters for registering the sub-images to a
reference image using an automated feature mapping technique (Büttner and Parareda,
1993). A composite transformation formed by the image registration parameters and a
previously established reference image to map transformation was used to subsequently
transform the images to the Hungarian National Datum.
Ground control points were selected on 1:100,000 scale maps. Root mean square
error (RMSE) of image-to-image registration was reported to be approximately 12.5 m,
while the RMSE of image to map registration was approximately 25 m. FOMI used
nearest neighbor (NN) re-sampling with a 25 m pixel size. The NN method was chosen
over a bilinear interpolation or bi-cubic convolution because, comparatively, the process
28
alters the original input pixel values less than the others (Smith et al., 1995). The images
were imported to an Imagine file format for processing. Due to cloud cover, the 1997
image required additional atmospheric and radiometric preprocessing.
The 1988 image was supplied in a generic binary format. After importing to an
Imagine file format, the image was registered to the 1992 image through 21 ground
control points (GCP). The GCPs were evenly distributed across the scene and located at
features recognized in both images. An overall RMSE of less than one pixel, or 25 m,
was achieved. Positional accuracy was verified through visual comparison of the 1988
image with the 1992 image at recognized locations. Table 5 presents the GCPs used in
rectification.
Table 5 1988 Rectification Parameters.
GCP Column Row X Coordinate
Y Coordinate
X Residual
Y Residual
RMSE (m)
GCP #2 1450.465 2820.544 516750.025 303794.369 -12.318 2.887 12.652GCP #7 1373.460 2920.421 515139.180 307181.097 -0.813 0.985 1.277GCP #9 1448.846 2788.479 516454.708 302890.973 27.169 -4.023 27.465GCP #14 1893.557 2801.705 529573.264 300179.539 -13.158 -7.744 15.268GCP #15 2030.773 2923.491 534457.833 302711.540 -0.001 1.889 1.889GCP #19 2481.270 2877.476 547327.177 298215.743 8.055 2.580 8.458GCP #25 2815.217 2716.840 555884.260 291236.933 -8.321 -3.000 8.845GCP #28 2920.471 2342.666 556256.381 279606.163 11.518 4.063 12.213GCP #29 2871.426 2063.550 552938.960 271823.286 -11.702 -3.723 12.280GCP #34 2994.589 1626.830 553445.678 258252.735 3.470 0.856 3.574GCP #36 2702.524 1603.134 544760.209 259598.706 -3.679 -0.844 3.774GCP #38 2485.210 1583.125 538307.231 260557.809 -7.787 0.194 7.790GCP #40 2372.638 1697.931 535814.066 264705.482 17.077 0.697 17.091GCP #47 1782.605 1957.538 520427.038 276382.084 -9.528 13.572 16.582GCP #45 1773.116 1812.952 519127.889 272287.008 -1.224 -7.203 7.307GCP #48 1388.780 1988.881 509226.585 280089.129 3.627 2.768 4.562GCP #49 1584.960 2248.984 516732.350 286264.759 1.543 -18.590 18.653GCP #50 1464.729 2555.089 515388.143 295993.365 -1.360 19.267 19.315GCP #51 1264.790 2676.613 510461.711 300959.591 -8.148 -5.720 9.955GCP #52 1630.352 2513.871 519901.371 293649.028 1.821 -2.057 2.747GCP #53 1949.091 2473.085 528897.788 290215.175 3.760 3.147 4.903
29
The rectification utilized a 4th Order polynomial. A single GCP, #9, had a high RMSE.
This point should have been deleted from the model and a lower polynomial used.
The final pre-processed images are presented in Figure 11 to Figure 14. The
images are a false color composite utilizing TM bands 4, 3 and 2.
Figure 11 1988 False Color Composite (TM Bands 4,3,2).
Figure 12 1992 False Color Composite (TM Bands 4, 3, 2).
30
Figure 13 1993 False Color Composite (TM Bands 4, 3, 2).
Figure 14 1997 False Color Composite (TM Bands 4, 3, 2).
Image Post-Processing
The floodplain study area was delineated on the TM false color composite (Bands
4, 3 and 2) by following the dikes on both sides of the river and saved as an AOI. The
study area was extracted from the larger image to reduce the variance in the distribution
31
of digital numbers (DN) and thus reduce pixel class confusion. Three regional control
areas for this research consistently identified across all years were also extracted from the
larger image but the vegetation was not classified. Control and study locations were
shown in Figure 5. Image NDVI, GVI and WI values were generated for control and
study areas. Annual, statistical analysis zone images were developed which combined
distance from water, country of origin, study area region and vegetation class.
Image Classification
Previous studies used supervised classification techniques to classify the 1992 and
1993 images. Training areas were selected from color infrared aerial photographs taken
in 1992. Training areas were selected for 3 water classes, 2 forest classes, 2 grassland
classes and an exposed surface class. The 3 subcategories for water corresponded to the
main river, the two lakes and the laterals, all having different turbidity status. The forests
were classified into older and younger forest stands. Grasses were classified into wet and
dry categories. Highly reflective targets, such as disturbed areas associated with the dam
and weir construction or point bars within and next to the riverbed, made up the exposed
class. The selected categories were highly separable in terms of transformed divergence
(Swain and Davies, 1978), except some pairs of subclasses of the same category.
A separate unsupervised maximum likelihood classification was performed using
the six reflective channels of 1988 and 1997 images. A total of 16 unsupervised classes
were created. The sixteen classes were pared down to the 8 previously identified classes.
32
Classes were grouped based on visual interpretation and comparison of the 1992 image
and the 1992 classified result versus the 1988 image and its associated classes. The
visual interpretations are summarized in Table 6.
Table 6 Visual Interpretation Standard. Image Appearance Interpretation Original Class Reclassified Light Blue/Grey "Turbid" Water 1 Water Slate Gray-Light Purple "Deep/Turbid" Water 2 Water Purple-Black "Deep" Water 3 Water Light Brownish Red "Young" Forest 1 Forest Brown-Deep Brown "Established" Forest 2 Forest Bright Red-Magenta "Recent" Grass 1 Grass Bright Red/Brown-Deep Magenta "Established" Grass 2 Grass White-Bright Blue-Light Grey/Green "Barren" Exposed Exposed
The lack of pixel homogeneity in some areas caused classification confusion. In
those areas, the mixed pixels were extracted and a separate maximum likelihood
classification applied to the extracted pixels. The newly classed pixels were then
replaced into the final product, a process commonly called “cluster busting”. The same
classification methods were applied to the 1997 image. For the purposes of this research,
classes for all years were aggregated to the 4 main classes, water, forest, grass and
exposed, Figure 15. Because field visits were not possible, classification accuracy
assessment was not performed. The lack of an accuracy assessment forced the use and
analysis of vegetation and moisture index values.
