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Comparing Landsat7 ETM+ and NAIP imagery for precision agriculture application in small scale farming: a case study in the south eastern part of Pittsylvania County, VA

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Comparing Landsat7 ETM+ and NAIP imagery for precision agriculture application in small scale farming: a case study in the south eastern part of Pittsylvania County, VA

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COMPARING LANDSAT7 ETM+ AND NAIP IMAGERY FOR PRECISION
AGRICULTURE APPLICATION IN SMALL SCALE FARMING: A CASE
STUDY IN THE SOUTH EASTERN PART OF PITTSYLVANIA COUNTY, VA

by

Robert Kevin Bohon Jr.

A Thesis Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMATION SCIENCE AND TECHNOLOGY)

June 2014

2014 Robert Kevin Bohon Jr.

ii

DEDICATION

This is dedicated to my wife Lindsey, and my family for supporting me.

iii

ACKNOWLEDGMENTS

I would like acknowledge all those who have helped me with research and data analysis:
Ryan Jensen, Dan Mertz, Rich Curran, and Charles Nickerson. I would also like to thank
the USC faculty for their direction throughout the GIST program, especially my advisor
Dr. Flora Paganelli and committee members Dr. Su Jin Lee and Dr. Darren Ruddell.

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TABLE OF CONTENTS
DEDICATION................................................................................................................... ii
ACKNOWLEDGMENTS ............................................................................................... iii
LIST OF TABLES ........................................................................................................... vi
LIST OF FIGURES ........................................................................................................ vii
LIST OF EQUATIONS ................................................................................................... ix
LIST OF ABBREVIATIONS .......................................................................................... x
ABSTRACT ...................................................................................................................... xi
CHAPTER 1: INTRODUCTION AND LITERATURE REVIEW ............................. 1
1.1 Introduction to Precision Agriculture (PA) ............................................................... 2
1.2 Review of Remote Sensing in Precision Agriculture studies .................................... 6
1.2.1 Remote Sensing of Vegetation .................................................................................... 8
1.2.2 Vegetation Indices .................................................................................................... 10
1.2.2.1 Type of Vegetation Indices and their use ..................................................................... 11
1.2.2.2 Data Output from Vegetation indices .......................................................................... 14
1.2.3 Benefits of Precision Agriculture ............................................................................. 15
1.3 Research question and Objectives ............................................................................. 17
CHAPTER 2: STUDY AREA ........................................................................................ 19
2.1 Location ....................................................................................................................... 19
2.2 Sites Characterization Criteria ................................................................................. 21
2.2..1 Presence of vegetation ......................................................................................... 21
2.2.2 Size and shape of site ............................................................................................... 22
2.2.3 Site Additional Features Characteristics ................................................................. 23
2.2 Sites Selections ............................................................................................................ 25
CHAPTER 3: DATA AND METHODOLOGY........................................................... 29
3.1 Data Sources and Selection for Landsat7 ETM+ and NAIP Imagery ................... 29
3.1.2 Data selection........................................................................................................... 30
3.1.2.1 Landsat7 ETM+ Imagery ............................................................................................. 30
3.1.2.2 NAIP Imagery .............................................................................................................. 32
3.2 Methodology ................................................................................................................ 33
3.2.1 Vegetation Indices Analysis ..................................................................................... 34
3.2.2 Percent Error Analysis ............................................................................................. 36
3.2.3 Quantifying Values ................................................................................................... 37
CHAPTER 4: RESULTS ............................................................................................... 39
4.1 Vegetation Indices Calculations ................................................................................ 39
4.2 Percent Error Results ................................................................................................. 47
4.3 Assessment of Resolution Differences ....................................................................... 49
CHAPTER 5: CONCLUSIONS AND FUTURE WORK ........................................... 54
5.1 Conclusions ................................................................................................................. 54
5.2 Future Work ............................................................................................................... 56
REFERENCES ................................................................................................................ 58
APPENDICES ................................................................................................................. 62

v

Appendix A: Site Selection Footprints .......................................................................... 62
Appendix B: VI Analysis Results .................................................................................. 67
Appendix C: Percent Error Analysis Results ............................................................... 77

vi

LIST OF TABLES

Table 1: RS Products and Suggested Uses 4

Table 2: Study Sites 25

Table 3: Landsat7 ETM+ Data 31

Table 4: NAIP Data 33

Table 5: Percent Error Results 47

Table 6: Group 5 PE results for all VI values 48

Table 7: Sites 10 & 13 NDVI PE Results 52

Table 8: Average PE Results by Size 53

Table 9: RVI PE Results 76

Table 10: NDVI PE Results 77

Table 11: GNDVI PE Results 78

Table 12: SAVI PE Results 79

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LIST OF FIGURES

Figure 1: Remote Sensing in Agriculture 6

Figure 2: Plant Spectral Profile 9

Figure 3: NDVI Calculation 12

Figure 4: False Color Composite 15

Figure 5: Study Location 21

Figure 6: Site Characteristics 24

Figure 7: Study Sites 27

Figure 8: Site Footprints 28

Figure 9: Landsat7 ETM+ Scene 31

Figure 10: NAIP Scene 32

Figure 11: Methodology Flow Chart 34

Figure 12: Group 5 Low Resolution 41

Figure 13: Group 5 High Resolution 42

Figure 14: Group 5 Landsat 43

Figure 15: Group 5 NAIP VI Analysis 44

Figure 16: Site 1l PE Results 45

Figure 17: Site 11 PE Results 46

Figure 18: Site 1 PE Example 50

Figure 19: Site 10 PE Results 51

Figure 20: Group 1 Footprint 61

Figure 21: Group 2 Footprint 62

Figure 22: Group 3 Footprint 63

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Figure 23: Group 4 Footprint 64

Figure 24: Group 5 Footprint 65

Figure 25: Group 1 Landsat7 ETM+ VI 66

Figure 26: Group 1 NAIP VI 67

Figure 27: Group 2 Landsat7 ETM+ VI 68

Figure 28: Group 2 NAIP VI 69

Figure 29: Group 3 Landsat7 ETM+ VI 70

Figure 30: Group 3 NAIP VI 71

Figure 31: Group 4 Landsat7 ETM+ VI 72

Figure 32: Group 4 NAIP VI 73

Figure 33: Group 5 Landsat ETM+ VI 74

Figure 34: Group 5 NAIP VI 75

Figure 35: Group 1 PE Results 81

Figure 36: Group 2 PE Results 82

Figure 37: Group 3 PE Results 83

Figure 38: Group 4 PE Results 84

Figure 39: Group 5 PE Results 85

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LIST OF EQUATIONS

1: Ratio Vegetation Index (RVI) 11

2: Normalized Difference Vegetation Index (NDVI) 12

3: Green Normalized Difference Vegetation Index (GNDVI) 13

4: Soil Adjusted Vegetation Index (SAVI) 13

5: Percent Error (PE) 36

x

LIST OF ABBREVIATIONS

AAG Association of American Geographers
GIST Geographic Information Science and Technology
JEP Joint Educational Project
SSI Spatial Sciences Institute
USC University of Southern California
RS Remote Sensing
VI Vegetation Index
PA Precision Agriculture
NAIP National Agriculture Imagery Program
USGS United States Geological Survey
USDA United States Department of Agriculture
FSA Farm Service Agency
NRCS Natural Resources Conservation Service
DOQQ Digital Orthophoto Quarter Quads
NDVI Normalized Difference Vegetation Index
GNDVI Green Normalized Difference Vegetation Index
RVI Ration Vegetation Index
SAVI Soil Adjusted Vegetation Index
ROI Return on Investment
PE Percent Error
VRT Variable Rate Technology

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ABSTRACT

Small scale farming identify farms with less than 300 acres of agricultural land
and represent a large population of producers in the US, thus the interest in procedures
such as Precision Agriculture Application in productivity cycles. This study compares
publically available Landsat7 ETM+ imagery, at nominal 30 meters pixel resolution, and
National Agricultural Imagery Program’s (NAIP) imagery, at nominal 1 meter pixel
resolution, to evaluate their use in Precision Agriculture (PA) applications for small-scale
farming. The selected study area was determined based on crop characterization and land
size criteria identified in the South Eastern part of Pittsylvania County, VA. The selected
agricultural fields within the study area, 14 in total, were of varying shapes, ranging from
7.5 to 150 acres in size, and characterized by a specific crop type such as non-alfalfa hay.
The methodology for this study consisted in the computation and analysis of four
vegetation indices (VIs) to evaluate the effect of imagery resolution to depict vegetation
maturity in the selected 14 sites. The VIs used consisted of: Ratio Vegetation Index
(RVI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference
Vegetation Index (GNDVI), and Soil-Adjusted Vegetation Index (SAVI). In addition to
the Vis analysis, a pixel Percent Error estimate was derived from the low- and high-
resolution VIs products to evaluate the amount of variance between Landsat7 ETM+ and
NAIP data.
As expected, NAIP’s VIs results provided more detail about the study sites
compared to the Landsat7 ETM+ VIs products. This was evident as NAIP’s ability to
locate and visualize vegetation and non-vegetation features within the study sites, which
is of particular importance for PA applications. In contrast, Landsat7 ETM+ imagery

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were not able to provide adequate identification and monitoring capabilities when used in
limited areal extent, specifically required for small scale farming PA applications.
Spectral mixing of land features smaller than the 30 meters pixel resolution imagery were
causing vegetation differences to be diluted across the fields rather than being isolated
and identifiable like in the NAIP’s VIs results.
Results from the PE analysis confirm the VI results and show a great difference
between VI values derived from the low resolution Landsat7 ETM+ and high resolution
NAIP imagery. The majority of the sites contain a high percentage of pixels error above
the acceptable percentage, which outline that VI values derived from low resolution
imagery do not provide results comparable to the high resolution imagery. Moreover, the
size of the sites do have an effect on the amount of acceptable PE within each field, with
larger fields containing higher percentages of Acceptable PE than smaller sites.
Therefore, due to the use of reduced size fields in small scale farming, the use of low
resolution imagery might not be appropriate to adequately represent the actual ground
conditions necessary for reliable PA use.

