Master Geo-Information Science Internship Report GRS

Transcription

Master Geo-Information Science
Internship Report GRS-70424
UAV imagery as tool to monitor crop growth in West Africa
A case study in a crop fertilization experiment
January 2015
Bastiaen Boekelo
Student nr.: 901212-078-130
Supervisor Internship Provider:
Organisation Internship Provider:
WUR Supervisor - Plant Production Systems (PPS):
WUR Supervisor - GIS and Remote Sensing (GRS):
Dr. ir. P. C. S. (Sibiry) Traore
ICRISAT - Mali
Dr. ir. A. G. T. (Tom) Schut
Dr. ir. L (Lammert) Kooistra
0
Document state
Date
Version
Description
January 11th, 2015
1.0
Final internship report Bastiaen Boekelo
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Abstract
UAV imagery as tool to monitor crop growth in West Africa
-
A case study in a crop fertilization experiment
ICRISAT - International Crop Research Institute for the Semi-Arid Tropics.
Bamako, Mali
January, 2015
In the semi-arid tropical zone of Mali, little is known about local growth limiting factors of crops
present in the region. A fertilization study was conducted to get more insight in the effect of different
fertilizers on the growth of cotton, sorghum, millet, maize and peanuts. Crop growth was monitored
with on-the-ground measurements and with imagery derived from eBee: a light weight Unmanned
Aerial Vehicle (UAV). This study evaluates the effect of fertilization on crop growth and the potential
of crop monitoring based on UAV technology. Results indicate that application of fertilizers improves
crop growth in the Sukumba region. Yet, the relative impact of fertilization seems to depend on the
local environment of the agricultural fields. There is a strong relationship between crop height and
Normalized Difference Vegetation Index (NDVI) values, suggesting a potential for UAV-based crop
growth monitoring. Moreover, basic temporal profiles show differences in NDVI values between crop
types, which could be useful for image-based crop recognition. However, current image quality issues
decrease precision of the imagery and constrain pixel-to-pixel comparison. More research with
inclusion of all experimental fields and all crop growth variables will provide more insights in how the
fertilization effect on crop growth relates to variation in growth caused by environmental
heterogeneity. Additionally, this will provide insights in the relationship between biomass and UAV
imagery for NDVI-based yield prediction. Temporal NDVI profiling covering the overall growing
season should be conducted to investigate its value for semi-automated crop type differentiation.
Keywords:
UAV; eBee; Crop monitoring; Fertilization; Spatial variation; Mali; NDVI; Temporal profile; Image
Quality; Radiometric Calibration; Sorghum; Millet; Cotton; Maize
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Table of Contents
ABSTRACT .................................................................................................................................... 2
1.
THE STARS PROJECT ......................................................................................................... 5
1.1.
1.2.
PROJECT AND ORGANIZATIONS .......................................................................................................... 5
PERSONAL WORK PROCEEDINGS ........................................................................................................ 6
2.
INTRODUCTION .............................................................................................................. 10
2.1.
RESEARCH QUESTIONS AND OBJECTIVES ............................................................................................ 11
3.
METHODS – DATA ACQUISITION ..................................................................................... 12
3.1.
3.2.
3.2.1.
3.2.2.
3.2.3.
3.3.
3.3.1.
3.3.2.
3.3.3.
3.4.
3.4.1.
3.4.2.
3.4.3.
3.4.4.
3.5.
3.5.1.
3.5.2.
3.5.3.
3.5.4.
EXPERIMENTAL SETUP .................................................................................................................... 12
CROP GROWTH CHARACTERISTICS .................................................................................................... 13
Variables .............................................................................................................................. 13
Data recording method ........................................................................................................ 14
Data products....................................................................................................................... 15
GROUND CONTROL POINTS ............................................................................................................. 16
GCP Construction ................................................................................................................. 16
GCP Measurements ............................................................................................................. 16
Data product ........................................................................................................................ 16
MEASURING PHYSICAL REFLECTANCE ................................................................................................ 17
Radiometric Panel Construction .......................................................................................... 17
Measuring reflectance ......................................................................................................... 17
Data product ........................................................................................................................ 19
Use radiometric panel in the field ....................................................................................... 20
FLYING THE UAV .......................................................................................................................... 20
Specifics UAV and camera.................................................................................................... 20
Camera settings ................................................................................................................... 20
Flying location and time of day ............................................................................................ 20
Metadata product ................................................................................................................ 21
4.
METHODS – DATA ANALYSIS........................................................................................... 22
4.1.
4.2.
4.3.
4.4.
4.5.
4.6.
OVERVIEW INPUT DATA & DATA ANALYSIS ......................................................................................... 22
SELECTION OF CLUSTER .................................................................................................................. 23
UAV IMAGE ANALYSIS ................................................................................................................... 23
UAV RADIOMETRIC CALIBRATION .................................................................................................... 24
NDVI CALCULATION, EXTRACTION AND CORRELATION WITH PLANT HEIGHT............................................. 27
SPATIAL VARIATION ....................................................................................................................... 28
5.
RESULTS ......................................................................................................................... 29
5.1.
5.2.
5.3.
5.4.
5.5.
FERTILIZATION EFFECT ................................................................................................................... 29
UAV IMAGE QUALITY .................................................................................................................... 33
NDVI & PLANT HEIGHT ................................................................................................................. 37
SPATIAL VARIATION ....................................................................................................................... 40
TEMPORAL PROFILE ....................................................................................................................... 42
6.
DISCUSSION ................................................................................................................... 43
6.1.
FERTILIZATION EFFECT ON CROP GROWTH ......................................................................................... 43
6.1.1.
Fertilization and crop height ................................................................................................ 43
6.1.2.
Environmental effects .......................................................................................................... 43
6.2.
POTENTIAL OF UAV IMAGERY FOR CROP GROWTH QUANTIFICIATION ..................................................... 44
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6.2.1.
Image quality........................................................................................................................ 44
6.2.2.
Correlation NDVI and height of the crops............................................................................ 45
6.2.3.
eBee as a source of information .......................................................................................... 46
6.3.
FUTURE RESEARCH PERSPECTIVES..................................................................................................... 46
7.
CONCLUSION.................................................................................................................. 47
8.
RECOMMENDATIONS ..................................................................................................... 48
9.
LITERATURE ................................................................................................................... 50
APPENDICES ............................................................................................................................... 52
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1. The STARS project
1.1. Project and organizations
This study is an offshoot of the STARS-ISABELA project (Spurring a Transformation in Agriculture with
Remote Sensing), which (in short) has two research objectives. The first one (i) aims on the
implementation of a subscription based and transparent rural cadastral land tenure information
system by remote sensing imagery. This involves the development of an information service about
financial markets for local smallholder farmers. The second research objective (ii) is to develop digital
libraries and algorithms for (semi-)automated crop recognition. This can subsequently allow the
development of a crop monitoring or crop prediction system. A fertilization experiment has been set
up inside the borders of the project which provides supporting data for both research objectives. This
fertilization trial will be the focus of this study. Two experimental sites have been set-up: one in Mali
and one in Nigeria.
The key parties of the project and the role they have played will first briefly be explained.
ICRISAT: This organization is situated in Bamako and it has been entrusted to lead the research by
the funder. ICRISAT stands for ‘International Crops Research Institute for the Semi-Arid Tropics’.
Their main goal is to “overcome poverty, hunger and a degraded environment by better agriculture”1.
Manager of the project is Pierre C. Sibiry Traore.
UCL: UCL stands for ‘Université Catholique de Louvain’; situated in Belgium. Project representative is
Pierre Défourny. Xavier Blaes is coordinating the measurements and steering the people of both UAV
and crop seasonal development measurements. Guillaume Chomé, an UCL student has also played
an important role for the project by - amongst others - ensuring the continuation of the UAV flights.
AMEDD: Situated in Koutiala. The name stands for ‘Association Malienne d’Eveil au Développement
Durable’, which can be translated as ‘Malian Association for Awareness for Sustainable
Development’. They play a facilitative role in the project by providing a field measurement team and
a team focused on storage and processing of the data. Also, they have provided housing and
transport during for the ICRISAT employees in Koutiala. Ousmane Dembele and Gilbert Dembele
played a key role for this project by (amongst others) coordinating the field measurements and
processing the data respectively.
Manobi: This is a mobile data services provider and finds it origin in Senegal. They provided the
project with an application called mInventaire and a platform to record and store/view the ground
measurements respectively. Also, they will be involved in the development of the information service
for the smallholder farmers.