33
Figure 15 Study Area Land Cover Classifications.
Vegetation and Moisture Indices
Using Imagine (v. 8.3) Tasseled Cap Greenness and Wetness indices as well as
NDVI values were calculated for control and study area pixels. In order to detect subtle
changes in NDVI and Tasseled Cap indices, floating-point values were calculated and
pixel values were not re-scaled to 8 bit integer (0,255) as they were in previous studies.
To avoid edge effects in the control areas, an area inside the border was selected for
analysis.
Using the previously discussed matrix formulas for GVI and WI, software
Tasseled Cap algorithms were verified by hand calculating sample pixel values for
Tasseled Cap Indices (Table 7). Results from hand calculations utilizing the tasseled cap
matrix approximated those from software algorithms (Table 8).
34
Table 7 Digital Numbers by Image Band for a Sample pixel.
X-Coor Y-Coor Sample Rectified Value
Map 527243.881 286880.226 Band 1 71File 769 804 Band 2 29 Band 3 25 Band 4 82 Band 5 43 Band 6-dup7 11 Band 7 11
Table 8 Software vs. Hand Calculated Tasseled Cap Values. Tasseled Cap Band Software Hand
Calculated Difference
Brightness 116.924 116.8857 0.0383Greenness 20.346 20.3769 -0.0309Wetness 16.884 16.8353 0.0487Haze 44.916 44.8940 0.0220Other1 -5.009 -4.9945 -0.0145Other2 -1.320 -1.3183 -0.0017
Statistical Analysis
Separate statistical zone images were generated in an attempt to analyze potential
study area NDVI, GVI and WI changes based on 4 land cover classes, 2 countries of
origin, 6 buffer distances from water, and 3 study area regions. Using map algebra, the
separate zonal images were recoded and combined to form an image with 144 unique
statistical analysis zones. The process was repeated for each of the 4 classified TM
scenes.
Country Zones
The country border, provided in ESRI’s Arc/Info format by FOMI, was used to
produce a 2 country zonal image (Figure 16).
35
Slovakia
Figure 1
Buffer zo
U
0m to 25
backgrou
areas wit
Figure 1
Regional
R
affects o
Hungary
6 Country Statistical Zones.
nes
sing the 1992 water class, 6 water buffer zones were generated for 0m (Water),
m, 25m to 50m, 50m to 100m, 100m to 200m distances. The remaining
nd area was classed as greater than 200m. The buffer zone image was limited to
hin the flood plain dikes. An example of the buffer zones is shown in Figure 17.
7 Water Buffer Statistical Zones.
zones
egional zones were chosen to study potential backwater affects. Backwater
ccur where the diverted water channel meets the original Danube. Within the
36
study area, 3 regions, upper, middle and lower, were arbitrarily chosen along the diverted
portion of the Danube (Figure 18). Each region represented approximately one third of
the area.
Middl
Figure 18 Reg
Map Algebra
Throug
were recoded
unique zonal c
values 1 (Wate
image were gi
image (Water,
increments of
image were ca
Upper
e Lower
ional Statistical Analysis Zones.
h map algebra functions, the country, water buffers, and region images
and added to each year’s classed image to produce 4 images containing 144
lasses (Figure 19). The 4 classes of the land cover images were assigned
r), 2 (Forest) 3 (Grass) and 4 (Exposed). The countries of the country
ven values 0 (Slovakia) and 5 (Hungary). The 6 buffers of the water buffer
0-25m, 25-50m, 50-100m, 100m-200m, <200m) were recoded in
10 from 0 to 50. Lastly, the upper, middle and lower regions of the region
lculated equal to 0, 60 and 120, respectively.
37
Figure 19 1988 Map Algebra and Zonal Image Creation.
When added together, each of the 144 unique zonal classes reflected the land
cover, country, buffer and region for each image date (Figure 20). For example: Forested
Class (2) + Country Hungary (5) + Water Buffer 100-200m (40) + Upper Region (0) =
Zone 47.
Figure 20 Zonal Statistics Image Example, 1988.
Statistics
Due to Imagine’s (v.8.3.1) zonal statistic limitations, the double floating point
vegetation indices and zonal images were exported to Arc/Info Grid format. For each
year’s zonal and study area image, Arc/Info’s GRID command Zonalstats generated
38
NDVI, GVI and WI zonal means. Control NDVI, GVI and WI statistics were obtained
using the same export conversion and grid command. The process generated an Arc/Info
INFO file containing the NDVI, GVI and WI zonal means. The INFO file was converted
to a dBase with Arc/Info’s INFODBASE command. Using Microsoft’s Excel program,
the dBase file data were imported and analyzed
Histogram weights were calculated by dividing the unique zone’s histogram value
by the total histogram value. A unique zone’s weighted index mean was obtained
through multiplying the zone’s weight and its mean. Table 9 is an abbreviated table of
the 1988 histogram weighted GVI means for a sample of unique zones.
Table 9 An Example of 1988 Histogram Weighted GVI Means (WTMN) by Zone.
Class Country Buffers Region Area Histogram Mean 88 GVI
Zonal Weight
WTMN 88 GVI
Water Slovakia < 200m Upper 16.0625 257 1.7090 0.0015 0.0026Forest Slovakia < 200m Upper 344.9375 5519 17.7180 0.0324 0.5737Grass Slovakia < 200m Upper 72.8750 1166 15.3548 0.0068 0.1050Exposed Slovakia < 200m Upper 6.3125 101 -12.1530 0.0006 -0.0072Water Hungary < 200m Upper 22.0625 353 -1.4013 0.0021 -0.0029Forest Hungary < 200m Upper 798.2500 12772 18.2146 0.0749 1.3650Grass Hungary < 200m Upper 150.5625 2409 17.9269 0.0141 0.2534Exposed Hungary < 200m Upper 6.1250 98 -18.3730 0.0006 -0.0106……… ……… ……… ……… ………. ………… …………………… …………
Total 10,652 170433 1.0
The formula: Weighted Mean = (wti)(xi) + … (wtj)(xj) ∑(wtij)
was used to produce total histogram weighted mean values for control areas and study
vegetation classes, water buffer areas, regions, and countries. The weighted means for
these larger areas were obtained through dividing the sum of the products, unique zonal
39
weights times unique zonal means from element i to element j, by the sum of the unique
zonal weights from element i to element j. Total histogram weighted mean NDVI values
were compared to total histogram weighted mean GVI and WI values for the same zones.