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CHAPTER 1: INTRODUCTION AND LITERATURE REVIEW

While there are still tractors, harvesters, combines and other typical machines involved,
those machines and their operators are now equipped with GPS units, various
environmental sensors, and other forms of technology such as the sue of satellite and
airborne imagery, commonly identified as Remote Sensing (RS), that help monitor and
track almost every element of traditional farming. The leap into a new generation of
small scale agricultural technology, defined as Precision Agriculture (PA), is made
possible through the use of RS and its innovations. PA has its benefits, however, it brings
to farmers some difficult choices due to the implementation of a new technology, which
has its challenges and costs. Implementing PA requires startup costs for training,
hardware, and software that could intimidate potential new users. As with all new
technology, there is an inherent amount of risk associated, especially in terms of cost.
Identifying cost effective methods with new technology can be a trying process.
Small scale farming accounts for around 92% of farms within the US (Poole,
2004) and the majority fall in the low producing section of that category where the farm
produces barely enough to cover the costs of maintaining a working farm. The other 8%
are large family or commercial farming outfits that produce over $250,000 in revenue. In
a study conducted in 2010 throughout the state of Ohio, of 3,000 farmers surveyed,
38.7% stated that they had adopted 1 or more elements of PA. This percentage identified
farms of the largest size and highest gross income (Diekmann & Batte, 2010).
Unfortunately this trend is not limited only to Ohio but it is common across the United

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States, as stated in the USDA’s National Institute of Food and Agriculture (NIFA)
website (USDA National Institute of Food and Agriculture 2009):
“Small and medium sized producers have a distinct disadvantage over
large producers. In high-volume agriculture, economies of scale and
narrow profit margins provide an economic advantage to large producers.
Furthermore, large producers tend to have more education and are less
wary of technology than smaller producers. These characteristics of
production agriculture suggest that most technological advances, including
site-specific management, are not scale neutral.”

1.1 Introduction to Precision Agriculture (PA)

While there is a noted disadvantage, there are some areas where PA applications
can be utilized by small scale operations. PA contains a variety of technology, therefore
individual elements can be implemented over time at a more manageable level rather than
purchasing multiple elements at one time. PA by definition refers to:
“a management system that is information and technology based, is site
specific and uses one or more of the following sources of data: soils,
crops, nutrients, pests, moisture, or yield, for optimum profitability,
sustainability, and protection of the environment” (McLoud & Gronwald
2007).

The information and technology used include a host of hardware (GPS Units,
vehicle mounted sensors, auto-steering, etc.) and software (GIS software, recordkeeping,
sampling collection, etc.) which drive the management styles and processes of farms.
While it is not as efficient to pick and choose which elements to use, cost savings and
increased revenue from PA over time could eventually lead to increased implementation.
Modern PA can trace its history back to when emerging technology became
available to the public. Early uses of GPS for precision agriculture began in the early
1990’s with the availability of NAVSTAR GPS (Sturdevant 2007). Variable rate

3

dispersion of fertilizers and pesticides, yield mapping, and isolating field damage from
weather related events were the first of many adoptions of modern PA through the
availability of GPS technology. Early estimations in 1994 predicted that only 5% of
farmers were utilizing the new GPS enabled PA, yet this was considered ‘booming’ for
the time (Sturdevant 2007).
Implementation of PA in agriculture since the early adoptions has increased, but
not across the entire spectrum of PA. The Agricultural Resource Management Survey
(ARMS) analyzed data from 2001 to 2010 of corn, winter wheat, and soybeans to try to
understand the adoption rates of PA. In the study, it found that while the PA technology
is becoming more readily available, it is still developing, and the adoption rates reflect
this. Easier forms of PA, such as Yield Monitoring, are first to be implemented, with
Yield Monitoring adoption rate of 45% for soybeans, 42% for corn producers, and 35%
for winter wheat (Schimmelpfennig 2011). Other forms, such as Variable Rate
Technology (VRT), which require more sophisticated analyses and technology, are less
likely to be utilized. VRT rates for soybeans was 8%, corn was higher at 12% and winter
wheat topped at 14% and is steadily increasing (Schimmelpfennig 2011).
PA utilizes RS as a source of data used to create products that can be used across
a wide range of practices. Table 1 contains a listing of RS products and suggested uses
developed by the Missouri Precision Agriculture Center (Casady & Palm, 2002). A
wealth of information is available through RS, which directly assists with the day to day
management of farming operations.
RS products differ in temporal availability, spatial resolutions, and spectral
resolution. These factors require evaluation and the pros and cons of the different RS

4

products need to be determined for best use in each individual PA application, whether
large scale or small.
Table 1: RS products and suggested uses.
RS Product Use in PA
Soil Brightness Construct soil maps or direct soil sampling
Crop Vigor or Health Various uses including replanting, fertilizer use,
pesticide use, and yield predictions
Vegetation Cover Replant decisions
Chlorophyll Content Nitrogen management
Yield Predictions General management
Weed Escapes Weed management
Stress due to Canopy Irrigation management moisture deficits
Crop Residue Evidence of compliance with erosion prevention
guidelines
Source: Casady & Palm, 2002
Farming is critical to the livelihood of civilization, the best available technology
and practices needs to be utilized to operate at peak efficiency. As previously mentioned
in the Ohio study (Diekmann & Batte, 2010), only 38.7% of the Ohio farmers surveyed
were using PA. Thus, the motivation for this study stems from the lack of widespread use
of PA within the farming community. Technology evolves and advances as time
progresses, and so it is necessary that adoption rates of technology follow suit. New tools
and processes are being developed to modernize and streamline processes that are
currently being used. The goal is to be more efficient with resources and increase output,
essentially do more with less. Unless these new practices and technology are used, these
goals will not be met. Spreading the word and educating potential users about how PA
can be implemented needs to go hand in hand with the development of new technology.

5

Small farms are an important part of American agriculture. In the 1998 National
Commission on Small Farms, a renewed dedication to the improvement of small farms
was created (Volkmer et al., 1998). The purpose of this commission was to develop goals
and strategies for small farms to succeed in a very competitive economy. As stated in the
report (USDA National Commission on Small Farms 1998):
“Small farms have been the foundation of our Nation, rooted in the ideals
of Thomas Jefferson and recognized as such in core agricultural policies.
It is with this recognition of our Nation’s historical commitment to small
farms that we renew our dedication to the prominence of small farms in
the renewal of American communities in the 21
st
century.”

The importance of small farms stretches far beyond the production of crops.
Communities benefit from the presence of small farms in terms of divers types of owners,
cropping systems, landscapes, biological organizations, culture, and tradition (Volkmer et
al., 1998). These small farms are a source of employment for the rural communities they
reside in. Farms can be a great learning environment for children, where they can learn
about responsibility and the fruits of hard work. Additionally, with the majority of
farming across the Nation being small scale, the ecological and environmental
management are more personal and results with more involvement of farmers and their
environment. The benefit of small scale farming is shaping the rural parts of the country
and needs to be protected. With new technology and modern farming practices, these
small farms can continue to operate and have the ability to increase production and
revenue.
Education is critical to changing the minds of those that are unfamiliar about how
PA can be used on a farm. The more familiar PA is to potential users, the greater the
chances of implementation. There is a substantial amount of academic research articles

6

and journals aimed at agricultural developments, most of which are written for a technical
audience.
1.2 Review of Remote Sensing in Precision Agriculture studies

The point of view of a farmer looking at their crops is very limited, and the more
they can see, the more they can understand and act on. Remotely sensed images can be
used to identify nutrient deficiencies, diseases, water deficiency or surplus, weed
infestations, insect damage, hail damage, wind damage, herbicide damage, and plant
populations, to name a few (Nowatzki, Andres, & Kyllo, 2004). In Figure 1 is shown a
very simplified sequence of the main elements of a complete remote sensing system,
from beginning to end, of remote sensing of vegetation and use for PA purposes. .There
are many elements that are required, which encompass acquisition, processing, analysis,
and interpretation of RS imagery.

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Figure 1. Remote Sensing in Precision Agriculture: The sun (A) emits
electromagnetic energy (B) to plants (C). A portion of the electromagnetic energy is
transmitted through the leaves. The sensor on the satellite detects the reflected
energy (D). The data is then transmitted to the ground station (E). The data is
analysed (F) and displayed on field maps (G) and then used in the field (Nowatzki et
al., 2004).

Agricultural RS applications can trace their roots back as early as the 1920’s. In
1927 aerial photography was used to differentiate the difference between healthy and
diseased cotton plants (Neblette, 1927). This is a very early successful instance of using
remotely sensed information for agricultural purposes. After aerial photography came the
use of satellites for remote sensing. Crop imagery began to be obtained by Landsat in
1978 (Tenkorang & Lowenberg-DoBoer, 2008). Since then many different satellite
constellations have been launched and cover the entire earth, such as the ASTER,
IKONOS, GEOEYE, QuickBird, RapidEye, and SPOT systems. Each has their own
specialties that range from high resolution to different scanners, which include thermal
and panchromatic bands. In addition to satellite based sensors, low altitude sensors
provide another method for collecting RS data. Airplanes, helicopters, and unmanned
aerial vehicles (UAVs) provide a great service when satellite platforms are not an option.
Remote sensing in general has many advantages, and those advantages translate
well toward agriculture. Remote sensing technology is a non-destructive method of data
collection, it is systematically collected over large geographical areas, can reveal data
about places inaccessible by humans, the systematic nature of data collection can remove
sampling bias, provides biophysical information usable by other sciences, and remote
sensing data is independent from other mapping sciences such as cartography or GIS
(Jensen, 1996). Of these advantages, we can see how useful remote sensing is. Systematic

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collection of data that is unbiased can eliminate a majority of field work previously
performed by individuals with in situ surveying (Liaghat, 2010), as well as monitoring
distant areas of a large scale farming installation.

1.2.1 Remote Sensing of Vegetation

Remote sensing of vegetation requires knowledge of the structure and function of
vegetation itself and how its energy is recorded through sensors. This allows a user to
better understand what is being seen through the data that has been collected and apply
that data to real life plant identification or condition. Plant life differs between species
and their chemical composition is what is reflected in the data. Vegetation Indices (VI)
are derived from the reflectance properties of plants and are used to identify
characteristics of plant life. These properties correspond to data points that are measured
in the electromagnetic spectrum, particularly in the visible, near-infrared and infrared
portions. In the case of using RS for agricultural purposes, the focus is directed to the
energy reflected from plant foliage.
The most important parts of plant foliage that are indicated through RS data is
pigments, water content, carbon content, and nitrogen content (Exelis, 2013). These
components all contribute to the spectral characteristics of each plant. Figure 2 below
contains a reflectance graph showing portions of the electromagnetic spectrum and the
respective plant components. Each part of the plan affects the reflectance values. This is a
major factor when utilizing RS data for PA applications. Spectral profiles like Figure 2
tell us a great deal about the condition of vegetation.