WUR: This university is situated in The Netherlands and stands for ‘Wageningen University and
Research Centre’. Goal is to improve quality of life. Project representative is Tom Schut. For the
project he assisted in the training of people and he played an important role in the development of
the measurement protocol. As for me, I have played more or less the same role as Guillaume: the
central part has been flying the UAV and creation of ground control points (GCPs).
Other partners that are involved in the project for the part in Mali are: ITC (Faculty of GeoInformation Science and Earth Observation); UDS (Universite de Sherbrook), GERSDA (Groupe
d'études et de recherche en sociologie et droit appliqué); Pixela SARL, IER and MSU. For a more
extended elaboration on the roles of the project parties one should read Appendix 7.
1
http://www.icrisat.org/Icrisat-aboutus.htm
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1.2. Personal work proceedings
It will be hard to describe my work in Bamako, Koutiala and Sukumba in only a few lines because of
its versatile character. My occupations can be split up in several main components. In this section I
will elaborate upon those ‘work components’ one by one, but before that I will sketch the context of
the overall situation to get an overview of the proceedings.
My first few weeks have been dedicated in assisting the development of the measurement protocol
document and exploring image quality and operability of the eBee. Here I have put effort in how to
store the data efficiently and, together with Guillaume Chomé (UCL), how to ensure the quality of
the data. Because of the rapid change of data recording method later (smartphones instead of
JUNO), this work has not been used afterwards. Further, I translated the document used for the crop
development stage made by Adja Sangare from French to English, did some data analysis with early
eBee flights and I made small contributions to the protocol, like the implementation of numbering of
the marked quadrats.
After this training period was finished, Guillaume (a Belgium student of UCL) and me were assigned
to fly the UAV in the field. We were staying in Koutiala which is situated more or less 50km from the
field site in Sukumba. Here we inhabited a guest house ICRISAT rented from AMEDD, which served as
our working office as well. The next table gives an indication of how an average day of work looked
like:
Time
06:30 – 08:30
08:30 – 09:00
09:00 – 13:00
13:00 – 14:00
14:00 – 16:30
16:30 – 17:00
17:00 – 18:00
18:00 – 20:00
20:00 – 21:00
21:00 – 22:00
Work
Mail, Skype, breakfast
Travel to the field
Field work
Lunch + field team meeting
Field work
Travel to Koutiala
Data control and transfer
Mail + other work PC
Diner
If needed: remaining work PC
Where
Guest house - Koutiala
Sukumba
Sukumba
Sukumba
AMEDD - Koutiala
Guest house
Maman j’ai faime - Koutiala
Guest house
The tasks of the day were shared equally between me and Guillaume together for the month August.
The first two weeks of September, Guillaume worked roughly half-time for the project and half-time
for his thesis and the last two weeks of September I have worked independently.
Now I will elaborate a bit more upon the content of the field work. The fieldwork can be split in
several matters that needed attention: ground control points, coordination field team and flying the
UAV:
Ground Control Points
Much work has been done for the construction and painting of the Ground Control Points (GCPs).
Especially in the first two weeks of measurements of the field team, it was very important that
suitable locations for GCPs were quickly identified and painted in order to perform a geometric
correction of the eBee imagery. For every the seven clusters, each typically about 120 ha, the area
has been surveyed for flat bedrocks or wells or concrete constructions to see where GCPs could be
painted easy. Besides a suitable subsurface, the GCPs should also be visible from the air and not be
built in a cropped field. We also explored local farms to see if there were suitable locations. If this
was the case, we only painted it when the owner of the farm agreed with it, which has always been
the case. No farmer has made objections against painting or constructing a GCP on their farm. For
some clusters there were plenty of suitable locations with the right surface, but for some clusters it
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was not or barely possible to find. This meant that an artificial subsurface needed to be made in
order to paint the white crosses. Therefore, we hired two masons. Again with help of the driver
(Massaman, Moussa, Abdulah or Malik, who all speak Bambara) we have explained – and also shown
– them how the concrete cinderblocks (concrete blocks / bricks) should be placed and connected in
order to make beautiful and usable GCPs. After a GCP (location) was identified, shown or painted, a
geotagged photo was made of the location and later on exported into a shapefile. With this shapefile
I started a documentation describing when we (i) showed suitable GCP locations to the masons, (ii)
when they were build, (iii) when a GCP was painted and (iv) when it was repainted. Guillaume and I
have updated this shapefile together regularly when progress had been made with the GCPs.
We made sure that every cluster at least had a few finished GCPs before we were flying over it. With
the field team we communicated in which order they needed to measure which fields (in which
cluster) in order to streamline the process and to have as many GCPs as possible on the photos. After
the first two weeks every cluster was equipped with at least several GCPs. However, since the
number of GCPs was still minimal, the masons were given the assignment to build more GCPs where
most necessary, which we referred to as the ‘GCP densification’. Unfortunately, because of financial
issues the construction of much GCPs was postponed three or four weeks. The last seven GCPs have
been built during the last two weeks of my stay. Once painted, the crosses were checked regularly on
‘whiteness’. Where necessary, GCPs have been repainted. It appeared that crosses painted on
bedrock were staying white the longest, whereas the concrete crosses needed to be repainted about
biweekly.
In total 51 ground control points are created and maintained.
Communication and instruction field team
The field team always consisted of four persons. This were Nema Dembele, Sanou Daouda, Oumar
Diabate and Birama Sissoko. Because of health issues, Nouhoum Dembele has later taken over the
place of Sissoko. All are employees of AMEDD. Team coordination was done in cooperation with
Ousmane Dembéle, the head of the ‘natural resources’ department of AMEDD. Most of the meetings
we had were during lunch time, since this was the most convenient time for Guillaume and me and
the field team. In the beginning we were also sometimes supervising them during the
measurements. This way, we gave instructions and we could give tips how to do things better (e.g.
they marked the quadrat with digging lines in the soil which was not necessary and made them loose
time). When the measurements seemed to go smoothly, we only came by occasionally. During the
lunch meetings we asked them which field they had done during the morning and the afternoon of
the previous day. In this way we knew what the best time was to fly which cluster and we could make
estimations about measuring time per field, which was used to make a feasible planning. Later I
made a form for this, so there was a non-digital way of knowing what has been done when at that
moment. We also exchanged batteries with the field team (which were charged in the guest house)
of the smartphones and the SD card of the camera of the vertical photograph. We always asked if
everything was going well and if there were problems. Not rarely there were problems with
smartphones which needed to be fixed in the field and/or in the guest house (often the problems
were related with the 3G connection, username login fail, synchronization of the application etc..).
We also always asked if they encountered problems or if they had new information (e.g. in my last
two weeks about fields to be harvested).
Flying the eBee
We also were flying the eBee. Biweekly all clusters were flown and as good as possible synchronized
with the field measurements. Also, we tried to keep the moment of flying as constant as possible.
Mostly we tried to fly in the morning at 10:00 since cloud conditions were often most favorable at
these times. For this we prepared the flight plans and in the beginning we explored which locations
are good landing sites. All landing sites were cotton fields since they were high enough to break the
landing and avoid the eBee from scratching the ground and low enough to avoid the eBee from
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falling down too much in the field. Before flying, the pre-flight checks needed to be performed to
ensure a safe flight. Sometimes flight plans were adjusted depending on the circumstances (clear
landing area, wind direction). During flights one of us was monitoring the eBee (battery, connection
quality etc.).
At the end of the field work in Sukumba, we went back to AMEDD to transfer the data (eBee and SD
card of the vertical photo camera) and to see if there are problems with the data processing. Then
we returned to the guest house to inform the others (AMEDD, Sibiry and Xavier) and to be informed
by email and to do other necessary work.
Radiometric calibration
For radiometric calibration of the images, there needed to be a radiometric panel, which has been
constructed in Bamako. Till about half September we used only radiometric panel A and B (mostly B).
I have painted those panels in 2 or 3 layers with some help of Guillaume in the guest house. This
work was done in the weekends and evenings since we were in the field during the day. Soon we
found out that camera optics were severely restricting the possibility to extract reliable pixel values
for an adequate radiometric calibration. Therefore, I have requested a bigger radiometric panel
which came in September, which I painted as well. Since images appeared often to be blurry, I
decided to paint this panel in two colours with a bigger surface.
All panels were measured by a spectrometer to determine the spectral reflectance of the colours of
the panel. I have measured those around noon, since the eBee also flies around these times. Before
that I made myself familiar with the software and Guillaume helped with connecting the Windows 98
laptop to the internet. After that, I processed the data to come with an absolute value for reflectance
for every colour and made a document describing how to do so for the next person working for the
project using this spectrometer.