Simple linear correlations across years and between indices were obtained using MSN’s
Excel linear correlation formula (Figure 21).
Figure 21 MSN Excel's Linear Correlation Formula.
Precipitation Data
Since WI is a moisture related index, and it is presumed that plants have short and
long term responses to moisture fluctuations (NDVI, GVI), readily available precipitation
data for the study area was obtained to access possible trends in index values. Regional
precipitation station locations can be seen in Figure 22.
Monthly station precipitation totals for the years 1988, 1992, and 1993 were
supplied by the Hungarian Meteorology Department. Graphs of monthly precipitation
totals are presented in Figure 23, Figure 24 and Figure 25. Monthly precipitation totals
for 1997 were unavailable; however, annual precipitation data for the 1989-1997 water-
years were obtained from FOMI and are presented in Figure 26.
40
Figure 22 Precipitation Recording Stations.
1988 Monthly Precipitation
0.020.040.060.080.0
100.0120.0140.0160.0
Janua
ry
Februa
ryMarc
hApri
lMay Jun
eJul
y
August
Septem
ber
Octobe
r
Novem
ber
Decembe
r
Month
Prec
ipita
tion
(mm
) BRATISRAJKAMOSONMGYORSALAHURBAN
Figure 23 1988 Monthly Precipitation Totals.
41
1992 Monthly Precipitation
0.020.040.060.080.0
100.0120.0140.0
Janua
ry
Februa
ryMarc
hApri
lMay Jun
eJul
y
August
Septem
ber
Octobe
r
Novem
ber
Decembe
r
Month
Prec
ipita
tion
(mm
)
BRATISMOSONMGYORHURBAN
Figure 24 1992 Monthly Precipitation Totals.
1993 Monthly Precipitation
0.020.040.060.080.0
100.0120.0140.0
Janua
ry
Februa
ryMarc
hApri
lMay Jun
eJul
y
August
Septem
ber
Octobe
r
Novem
ber
Decembe
r
Month
Prec
ipita
tion
(mm
) BRATISMOSONMGYORSALAHURBAN
Figure 25 1993 Monthly Precipitation Totals.
42
Study Area Precipitation Totals
0100200300400500600700800900
1989
-1990
1990
-1991
1991
-1992
1992
-1993
1993
-1994
1994
-1995
1995
-1996
1996
-1997
Water Year
Prec
ipita
tion
(mm
)
Precipitation
Figure 26 Water Year Precipitation Totals.
It is important to consider the image date when using monthly precipitation totals.
Since the image dates used in this study fell at the beginning of the month, precipitation
station totals for the months prior to and the month during satellite overpass are provided
in Table 10. The Hurban station values were not used in the average calculation. The
station was not used because it is very distant, approximately 40 km from the study area,
and the 116.7mm July 1992 value compared to the other July 1992 station values
suggests a possible recording error. The average monthly rainfall for the month prior to
the image date is presented in Table 11.
43
Table 10 Regional Monthly Precipitation Values Surrounding Image Dates.
Year Sta# Name July (mm)
August (mm)
September (mm)
1988 111816 BRATIS 14.5
90.7 61.41988 80 RAJKA 12.5 137.7 34.01988 91 MOSONM 16.7 88.9 73.71988 212822 GYOR 25.5 105.3 66.61988 142479 SALA 15.0 95.0 57.01988 111858 HURBAN 31.3 125.5 78.2
b
Year Sta# Name June (mm)
July (mm)
August (mm)
1992 111816 BRATIS 82.1 23.7 8.71992 91 MOSONM 74.3 33.4 25.51992 212822 GYOR 74.1 95.3 1.61992 111858 HURBAN 83.6 116.7 1.0
c
Year Sta# Name June (mm)
July (mm)
August (mm)
1993 111816 BRATIS 39.2 77.1 69.81993 91 MOSONM 69.7 65.3 53.61993 212822 GYOR 44.7 63.5 35.21993 142479 SALA 29.0 72.0 59.01993 111858 HURBAN 51.3 37.6 28.7
104 8/92 51 -- 8/93 63 -- 9/97 -- --
Table 11 Average Monthly Precipitation Prior to Image Date.
Image July
precipitation (mm)
August precipitation
(mm) 9/88 --
CHAPTER 4 RESULTS AND DISCUSSION
Morphological changes in the original Danube were readily apparent through
observation of the satellite imagery. For example, reduced surface water and exposed
riverbanks can be seen throughout the Szigetköz region (Figure 27).
Figure 27 Pre- and Post-Danube Diversion, 1992 and 1993, Respectively.
Image Classification
Land cover total aerial extent and percent composition for the four study area land
cover classes, are summarized in Table 12. Land cover composition for the Szigetköz
portion of the study area is displayed separately in Table 13. Control areas were not
classified since they were visually similar to the study area forest class.
44
45
Table 12 Study Area Land Cover Classes (ha and percent cover), 1988-1997. Category Water Forest Grassland Exposed Total
(a) Total Area in Hectares (ha) 1988 2,644 6,627 1,242 140 10,652 1992 2,422 5,252 1,858
1,120 10,652 1993 2,102 4,948 3,252 350 10,652 1997 2,329 5,499 2,005 223 10,056a
(b) Percent of Total Area (%) 1988 25 62 12 1 100 1992 23 49 17 11 100 1993 20 46 31 3 100 1997 23 55 20 2 100
aArea reduction due to cloud cover.