9

Figure 2. Plant Spectral Profile: Typical reflectance sensitivities as controlled by leaf
pigments, cell structure, and water content. Crop health and other plant
characteristics can be identified based on the specific values returned in a spectral
profile. The variation of spectral values tells the story of the plant and gives a
molecular breakdown of what is happening inside (Exelis, 2013).

Pigments within the plant include chlorophyll, carotenoids, and anthocyanins
(Exelis, 2013). Each of these components that are found within a plant responds
differently depending on the health and condition of the plant. Chlorophyll is commonly
known for giving the green color to plants and helps see the general health of the plant.
Plants registering with high levels of chlorophyll are healthy and have high rates of
photosynthesis, meaning that they are well nourished and are able to sustain themselves.
The presence of high levels of carotenoids generally indicates stress, which can be caused
by low moisture content or disease, but can also indicate death of the plant. Identifying
carotenoid content can be very beneficial early on in the growing season to help identify
crop diseases and failures in irrigation systems. The last pigment compound is

10

anthocyanins, which shows changes in the foliage. This lends itself to identifying plants
undergoing senescence. All of these pigments within a plant are represented through RS
imagery.
The water content within a plant helps facilitate many processes that are necessary
for the plant to survive and sustain itself. Nutrients and minerals are transported
throughout the plant by water, and without water, the plant could not survive. Measuring
water content through RS is performed using the water content found within the leaves
using the near-infrared and shortwave infrared regions of the electromagnetic spectrum.
The values associated in this portion of the spectrum can then be used to identify the
water content of the plant and whether it has an appropriate amount and can survive or if
there is a lack of sufficient water.
Plants need carbon to survive, as it is the main requirement to perform
photosynthesis. Plants use carbon in forms as sugar, starch, cellulose, and lignin.
Cellulose and lignin are used in cell structure and have spectral characteristics that appear
in the shortwave infrared range of the electromagnetic spectrum. In addition to carbon,
nitrogen is also found in plants and has spectral characteristics that affect VIs that
measure plant pigments. Nitrogen can be found in plant leaves, contained in chlorophyll,
proteins and other molecules.

1.2.2 Vegetation Indices

Vegetation indices use a combination of two or more wavelengths of spectral
values into a single value, which can identify and highlight functional characteristics of

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vegetation (Department of Ocean Earth and Atmospheric Sciences, ODU, 2003). Because
of the different chemical compositions of vegetation, these reflectance values change
plant to plant, including different stages of growth and during times of plant stress. In
addition to identifying plant characteristics, VIs can also be used as mapping tools. Image
classification, such as land use can be identified through the use of VIs that is specifically
tailored toward plant identification. Other uses include canopy mapping, and tracking
deforestation.
The use of VIs requires the same knowledge of plant physiology as using RS.
With over 150 different VIs present today, the desired output and land conditions will
determine the type of VI used. Some of the most popular and widely used VIs developed
over the years includes Normalized Difference Vegetative Index (NDVI), Soil Adjusted
Vegetation Index (SAVI), and Ratio Vegetative Index (RVI). Each of these VIs uses the
same basic ratio based approach, but there are advantages to use one over the other.
1.2.2.1 Type of Vegetation Indices and their use
Ratio Vegetation Index (RVI)
The Ratio Vegetation Index (RVI) is the simplest VI as it is a basic ratio of near
infrared and red bands (Birth and McVey 1968). The use for this is a general view of
vegetation in a given scene. The equation (1) for calculating RVI is as follows:
RVI = NIR / RED (1)
Normalized Difference Vegetation Index (NDVI)
The Normalized Difference Vegetation Index (NDVI) calculates the different
between the near infrared and red reflectance values divided by their sum (Tucker 1979,

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Hunt and Yilmaz 2007). The equation (2) produces a value ranging from -1 to 1, where
positive values generally denote vegetation and values approaching 0 and below are
devoid of vegetation, such as barren rock and snow. The example in Figure 3 shows the
difference of NDVI values for healthy and unhealthy vegetation.
NDVI = (NIR – RED)/(NIR + RED) (2)

Figure 3. NDVI Calculation: NDVI is calculated from the visible and near-infrared
light reflected by vegetation. Healthy vegetation (left) absorbs most of the visible
light that hits it, and reflects a large portion of the near-infrared light. Unhealthy or
sparse vegetation (right) reflects more visible light and less near-infrared light. The
numbers on the figure above are representative of actual values, but real vegetation
is much more varied (NASA Earth Observatory, 2013).

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Green Normalized Difference Vegetation Index (GNDVI)
A variation of NDVI has been developed (Hunt et al. 2007) that uses the green
band portion of the electromagnetic spectrum rather than the red band. This is referred to
Green Normalized Difference Vegetation Index (GNDVI). This is calculated using the
same equation (3) as NDVI, but with green band substituted for Red:
GNDVI = (NIR - GREEN) / (NIR + GREEN) (3)
The benefit of GNDVI over NDVI is that the green band can cover a broader
range of chlorophyll in plants than the red band. This applies to mature plants, and can be
useful for crop yield monitoring late in the growing season. NDVI’s use of the visible red
band works well for young, adolescent vegetation and is suitable for general greenness
and growth monitoring.
Soil Adjusted Vegetation Index (SAVI)
The Soil Adjusted Vegetation Index (SAVI) uses the same band as NDVI (red)
but introduces a constant to account for the present of soil in the data (Huete 1988). The
equation (4) for calculating SAVI is as follows:
SAVI = ((NIR – RED)x(1 + L))/(NIR + RED + L) (4)
The L value is the constant for soil brightness and produces values that are
independent of background noise (soil reflection). The L value ranges from 0 to infinity,
but Huete suggested that a value range be determined for vegetation density (Qi et al.,
1994). The value range suggested is L=1 for low vegetation, L=0.5 for intermediate
vegetation and L=0.25 for high vegetation. Note that when L=0 the output would equal
NDVI. This would be appropriate to use early on in the growing season where there is an

14

abundance of bare soil while seedlings are growing, or other situations where there is a
large amount of soil present when taking imagery.

1.2.2.2 Data Output from Vegetation indices

Once the calculations of VIs have been performed, the resultant data can be
visually examined. One of the advantages of using RS imagery is the ability to isolate
individual spectral bands and perform false color composite images. This process alters
the natural color scheme of what is seen by the eye to highlight a specific feature or
collection of features that cannot be seen by the naked eye. Figure below shows part of
the City of Ukiah, California. NDVI was calculated using 1 meter resolution imagery and
a false color composite color ramp is shown ranging from brown (unhealthy vegetation),
to red to green (healthy vegetation). Agricultural fields can be seen with lots of green
present, and surrounding neighborhoods and roads are represented with darker browns
and reds.

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Figure 4. False Color Composite: NDVI calculated Ukiah, CA, using NAIP 2010 1-
meter resolution NDVI imagery. False color composite images allow a different view
where subtle differences can be identified. In a true color image, green grass would
be visible and dominant, but with this alternate view, all rooftops and other features
that would be camouflaged by their colors stand out and can be easily seen. In this
image green tones depict healthy vegetation, yellow tones a mix of vegetation and
soil, and red tones bare soil or manmade infrastructures.
Source: ArcGIS Online, 2014

1.2.3 Benefits of Precision Agriculture

The benefits of PA derive from streamlined processes and added monitoring
ability. The results of efficient processes and monitoring include reducing the amount of
wasted resources, increased ability to monitor soil and crop conditions, and the potential
for increased yield with enhanced crop management. In a Cornell Precision Agriculture
case study, the farm of Elmer Richards and Sons was used as a test sight. The Richards

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farm is a dairy farm that grows 1,300 acres of corn and 1,000 acres of wheat, along with
their 800 cow dairy operation. In 1997 a yield monitor was purchased to assist with
identifying problem areas and to create field maps. As stated in the study, it was an
additional 10 hours of work for the entire year to manage data with an additional hour to
calibrate the sensor as needed, both of which is considered minimal (Kahabka, Staehr,
Hanchar, & Knoblaunch, 2000). Through their use of the yield monitor, crop maps were
produced utilizing yield data combined with GPS data. Based on the information gained
from implementing one PA element, numerous changes in crop selection were made as a
result of having accurate data available when making decisions.
Additional benefits of PA present opportunities that do not apply the farmer’s
bottom line; there are environmental advantages and benefits of using PA. Utilizing
advanced resource management, the amount of farm related pollution and erosion can be
lessened. Through targeting specific areas with a predetermined amount of pesticides,
fertilizers, or any other additives, runoff can be curbed to reduce ground water
contamination.
Erosion is another source of water contamination and removes valuable topsoil.
Monitoring key areas through PA can prevent many practices such as over irrigation on
sloped fields and unnecessary field and crop treatments. In addition, with the capability to
track farm equipment using GPS, high traffic areas can be identified and addressed to
reduce unnecessary traffic and movement on and around sensitive areas that are prone to
erosion.

17

1.3 Research question and Objectives
Small scale farms rely heavily on the success of their crops as it is generally the
only source of income. Because of the limitations of available capital, any potential
addition to current operations is a financial risk. In addition to financial risks, new
technology brings new challenges that include hardware, software, and the technical
capabilities of the user. The uncertainty about new technology and financial risks are
factors that keep potential users from considering using.
The research topic for this case study is comparing low and high resolution RS
products for use in PA applications for small scale farming. Due to the limited land and
resources available with small scale farming, utilizing RS data needs to be as efficient as
possible. Resolution differs between RS sensors and sources, and cost is influenced by
resolution. This comparison used publicly available Landsat7 ETM+ low resolution data,
30 meters nominal resolution, and efficient NAIP high resolution data, 1 meter nominal
resolution, to achieve the following measurable objectives:
- quantify the amount of error between Landsat7 ETM+ and NAIP data through
the use of VI and PE analyses
- quantitative comparison of Landsat7 ETM+ vs. NAIP,
The predicted outcome is that there will be a large amount of difference between
the two based on the large difference in pixel size, so much so that the low resolution
imagery will not provide the optimal results. Small scale farming fields are small and low
resolution pixels are large and cannot collect the same level of detail and information
compared to their high resolution counterpart.