Communication
Every relevant progression of the project was communicated to the project decision makers. Also,
together with Ousmane we maintained the communication between the field team and AMEDD. This
all means that problems with smartphones, decisions concerning location or number of GCPs, eBee
errors, financial matters, image quality issues etc.. are all discussed mostly via email. Also, Guillaume
(in August) and I (in September) updated an online agenda, describing in short the activities of the
field team and ourselves. Furthermore, we attended the biweekly scheduled skype sessions at
Tuesday morning for a general meeting with UCL (Xavier Blaes), AMEDD (Gilbert Dembele and
Ousmane Dembele) and ICRISAT (Pierre Sibiry Traore). This way, we transferred the information
acquired during these meetings to the field team and vice versa. Most of the computer work was
spent on communication.
Besides AMEDD, UCL and ICRISAT we also discussed with Geometius and Manobi the problems that
arose with the eBee and the measurement smartphones respectively. With Geometius we had many
mail conversation concerning image quality and the errors of the eBee, whereas Manobi needed to
improve the user interface of the online data viewer and the synchronization problems.
Last two independent weeks
During the last two weeks I mainly have continued the work as I did together with Guillaume before.
Some things were different. The main change was the communication with the field team and
AMEDD. This was before mainly performed by Guillaume, since French is his mother tongue and it
was often faster and easier. However, in general the communication went without problems. After
Guillaume’s departure, the harvest measurements needed to be done and for that I instructed the
field team how to do the measurements and how to record the measurements into the smartphone
application (ODK collect). During the instruction day, I invited Julie Champeau (AMEDD) to the field
to translate where necessary.
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I also trained Joël Davidse during in the last week of September how to fly the eBee and to make him
familiar with the organization, the people and Sukumba.
Ancillary activities
The most important working proceedings have already been discussed. Nevertheless, there were
plenty of smaller tasks that needed to be performed to be able to do the main work. For example,
maps were created, printed, cut and glued for every cluster and one for the whole experimental site.
These were used for orientation in the field and documentation of important sites and locations of
ground control points. Also small other datasets were made. Since the radiometric panel is hard to
find in an eBee derived orthomosaic, I made a shapefile with the rough locations. This way, it will be
easy for people to find the panels.
We made a document of when we used an AMEDD car and how many nights we were staying in the
guest house. The agenda needed to be updated, normally the weather was checked before we left
the guest house to change the planning when necessary, batteries of smartphones, GPS, eBee and
tablet needed to be loaded every day etcetera.
Altogether the work in Mali has been a very condensed and diverse experience, which has served as
a great environment for my personal development and a strengthening of my skillset.
Because of the pressure on the operational part, I have not been able to spent time to data analysis
during my stay. Therefore, once in The Netherlands I have done some research focusing on
measurements with a limited spatial and temporal extent.
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2. Introduction
Farmers with a profound understanding of which environmental factors are determining and limiting
the growth of their crops have the potential to significantly increase yield, because they can make
the right interventions. Understanding the ecosystem and the relative influence of (changing) its
elements on plant growth is therefore crucial. For the African semi-arid tropics nutrients rather than
water availability is the major limiting factor for plant development (Breman and De Wit, 1983).
Because of this restricted accessibility of nutrients, it is extremely important that fertilization is
applied in the right manner. Van Lauwe et al. (2014) proposed that the right application of fertilizer
should gain more attention in conservation agricultural for the sub-Saharian region. This way,
smallholder farmers can boost their amount of organic residue and apply it in an efficient manner.
However, Tittonel and Miller (2013) recognize that it is difficult to identify which factors are most
critical for plant growth in smallholder cropland systems, since this is often determined by multiple
interacting components. Still they acknowledge that fertilization is a major limiting factor. Despite
the available knowledge there is in general still much unknown about which other (physical and
biological) factors are determining crop growth in the region (soil temperature, soil moisture, pH,
etc..) and it is worth exploring how one can derive effectively such information at various scales.
At this moment there is a growing understanding of how remote sensed imagery can be an effective
tool to monitor plant and crop. A considerable range of crop (disease) characteristics have the
potential to be monitored from the air, only using physical reflectance values. To illustrate this,
reflectance values can be related to corn grain yield (Shanahan et. al, 2001), wheat grain yield (Schut
et. al, 2009), plant N status in corn (Bausch & Duke, 1996), leaf nitrogen status in rice (Xue et. al,
2004), yellow rust in wheat (Huang et. al, 2007) or even water stress in orchards (Suárez et al, 2010).
Because these techniques provide information in a spatial format, farmers can use it for precision
agricultural purposes. This way, WDVI values have been used in potato fields to estimate the N
content for semi-automated application of the right amount of fertilizer at the right place (Evert et al,
2012).
Unmanned aerial vehicles (UAVs) are more and more used to establish these relationships. However,
though the potential of UAV technology is high, it also faces limitations. Data quality is highly
dependent on acquisition procedure, flying device and the camera. After image acquistion, they need
to be post-processed for mosaicing and in some cases pre-processed. LeLong et. al (2014) describe
data quality issues of UAV derived images of an agricultural wheat field. Before establishing the
relation between data collected at the ground level and image derived values (e.g. LAI and NDVI), the
images needed to be corrected by vignetting and bidirectional effects and a geometrical and
radiometrical correction as well. They also acknowledge that data quality issues depend on
measurement device. For our research an eBee (Sensefly) and a Canon Powershot S110 NIR (which is
an adapted version of the S110 RGB) are used to collect reflectance data. There is still not much
known about its image quality. One important finding of the eBee (in this case in combination with a
Canon IXUS 125 HS) is that the orientation of the camera can be changed inside eBee as a side-effect
of the impact of the landing (Shih and Teo, 2013).
In Mali, not much is known about the relation between reflectance and crops. Moreover, although it
is commonly accepted that nutrients are limiting the growth, it has been scarcely studied to which
extent those crops are reacting to (combinations of) nitrogen, potassium and phosphate, urea and
compost application. This study aims on quantify this effect for sorghum, millet, maize and cotton,
which are cultivated crops in Mali. Especially cotton has a considerable economic value. An
Unmanned Aerial Vehicle (UAV) has been used to acquire spatial information in the region, which can
be used to gain more insight in i) fertilization effects and ii) underlying factors for crop growth
heterogeneity. It is the first time that UAV technology is used to map the smallholder agricultural
systems over a growing season on a regular base in Mali. Knowledge derived from the images could
enable smallholder farmers to have an overview of the growth of the plants in their fields, enabling
them to make the right interventions to locations where plants seem to be limited in growth.
10
The results presented in this study are site specific. Soil types and characteristics, climatological
conditions, ecological systems and available resources are to a certain extent bound to the
geographic location. Therefore, one should be cautious to extrapolate the results to bigger regions,
like e.g. the West-African zone.
2.1. Research questions and objectives
This study aims to answer questions at two levels. First it will elaborate upon what is happening at
the ground level to get insight in the fertilization experiment. Plant height has been used as growth
response variable, because measurements of other growth variables had not started yet or they
were still in need of further post-processing.
Secondly, there will be a focus on the correlation between that outcome and UAV imagery.
1) What is the effect of fertilization on the growth of maize, millet, sorghum and cotton?
•
•
What is the effect of fertilization on the height of the crops?
What is the effect of the environment on the fertilizer response?
2) What is the potential of UAV imagery for crop growth quantification?
•
•
•
What is the UAV image quality?
Does plant height correlate with NDVI?
What information could be derived from the UAV imagery?
The main objective of this research is to provide answers to the research questions. Time wise, the
data analysis is not extensive, but this study should be indicative for to which extent remote sensing
techniques can be used to monitor the growth of the agricultural system from above.
11
3. Methods – Data acquisition
Data of various sources were used for this study. This chapter will describe briefly how data has been
collected and what data products have been produced and used afterwards. The first paragraph will
give a short overview of the experimental setup.
3.1. Experimental setup
The field experiment is situated in Sukumba, Mali (WGS84: 5.202° W; 12.195° N). This region is
dominated by agriculture. Sorghum, millet, banana, rice, maize, beans and peanuts are the main
crops cultivated in the region. The five most frequently cultivated crops have been selected for the
experiment: sorghum, millet, maize, cotton and peanuts. For the year 2014 there were 48 fields in
total, varying from 0.5 to 3.5 ha. Every crop type has been assigned to 9 or 10 fields of local
smallholder farmers.