Table 13 Szigetköz Region Land Cover Classifications. Category Water Forest Grassland Exposed Total
(a) Szigetköz Area in Hectares (ha) 1988 1,596 3,696 725 69 6,086 1992 1,398 2,941 1,090 657 6,086 1993 1,138 2,766 1,985 197 6,086 1997 1,290 3,077 1,079 110 5,556a
(b) Percent of Szigetköz Area (%) 1988 26 61 12 1 100 1992 23 48 18 11 100 1993 19 45 33 3 100 1997 23 55 19 2 100
aArea reduction due to cloud cover.
Approximately 5% of the 1992 and 1993 classification from previous studies was
undetermined. Upon further image review, the 1992 portion was aggregated with the
water category and the 1993 portion was aggregated with the grass category for this
research. Figure 28 charts annual land cover percentages.
46
Percent Land Cover
0
10
20
30
40
50
60
70
1988 1992 1993 1997
Year
Perc
enta
ge WaterForestGrasslandExposed
Figure 28 Percent Land Cover Composition by Year.
Anecdotal Accuracy Assessment
Due to lack of ground-based data, a formal accuracy assessment of the
classification was not performed. Instead, local experts reviewed results (Buttner,
personal communication). Field data reported in the Hungarian Forest Database
estimated managed forests in the study area on the Hungarian side at 3575 ha (Smith et
al., 2000). The area indicated as a forest on the 1997 image is 3,077 ha, which represents
a difference between the field observations and classification measurements of 14
percent. This difference is most likely due to 1) a 9% loss total area due to cloud cover;
2) recording of forest roads and clearances as forest in the database; 3) the cut, but not yet
reforested areas are not distinguishable in the database, however it was classified as
grassland in the image.
Forested area in the image closely fits the ground data. Furthermore, the water
surface measured on the image is similar to the actual ground surface area. There are
virtually no croplands within the dikes on the Hungarian side and so the area classified as
47
grassland is actually grassland on newly reforested area and marshland on the image.
The exposed category on the image is negligible in size, but is similar in both image and
ground measurement (Smith, et. al. 2000).
The land cover data as classified indicates the following; 1) the most important
type of 1997 land cover is forest representing 55 percent of the total area; 2) areas
covered with water and grassland are approximately 20 percent each; 3) the barren area is
a minor component (2-3 percent); and, 4) ratios of the land cover on the Slovakian and
the Hungarian side are similar. The conclusion by the expert forester was that these
classifications accurately represented the land cover conditions in the study area (Butner
and Somogyi, personal communication, 1998).
Analysis of Land Cover Changes
A comparison of land cover consistency and change for the period 1988 to 1997 is
presented in Table 14. The table is the product of between-year cross-classification
matrices.
Table 14 Land Cover Classification Comparisons. Year
Comparison Constant
water (ha)
Constant forest (ha)
Constant grassland
(ha)
Constant exposed
(ha)
Former water (ha)
Former forest (ha)
Other changes
(ha)
Total Area (ha)
1988/1992 1,844 4,717 554 94 799 1,910 734 10,652 1988/1993 1,650 4,434 817 48 993 2,193 517 10,652 1992/1993 1,697 4,327 1,463 198 725 924 1,317 10,652 1992/1997 1,628 3,962 601 88 681 1,102 1,993 10,056a 1993/1997 1,624 3,784 1,008 82 396 967 2,194 10,056a 1988/1997 1,753 4,727 444 16 767 1,598 750 10,056a aarea reduction due to 1997 cloud cover.
48
In Table 14, changes before the diversion are represented by data for the years
1988 and 1992. The short/term changes are characterized by the data of 1992 and 1993.
The midterm changes after the diversion are demonstrated by the data of the years 1993
and 1997. Constant values represent areas classified the same in both years. Conversely,
former values represent areas previously classified as one land cover and classified
differently in the subsequent year. The “other changes” category reflects change from
areas previously classed as grass or exposed that shifted to any of the other 3 classes.
The data indicate a continued fluctuation in all classes. As surface water and
forested classes declined, grass and exposed classes increased over their 1988 values.
Some changes were substantial. For example, comparison of the data for 1988 and 1997
reveals some changes in aerial extent. Previous studies data showed that between 1990
and 1992, cuttings of dead trees were 214 m3/year, while the same value for the period of
1993-1997 was almost five times higher (1158 m3/year) (Smith et al., 2000). Whether
the increase in dead trees was a result of the diversion or climate is unclear. While the
data of Table 12, Table 13, and Table 14 summarize study area affects, they do not reflect
the over all spatial distribution of the changes.
Several explanations for class shifts are possible. One explanation for a forest-
exposed-grass-forest class shift is forestry practice. Harvested and cleared forested land
is first seen as exposed. It is then replanted with seedlings and grass is grown as cover.
As trees mature, the area is interpreted as forested.
Another explanation for some of the water-exposed-grass shift relates directly to
the diversion and ecological succession. Reduced water depth exposes banks. Exposed
banks are colonized by vegetation growth and subsequently classed as grass. As time
49
passes and water levels are stabilized, the grass areas may become established with trees
or other woody vegetation and classed as Forest.
Lastly, the forest-exposed-grass-water shifts can be related to the addition of side
channels and weirs to the branches of the Danube. During side channel excavation for
new weirs some areas previously classed as forest or grass become exposed. The new
weirs cause increased surface water retention and inundation and consequently a shift in
the next year’s water class. Construction of the weirs is clearly visible in the 1993
imagery (Figure 29). In Figure 30, the 1997 image revealed the 1993 weir construction
area was inundated with water and the exposed area colonized by vegetation. Figure 29
also shows exposed banks along the Danube and Figure 30 shows their colonization by
vegetation.
Weir
Figure 29 1993 Weir Construction.
Weir
Figure 30 1997 Post-Weir Construction.
50
Vegetation and Moisture Index Images
For presentation and discussion of NDVI, GVI and WI index image results,
portions representative of study area and controls were chosen. For the study area, a
section containing the lower half of the upper region and a small portion of the upper half
of the middle region was selected and the southern control was chosen to represent all
controls. Locations of examples are shown in Figure 31.
Figure 31 Study Area and Control Example Image Locations.
Index results in Figure 32 through Figure 37 each contain 4 gray scale images.
From left to right, top images represent 1988 and 1992 index results and bottom images
represent 1993 and 1997 results respectively. Portions of the 1997 image excluded from
analysis due to cloud cover are seen in top left portions of the 1997 study area examples.