18

Alternately, high resolution imagery provides a large amount of data, which could
actually be in excess of the amount of needed information. If the larger pixel size can
produce results comparable to the high resolution, then the use of low resolution imagery
can be considered optimal. Even with this as a possibility, the expected outcome is that
the low resolution imagery is not optimal for small scale farming.
The following chapters of this thesis discuss and explain the process that took place to
achieve the thesis objectives. Chapter 2 discusses the study site selection process, with
details related to the site selection criteria. Chapter 3 provide details on the data selection
and methodology used for the vegetation indices extraction, statistical analysis, and
values quantification and comparison. Chapter 4 lists the results and chapter 5 presents
the conclusions and potential future work.

19

CHAPTER 2: STUDY AREA

Precision Agriculture’s use of imagery provides a data driven view of a specific area. RS
imagery provides the spectral vegetation values for a field and after further analysis (such
as a vegetation index calculation or false color composite image) a crop health map is
derived that allows a farmer to understand the general health their field.
The accuracy of this data depends on several factors, which include the
characteristics of the field. Agricultural fields come in various shapes and sizes, terrains,
climates, locations, and other characteristics that will affect the RS imagery and resultant
data. These variations occur both naturally and manmade, which needs to be taken into
account when gathering data. To present the best possible study locations, several factors
were selected and included in the site selection process.
Small scale faming exists throughout the country and across a wide variety of
land types. To be able to represent small scale farms for this comparison, features and
characteristics of small scale farms were used that can be applied to most small farms in
one way or another. Study sites were chosen based on location, presence of vegetation,
size and shape of the site, and field characteristics. These categories represent real life
factors of small farms and topographic elements that challenge the abilities of RS
imagery.
2.1 Location

The study area for this comparison consists of agricultural areas within
Pittsylvania County; Virginia. Figure 5 gives reference to the location of Pittsylvania

20

County. The Commonwealth of Virginia grows a wide variety of crops and has a large
number of small farms. The 2011 average farm size was 171 acres, which falls under the
initial definition of a small scale farm. While the average farm size is 171 acres, the
majority of farms within the state fall under 100 acres, with multiple fields per farm.
The 2007 Agricultural Census showed that the majority of acres harvested came
from farms averaging less than 100 acres in size. Pittsylvania County is the largest county
in Virginia and is comprised of 44% agricultural land (Rephann, Ellis, Rexrode, &
Eggleston, 2013). In addition to having an abundant supply of available agricultural land,
Virginia also participates in USDA’s National Agricultural Imagery Program (NAIP).
The NAIP program acquires ‘leaf-on’ aerial imagery during growing seasons across the
United States. NAIP imagery is available to the public and government agencies for use
in both agricultural and non-agricultural purposes. Virginia’s participation in this
program provided key RS data for this comparison as it is both publically available and
high resolution.

21

Figure 5. Study Location: Pittsylvania County is located in the South Central
portion of the state of Virginia. It is the largest county in Virginia and is 44%
agricultural land. The terrain includes hills, low land, and low mountains in the
northern portion of the county. Rich soil content and a healthy growing season
present optimal conditions for growing a variety of crops and livestock
Source: Yellow Maps, Blank County Maps of Virginia 2014

2.2 Sites Characterization Criteria
2.2..1 Presence of vegetation

Vegetation index analyses were used for the comparison, which required
vegetation to be present within the imagery. Due to the wide range of vegetation grown in
Pittsylvania County, there was a good chance of having vegetation within the collection
areas for each set of imagery. In the case of Pittsylvania County, VA, the collection dates

22

were rather early within the growing season, but despite this, there was usable vegetation
within the hay and agricultural grasses category. These particular crops can have earlier
harvesting dates, which allowed for the presence of Non-Alfalfa Hay vegetation. This
type of vegetation served as a viable agricultural medium for VI analysis. To keep results
uniform throughout the study, fields containing the same crops were sought. This was
accomplished with the assistance of the USDA’s National Agricultural Statistical Service
(NASS). NASS collects and updates a national database, the Cropland Data Layer, with
agricultural data, specifically crop types. The NASS utilizes the AWiFS sensor aboard
the Resourcesat-1 satellite to gain 56m imagery. The data is analyzed in-house with
commercial software (Erdas Imagine, ESRI ArcGIS, and Rulequest See5) to develop and
process accurate ground cover types (Research and Development Division 2014). With
the development of appropriate methodology, including the development of the Common
Land Unit (CLU) program as a result, the information produced by this service was used
to identify crop types for all fields during the selection process.
2.2.2 Size and shape of site

Small Scale farming is defined by the USDA as having an income less than
$250,000 per year (Poole, 2004). A set range of 5 - 150 acres per field has been chosen
for this analysis due to the average farm size for the middle income range ($10,000-
$99,999/year) of 2011 is around 300 total acres. This accounts for 30% of the number of
farms classified as small (the majority fall in the < $10,000/year, at 60%) (National
Agricultural Statistics Service, 2012). Even though the majority of farms are well below

23

the 300 sq. acre range, the same results can be used on any size farm by utilizing the
information and processes as stated in this comparison.
The topology and terrain of small farms varies depending on a variety of factors,
including availability of arable land, water features, irrigation practices, etc. Typical
small scale farms utilize multiple fields, comprised of all shapes and sizes to maximize
all available land that is owned by the farmer. Pittsylvania County’s agricultural land
showed no standard or consistent field size or shape throughout the county. To reflect this
within the comparison, a range of field sizes and shapes was used. This variation in field
size was necessary to test the limits of how the different resolutions represented fields
varying in size and shape.

2.2.3 Site Additional Features Characteristics

In addition to selecting sites of different size and shapes, certain features
characteristics were chosen to challenge the resolution of the imagery. These included the
presence of non-vegetation features such as bodies of water, and erratic boundaries
between vegetation features. The reasoning behind these characteristics is centered on
the problem of mixed pixels. Mixed pixel is the result of a single pixel representing an
“area occupied by more than one ground cover type” (Roosta, Farhudi, & Afifi, 2007).
Mixed pixel situations occur in the following situations: 1) the pixels that are located at
the edges of large features, like agricultural fields, present a mixed signature between 2
vegetation types or vegetation and non-vegetation ground cover materials; 2) objects that
are relatively small, compared to the spatial resolution of the sensor, do contribute to the

24

pixels signature value but cannot be isolated (Roosta et al., 2007). In the low resolution
imagery, due to the larger area per pixel coverage, each pixel contains a larger amount of
spectral variation.
In a single 30 x 30 meter plot of land (size of Landsat7 ETM+ pixels), there can
be part of a water body, vegetation, a road, or bare ground. Each of those produces a
different spectral value, which is then averaged out to give a single value for the pixel.
With high resolution imagery, this issue becomes less apparent. Within a 1x1 meter pixel
area (size of NAIP pixels), the potential differences in ground cover and features are
limited. Areas along boundary features, such as roads and tree lines, have fewer mixed
pixels which result in more defined boundary edges. An example of this can be found in
one of the study areas, Study Site 14, shown in Figure 6. Site 14 has 3 lines of trees that
extend out into the field. This pattern is easily distinguished when using high resolution
imagery, but when viewed with low resolution imagery, the line of trees is somewhat
delineated but not clearly and uniquely identifiable.
In order to challenge the data analysis a variety of features such as tree lines, dirt
roads, tranches, ponds, and irregular boundaries were common in all selected sites.
Moreover, the wide variety of different field sizes created a very diverse dataset for this
study.

25

Figure 6. Site Characteristics: Study Site 14 shown utilizing both NAIP (right panel)
and Landsat7 ETM+ (left panel) imagery. The red arrows point to three different
tree lines that are within the field. Notice how defined the tree line is in the NAIP
imagery (right panel). As for Landsat7 ETM+ imagery (left panel), the tree line is
drastically exaggerated to the point of being almost unrecognizable. The area
affected by mixed pixels is greater in the Landsat7 ETM+ imagery as compared to
the NAIP. This larger area contains pixel averages that include unrelated tree
vegetation, which skew vegetation reflectance values specific to the non-alfalfa hay
crop that is being investigated in this study.

2.2 Sites Selections
After determining the criteria, the site selection process could begin. Both
Landsat7 ETM+ and NAIP imagery were utilized during the site selection process. Each
site is defined by a site number, group number, size in acres, and the different field
characteristics found within it. A total of 14 study sites, ranging in field sizes from 7.5 to
152 acres, were chosen and by relative location separated into 5 groups, as shown in
Figure 7. The 14 selected sites and attributes are summarized in Table 1. Footprint
examples for Group 5 (sites 10 through 14) are shown in Figure 8 and all remaining site
footprints are listed in Appendix A.

26

Table 2: Study Sites Characteristics
Site# Group#
Size
(Acres)
# of Pixels
Landsat7
ETM+
# of Pixels
NAIP
Crop Type Site Characteristics
1 1 110.57
499

436,155
Non-Alfalfa
Hay
Rounded boundaries, forested section
within field boundary, hilly terrain
2 2 12.72 54 46,374
Non-Alfalfa
Hay
Straight boundaries, no presence of
foreign objects or vegetation
3 2 71.98 320 273,157
Non-Alfalfa
Hay
Irregular boundaries, narrow field
sections, outbuilding present and small
wooded area
4 3 14.30 65 54,382
Non-Alfalfa
Hay
Straight boundaries, no presence of
foreign objects or vegetation
5 3 30.56 137 117,965
Non-Alfalfa
Hay
Irregular boundaries, dirt road present
6 3 17.33 78 64,407
Non-Alfalfa
Hay
Rounded boundaries, narrow field
section
7 3 11.29 50 40,957
Non-Alfalfa
Hay
Narrow field with barren patch of land
within
8 4 33.49 151 128,512
Non-Alfalfa
Hay
Irregular shape with a tree line and
individual trees throughout
9 4 21.76 97 81,697
Non-Alfalfa
Hay
Irregular shaped field with trees and
barren sections contained within
10 5 152.35 686 605,657
Non-Alfalfa
Hay
Large field with a dirt road, grove of
trees and slight terrain variation
throughout
11 5 23.42 104 88,032
Non-Alfalfa
Hay
Straight boundaries with a narrow
section surrounded by trees
12 5 7.5 31 26,173
Non-Alfalfa
Hay
Rectangular shaped field with uniform
vegetation and slight terrain variation
13 5 23.89 108 92,087
Non-Alfalfa
Hay
Rectangular shaped field, a grove of
trees, and a pond
14 5 86.61 391 331,107
Non-Alfalfa
Hay
Sprawling field with narrow sections
and tree lines entering the field

27

Figure 7. Study Sites on NAIP image. Their locations are contained within the
usable NAIP and Landsat7 ETM+ imagery overlapping area (as discussed in section
3.1.2), and have been assigned to 5 Groups. Site details are contained within Table 1
and complete list of site footprints are contained in Appendix A.