Inside these fields, several 15*15 m areas are situated, called plots. Depending on crop type, a field
contains five or six plots (A, B, C, D, E, (F)). Plot A is the “farmer’s practice”, which serves as a positive
control, no interventions have taken place here. Plot B is the negative control; no fertilizer has been
applied here. The other plots have been given a certain fertilization treatment, which can be found in
page 4 of Appendix 1. An overview of the number of fields and plots per crop type for 2014 can be
found in table 1.
Table 1: Overview size of the experimental setup
Crop
Maize
Millet
Sorghum
Cotton
Peanuts
# Fields
10
10
9
10
9
# Plots
5
6
6
5
5
In every plot measurements were performed, which are explained in the next paragraph. To get a
representative sample of the 225 m2 area, the plot is divided into five quadrats of 2*2m. A schematic
overview of a field with all its elements is shown in figure 1.
Figure 1: Overview experimental design
12
For this study, we will zoom in on a cluster. A cluster is an arbitrary drawn area which can be covered
by one flight of the UAV. Typically a cluster covers between 5 and 10 fields. The total region is
covered by 7 clusters.
An overview of the area can be found in figure 2. Here the geographical locations of the fields and
clusters are visualized. Also, the location of the ground control points (10 m precision) are shown
(see chapter 3.3).
Figure 2: Overview of the experimental site in Sukumba. Large areas delineated by thin black lines are
clusters. Smaller areas delineated by thick black lines are the fields. Blue circles indicate locations of GCPs.
3.2. Crop growth characteristics
Several crop growth characteristics are being measured: (1) plant height, (2) crop development stage,
(3) green ground coverage, and (4) weed quantity. Moreover, photos are made for visual
interpretation of field, plot and quadrat status. Before the measurement season started, the central
plant of every quadrat is marked with a colored ribbon and an indication of the name of the quadrat
(1, 2, 3, 4 or 5). Main aspects of the measurement protocols are briefly described. For an elaborated
description with visualizations of measurements, one should read Appendix 1.
3.2.1.
Variables
Plant Height
This variable is measured from ground level till the stem incision of the last fully developed leaf. This
is done for 5 plants in all 5 quadrats for all plots. The measuring order in the quadrat always stays the
same and is relative to the entry point / closest road. One measures clockwise, starting at the left
proximal plant and ending with the central plant.
13
Crop development stage
A document has been developed which describes the different stages of plant development for
peanuts, sorghum, millet, cotton and maize. There are eight stages defined:
1. Germination
5. Booting
2. Leaf development
6. Inflorescence emerge, heading
3. Tillering
7. Flowering
4. Stem elongation
8. Senescence
An elaborate description of the development stages is described in appendix 2. This is also done per
quadrat level.
Green ground coverage
This variable describes how much surface crops and weeds are covering the ground together.
Weed cover
An indication about the amount of weeds is given by estimating the total weed coverage in every
quadrat. Estimations are split up in three ordinal classes: <10%, 10-50% and >50%.
3.2.2. Data recording method
A field team of 4 persons, working in couples, are biweekly measuring all variables mentioned in
previous section for every 48 fields. Data of plant height, crop development stage and weed quantity
are entered into smartphones (1 smartphone per couple). These smartphones are, via a 3G network,
connected to the Manobi platform. The application in which the field teams enter the data, called
‘mInventaire’ originates from the same organization. This way, the smartphones serve as a tool for
filling in digital field forms. Moreover, the application links the data to the location, using the
smartphone built-in GPS. When no server connection problems occurred, the field team couples
could synchronize the data to this server at the end of each measuring day. Data can be visualized
online at the site of mInventaire2. A username and password (to be given by the administrator) are
required.
The acquisition of green ground coverage values are less straightforward and requires postprocessing. First, images are made, with a regular RGB camera, of the quadrats from a height of 3
meter. This distance is kept constant by means of a 3 m high pole (see figure 4, page 20 in appendix
1). Later the image is post-processed by ‘CAN-EYE’, a software program that semi-automatedly
classifies all pixels present in the picture into ‘soil pixels’ or ‘plant/green pixels’. It also enables the
user to crop a certain area of interest out of the picture. The result of the processing is a value
describing the soil fraction (%) per quadrat. To know ground coverage, one needs to subtract the
fraction value from 100%.
2
http://minventaire2.manobi.com/?_H=653
14
3.2.3. Data products
A .csv file of the data for plant height, development stage and weed quantity can be downloaded
from the mInventaire website. Data is split up into three files, thereby providing the data by level of
measurements: field, plot and quadrat. Files can be linked using a unique ID for the right
measurements. The data recorded are listed in table 2. “ID_Objet” and “ID_Object_pere” can be
used to join the different tables.
Table 2: Overview variables recorded in mInventaire .csv files
Field
ID_Objet
ID_Objet_pere
Date
no_field
Plot
ID_Objet
ID_Objet_pere
Date
nom_du_plot
Quadrat
ID_Objet
ID_Objet_pere
Titre
Date
numero_quadrat
indice_bbch
plante_1
plante_2
plante_3
plante_4
plante_centrale
The data of the post-processed green ground coverage is stored in different formats. There is one
excel sheet containing plot information; it stores the fraction of soil pixels (per quadrat). For fraction
ground cover one needs to subtract this value from 1.
15
3.3. Ground control points
For the geometric correction of the UAV imagery, exact coordinates (preferably a precision of <1 cm)
from stationary objects at ground level are needed, which should be recognizable in the images.
These points have been constructed in the Sukumba site and will be referred to as ground control
points (GCPs).
3.3.1. GCP Construction
There are two different GCPs, differing in construction. The first type is painted on plain bedrock. A
plastic sheet, with a cross-shaped hole has been used to paint the white crosses on the rocks. The
dimensions of these crosses are 1.50 m * 1.50 m; the width of the lines are 20 cm.
For places where it is not possible to paint on rocks, concrete crosses have been constructed. These
crosses are made of concrete blocks. Typically, the blocks are placed a few cm below the surface on a
layer of cement. Then, the blocks are ‘connected’ with each other by cement as well. This way, the
construction of a concrete cross is a fact. After at least a day drying in the sun, the crosses are
painted white. The dimensions are 1.60 m * 1.80 m and width of lines is 20 cm.
Examples of both cross types are visualized in figure 3.
Sometimes crosses have been painted on a well. In that case, the white painted surface is often Lshaped. Also, temporary crosses have been painted on small rocks. However, both cases are
exceptional. In total 51 GCPs have been constructed.
3.3.2. GCP Measurements
The crosses can only be used when the exact coordinates are known. Therefore, a base station has
been set in the Sukumba region and all GCPs have been measured with a dGPS. All points are
measured exactly in the middle of a cross. Also the L-shaped crosses are measured in the middle of
the corner.
3.3.3. Data product
After GCP acquisition of the dGPS, points have been converted to known coordinate systems
(‘WGS84 UTM zone 30N’ or ‘WGS84’). This has been exported to an excel sheet and a shapefile.
Figure 3: Ground control points (GCPs). Left: GCP painted on bedrock. Right: GCP painted on constructed
concrete cross.
16
3.4. Measuring physical reflectance
After geometric correction of the image, a radiometric correction needs to be performed in order to
do quantitative and comparable analyses. Raw DN values need to be transformed into physical
reflection values. Therefore, the relationship between reflection and pixel value should be known.
3.4.1. Radiometric Panel Construction
To be able to correct the image during the post-processing phase, three radiometric panels (RPs)
were constructed. Radiometric panel A and B are foldable wooden structures of 1.20 m * 1.20 m,
consisting of four wooden plates of 60 cm * 60 cm. Every plate has its own colour: black, dark grey,
light grey and white. Black was painted with 2 layers, the others were painted three times.
Radiometric panel C was split into two wooden parts, both 1.00 m * 2.00 m. This panel has been
constructed later. An image of radiometric panel B can be found in figure 4.
Figure 4: Radiometric panel B
3.4.2. Measuring reflectance
Reflectance of the different colours was measured with a spectrometer (ASD Field Spec PRO) with a
spectral resolution of 1 wavelength. Unfortunately, no fore-optics - with known field of view (FOV) were available and the spectralon lab certificate describing was absent. Therefore, a 100% spectralon
reflectance for every wavelength is assumed, the bare fiber (with unknown FOV) of the spectrometer
is used for the measurements and no extra reflection post-correction based on measurement
distance has been performed. It should also be noted that the spectralon was not perfectly clean.