For display purposes calculated index image values were stretched from 0 to 255 to
produce the gray scale images. The gray scale is indicative of index values, where the
color range of white-gray-black equals high-medium-low index values.
51
NDVI and GVI Images
Study area NDVI and GVI image examples are presented in Figure 32 and Figure
33. Control NDVI and GVI images are presented in Figure 34 and Figure 35.
Figure 32 Sample Study Area NDVI Images 1988, 1992, 1993 and 1997.
52
Figure 33 Sample Study Area GVI Images 1988, 1992, 1993 and 1997.
Figure 34 Southern Control Area NDVI Images 1988, 1992, 1993, 1997.
53
Figure 35 Southern Control Area GVI Images 1988, 1992, 1993, 1997.
Study area landscape pattern changes visible in Figure 27 and are also visible in
the NDVI and GVI gray scale images. Morphological changes in the main river channel
and side branches are visible. Also, large, well-defined straight edged rectangular and
triangular patterns indicative of silviculture practices are present in all the study and
southern control image examples; however, visually detectable evidence of silviculture
was absent from the northern and south-central controls.
In Figure 32, the 1993 NDVI study image (lower left) shows the newly exposed
riverbanks as dark gray, or low NDVI. The 1997 riverbank area, which had been
colonized by vegetation, appears light gray, indicating higher NDVI than the 1993
riverbanks.
54
The GVI results displayed in the study GVI and control GVI figures provide
higher contrast images and thus better visual interpretation than the NDVI images.
Water and exposed areas appear dark gray to black and had low NDVI and GVI values.
Forested areas appear as gray patches. Grass and newly reforested areas had high NDVI
and GVI causing them to appear white to light gray.
WI Images
Example study and control wetness index image results are shown in Figure 36
and Figure 37 respectively.
Figure 36 Sample Study Area WI Images 1988, 1992, 1993 and 1997.
55
Figure 37 Southern Control Area WI Images 1988, 1992, 1993, 1997.
Wet surfaces such as exposed, grass and newly replanted areas, display as black
to dark gray, less exposed (forested) as gray and dry areas as light gray to white. Land
cover patterns distinguishable in NDVI and GVI images are not as apparent as in the WI
images; however some trends can be seen along the main Danube channel. In Figure 36,
the 1993 image contains areas immediately adjacent to the center channel, which were
recently exposed and remained very wet or black. In the 1997 image, while some dark
areas around the main river channel remain, a majority has become lighter gray, or dry.
56
Observations and Comparisons
Precipitation Comparisons
Annual precipitation amounts are needed to interpret trends in environmental
moisture (WI) and ultimately, to interpret measured vegetation responses (NDVI, GVI) to
those trends. FOMI provided regional precipitation summaries for the water-years 1989
to 1997 (Table 15). The 8-year mean precipitation was 587 mm and 1 standard deviation
was 121.026 mm. The annual deviation from the mean provided in Table 15 was
calculated and described.
Table 15 Annual Precipitation for the Water-Years 1989 to 1997.
Period Sum of precipitation (mm)
Number of Standard
Deviations Description
1989-1990
488 -0.821 Droughty 1990-1991 532 -0.458 Droughty 1991-1992 480 -0.887 Droughty 1992-1993 508 -0.656 Droughty 1993-1994 637 0.410 Wet 1994-1995 721 1.104 Wet 1995-1996 806 1.806 Extremely Wet 1996-1997 527 -0.499 Droughty
The spring and early summer months of 1992 were unusually arid. This
coincided with the time of the diversion and so the effects of the diversion might have
been accentuated by the dry climate conditions. The summer of 1994 had average
precipitation, however, arid periods were frequent. 1995, 1996 were wet years with
exceptional floods on the Danube. These meteorological observations are important to
this study in that the drought conditions in 1991 and 1992 probably would have caused
57
stress to the natural and planted vegetation irrespective of the diversion. The wet period
of 1994-1996 may have masked the effect of the diversion.
In summary:
�� The 1988 spring was wet and the month during and prior to satellite
overpass was also wet.
�� The 1992 spring was dry and the rainfall before the image date was below
average.
�� The 1993 spring was dry but the average rainfall before the image date
was higher than the 1992 average for the same month.
�� The 1997 yearly summary indicates below average rainfall for the year.
�� The annual mean precipitation and standard deviations reflect long-term
drought conditions from 1989 to mid 1994, a brief period of drought relief
from mid 1994 to mid 1996, and a return to conditions in mid 1996.
Control NDVI and GVI Observations and Comparisons
Mean NDVI and GVI values for all control areas were graphed and compared for
trends. Figure 38 and Figure 39 show control NDVI and GVI trends were similar. In
1988, a relatively wet year, and in 1992, a relatively dry year, NDVI and GVI levels were
low. In 1993, a year with moderate rainfall, mean values were highest and in 1997 values
dropped to levels similar to 88 and 92. With the exception of 1992, NDVI levels for all
controls were similar. The northern 1992 NDVIs approximated the 1988 levels. South-
central means were highest in all years except 1988, when the southern control mean was
the highest.
58
Yearly Control NDVI Values
0.00000.10000.20000.30000.40000.50000.60000.7000
88 92 93 97
Year
Mea
n N
DV
I
NDVI-NNDVI-SNDVI-SC
Figure 38 Mean Control Area NDVI per Year.
Yearly Control GVI Values
0.00005.0000
10.000015.000020.000025.000030.000035.0000
88 92 93 97
Year
Mea
n G
VI V
alue
Green-NGreen-SGreen-SC
Figure 39 Mean Control Area GVI per Year.
The graph of mean control GVI values in Figure 39 shows greater differentiation
between yearly values. The amplitude of the difference between values may be a result
of the Tasseled Cap GVI calculation, which includes a scaling factor. Further difference
may be due to inclusion of a moisture component in NDVI values (Richardson and
Wiegand, 1977, van Leeuwen and Huete, 1996, Todd and Hoffer, 1998, and Fisher,
1999), which is separated in Tasseled Cap calculations. Northern and Southern control
GVI levels were similar in 1988. Southern and south-central means were similar in 1992
59
and 1993. The northern mean for 92’ and 93’ was lowest and then intermediate in 1997.
South-central GVI control means were highest in all years.
Simple linear NDVI and GVI correlations (r) presented at the end of Table 16
were similar for all analysis zones and controls. High correlations of NDVI and GVI are
consistent with Hill and Aifadopoulou's 1990 NDVI conclusions.