28

Figure 8. Site Footprints of Group 5. Landsat7 ETM+ (top panel) and NAIP
(bottom panel). Group 5 consists of Sites 10 through 14. Several features exist that
appear in the imagery, for instance Site 10 contains a dirt road and tree lines casting
shadows, site 13 contains a pond, and site 14 contains tree lines casting shadows.

29

CHAPTER 3: DATA AND METHODOLOGY
This chapter describes in detail the data sources and their selection process, then focus on
the selected methodology encompassing the analysis of four vegetation indices,
vegetation indices percent error analysis, and values quantification and comparison.
3.1 Data Sources and Selection for Landsat7 ETM+ and NAIP Imagery

The search of available data was conducted with collection dates as close to one
another as possible. This was done to reduce the variance between crops due to
vegetation maturity. Vegetation at different stages in maturity reflect differently, which
would not present identical site data between resolution datasets.
The Landsat7 ETM+ low resolution imagery were publicly available from the
USGS’s Landsat Archive, through the USGS web based Earth Explorer system
(http://earthexplorer.usgs.gov/). The data was identified using the ground coordinates
of the study area (Lat: 36.7440, Lon: -79.1704) among the available imagery from the
data set L7 ETM+ SLC-off (2003-present).
The NAIP imagery were obtained from the USDA/NRCS imagery program and
web portal (http://datagateway.nrcs.usda.gov/) in which a county system ID is used to
retrieve county mosaics and DOQQs. The county ID used for the Pittsylvania County,
located in the South Central portion of the state of Virginia, is 51143.
The similarity of the Green, Red, and Near Infrared spectral bands of the NAIP data
and Landsat ETM+ data (Lillesand and Kiefer 1994; USDA 2008) provide the perfect
data sets for this study.

30

3.1.2 Data selection
3.1.2.1 Landsat7 ETM+ Imagery
Landsat7 ETM+ multispectral data with less than 10% cloud cover factor was
used for the comparison. In addition to cloud cover restraints, Landsat7 ETM+ data
contains gaps within each scene due to a malfunction on the satellite platform that
occurred in 2003. On May 31, 2003, the Scan Line Corrector (SLC), which compensates
for the forward motion of Landsat 7, failed. The SLC-off effects are most pronounced
along the edge of the scene and gradually diminish toward the center of the scene (Figure
9). The middle of the scene, approximately 22 kilometers wide on a Level 1 (L1G)
product, contains very little duplication or data loss, therefore this region of each image is
very similar in quality to previous ("SLC-on") Landsat 7 image data. Landsat 7 ETM+
inputs are not gap-filled in the surface reflectance production available through USGS
Landsat Archive: L7 ETM+ SLC-off (2003-present). Because of this, the areas chosen
using the Landsat7 ETM+ scene were within the unaffected areas as shown in Figure 9.
The selected Landsat7 ETM+ imagery was collected on June 9, 2008, and was acquired
as L1G Product or Surface Reflectance. In Table 2 are listed the bands characteristics of
the acquired Landsat7 ETM+ scene encompassing bands B2 ( Green), B3 (Red), and B4
(Near Infrared). Table 2 contains a summary of the Landat7 ETM+ dataset.

31

Figure 9. Landsat7 ETM+ scene. The yellow rectangle shown contains the area
within the Landsat7 ETM+ scene is unaffected by the data gaps.

Table 3. Landsat7 ETM+ data
Landsat7 ETM+
# of Bands
Collected
B2 (.525-.605 μm)
Green
B3 (.63-.690 μm)
Red
B4 (.75-.90 μm)
NIR
Surface
Reflectance
Spatial
Resolution
30 meters
Date of
Collection
9 June 2008
Data Set LE70160352008161EDC00
Data Source: USGS Landsat Archive: L7 ETM+ SLC-off (2003-present) 2008

32

3.1.2.2 NAIP Imagery
The NAIP data for this study was collected in 2008, as it was the only available
dataset with 4 bands for Pittsylvania County. NAIP procedures allow states to acquire 3
or 4 band imagery, and with the use of VI the 4 band imagery is required. The acquisition
date on May 23, 2008, was the closest available dataset to match the Landsat7 ETM+
dataset. Figure 10 shows the same available data rectangle as seen in Figure 9, which
represents overlapping data with Landsat7 ETM+. A summary of the NAIP Surface
Reflectance dataset used in this study is listed in Table 3.

Figure 10. NAIP Scene. The yellow rectangle shown correlates with the available
data section of the Landsat7 ETM+ scene. The NAIP scene contains only

33

Pittsylvania County Virginia, whereas the Landsat7 ETM+ scene contains a larger
portion of counties within Virginia and North Carolina.

Table 4. NAIP data
NAIP
# of Bands
Collected
B2 (.480-.640 μm)
Green
B3 (.580-.700 μm)
Red
B4 (.680-.940 μm)
NIR
Surface
Reflectance
Spatial
Resolution
1 meter
Date of
Collection
23 May 2008
Data Set 51143_1m2008_6 Pittsylvania
Data Source: USDA/NRCS NAIP 2008

3.2 Methodology

The methodology used in this study encompass a first phase for data preparation
and vegetation indices extraction (Section 3.21, Vegetation Indices Analysis), a second
phase of vegetation indices statistical analysis (Section 3.2.2, Percent Error Analysis),
and a third phase for data products resolution comparison (Section 3.2.3, Quantifying
Values).
The full methodology workflow, shown in Figure 11, was conducted using
Model Builder (ESRI, 2014) Image bands were imported into ArcMap to begin the
extraction process. Prior to the VI’s calculations, statistical analysis, and value
quantification processes, the spectral band from both Landsat7 ETM+ and NAIP imagery
were extracted for each study site within the study area. This was accomplished by using
the Extract by Mask tool in order to reduce the amount of disk space used and increase
speed during processing time.

34

Figure 11. Methodology workflow. This ModelBuilder model illustrates the 4 main
processing phases: 1) band extraction, 2) VI Analysis, 3) PE Analysis, and 4)
Quantifying Values used to evaluate the effect of image resolution for use in PA
applications.

3.2.1 Vegetation Indices Analysis
Four different VI’s were utilized to give a variety of available indices used in the
vegetation analysis:
1) Ratio Vegetation Index (RVI)

35

RVI = NIR / RED
2) Normalized Difference Vegetation Index (NDVI)
NDVI = (NIR – RED)/(NIR + RED)
3) Green Normalized Difference Vegetation Index (GNDVI)
GNDVI = (NIR - GREEN) / (NIR + GREEN)
4) Soil Adjusted Vegetation Index (SAVI)
SAVI = (NIR – RED)/(NIR + RED + L) x (1 + L)
These equations use spectral values associated with each individual pixel within a
spectral band. NAIP’s used spectral bands B2 (Green), B3 (Red), and B4 (Near Infrared).
Analogously Landsat7 ETM+ used bands were B2 (Green), B3 (Red), and B4 (Near
Infrared) spectral bands. Due to the spectral characteristics and interaction of red, near-
infrared, and green bands with plants, these bands are used in most vegetation related
indices. In addition to spectral band values, the SAVI equation uses a constant variable
‘L’ to adjust for bare soil. Based on the collection date, type of crop, and visual
inspection of the imagery, a constant of 0.25 was used when calculating for SAVI. The
0.25 constant represents high vegetation and limited soil interference. From a visual
inspection between both sets of imagery, each site contains a generous covering of
vegetation with limited bare soil present.
Each site area was isolated from surrounding areas to reduce VI calculation times,
and using the above mentioned equations on each pixel, VI values for both Landsat7
ETM+ and NAIP were calculated. These values were then used to complete the PE
analysis.

36

3.2.2 Percent Error Analysis

To compare the differences between VI values at different resolutions the Percent
Error (PE) analysis was used (University of California, Davis 2014). The PE analysis is
not commonly used in agricultural purposes and has not been applied for PA applications
observed in the literature, however, it is commonly used in chemistry and other sciences,
where it involves measuring the difference between a known value (exact value) and an
experimental (approximate value) value. The PE equation (5) used in this study measures
the percent of error between a known value, exact value in this case assigned to NAIP VI
value, and a measured value or approximate value that in this case was assigned to
Landsat7 ETM+ VI value:
–

(5)
where : Exact value = NAIP VI value
Approximate value = Landsat7 ETM+ VI value.

The approximate value was designated as Landsat7 ETM+ values as it is being
compared to NAIP and it is the average value of the largest pixel size. Exact Value is
NAIP values as it is the value being compared to and is used as a more exact value due to
the reduced pixel size. The equation was performed for each NAIP pixel, and results
were collected and grouped into percentage categories. The resulting calculations give the
amount of error present between the two resolutions. Because a single low resolution
pixel represents a 30m x 30m land area within that pixel, there are 900 high resolution
pixels for the same size area, therefore there will likely be a given percentage of error

37

between the two. The percentage error calculation is two-fold as its output can give the
percentage of value differences that are: 1) over-estimated, where value of the low
resolution pixel is above what of the high resolution pixel value; 2) under-estimated,
where value of the low resolution pixel value is lower than the high resolution pixel.

3.2.3 Quantifying Values
The PE analysis is performed on each site’s VI values and the results are then
categorized. Three categories were established based on the amount of error present. The
categories are Grossly Over-estimated (+/- 100%), Debatable (between +/-100 & +/-
25%), and Acceptable (between +/- 25%). Each category’s percent error range was based
on the amount of error, compared to the amount of change between VI values.
The Acceptable category contains the percentage of pixels where the values fall
closely together, with a +/- 25% error range. This range represents a minimal change in
value. The Debatable category is separated into positive and negative percentages, which
represent over and under estimation of values. This particular range is considered
debatable, as the values have more than the accepted amount of error, but could
potentially rest within an acceptable range of error if so decided by the user. The Grossly
Overestimated category contains the percent of error +/- 100%, which is a significant
difference between values.
The results of the PE analysis provided a basis for comparison, as it defined a
measurable amount of error present. The greater the amount of error, the greater the
difference between VI values, which translates to incorrect VI representations for the
ground within that specific pixel. This applies directly to the use of RS data in PA, where

38

monitoring applications, resource allocation, or yield estimation rely on accurate ground
information. The greater the error, the more likely the resulting PA practices will not
produce the intended results, such as incorrect moisture monitoring results leading to
either over-watering or withholding irrigation. These actions would be detrimental to the
crops and negate the intended use of RS and PA.