During measurements the following elements are kept constant:
 Time of day (between 11:00 AM and 02:00 PM)
 Measurement angle (80 -85O, not 90 O to avoid measuring shadow)
 Measurement angle perpendicular to the direction of the sun
 Weather type (only measured in clear sky conditions)
 Distance between sensor and RP (distance 20 cm, estimated st.dev 3 cm)
 Distance between spectralon and RP (distance 20 cm, estimated st.dev 3 cm)
An elaborate description of the spectrometer measurement protocol and the use of the software is
attached in Appendix 4.
17
After this has been done, one ends up with a dataset with values describing (DN - DNmin) per
wavelength (wl). Following the protocol one has two replicate values recorded for every measured
colour of the panel. Now the reflectance should be calculated with the next formula:
𝑅𝑒𝑓𝑙𝑒𝑐𝑡𝑎𝑛𝑐𝑒(𝑤𝑙) = 𝑅𝑤𝑙 = 𝑅𝑅𝐸𝐹(𝑤𝑙) ∗
Where:
RREF(wl)
DNSPEC1(wl) - DNMIN(wl)
DNSPEC2(wl) - DNMIN(wl)
DNRP1(wl) - DNMIN(wl)
DNRP2(wl) - DNMIN(wl)
(𝐷𝑁𝑅𝑃1(𝑤𝑙) − 𝐷𝑁𝑀𝐼𝑁(𝑤𝑙) ) + (𝐷𝑁𝑅𝑃2(𝑤𝑙) − 𝐷𝑁𝑀𝐼𝑁(𝑤𝑙) )
(𝐷𝑁𝑆𝑃𝐸𝐶1(𝑤𝑙) − 𝐷𝑁𝑀𝐼𝑁(𝑤𝑙) ) + (𝐷𝑁𝑆𝑃𝐸𝐶2(𝑤𝑙) − 𝐷𝑁𝑀𝐼𝑁(𝑤𝑙) )
= Absolute reflectance value of spectralon (‘1.00’ for every wl for this study)
= value recorded by first measurement of spectralon
= value recorded by second measurement of spectralon
= value recorded by first measurement of a colour of the RP
= value recorded by second measurement of a colour of the RP
After calculation, the dataset should describe the absolute reflectance values for every wavelength in
the range of the spectrometer. Now the average reflectance should be calculated for the sensitivity
range for the sensors of the S110 camera: Red, Green and NIR. A dataset has been used to derive the
sensitivity values and the corresponding graph is shown in figure 5.
Since this dataset only describes the sensitivity every 10th wavelength, the average 10th wavelength
of the reflectance dataset has been calculated first, according to the following example calculation:
635
1
𝑅(𝑤𝑙630 ) =
∗ ∑ 𝑅𝑤𝑙
10
𝑤𝑙= 615
Where R(wlX) = Reflectance value at wavelength X.
Then, the reflectance values per wavelength calculated before are weighted and averaged by this to
come to one value, using the next formula:
𝑅𝑏𝑎𝑛𝑑 =
Where:
Rband
Wwl
∑(𝑅𝑤𝑙 ∗ 𝑊𝑤𝑙 )
∑ 𝑊𝑤𝑙
= Average weighted reflectance for one band of the camera (Red, Green or NIR)
= relative weight corresponding to a wavelength
This calculations have been performed for every colour for every radiometric panel.
18
Figure 5: Spectral response ( sensitivity) of the three sensors (Canon Powershot S110 NIR) for the different
parts of the spectrum.
3.4.3. Data product
The outcome of the experiment is a dataset describing the physical reflectance values for Red, Green
and NIR for all the colours of the radiometric panels as visualized in figure 6. These values again can
be used to correct the UAV acquired images.
Figure 6: Obtained reflectance values of the radiometric panels for each painted colour.
19
3.4.4. Use radiometric panel in the field
Using the RP during a flight one should pay attention to the following:
 The panel was placed at a location of high contrast. Mostly this is low vegetation. When the
panel is placed on soil or rock, it becomes less visible in the images since the brightness of
the soil causes the values of the ‘panel pixels’ to be influenced.
 The panel was placed on a horizontal surface.
 Shadows were avoided on the panel. High grass causing shadow around the panel were
removed.
 Panel was not placed close to high objects as trees, to ensure the UAV to make a photo of it
from all viewing angles.
 Optional: If panel was placed exactly under a flight line, we noticed that image quality of the
panel pixel values are often less blurry.
3.5. Flying the UAV
3.5.1. Specifics UAV and camera
The UAV used for the experiment is a eBee and it has been bought from ‘Sensefly’. On their site one
can find the specifics of the UAV: https://www.sensefly.com/drones/ebee.html. It is a light-weight
UAV, which has as advantage it can last relatively long with a battery (about 40 minutes). Downside is
that it is relatively unstable in the air. Several cameras can be attached to the eBee, which are also
listed at the site: https://www.sensefly.com/drones/accessories.html. The Canon Powershot S110
NIR camera has been used, where the sensor for the blue part of the light spectrum is replaced by a
sensor for the NIR part.
3.5.2. Camera settings
During a flight all camera settings were kept constant (shuttertime, aperture, ISO, white balance).
Shuttertime has been 1/2000 seconds for all flights and white balance was set to sunny conditions.
The aperture and, if necessary, ISO were adjusted before the flight depending on the light conditions
(meaning radiance reflecting from the soil). The settings were based on the histograms of the
different bands of a test photo that was made of a bright object (mostly soil or rock) before every
flight. Histograms were adjusted in such a way that the image would not be under- or oversaturated.
3.5.3. Flying location and time of day
The eBee has always flown between 10:00 and 14:00 hrs, preferably around noon to reduce
shadowing of the plants. The cluster where the UAV was flown depended on where the ground
measurements were taking place. The variables extracted from the acquired imagery can then
directly be linked to the data collected at ground level. The eBee was always flown at an altitude of
286.1 m, corresponding to a pixel resolution of 10 cm.
20
3.5.4. Metadata product
Of every flight of the eBee metadata has been recorded in a ‘flight sheet’. The information that has
been noted down here is listed in table 3.
Table 3: Metadata of a single flight
Metadata
Flight ID
Operator
Date
Cluster
Cloud coverage
Radiometric panel
eBee
Battery
ISO
Aperture
Mission Area
Pixel resolution
Altitude
Overlap (%) lat.
Overlap (%) long.
Start time
Flight time
Wind (m/s)
Comments
Explanation
Number of the flight as it is stored in the UAV
Who is/are flying the UAV
Date of flight
Which cluster the eBee flies
Clear sky, partly clouded or fully overcast
Which panel has been used (A, B or C)
Which eBee is used (A, B or C)
Which battery is used (1 or 2)
ISO of the S110
Aperture of the S110
Size of the area that is coverd by the flight (ha)
Pixel resolution of the images
Height of flight above take-off location (m)
Lateral overlap between individual photos
Longitudinal overlap between individual photos
Time of day at which eBee is launched
Time the eBee is in the air
Measured wind speed during flight
Remarks about flight (conditions), errors of eBee, peculiarities etc.
21
4. Methods – Data Analysis
4.1. Overview input data & data analysis
For this study several datasets have been used. These are listed in table 4, together with a short
description of the data’s origin and where it is used for.
Table 4: Datasets used in this research.
Dataset
Measuring device
Experimental fields
Non-differential GPS
Experimental plots (1)
Experimental plots (2)
Spectrometer output
Ground measurements
Photos of the clusters
Ground Control Points
differential GPS
Non-differential GPS
ASD Fieldspec PRO
Smartphone (Alcatel)
S110 NIR camera + eBee
differential GPS
Crop type reference
non-differential GPS
Satellite imagery
GeoEye: 1st of May 2014
Used for
indication of geographic location of the
fertilization experimental fields in Sukumba
NDVI value extraction
identification of plot names of dGPS plots
Radiometric calibration of orthomosaic
Plant height analysis & NDVI correlation
Calculating NDVI
Geometric rectification of orthomosaic
Determination of crop type for non-experimental fields (collected by G. Chomé)
Locating trees for proper field delineation for
non-experimental fields
In this study, the scheme for analysis as visualized in figure 7 has been followed. The following
paragraphs will zoom in and elaborate upon it where necessary.
Coordinates
GCPs (shp)
Image
processing
Orthomosaic
Radiometric
calibration
Raw eBee
images (CR2)
Conversion to
TIFF format
DSM
NDVI
calculation
Zonal
statistics
NDVI image
cluster 1
Experimental
plots (shp)
Input data
Ground
Measurements
Regression
Analysis
NDVI value per
plot
Data product
Operation
Figure 7: Schematic overview of the overall analysis
22
4.2. Selection of cluster
For the selection of the cluster the next criteria are used:
 Two flight dates should be chosen with at least 14 days difference in order to analyze growth
over time;
 Cloud shadows on the fields should be minimal for the two dates;
 The cluster should contain fields with at least three, but preferably four different crop types.