Table 16 Mean Weighted GVI and NDVI Values. Buffer 1988 1992 1993 1997
(a) GVI 0m -6.82 -23.92 -23.66 -6.5025m 3.28 -0.46 3.40 5.1250m 9.78 12.34 19.27 12.73100m 14.44 16.36 25.19 17.43200m 14.09 17.37 23.31 16.31Outside 17.82 19.24 26.40 19.31
(b) NDVI 0m 0.22 0.00 -0.10 0.2725m 0.33 0.33 0.35 0.3950m 0.39 0.45 0.52 0.45100m 0.42 0.48 0.58 0.49200m 0.43 0.50 0.57 0.48Outside 0.46 0.51 0.60 0.50
(c) GVI to NDVI Correlation r = 1.00 0.99 0.99 1.00
Control WI Observations
The yearly mean control WI values are presented in Figure 40. All south-central
yearly means were highest, or driest, while 1988 and 1992 southern control values were
lowest, or most wet, and northern values for the same years were intermediate. In 1993,
the northern control was lowest, or most wet, and the 1993 southern control was slightly
higher, or drier than the southern control. The south central control remained the driest in
all years.
60
Yearly Control WI Values
-4.0000-2.00000.00002.00004.00006.00008.0000
10.000012.0000
88 92 93 97
Year
Mea
n W
I Wet-NWet-SWet-SC
Figure 40 Yearly Mean Control WI Levels.
Control GVI and WI Trends
Tasseled Cap scale factors allow GVI and WI values to be concurrently graphed.
On the other hand, variations in calculated NDVI values, which range between –1 and 1,
cannot be mapped in the same value range as GVI and WI. A composite graph of Yearly
NDVI, GVI and WI means is presented in Figure 41. Since a high NDVI and GVI
correlation has been established, it is presumed that WI-GVI relationships are the same as
WI-NDVI trends.
Yearly Control Index Values
-5.00000.00005.0000
10.000015.000020.000025.000030.000035.0000
88 92 93 97
Year
Mea
n In
dex
Val
ue
NDVI-NNDVI-SNDVI-SCGreen-NGreen-SGreen-SCWet-NWet-SWet-SC
Figure 41 Mean NDVI, GVI and WI Comparison.
61
The graph of yearly control index values shows that during extreme
environmental moisture conditions, i.e. very wet (1988) and very dry (1992), GVI control
means are similar. During moderate moisture conditions such as 1993, GVI values are
highest and with less moisture, as in 1997, the mean GVI declined.
Control and Study Area Forest Index Value Comparison
Total forest class weighted mean NDVI, GVI and WI values were highly
correlated with all control locations, range of r = 0.897 to 0.998 (Table 17).
Table 17 Histogram Weighted Mean Study Area Forest Indices to Control Indices Correlations.
Index North (N)
South (S)
South-Central
(SC) NDVI 0.924 0.977 0.994 GVI 0.897 0.948 0.991 WI 0.899 0.998 0.977
The highest study area forest WI to control WI correlation was with the southern control
(r=0.998). Forest NDVI and GVI were most correlated with south-central control NDVI
and GVI. A graph of the control and forest mean comparison is shown in Figure 42.
The high WI correlations indicate the study area forest and control areas are following
the same "wetness patterns", however, Figure 43 shows histogram weighted mean study
forest WI levels higher, or drier, than controls. This higher value indicates dryer
conditions in the study area except in 1988 when heavy rainfall occurred and all areas
were inundated. As noted previously, the south-central control 1993 WI was higher, or
drier, than the northern 1993 control WI (Figure 40 and Figure 43).
62
Yearly Conrtol and Forest GVI Means
0.005.00
10.0015.0020.0025.0030.0035.00
88 92 93 97
Year
Mea
n G
VI GVI-N
GVI-SGVI-SCForested
Figure 42 Yearly Control GVI Means.
Control and Forest Class WI
-5.00
0.00
5.00
10.00
15.00
20.00
88 92 93 97
Year
Mea
n W
I WI-NWI-SWI-SCForested
Figure 43 Mean Control and Weighted Mean Forest WI Values.
Control and Water Buffer Distance Comparison
Histogram weighted water buffer distance index means for the forest class were
compared to weighted forest class means and control means. A graph of the GVI
comparison is presented in Figure 44. In the graph, the 1992 mean 0-meter buffer
distance (water) GVI equals 0 since the 1992 river boundary was used as the 0-meter
buffer class. Consequently, no forested vegetation occurred within the 1992 0m buffer
class. Higher 0m means for all other years are a result of morphological changes in water
features. In general, the forested portions of the buffer distances follow control and total
63
forested trends. The 25m buffer distance more closely follows the northern control GVI
response. With the exception of the 0-meter class, means within the 25-meter zone are
lower than all buffer zones and years
Control and Forest Class GVI
0.005.00
10.0015.0020.0025.0030.0035.00
88 92 93 97
Year
Mea
n G
VI V
alue
GVI-NGVI-SGVI-SCForested0m25m50m100m200mOutside
Figure 44 Mean Control, Weighted Mean Forested and Buffer Distance Forested GVI Values.
A graph of the WI comparison is presented in Figure 45. The graph shows all
forested buffer zone means were higher or dryer than controls. The 1992 WI mean
equaled 0 for reasons previously discussed in the buffer distance GVI comparison. The
graph shows 1988 was a relatively wet year, 1992 was an extremely dry year, 1993 was
intermediate and 1997 was slightly drier than 1993. While the wetness patterns are the
same as controls, they were drier than controls. Particularly, the 25m buffer distance
class was the driest in 1992, 1993, and 1997. Conversely, the outside buffer distance
class (200m+) was the most wet.
64
Control and Forest Study WI
-5.00
0.00
5.00
10.00
15.00
20.00
88 92 93 97
Year
Wei
ghte
d M
ean
Inde
x V
alue
WI-NWI-SWI-SCForested0m25m50m100m200mOutside
Figure 45 Mean Control, Weighted Mean Forest and Buffer WI Values.
Control Comparison Summary
The control and study area comparison can be summarized as follows:
�� Study area and control moisture changed across years.
�� 1988 was the wettest year and 1992 was the driest.