39

CHAPTER 4: RESULTS

The results of both the VI analysis and PE analysis gave two different viewpoints for
imagery comparison. The VI analysis successfully calculated VI values for each field,
allowing for the PE analysis to measure any differences between resolutions. The results
are illustrated using the NDVI values due to their popularity in the agricultural literature,
however, all VI’s values are reported in Appendix B and C.
The results of the sites from Group 5 contain the largest number of fields, largest
range of field size, and largest amount of site characteristics available, therefore are
chosen as representative example of the general results. The remaining results obtained
from Group 1, 2, 3 and 4 are listed in Appendices B and C.
The PE analysis results provided a statistical look at the differences between
resolutions. As expected, there was a large amount of error present between the two
resolutions. This error was present for each VI analysis with the exception of the RVI
outputs. For this particular VI, the PE results were opposite of what was expected. PE
values were calculated as within the Acceptable category for every field, which was
drastically different than the other site PE results. Possible reasons for this to occur is that
the RVI formula is not normalized and therefore the results would not conform to the rest
of the VIs results.

4.1 Vegetation Indices Calculations
As mentioned in the site selection section, fields were chosen to include various
characteristics and features to test the abilities of the RS imagery. Both the Landsat7

40

ETM+ and NAIP imagery showed these differences, but with different degrees of details.
Results varied with each VI analysis due to the different spectral bands used in the VI
formulas, therefore different reflectance response are at times emphasized and others
subdued.
The NDVI values for the sites in Group 5 are shown in Figure 12. and 13. In
figure 12 are shown the low resolution Landsat 7 ETM+ imagery (panel A) and NDVI
(panel B), while figure 13 show the NAIP high resolution imagery (panels C) and NDVI
(panel D). Figure 14 and 15 show the VI results on sites 10 through 14, for the Landsat7
ETM+ and NAIP imagery respectively. Looking at Group 5 for each resolution, the VI
results do follow similar patterns and highlights the same general areas for vegetation and
non-vegetation areas.
The greater differences are observable when comparing resolutions, not just
between the VI results. For example Site 11, Figures 16 and 17, shows a substantial
difference with regards to VI values between the low and high resolutions imagery. The
difference suggests a great effect due to mixed pixels in the NDVI product form the
Landsat 7 ETM+ imagery, which is unable to depict the high resolution features
observable in the NADVI products derived from the NAIP imagery. Additionally, on a
pixel level, PE analysis adds to the vast differences in VI values. The remaining VI
results for the other sites and relative descriptions can be found in Appendix B.

41

Figure 12. Group 5: low resolution Landsat7 ETM+ imagery (panel A) and NDVI
(panel B). The Landsat7 ETM+ imagery (panel B) does outline similar features,
however, they are not as well defined and are not outlined as well through the NDVI
analysis.

42

Figure 13. Group 5: high resolution NAIP (panel C) and NDVI (panel D). There are
obvious differences between the VI’s products due to the different resolutions. The
NAIP NDVI analysis (panel D) shows more detail and outlines the such as a dirt
road and tree lines casting shadows (site 10), site 13 contains a pond, and site 14
contains tree lines casting shadows.

43

Figure 14. Group 5 Landsat7 ETM+ imagery VI analyses. VI values are overlying
NAIP base imagery for clarity. Values for each VI vary for each field, but follow
similar trends for vegetative areas and low vegetative areas. Site 13 contains a large
red spot, which is a pond. Each VI identifies it, but the effects of mixed pixel
averaging of surrounding ground values creates a disproportionate size and shape
of the pond.

44

Figure 15. Group 5 NAIP imagery VI analyses. High resolution analysis reduces the
impact of mixed pixels. Comparing Site 13, from Figure 14, same the pond is more
defined and the effect of surrounding areas reduced. Additionally, distinct lines of
low vegetation appear in Site 10, which are not clearly defined in the low resolution
imagery (Figure 14).

45

Figure 16. Site 11: VI products from Landsat7 ETM+. Similar results are
observable among VI’s values (overlying on NAIP base imagery for clarity). Pixels
are noticeably larger and effect of mixed pixels occur, resulting in the inability to
isolated higher resolution features visible in the NAIP results (Figure 17).

46

Figure 17. Site 11: VI products from NAIP. Similar results are observable. A large
amount of difference is though present when comparing VI products from NAIP
and Landsat7 ETM+ (Figure 16), in particular most of the low value vegetative
areas are singled out within the eastern part of the field in the NAIP imagery.

47

4.2 Percent Error Results
The PE analysis results for the NDVI values, shown in Table 5, are broken into
three different percentage categories: Grossly Overestimated, Debatable, and Acceptable.
NDVI values for each site is represented in the table. NDVI values and the results from
the remaining VI values are located in Appendix C. Referring back to Site 11, the large
amount of visual difference equated to only 10% of the pixels (8,803 pixels) being within
the Acceptable range, which represents a very low amount. The remaining 90% was
found split between the Debatable and Grossly Overestimated categories.
Table 5. NDVI Percent Error Results
NDVI
Site
#
Grossly Overestimated Debatable Acceptable
Below -100% Above 100%
Between -25% and
-100%
Between 25%
and 100%
Between -25%
and 25%
PE # of Pixels PE # of Pixels PE # of Pixels PE
# of
Pixels
PE
# of
Pixels
1 14% 61062 12% 52339 45% 196270 10% 43616 19% 82869
2 25% 11594 10% 4637 49% 22723 5% 2319 11% 5101
3 51% 138713 20% 56384 20% 55019 3% 9653 6% 17459
4 62% 33717 6% 3263 18% 9789 9% 4894 5% 2719
5 84% 99091 2% 2359 10% 11797 1% 1180 3% 3539
6 75% 48305 1% 644 18% 11593 1% 644 5% 3220
7 3% 1229 41% 16792 8% 3277 36% 14745 12% 4915
8 45% 57830 1% 1285 52% 66826 1% 1285 1% 1285
9 24% 19607 1% 817 69% 56371 1% 817 5% 4085
10 7% 42396 24% 145358 8% 48453 29% 175641 32% 193810
11 30% 26410 13% 11444 42% 36973 5% 4402 10% 8803
12 24% 6282 62% 16227 1% 262 5% 1309 8% 2094
13 2% 1842 1% 921 5% 4604 12% 11050 80% 73670
14 2% 6622 3% 9933 51% 168865 7% 23177 37% 122510

In reference to the sites in Group 5, Table 6 contains the results for all the VI
calculations. With the exceptions of RVI (which will be discussed later in Chapter 5) and

48

Site 13, all remaining VI results fall in the low percentages of Acceptable. This trend
extends throughout the rest of the sites, with remaining amounts of Debatable versus
Gross Overestimation varying.

Table 6: Group 5 PE results for all VI values
Site Info
Grossly Overestimated Debatable Acceptable
Below -100% Above 100%
Between -25%
and -100%
Between 25%
and 100%
Between -25%
and 25%
Sit
e #
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
RVI
10 605,657 0% 0 0% 0 <1% 595 9% 54509 91% 551148
11 88,032 0% 0 <1% 2 0% 0 2% 1761 97% 85391
12 26,173 0% 0 0% 0 0% 0 37% 9684 63% 16489
13 92,087 0% 0 2% 1842 <1% 169 6% 5525 91% 83799
14 331,107 0% 0 <1% 2 <1% 8 1% 3311 98% 324485
NDVI
10 605,657 7% 42396 24% 145358 8% 48453 29% 175641 32% 193810
11 88,032 30% 26410 13% 11444 42% 36973 5% 4402 10% 8803
12 26,173 24% 6282 62% 16227 1% 262 5% 1309 8% 2094
13 92,087 2% 1842 1% 921 5% 4604 12% 11050 80% 73670
14 331,107 2% 6622 3% 9933 51% 168865 7% 23177 37% 122510
GNDVI
10 605,657 23% 139301 74% 448186 0% 0 1% 6057 1% 6057
11 88,032 34% 29931 62% 54580 0% 0 2% 1761 0% 0
12 26,173 74% 19368 26% 6805 0% 0 0% 0 0% 0
13 92,087 64% 58936 32% 29468 1% 921 1% 921 1% 921
14 331,107 5% 16555 90% 297996 0% 0 4% 13244 <1% 11
SAVI
10 605,657 6% 36339 25% 151414 8% 48453 29% 175641 32% 193810
11 88,032 34% 29931 13% 11444 43% 37854 5% 4402 5% 4402
12 26,173 24% 6282 63% 16489 0% 0 6% 1570 7% 1832
13 92,087 34% 31310 31% 28547 7% 6446 13% 11971 15% 13813
14 331,107 2% 6622 3% 9933 51% 168865 7% 23177 37% 122510

49

4.3 Assessment of Resolution Differences
The Percent Error analysis produced some very interesting results. The expected
outcome was that there would be a high level of error between resolutions, which was
due to the large pixel size and averaging of reflectance values within Landsat7 ETM+
pixels. A single Landsat7 ETM+ pixel value was compared to the 900 NAIP pixels
contained within, and a single non-vegetative feature would produce many low or
negative NAIP pixel values, which would differ drastically to the corresponding averaged
Landsat7 ETM+ pixel value.
Because of the potential for difference between Landsat7 ETM+ and NAIP pixel
values, there was a chance that the percentage would be greater than 100%. This happens
when the approximate value (Landsat7 ETM+) is far greater than the known or expected
value (NAIP). An example of this is found in Figure 18. The Landsat7 ETM+ pixel
#213’s NDVI value is 0.011 and the NAIP pixel # 188184 NDVI value is -0.077. Those
values used in the PE formula result in a percent error of -114.669, which is greater than
the +/- 100% mark.

50

Figure 18. Site 1 PE example. The yellow square represents the Landsat7 ETM+
pixel and the red dot represents the location of the NAIP pixel. With their values
used in the PE formula, the resulting PE is outside the +/- 100% mark, which falls
within the Gross Overestimation category.

For this comparison, examples of the gross over or under estimation values can be
attributed to VI values of shadows from vegetation found in high resolution data and not
low resolution, or non-vegetation feature VI values in high resolution pixels that
averaged out in low resolution pixels, and non-vegetation features within the low
resolution pixel that are not being recognized due to pixel size vs. feature size. Site 10
contains site characteristics that fit the description above and causing PE of +/- 100%.
Figure 19 shows a color representation of NDVI PE values, where the areas containing
+/- 100% error follow the dirt road, tree lines with shadows, and changes in terrain.