Based on these criteria cluster 1 appeared to be the best choice, since it contains 1 maize field, 2
cotton fields, 2 sorghum fields and 3 millet fields. Moreover, the two flights of the 27th of August and
the 18th of September have both minimal shadow cover over the fields. An image of cluster 1,
showing the location of the fields, is shown in Appendix 5. These fields are used for the analyses
described in paragraph 4.5.
4.3. UAV image analysis
All images are stored with a compressed raw format of Canon (.CR2). With eMotion, a software
package delivered with the eBee, it is not possible to manually adjust parameters for the creation of
the orthomosaics. Therefore, all images of cluster one of both dates have been processed with
Agisoft photoscan (v1.0.0.1). Processing included the following steps:
1. Aligning photos (point limit: 20.000)
2. Manual removal of outliers of the point cloud
3. Manual identification of GCPs in individual photos
4. Optimize image using GCPs
5. Removal of outliers of the optimized point cloud + removal of edges
6. Point cloud densification (Quality: medium, depth filter: moderate)
7. Building mesh of point cloud (Surface Type: Height field, Source Data: Dense point cloud, Polygon
Count: Medium)
8. Export to GeoTIF
23
4.4. UAV radiometric calibration
When one georectified image is constructed, it needs to be radiometrically corrected in order to
transform the digital numbers of the three bands (red, green and NIR) of the image to physical
reflectance values. This allows comparison of images acquired at different moments.
The radiometric panel was used to calibrate the image acquired at 27th of August and the 18th of
September 2014.
First, an example of a normal calibration procedure is given. In figure 8 two picture are shown of
radiometric panel C (having only two colours: black and light grey) for the red band and NIR band
respectively. First, an area was defined with pixels that are minimally compromised by mixing of
other pixels. In figure 8, this are the blue polygons. Once the mean of the red and NIR band was
known for both the light grey and the black colour of the panel, the values were plotted against the
reflectance values measured (as described in paragraph 3.4.2), resulting in figure 9. A linear
relationship is plotted of these two values, resulting in the formulas shown. This formula could be
used upon the image to radiometrically transform it to physical reflection values. Theoretically, using
panel A or B one could plot a logarithmic line as well through the points, since these panels have four
different colours painted on them.
Figure 8: Images indicating the area used for extraction of pixel averages and mean. Photos are made by the
UAV with the Canon Powershot S110 NIR. Left: band 1 (Red). Right: band 3 (NIR). Pixel size = 10 cm. Images
are based on the orthomosaic of the 18th of September.
24
Figure 9: Example calibration curves, based of reflectance and DN measured by UAV of radiometric panel C.
Flight date = 18th of September .
However, there are two reasons why there has not been chosen for this method for this study. The
first reason is that reliable DN values of the black and grey colour cannot be obtained, because of
image quality issues (described in paragraph 5.2). The second reason is that the image of the 27th of
August does not have the – big – radiometric panel C yet. Therefore, the radiometric calibration of
the orthomosaics of both dates will be performed using only 1 value, which is the DN of the brightest
pixel in the white colour of radiometric panel B. The DN’s of the other colours of the radiometric
panel are not used because those are influenced by the reflection of the white colour of the panel.
Normally, at least 3 colours should be used to obtain a calibration line that is reliable enough. The
extracted ‘white’ values are listed in table 5.
Because the same calibration method is used for both images, it can be assumed that the calculated
reflection values, thus NDVI values will be more similar between the two dates compared to where
two different calibration techniques would have been used.
Table 5: Reflectance values obtained with the spectrometer and the DN values extracted from images of the
lightest part of radiometric panel B.
Reflectance (%)
Red
NIR
91.14
88.82
Extracted DN
27-08-2014
Extracted DN
18-09-2014
25296
7859
40680
9710
Plotting the values of radiometric panel B - and assuming a linear line through zero! - results in the
graphs visualized in figure 10.
25
Figure 10: Curves used for calibration of the Red band and NIR band for both flight
dates
The next formulas are used to radiometrically correct the two bands of the two dates:
Correction Red band, 27th of August:
𝑅𝑒𝑓𝑙𝑒𝑐𝑡𝑎𝑛𝑐𝑒 = 𝐷𝑁 ∗ 3.6029 ∗ 10−5
Correction NIR band, 27th of August:
Correction Red band, 18th of September:
Correction NIR band, 18th of September:
The calibration formula’s based on the values of radiometric panel C (shown in figure 9) would have
resulted in lower values than the formulas of the red and NIR band of the 18th of September. Perhaps
these values are more precise / accurate, but using these values for the calibration would make the
two orthomosaics less comparable, since reliable zonal pixel extractions for radiometric panel B on
the 27th of August would not be possible, because of image quality issues. These quality issues
comprise noise and blurriness and are described in detail in paragraph 5.2.
26
4.5. NDVI calculation, extraction and correlation with plant height
Once the reflection values were known NDVI was calculated, using the corresponding formula:
𝑁𝐷𝑉𝐼 =
𝑁𝐼𝑅 − 𝑅𝑒𝑑
𝑁𝐼𝑅 + 𝑅𝑒𝑑
Where:
NIR = Light reflection of the NIR part of the spectrum
Red = Light reflection of the red part of the spectrum
This calculation was performed for all pixels of the orthomosaics. Then the zonal extractions needed
to be performed per plot. Where possible the plot name was identified using the non-differential GPS
plot dataset of the experimental plots (which has been measured early in the season). This dataset
indicates which treatment belongs to which plot. Only for the maize field (field 2) plot treatment
identification was not possible, because no information was recorded with the non-differential GPS
(which provided the names of the plot). When the locations of the plots and the treatments were
known, polygons were made out of the points of the experimental plot dataset of the dGPS. Only the
location of plot ‘F’ for field 4 (sorghum) was missing. This polygons were used to perform the zonal
extractions of the NDVI values calculated in that plot. With these zonal extractions the mean NDVI of
the pixels inside the plot and its standard deviation could be obtained.
When the mean NDVI value per plot was known, it was linked to the mean plant height
measurements of the plots. Five plants were measured inside a quadrat (of 2 * 2 m). These five
values were averaged to obtain 5 plant height values per plot. The mean plant height (per plot) and
standard deviation has been calculated over these five plant heights (per quadrat). Visual inspection
of the data was needed to remove outliers (e.g. plant heights higher than 10m). Sometimes a
quadrat was measured twice, resulting in six plant height values per quadrat. One record of such
replicate measurements was removed before a quadrat average was calculated. Lastly, mean plant
height was plotted against NDVI values found per crop type and a linear line has been fitted through
the points using least square procedure. R2 was used for reliability interpretation. Because of the
image quality issues, it has been chosen not to merge the data of the 27th of August and the 18th of
September for the correlation between NDVI and plant height.
Lastly, a temporal profile was constructed of the four crop types to see whether there are differences
in mean NDVI values between the crops.
27
4.6. Spatial variation
Lastly, there has been a small investigation to what is happening in the fields that have not been
subjected to the experimental treatments. Several fields with known crop type (based on the crop
reference dataset) has been chosen to get insight in imaged quality and field heterogeneity. The
satellite image has been used to verify locations of trees to exclude those in the field boundary
delineation. A field delineation with trees could look similar to the field shown in figure 11.
Figure 11: Example of a delineation of field boundaries. Trees are not included.
Location: -5,180341; 12,184086 (WGS84). Date: 18/9. Crop: millet
This delineation has been used for zonal extraction of NDVI values. Because of a limited number of
available reference points in cluster one, only ten fields have been selected. However, this could be
indicative for the degree of field heterogeneity.
28
5. Results
5.1. Fertilization Effect
The graphs in figure 12 show the average plant height in the experimental plots. NPK refers to the
15-15-15 fertilization treatment.
Figure 12: Fertilization effect on plant height per field. Error bars represent standard deviation over 5
quadrat composite samples inside every plot.
29
Fertilization effect
Several elements are interesting. First of all, it seems that the biggest treatment effect is visible in the
sorghum plants in field 4, while the sorghum plants in field 3 are responding less to the treatment.