�� Mean control WI patterns were similar to rainfall.
�� Study WI and GVI patterns approximated control WI and GVI patterns.
�� Study area forest WI’s were higher, or dryer, than controls, except in
1988, a pre-diversion and very wet year.
�� South-central mean control GVI was higher than study area forest GVI.
Study Area Indices by Buffer Distance
The highest buffer distance GVI means occurred during 1993 and at distances
greater than or equal to 50 meters from water (Figure 46). For the same buffer distances,
1988 means were lowest. All means were aggregated at the 25-meter distance. High
turbidity visible in the 1988 image, and decreased river depth in 1997 may have
contributed to higher TM band 5 reflectance and lowered Tasseled Cap GVI.
65
GVI vs Water Buffer Zones
-30.00
-20.00
-10.00
0.00
10.00
20.00
30.00
0m 25m 50m 100m 200m outside
Buffer Zone
Wei
ghte
d-M
ean
GV
I
GVI-88GVI-92GVI-93GVI-97
Figure 46 GVI Means by Buffer Distance from Water.
As predicted by control WI means, 1992 study area WI buffer zone means were
highest, or driest, for all years (Figure 47). The WI means ranked from driest to wettest
are: 1) 1992, 2) 1997, 3) 1993, and 4) 1988. A lower WI mean for the 0m 1997 buffer
distance may be a result of higher water levels reported at Rajka (Smith 2000) and
backwater effects occurring at the power channel and river channel confluence.
WI vs Buffer Zone
-5.00
0.00
5.00
10.00
15.00
20.00
25.00
0m 25m 50m 100m 200m outside
Buffer Zone
Wei
ghte
d-M
ean
WI
WI-88WI-92WI-93WI-97
(1992 Used as Om Start)
(1992, 1997 Drier than 1993)
Figure 47 WI Means By Buffer Distance From Water.
66
Separate yearly graphs of buffer distance GVI means versus WI means are
displayed in Figure 48 through Figure 51, and the negative correlations of GVI to WI and
NDVI to WI are presented in Table 18. Higher correlations were observed in 1992 and
1993, while lower correlations were observed in 1988 and 1997.
1988 GVI and WI vs Buffer
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
0m 25m 50m 100m 200m outside
Distance
Mea
n W
eigh
ted
Inde
x
GVI-88WI-88
Figure 48 1988 Mean GVI vs. WI By Buffer Distance To Water.
1992 GVI and WI vs Buffer
-30
-20
-10
0
10
20
30
0m 25m 50m 100m 200m outside
Distance
Mea
n W
eigh
ted
Inde
x
GVI-92WI-92
Figure 49 1992 Mean GVI vs. WI By Buffer Distance To Water.
67
1993 GVI and WI vs Buffer
-30
-20
-10
0
10
20
30
0m 25m 50m 100m 200m outside
Distance
Mea
n W
eigh
ted
Inde
x
GVI-93WI-93
Figure 50 1993 Mean GVI vs. WI By Buffer Distance To Water.
1997 GVI and WI vs Buffer
-10
-5
0
5
10
15
20
25
0m 25m 50m 100m 200m outside
Distance
Mea
n W
eigh
ted
Inde
x
GVI-97WI-97
Figure 51 1997 Mean GVI vs. WI By Buffer Distance To Water.
Table 18 Vegetation Index to Wetness Index Correlations by Year. Comparison Correlation
(a) GVI to WI 1988-1988 -0.655 1992-1992 -0.921 1993-1993 -0.906 1997-1997 -0.610
(b) NDVI to WI 1988-1988 -0.687 1992-1992 -0.949 1993-1993 -0.940 1997-1997 -0.638
68
Study Area Indices By Land Cover
Graphs of index values by land cover class are presented in Figure 52 through
Figure 52. No simple linear correlations exist between index values based upon land
cover class.
Yearly Water Index Values
-20.00-15.00-10.00
-5.000.005.00
10.0015.0020.00
88 92 93 97
Water Category Year
Wei
ghte
d M
ean
Inde
x V
alue
NDVIGVIWI
Figure 52 Mean Water Class Index Values.
Yearly Forest Index Values
0.00
5.00
10.00
15.00
20.00
25.00
30.00
88 92 93 97
Forest Category Year
Wei
ghte
d M
ean
Inde
x V
alue
s
NDVIGVIWI
Figure 53 Mean Forest Class Index Values.
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Yearly Grass Index Values
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
25.00
88 92 93 97
Grass Category Year
Wei
ghte
d In
dex
Val
ues
NDVIGVIWI
Figure 54 Mean Grass Class Index Values.
Yearly Exposed Index Values
-30.00
-25.00
-20.00
-15.00
-10.00
-5.00
0.00
5.00
88 92 93 97
Exposed Category Year
Wei
ghte
d M
ean
Inde
x V
alue
s
NDVIGVIWI
Figure 55 Mean Exposed Class Index Values.
Study Area Indices by Region
Upper, middle, and lower region GVI means and WI means were graphed and are
presented in Figure 56 to Figure 58. The graphed weighted mean upper and middle
region values, Figure 56 and Figure 57, indicate that as WI rises, or becomes drier, GVI
lowers and as WI lowers, or becomes wetter, GVI rises. Divergent lower region GVI and
WI means in 1997 may be due to backwater effects occurring at the power channel and
river channel confluence (Figure 58).
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Upper Region Yearly Index Values
-5.000
0.000
5.000
10.000
15.000
20.000
Up-88 Up-92 Up-93 Up-97Index
Wei
ghte
d-M
ean
Inde
x V
alue
NDVIGVIWI
Figure 56 Upper Region Mean Index Values.
Middle Region Yearly Index Values
0.000
5.000
10.000
15.000
20.000
Mid-88 Mid-92 Mid-93 Mid-97
Index
Wei
ghte
d-M
ean
Inde
x V
alue
NDVIGVIWI
Figure 57 Middle Region Weighted Mean Index Values.
Lower Region Yearly Index Values
0.0002.0004.0006.0008.000
10.00012.00014.000
Low-88 Low-92 Low-93 Low-97
Index
Wei
ghte
d-M
ean
Inde
x V
alue
NDVIGVIWI
(“Back Flow”)
Figure 58 Lower Region Weighted Mean Index Values.