51

Figure 19. Site 10 PE. Dark red represents +/- 100% error pixels, which follow the
dirt road (a) and surround the tree lines (b). One other area of interest is (c), where
a change in terrain occurs and showing with a significant amount of error. This area
(c) was not originally identified as significant site characteristic for testing
resolution abilities.

In Table 6, the PE results for Group 5 show large amount of differences present
among the various sites. To put the data into perspective, NDVI PE values for sites 10
and 13 will be used to explain the results. Table 7 contains NDVI PE results for sites 10
and 13, while remaining results can be found in Appendix C.

a
c
b

52

Table 7: Sites 10 & 13 NDVI PE Results
Site # Grossly Overestimated Debatable Acceptable
10 31% 37% 32%
13 3% 7% 80%

In site 10 there is 31% of pixels falling within the Grossly Overestimated
category, 37% of pixels falling within the Debatable category, and 32% of pixels falling
within the Acceptable category. The overall results an even distribution across categories.
Looking at it from a usability standpoint, straight from the data, only 32% of the data
would result in correct ground values and conditions. Additional research and decisions
on acceptable levels from the Debatable category could bolster this amount, but without
that, there is too much error to safely rely on low resolution data to produce acceptable
results.
In site 13 the NDVI results are different than that of the other sites. In contrast to
the expected results, Site 13 produced high amounts of Acceptable error present. Site 13
resulted in only 3% of pixels falling within the Grossly Overestimated category, 17% in
the Debatable category, and 80% in the Acceptable category. Even grouping Grossly
Overestimated and Debatable together, the total amount of Acceptable error is significant
enough to suggest that low resolution imagery, using NDVI calculations will provide
correct ground values with an 80% accuracy. Although this is a significant amount, the
remaining fields surrounding site 13 did not calculate this amount of Acceptable error,
and it would not be efficient to utilize low resolution for a single field, where the
surrounding fields need higher resolution imagery to be assessed.

53

As far as field size is concerned, there is a significant trend of larger fields
containing more Acceptable percentages of Error than their smaller counterpart. Table 8
shows the average Acceptable PE for the larger 5 sites, medium 4 sites, and smaller 5
sites. Both RVI and SAVI show that larger sites correspond a higher number of pixels
with Acceptable Error. RVI results have the larger averages in both the large and small
sites, and respectively higher than the medium size fields. NDVI shows close percentages
between the large and medium sites, with the lowest average in smaller sites. GNDVI has
opposite results, with the larger fields averaging the lowest amount of Acceptable Error
percentages.

Table 8: Average PE by Size
Average Acceptable PE

Large Size
5 Sites
Medium
Size 4 Sites
Small Size
5 Sites
Avg. Landsat7 ETM+ Pixels 409 111 39
Avg. NAIP Pixels 354,918 90,622 49,917
RVI 88.20% 61.50% 74.60%
NDNI 19% 24.50% 8.50%
GNDVI 5% 11.50% 9.20%
SAVI 19% 10.25% 8%

54

CHAPTER 5: CONCLUSIONS AND FUTURE WORK
5.1 Conclusions
The results from the VI analysis of low resolution imagery did not provide results
comparable to the high resolution imagery. VI analysis on the low resolution imagery
resulted in a lower detail in features and characteristic delineation. Features within the
low resolution imagery were identifiable but with less detail due to mixed pixels,
therefore resulting in an exaggerated feature footprint, such as the tree lines and dirt roads
as seen in Sites 10 and 14, which was highlighted in Figure 8, 12 and 13. As expected,
the high resolution data provided a more detailed visual representation of the study sites,
including all site features and characteristics.
Results from the Percent Error Analysis showed a great difference between VI
values with the exception of RVI. These results showed that the size of the pixel affects
the accuracy of data values. Landsat7 ETM+’s large pixel size averaged out the
surrounding areas and fail to retain amount of usable data which is instead captured in the
NAIP data. Even though there were amounts of acceptable error between the two
resolutions, the majority of the error was found within the Grossly Overestimated and
Debatable categories.
Examples used in section 4.3 examined the results of two sites, 10 and 13. The
comparative results identified site 10 as the area with the higher number of pixels outside
the Acceptable category. In contrast, site 13 produced results that went against the
expected outcome, showing 80% in the Acceptable category. A similar results was found
also for site 3 (Appendix C) also showing a higher amount of Acceptable error.

55

Site size was also a contributing factor in amount of Acceptable error. The larger
the field, the greater the chance of having more pixels with Acceptable amounts of PE.
The cause of this could be attributed to the same challenges overall; pixel size and mixed
pixels, but also the limited amount of land and pixel field boundary overlapping. Smaller
boundaries around fields and large pixels have the greater chance to include neighboring
areas within each pixel averaging.
There was one exception to the VI results, where the entire set of RVI values
resulted in almost 100% of pixels showing percentages of error within the Acceptable
category. A potential reason as to why this occurred is that the output values for RVI
were outside the normal range of outputs for normalized VI formulas. Fluctuations
between RVI values between resolutions were nominal, and the largest difference
between resolution VI results were found in the other VI formulas.
Overall, the results favored the expected outcome showing a greater difference
between NAIP high resolution VI values compare to the Landsat7 ETM+ low resolution
VI values. With the small nature of land applications used in small scale farming, the
larger pixel size would retain less information to be of use compared to the high
resolution imagery. If there was a higher percentage of acceptable error, +/- 25% was
accepted here, then the results could be more in favor of the low resolution.
This study addressed issues related to the assessment of data resolution in PA
applications, however, other factors could affect the use of different RS data such as cost,
return on investment (ROI) on use of RS to manual site methods in distributing fertilizer
or pesticide. In the next session, some considerations are provided for future work for
development in the use of affordable RS imagery in PA applications.

56

5.2 Future Work
Precision Agriculture is not a brand new topic, but it is one that has seen an
increase in research and activity in the past 20 years. With RS imagery, GIS integration,
highly advanced computers, and a vast array of monitoring hardware steadily advancing
the room for continued research is wide open. The basis of this comparison is to limit the
financial strain on small scale farmers when adopting new technology, particularly RS
imagery to use in PA applications. Cost is a main factor, and imagery resolution
influences the cost. Any additional or future work associated with this comparison would
include research in cost effective ways to implement RS or other advanced PA practices
and policies.
An example of this research would be an in-depth look at the financial return on
investment (ROI) applications of RS imagery. Begin a case study comparing the use of
RS to manual site methods in distributing fertilizer or pesticide. The comparison would
look at the time/cost data of the amount of labor used (man hours with relative pay), and
a fertilizer/pesticide used, determining a time frame for a ROI of utilizing RS for a single
specific use. This could then branch off to other uses as necessary.
Branching off from the ROI research, additional work including researching cost
effective methods of implementing RS imagery. NAIP imagery is valuable, but imagery
containing the NiR band is not available for every mapped location. Nontraditional
sensor platforms would be a popular subject as drones or UAVs have become a
household name in society today. Moving away from space-born and high altitude
platforms could potentially reduce some costs associated with sensor tasking, flight
scheduling, and weather related issues. Depending on the source, on-demand flights

57

could be possible after weather related events, during specific growth timelines, or land
surveying. Work already being completed by schools such a Utah State University’s
AggieAir UAV system (http://aggieair.usu.edu/) could be expanded with additional
knowledge of resolution limitations for small scale farming applications.
Additionally, improving upon the data available would be beneficial, as some
might be limited due to the intrinsic resolution of the data. This could be explored using
Data Fusion, which refers to the combination of data from different sensors and
resolutions to improve imagery interpretability (Ranchin, 2014). One of the advantages of
data fusion is the capability to improve spatial resolution and thus increase the ability to
identify features of interest. Despite the low performance of Landsat 7 ETM+ low
resolution imagery in this study, data fusion could possibly be used as a combination of
Landsat 7 ETM+ and LiDAR data (Cartus, 2012) to increase the interpretability and
possibly discern the amounts of acceptable error, or simply use it to render the data into a
useful mapping product for use in a different aspect of PA.

58

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62

APPENDICES
Appendix A: Site Selection Footprints

Figure 20: Group 1 Footprints. Level of detail is noticeably different between
resolutions.

63

Figure 21: Group 2 Footprints. Site 3 has very erratic borders which as seen in the
Landsat7 ETM+ do not follow visual field boarders.

64

Figure 22: Site 3 Footprints. Site 7 footprint does not follow a noticeable field shape
in the Landsat7 ETM+ imagery, which could possibly affect the VI and PE analyses.

65

Figure 23: Group 4 Footprints. Great differences between resolutions. NAIP
imagery shows large individual trees and tree lines within the site, which are
completely lost within the Landsat7 ETM+ imagery. Features like these will affect
the pixel that they are contained within.

66

Figure 24: Group 5 Footprints. Just as with Group 4, NAIP imagery shows tree lines
and other features within each site that are not identified within Landsat7 ETM+
imagery.

67

Appendix B: VI Analysis Results

Figure 25: Group 1 Landsat7 ETM+ VI Results. VI results, overlying on NAIP base
imagery for clarity, show a low vegetation trend throughout the entire field, with
high vegetative values in the northern most part of the field.

68

Figure 26: Group 1 NAIP VI Results. With the smaller pixels, more high vegetative
areas are highlighted and a better contrast between areas is present. Rather than
the entire field having low vegetative levels, an improved look at the results show
more vegetation present as compared to Landsat7 ETM+ values.

69

Figure 27: Group 2 Landsat7 ETM+ VI Results. VI results overlying on NAIP base
imagery for clarity. Large amounts of low vegetative areas with the central portion
of field 3 showing a fluctuation of high and low values.

70

Figure 28: Group 2 NDVI VI Results. More detail is shown of the mid range
(yellow) areas, where vegetation is present, but not at high levels.

71

Figure 29: Group 3 Landsat7 ETM+ VI Results. VI results overlying on NAIP base
imagery for clarity. Wide spreading of low values across each site except Site 7.

72

Figure 30: Group 3 NAIP VI Results. In comparison to the Landsat7 ETM+ results,
here NAIP values show abundant vegetation with limited areas of lower vegetative
levels.

73

Figure 31: Group 4 Landsat7 ETM+ VI Results. VI results overlying on NAIP base
imagery for clarity. Large amount of low vegetative levels in each site with few high
values.

74

Figure 32: Group 4 NAIP VI Results. More detail about each site, including the site
characteristics mentioned in the Site Characteristics table in Chapter 2. More high
vegetative levels throughout, indicating more vegetation present than seen through
the Landsat7 ETM+ data.