Also for millet a similar result can be found. In the same amount of time, the plants in field 7 grew
much more than in field 36. Same for the two cotton fields. In general, it seems that there are
differences in mean plant growth and in fertilization effect as well. To be able to say more about
these results, the UAV images should be taken into account as well. These can be found in the next
pages. The left image is always the image taken at the 27th of August, the right picture at the 18th of
September. The blue polygons are the (dGPS derived) NDVI extraction areas (experimental plots). Six
different treatments can be found (A, B, C, D, E, F). The fertilization application belonging to these
plot names can be found in Appendix 1.
Field 3 – Sorghum
Field 4 – Sorghum
30
Field 7 – Millet
Field 36 – Millet
Field 38 – Millet
31
Field 2 – Mais
Field 6 – Cotton
Field 27 – Coton
32
Several aspects of these pictures are noteworthy. In field 3 (Sorghum) the treatment effect can
visually clearly be seen. In plot D, E and F (DAP+Urea, DAP+Urea [higher dose] and NPK+Urea
respectively) the NDVI values are much higher than the surrounding and the other plots.
This is consistent with the results shown in figure 12. All of those treatments have increased the
height of the sorghum with more than 100% compared to the farmer’s treatment. Also for field 3,
the DAP(75)+Urea(50) improves the height with more than 100 % compared to the farmers
treatment. For other crops differences are not that obvious, but it can also depend on the local
environment and growth limiting factors.
Field 3 looks (not taking the plots into account) homogeneous. Field 4 (a sorghum field as well)
however, seems very heterogeneous. The upper part of field 36 (millet) has higher NDVI values and
in general it seems that the treatment effect of the plots is only visible in the southern part of the
field. Crop rotation effects could play a role here. Based on these NDVI images, the degree of intrafield variability seems to depend on the location.
5.2. UAV image quality
This paragraph will give an overview of issues concerning image quality of the photos that have been
acquired by the Canon Powershot S110 with the UAV. The next figures will give an overview of how
blurriness can differ between images and in the same image as well. Since the GCPs have a known
cross shape, they can be used as reference to visually determine the degree of blurriness. Images are
screenshots of the geometric correction process in Agisoft Photoscan of the flight made at 18
september 2014. Images are not manually stretched.
Several problems can be identified. In figure 13 one can see what often happens: pixels which
appears to be located at the edge of a photo are more blurry than pixels located more at the center
Figure 13: Photo visualizing difference in blurriness. Number in the flags in pictures at the left correspond to
the number in the picture at the right. GCP located at the edge of the photo appears to be more blurry.
33
However, this is not always true. Figure 14 shows exactly the opposite.
Figure 14: Photo visualizing difference in blurriness. Number in the flags in pictures at the left correspond to
the number in the picture at the right. GCP located at the edge of the photo appears to be sharper.
These phenomena will be referred to as location-imposed sharpness. Figure 15 shows that not only
location appears to be determining blurriness. This are screenshots of a GCP of two subsequently
made photographs, where the blurry one is located more at the center of the photo. This problem is
referred to as moment-imposed sharpness; sharpness determined by the moment of taking the
picture. It can be assumed that both mentioned problems can occur at the same time.
Previous two phenomena describe the issues regarding variation in sharpness. However, there is also
a constant band-imposed sharpness. This means that band 1 (Red) and band 2 (Green) mostly appear
to be blurry, while band 3 (NIR) appears to be sharp. This anomaly can be seen in figure 8 (paragraph
4.5). Since this seems to be sensor issue, this will be referred to as sensor-imposed sharpness.
Figure 15: Image derived from photos made in a flight (18 september) ca. 10 seconds after each other of the
same GCP, showing the difference in blurriness.
Furthermore, a noteworthy feature of the images is that all bands seem to be noisy. Since the NIRband is the sharpest band, the noise effect is often the best visible here. This noise effect can also be
seen in figure 8.
Another feature of the camera influencing image quality is the sensitivity range of the NIR-sensor.
Figure 5 (section 3.4.2) shows that the NIR-sensor is sensitive for a range in the visible part of the
34
spectrum as well. Besides that, the sensitivity of the NIR band is low compared to the Red band and
Green band. This also results in significant lower pixel values for the third band. In
The pixel extraction used for radiometric calibration of the orthomosaic of 18th of September (as
described in section 3.4.2) can be used as example to quantify the noise effect and to show the low
values. Table 6 shows the statistics of the pixel extraction for both bands:
Table 6: Zonal statistics of the pixel extraction of radiometric panel C. ‘St.dev’ represents the standard
deviation of the values of the pixels inside the polygon. ‘CV’ stands for the coefficient of variation of the pixels
inside the polygon.
Black colour (panel C)
Grey colour (panel C)
Sensor
# pixels
Mean
St.dev
CV
# pixels
Mean
St.dev
CV
Red
20
8682.0
322.1
0.037
27
26672.4
1107.2
0.042
NIR
81
813.5
226.3
0.278
99
6120.5
906.4
0.148
The table clearly indicates that the noise of the NIR band is higher (CV is 3-8 times higher for the NIR
band than for the Red band).
Differences in blurriness can also be seen in NDVI images of the fields in cluster 1. Figure 16 shows
the differences found in sharpness / noise in the same maize field.
Figure 16: Screenshot of NDVI values in two locations in the same field. Location left: -5,181731; 12,175663.
Location right 5,181642; 12,176262 (both WGS84). Date: 18-9-2014. Crop: Maize
35
Also without zooming to the pixel level, this difference can be seen. Figure 17 shows a NDVI image of
a cotton field (not part of the experiment) which has a blurry northern part and a sharp southern
part:
Figure 17: NDVI image of cotton field. Delineation is based on sharpness.
Location: -5,180178; 12,181466 (WGS84). Date: 18-9-2014. Crop: Cotton
This difference in sharpness is not visible in the image of 27th of August, as visible in figure 18.
Figure 18: NDVI image of cotton field. Delineation is the same as in figure 17.
Location: -5,180178; 12,181466 (WGS84). Date: 27-8-2014. Crop: Cotton
36
5.3. NDVI & Plant Height
The next graphs show the plant height plotted against the NDVI values per date in figure 19.
Figure 19: Correlation of NDVI and plant height.
37
Based on the correlations one would assume the growth response would be visible from the UAV
imagery as well. Since the plots in field 4 (sorghum) are differing relatively much in plant height, this
field is chosen to visualize the change in NDVI as well. This is shown in figure 20.
Field 4 - Sorghum
0.8
0.7
0.6
NDVI
0.5
27-8-2014
0.4
18-9-2014
0.3
0.2
0.1
0.0
Farmer's
treatment
No treatment
Urea (50)
DAP (75), Urea DAP (150), Urea
(50)
(50)
Fertilizer
Figure 20: Mean NDVI value per plot. Error bars indicate standard deviation of the mean plant heights of the
five quadrats..
Even with the difference in time of plant height measurements and date of drone flight (and
inaccuracy of radiometric calibration), the pattern of the NDVI response to fertilization treatment for
both dates is similar to the pattern of plant height response to fertilization.
It can be hypothesized that with low plant height more soil is visible, thereby resulting in more
spatial variation in NDVI. For quantification of this spatial variation of NDVI the next formula
calculating was used for every plot:
𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑜𝑓 𝑣𝑎𝑟𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑁𝐷𝑉𝐼 =
𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑁𝐷𝑉𝐼
𝑀𝑒𝑎𝑛 𝑁𝐷𝑉𝐼
Figure 21A is indeed suggesting the described hypothesis. Next, assuming a correlation between
plant height and NDVI this variation should also be reflected in the coefficient of variation (CV) of
NDVI and the mean NDVI measured (as visualized in figure 21B). Indeed, it seems that there is a
negative exponential relationship between plant height and spatial variation of NDVI.
38
Figure 21: Upper graph (21A): plant height plotted against the coefficient of variation found in a plot. Low
graph (21B): mean NDVI plotted against the coefficient of variation found in a plot. Data originates from all
dates of all fields of all plots.
39
5.4. Spatial variation
The main focus of this report is on the experimental fields at a plot (15x15m) level. However, it is also
interesting to investigate spatial variation in the images of non-experimental fields. Means and
standard deviations are plotted in figure 22.
Fields in Sukumba
0.8
0.7
NDVI
0.6
0.5
27-8-2014
18-9-2014
0.4
0.3
0.2
0.1
0.0
cotton cotton cotton cotton
maize
maize
millet
millet sorghumsorghum
Crop type
Figure 22: Graph showing mean NDVI values found for several fields located in cluster 1. Error bars indicate
standard deviation of the NDVI values found in the fields.
Though the fields chosen are just a small sample of the all the fields in Sukumba, figure 22 already
highlights several aspects. Firstly, the differences between NDVI values found between the two dates
(which should be indicative for plant growth) are varying. The millet fields are showing a big
difference in growth. Interesting is also the apparent lack of growth for the two maize fields.