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Simple linear correlation between NDVI and WI or between GVI and WI does
not exist within a region and across years. Within an index and across image years,
regions were highly correlated with each other (Table 19).
Table 19 Index Correlations Across Regions. Correlation (r)
Index Lower to Middle Lower to UpperMiddle to UpperNDVI 0.905 0.875 0.985 GVI 0.895 0.749 0.893 WI 0.911 0.915 0.994
Within an individual year and across regions, NDVI to WI and GVI to WI correlations
were high except for 1997 (Table 20). In 1997 correlation was slight for NDVI to WI
and correlation did not exist for GVI to WI means. This may be due in part to the lower
region backwater effects.
Table 20 Correlations Within Year and Across Regions. Year NDVI-WI GVI-WI
88-UML 0.973 1.00092-UML 0.843 0.94693-UML -0.984 -0.99097-UML 0.699 -0.172
Study Area Indices by Country
Table 9 presents mean weighted index values for the study years. A graph of the
means is contained in Figure 59. The mean country WI values are closely matched and
the graph appears similar to the upper and middle region graphs, Figure 56 and Figure 57.
Slovakia GVI means for 1993 and 1997 were lower than Hungarian GVI means for the
same years. Correlations within an individual index across years and between countries
were very high, WI r = 0.99, GVI r = 0.99, and NDVI r = 0.98.
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Table 21 Mean Weighted Index Values. 1988 1992 1993 1997
Hun-WI 0.763 11.593 5.852 8.660Slov-WI -0.276 11.910 6.517 8.168Hun-GVI 10.978 8.874 15.566 12.955Slov-GVI 10.298 9.278 13.661 11.647Hun-NDVI 0.390 0.392 0.443 0.445Slov-NDVI 0.394 0.406 0.447 0.440
Country Index Values
-202468
1012141618
Wei
ghte
d M
ean
Inde
x V
alue n
Figure 59
Th
Todd and H
higher plan
less moistu
positive G
Similarly,
field study
Slovakia GVI Lower After Diversio
1988 1992 1993 1997Year
Hun-WISlov-WIHun-GVISlov-GVIHun-NDVISlov-NDVI
WI Closely Matched
Index Correlations between Countries.
e results of these comparisons contradict the mathematically modeled results of
offer (1998). A lower WI indicates more infrared (IR) IR reflectance and
t and background moisture. Higher WI equates with higher IR absorption and
re. Surface water, which absorbs IR also produces higher WI values. A
VI equates with higher IR reflectance and potentially healthier plant condition.
Todd and Hoffer (1998) concluded their modeled results were different from
observations of a single plant species study with varying vegetation cover.
CHAPTER 5 CONCLUSIONS
There were 4 objectives of this research; 1) classify Landsat Thematic Mapper
images to assess land cover change, 2) assess plant condition and hydro-period change
using NDVI and Tasseled Cap GVI and WI as estimators; 3) compare and correlate
indices; and 4) identify trends in GVI and WI.
Through these objectives, five research questions were formulated: 1) were WI
values as a measure of surface moisture similar to precipitation patterns 2) were the study
area forest NDVI, GVI and WI patterns similar to the control area patterns 3) were GVI
and NDVI correlated 4) were there correlations between GVI or NDVI and WI 5) were
NDVI, GVI and WI patterns discernable with respect to proximity to water, land cover
class, region, or country?
The following conclusions were reached:
�� WI is a useful index for comparing environmental moisture to rainfall.
�� NDVI and GVI are well correlated, and changed across image years.
�� NDVI and GVI measurements indicate vegetation responds to
precipitation patterns.
�� Tasseled Cap GVI scaling factors better differentiate displayed and
measured changes in vegetation condition.
�� Histogram-weighted mean floating point WI values are inversely related
to environmental moisture patterns.
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74
�� Control WI and GVI may be used to normalize study area imagery for
variance in environmental conditions.
Satellite imagery can be a valuable tool for monitoring large-scale environmental
change, but the data must be properly pre-processed, interpreted and verified in order to
be useful. Pre-processing consisted of both geometric and radiometric corrections to the
imagery in order to “normalize” it over time.
Environmental variables, such as precipitation pattern, had to be taken into
account so that any change in vegetation as measured by the satellite imagery could be
attributed to the change in the hydrological regime caused by the diversion project
instead of a change in climate patterns. This was accomplished in this study by
incorporation of weather statistics and the use of “control” zones well outside the study
area. The weather statistics showed extremely wet and extremely dry conditions
occurred during the image years. Based on observations of control indices related to
these environmental moisture extremes, an elastic vegetation response may have occurred
and masked any satellite detectable impacts.
The conclusions of this study with respect to the environmental impacts of the
GBS on the Szigetköz region were as follows:
�� Visible surface water area along the river channel and side branches
decreased.
�� Exposed Danube River banks were colonized by vegetation.
�� Satellite detectable moisture in the study area decreased.
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�� Study area land cover changes were due to mostly forestry management
rather natural succession.
�� Post diversion hydrologic alterations such as side channel dams and weirs
increased study area moisture.
Therefore, it is the overall conclusion of this study that, while Landsat TM
imagery is, potentially, a very useful tool for assessment of large-scale environment
impact, a longer period of record is needed in order to ascertain the actual impacts of the
GBS on the Szigetköz.
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BIOGRAPHICAL SKETCH
Mr. Aufmuth has accepted a new Documents Library Faculty and GIS
Coordinator position with the University of Florida Libraries. While completing his
Master of Science degree Mr. Aufmuth has been a research and teaching assistant in the
geomatics program at the University of Florida’s Department of Civil Engineering. Prior
to his enrollment in Civil Engineering, he spent 5 years as a geographic information
systems (GIS) manager and specialist in private consulting and government research.
Before becoming a Gator, he earned his Bachelor of Science degree in ecology, ethology
and evolution from the University of Illinois in 1984.
While working on his degree, Mr. Aufmuth spent a short time in Africa. He co-
instructed GIS and remote sensing short courses in Tanzania and Ethiopia. Amongst
other publications, he recently co-authored an article for ASCE 's Journal of Water
Resources Planning and Management on the effects of the Danube River diversion in
Hungary. GIS, remote sensing and data base development are his areas of concentration,
and he will provide a link to current University of Florida GIS efforts, the special needs
of those programs and the Library’s mission to assist in research and education.
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