75

Figure 33: Group 5 Landat7 ETM+ VI Results. VI results overlying on NAIP base
imagery for clarity. Higher vegetative levels present in these sites as compared to the
other sites. Water feature is present in Site 13, but site characteristics of other sites
are not present, such as the tree lines in Site 14 and dirt road in Site 10.

76

Figure 34: Group 5 NAIP VI Results. Lots of vegetation present in each site and site
characteristics are more present than with Landsat7 ETM+ data.

77

Appendix C: Percent Error Analysis Results

Table 9: RVI PE Results
RVI
Site Info
Grossly Overestimated Debatable Acceptable
Below -
100% Above 100%
Between -
25% and -
100%
Between
25% and
100%
Between -25%
and 25%
Site # # of Pixels PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
1 436,155 0% 0 <1% 300 <1% 177 3% 13085 96% 418709
2 46,374 0% 0 0% 0 1% 464 11% 5101 88% 40809
3 273,157 0% 0 <1% 48 <1% 138 6% 16389 93% 254036
4 54,382 0% 0 0% 0 55% 29910 2% 1088 43% 23384
5 117,965 0% 0 0% 0 66% 77857 1% 1180 33% 38928
6 64,407 0% 0 0% 0 75% 48305 0% 0 25% 16102
7 40,957 0% 0 0% 0 0% 0 4% 1638 96% 39319
8 128,512 0% 0 0% 0 36% 46264 1% 1285 63% 80963
9 81,697 0% 0 <1% 3 16% 13072 <1% 646 83% 67809
10 605,657 0% 0 0% 0 <1% 595 9% 54509 91% 551148
11 88,032 0% 0 <1% 2 0% 0 2% 1761 97% 85391
12 26,173 0% 0 0% 0 0% 0 37% 9684 63% 16489
13 92,087 0% 0 2% 1842 <1% 169 6% 5525 91% 83799
14 331,107 0% 0 <1% 2 <1% 8 1% 3311 98% 324485

78

Table 10: NDVI PE Results
NDVI
Site Info
Grossly Overestimated Debatable Acceptable
Below -100% Above 100%
Between -25%
and -100%
Between 25%
and 100%
Between -25%
and 25%
Site
#
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
1 436,155 14% 61062 12% 52339 45% 196270 10% 43616 19% 82869
2 46,374 25% 11594 10% 4637 49% 22723 5% 2319 11% 5101
3 273,157 51% 138713 20% 56384 20% 55019 3% 9653 6% 17459
4 54,382 62% 33717 6% 3263 18% 9789 9% 4894 5% 2719
5 117,965 84% 99091 2% 2359 10% 11797 1% 1180 3% 3539
6 64,407 75% 48305 1% 644 18% 11593 1% 644 5% 3220
7 40,957 3% 1229 41% 16792 8% 3277 36% 14745 12% 4915
8 128,512 45% 57830 1% 1285 52% 66826 1% 1285 1% 1285
9 81,697 24% 19607 1% 817 69% 56371 1% 817 5% 4085
10 605,657 7% 42396 24% 145358 8% 48453 29% 175641 32% 193810
11 88,032 30% 26410 13% 11444 42% 36973 5% 4402 10% 8803
12 26,173 24% 6282 62% 16227 1% 262 5% 1309 8% 2094
13 92,087 2% 1842 1% 921 5% 4604 12% 11050 80% 73670
14 331,107 2% 6622 3% 9933 51% 168865 7% 23177 37% 122510

79

Table 11: GNDVI PE Results
GNDVI
Site Info
Grossly Overestimated Debatable Acceptable
Below -100% Above 100%
Between -25%
and -100%
Between 25%
and 100%
Between -25%
and 25%
Site
#
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
1 436,155 37% 161377 54% 235524 1% 4362 4% 17446 1% 4362
2 46,374 95% 44055 3% 1391 1% 464 1% 464 0% 0
3 273,157 96% 262231 2% 5463 <1% 85 <1% 160 <1% 96
4 54,382 16% 8701 22% 11964 26% 14139 9% 4894 27% 14683
5 117,965 30% 35390 16% 18874 33% 38928 7% 8258 14% 16515
6 64,407 22% 14170 15% 9661 66% 42509 10% 6441 19% 12237
7 40,957 39% 15973 55% 22526 6% 2457 0% 0 0% 0
8 128,512 2% 2570 16% 20562 34% 43694 21% 26988 27% 34698
9 81,697 3% 2451 28% 22875 9% 7353 28% 22875 31% 25326
10 605,657 23% 139301 74% 448186 0% 0 1% 6057 1% 6057
11 88,032 34% 29931 62% 54580 0% 0 2% 1761 0% 0
12 26,173 74% 19368 26% 6805 0% 0 0% 0 0% 0
13 92,087 64% 58936 32% 29468 1% 921 1% 921 1% 921
14 331,107 5% 16555 90% 297996 0% 0 4% 13244 <1% 11

80

Table 12: SAVI PE Results
SAVI
Site Info
Grossly Overestimated Debatable Acceptable
Below -
100% Above 100%
Between -25%
and -100%
Between
25% and
100%
Between -25%
and 25%
Site
#
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE
# of
Pixels
PE # of Pixels
1 436,155 16% 69785 11% 47977 45% 196270 10% 43616 18% 78508
2 46,374 24% 11130 10% 4637 50% 23187 5% 2319 11% 5101
3 273,157 51% 138713 20% 56384 20% 55049 3% 9653 6% 17430
4 54,382 61% 33173 10% 5438 16% 8701 8% 4351 5% 2719
5 117,965 84% 99091 3% 3539 8% 9437 2% 2359 3% 3539
6 64,407 76% 48949 4% 2576 14% 9017 1% 644 5% 3220
7 40,957 3% 1229 41% 16792 8% 3277 36% 14745 12% 4915
8 128,512 45% 57830 <1% 539 52% 66826 <1% 588 2% 2570
9 81,697 11% 8987 1% 817 69% 56371 1% 817 18% 14705
10 605,657 6% 36339 25% 151414 8% 48453 29% 175641 32% 193810
11 88,032 34% 29931 13% 11444 43% 37854 5% 4402 5% 4402
12 26,173 24% 6282 63% 16489 0% 0 6% 1570 7% 1832
13 92,087 34% 31310 31% 28547 7% 6446 13% 11971 15% 13813
14 331,107 2% 6622 3% 9933 51% 168865 7% 23177 37% 122510

81

Figure 35. PE Results Group 1. NDVI and SAVI show more detailed results
throughout the field than GNDVI and RVI.

82

Figure 36. PE Results Group 2. Site 3 NDVI results show the largest amount of
Grossly Overestimated and Debatable pixels throughout its center. This is also
reflected in SAVI due to the similarity in the VI formulas.

83

Figure 37. PE Results Group 3. SAVI and NDVI show similar results, with GNDVI
showing some possible differences, which could depend on the use of the green band
over the red.

84

Figure 38. PE Results Group 4. Each VI shows a different set of results for each site.
The pixel appearance in the NDVI and SAVI is due to sharp differences in the VI’s
derived values from Landsat 7 ETM+ and NAIP data.

85

Figure 39. PE Results Group 5. Areas around main features within the sites contain
pixels that fall in the Grossly Overestimated category in both NDVI and SAVI.

Abstract (if available)

Abstract Small scale farming identify farms with less than 300 acres of agricultural land and represent a large population of producers in the US, thus the interest in procedures such as Precision Agriculture Application in productivity cycles. This study compares publicly available Landsat7 ETM+ imagery, at nominal 30 meters pixel resolution, and National Agricultural Imagery Program’s (NAIP) imagery, at nominal 1 meter pixel resolution, to evaluate their use in Precision Agriculture (PA) applications for small‐scale farming. The selected study area was determined based on crop characterization and land size criteria identified in the South Eastern part of Pittsylvania County, VA. The selected agricultural fields within the study area, 14 in total, were of varying shapes, ranging from 7.5 to 150 acres in size, and characterized by a specific crop type such as non‐alfalfa hay. ❧ The methodology for this study consisted in the computation and analysis of four vegetation indices (VIs) to evaluate the effect of imagery resolution to depict vegetation maturity in the selected 14 sites. The VIs used consisted of: Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Soil‐Adjusted Vegetation Index (SAVI). In addition to the Vis analysis, a pixel Percent Error estimate was derived from the low‐ and high‐resolution VIs products to evaluate the amount of variance between Landsat7 ETM+ and NAIP data. ❧ As expected, NAIP’s VIs results provided more detail about the study sites compared to the Landsat7 ETM+ VIs products. This was evident as NAIP’s ability to locate and visualize vegetation and non‐vegetation features within the study sites, which is of particular importance for PA applications. In contrast, Landsat7 ETM+ imagery were not able to provide adequate identification and monitoring capabilities when used in limited areal extent, specifically required for small scale farming PA applications. Spectral mixing of land features smaller than the 30 meters pixel resolution imagery were causing vegetation differences to be diluted across the fields rather than being isolated and identifiable like in the NAIP’s VIs results. ❧ Results from the PE analysis confirm the VI results and show a great difference between VI values derived from the low resolution Landsat7 ETM+ and high resolution NAIP imagery. The majority of the sites contain a high percentage of pixels error above the acceptable percentage, which outline that VI values derived from low resolution imagery do not provide results comparable to the high resolution imagery. Moreover, the size of the sites do have an effect on the amount of acceptable PE within each field, with larger fields containing higher percentages of Acceptable PE than smaller sites. Therefore, due to the use of reduced size fields in small scale farming, the use of low resolution imagery might not be appropriate to adequately represent the actual ground conditions necessary for reliable PA use.

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Asset Metadata

Creator Bohon, Robert Kevin, Jr. (author)

Core Title Comparing Landsat7 ETM+ and NAIP imagery for precision agriculture application in small scale farming: a case study in the south eastern part of Pittsylvania County, VA

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Publication Date 07/14/2014

Defense Date 06/17/2014

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Landsat7 ETM,NAIP,NDVI,OAI-PMH Harvest,percent error,precision agriculture,remote sensing,SAVI,small scale farming,vegetation index

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Paganelli, Flora (committee chair), Lee, Su Jin (committee member), Ruddell, Darren M. (committee member)

Creator Email rob.bohon@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-436911

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Legacy Identifier etd-BohonRober-2660.pdf

Dmrecord 436911

Document Type Thesis

Format application/pdf (imt)

Rights Bohon, Robert Kevin, Jr.

Type texts

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Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

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