Secondly, the standard deviation bars indicate that in general variation in NDVI pixel values is
relatively high, though it also seems to depend upon field. Thirdly, in general variation in pixel NDVI
values seems to be less in fields that are measured the 18th of September. This is also in line with
results found and presented in figure 21, since plants have grown in the meantime.
Not only quantitative information can be derived from the fields. While visually inspecting the fields
one could discover interesting features, like shown in figure 23.
Figure 23: NDVI Image showing ‘donut-shaped feature’. Location: -5,180200; 12,180643 (WGS84).
Date: 27-8-2014. Crop: millet
40
In this figure there are several ‘donut-shaped’ features visible. It characterizes itself by a more or less
circular shaped zone (with a diameter of 2 or 3 meters) of low NDVI values, followed by a
circumference with a width of about 1 or 1.5 meter characterized by high NDVI values. It is very well
possible that these spots are caused by the presence of ants. Walking through the fields, one will not
seldom come across a spot that looks like this. These ants probably start eating the crop, thereby
creating a non-growth gap in the field, but at the same time providing nutrients in the soil which
benefit the surrounding plants. How such a spot looks like at the ground level is shown in figure 24.
Figure 24: Ants spot. Ants have eaten the plants at this spot, creating more or less circular spot where
nothing grows. Note that this spot is located on a border between a cotton field and a sorghum field.
Another aspect of the field in figure 23 is that it seems that NDVI values are higher at locations close
to the tree in the centre. A possible explanation for this is that leafs falling from this tree are
supplying a nutrients to the soil, which is otherwise limiting plant growth.
In any case, these two examples show that spatial patterns could be identified. For proper
interpretations field verifications are required.
41
5.5. Temporal profile
Figure 25 shows a temporal profile of the four crops present in the experimental fields of cluster 1. It
is clearly visible that there are differences in the mean NDVI for both dates. Cotton and maize have
higher NDVI values, which is in line with the harvest time, since maize and cotton are harvested
about two months earlier than sorghum and millet. These results indicate that NDVI based temporal
profiles could be used for automated crop recognition.
Temporal Profile
0.8
0.7
0.6
NDVI
0.5
Cotton
0.4
Millet
0.3
Sorghum
0.2
Maize
0.1
0.0
24-aug
3-sep
13-sep
23-sep
Date
Figure 25: Temporal profile of cotton, millet, sorghum and maize.
42
6. Discussion
In this section the two main research questions will be answered separately in two paragraphs. Inside
these paragraphs, the answers to and discussion of the sub-research questions will be covered.
Finally, an overall conclusion will be drawn which evaluates the potential of using UAV technology as
a tool to monitor agriculture in Mali.
6.1. Fertilization effect on crop growth
6.1.1. Fertilization and crop height
The results shown in this study are indicating that fertilizers are improving crop growth. The biggest
effect is visible for the sorghum plants in field 3 and 4. Here, the plots of urea(50)+DAP(150) and
urea(50)+NPK(150) have much higher plants (in the order of factor 2) than no treatment or the
farmer’s treatment. This was also found in 1991 (Kagbo), where fertilization with N, rock phosphate
(P2O5) and K2O has been shown to stimulate cotton and maize growth in Malian soils. As in sorghum,
combined fertilization shows the best results. Also in field 6 (cotton), it seems that more fertilization
is resulting in higher cotton plants. Exceptional cases are present as well. A few examples: field 7
shows that the plants in the plot with no fertilization treatment is doing better than the farmer’s
practice, field 38 implies that increasing DAP fertilization is reducing the plant height and field 3
suggests that applying urea (50) is causing plants to be lower compared to no treatment. It can be
seen that there are many differences in fertilization response between fields of the same crop type.
This is visible for the two sorghum fields and the three millet fields. Also, the maize field does not
seem to react to the fertilization treatments, while even only urea treatments could improve maize
yield in the West-African zone (Vanlauwe et al., 2001). These examples suggest that plant height is
explained by other factors than fertilization alone.
6.1.2. Environmental effects
It is likely that differences and exceptions are explained by field heterogeneity. This heterogeneity
could be caused by abiotic variation, such as (limitation of) minerals, water or differences in soil
structure. A pot experiment (Soumare et al., 2003) shows that adding different kinds of compost to
Malian soils (from different locations) is significantly improving crop yield. This indicates that in the
soils itself there is a lack of essential minerals. In Sukumba, local farmers indicated that compost and
fertilizers cannot be applied everywhere because of a lack of material and financial resources, which
contributes to the existing field heterogeneity. Also, it is common for farmers in Mali to fertilize soils
as well using crop residues of the harvest of the previous year (Blanchard et al., 2013), which could
result in local soil fertility differences. For these reasons it is very important that, when possible a
visual inspection of the fields is performed, before one draws conclusions out of the non-spatial
analyses. For example, looking at field 36 (millet) it is very clear that plot D, E and F (DAP(75) +
Urea(50), DAP(150) + Urea(50) and NPK(150) + Urea(50) respectively) are responding very well to the
treatment. However, these plots are located in the southern part of the image, where the
surrounding plants are in general low, while the plants in the northern part seem to be higher
(assuming higher NDVI does mean higher plants). This means that there is not much difference in
mean NDVI value between all plots, since plant height of plots located in field 36 are indeed not as
much differing as one would expect. A possible explanation of the difference in plant growth
between the northern and southern part of field 36 might lie in the farmer’s crop rotation
management. Kouyaté et. al (2000) showed that growing legumes in the end of the raining season
significantly improved grain yield of crops growing at the same locations the next year. Since peanuts
are much cultivated in the Sukumba region, the difference in plant growth in the northern and
southern part of the field could very well be explained by differences in preceding crops. Also, Kagbo
43
(1991) showed that crop rotation of maize and cotton has an effect on the next year’s yield where a
maize-cotton rotation appeared to be more beneficial than a cotton-maize rotation. Since the two
parts are distinguishable by an unambiguous sharp line crop rotation could be a reasonable
explanation. None of this information can be retrieved by only looking at the graphs. In field 4
(sorghum) there are big differences in plant heights, but if one takes a look at the aerial image one
might wonder if this difference is caused by fertilization or other effects. Also here, it seems that
heterogeneity in the field is causing the differences found in the plot.
The results discussed in this report is only a small percentage of the total collected data. The overall
experiment is designed to take all kinds of variation into account by distributing the fields over a
large geographic region with varying local environments and to have ten replications per crop type.
Analyzing all fields together would give better insight to what extent varying local environments
relate to the relative effect of the fertilization. These results in this study should therefore be
considered indicative. Additionally, caution is needed when one is drawing conclusions of single field
data. Visual inspection of UAV derived imagery can be a considerable help in understanding crop
growth response to a given fertilization treatment.
6.2. Potential of UAV imagery for crop growth quantificiation
6.2.1. Image quality
Several issues concerning the quality of the images have been discussed. This paragraph will
elaborate on them one by one.
Blurriness
Moment-imposed sharpness, location-imposed sharpness and sensor-imposed sharpness cause
quality differences between (location of pixels of) photos. This has its consequences for the quality of
the orthomosaic, since it will consist out of a mixture of images which are sharp and images with a
certain degree of blurriness. Turner et. al (2014) also faced this problem with their octocopter
experiment. Since they had pictures with 90% overlap, they decided to select pictures based on its
sharpness. An algorithm had been developed to automatically filter out blurry pictures. Perhaps this
approach could be valuable as well for the STARS project to reduce the influence of momentimposed sharpness. However, the sensor-based sharpness will still be an issue and needs to be
solved at hardware level. Location-imposed sharpness can perhaps be reduced by cropping the
images or by implementing algorithms that, during stitching images together, favor pixels that are
taken more at the center of the photo. The sensor-imposed sharpness is most likely caused by a fault
in the fabrication of the NIR sensor in the camera, since the Red and Green band are often showing
equal degree of blurriness, while pixels in the NIR band are responding differently.
Noise
Secondly, the images have, partly dependent on the band, some level of noise. The consequence of
this is (together with sharpness issues) that (temporal) pixel-to-pixel comparisons will be unreliable.
However, for analysis purpose noise effects can be minimized by averaging pixel values of a bigger
area, enabling temporal profile construction. Small precision errors due to blurriness should always
be taken into consideration though.
Radiometric calibration
Several factors decrease the quality of the radiometric calibration:
 Lab certificate of the spectralon is missing. For this research a reflectance of 100% is
assumed for all wavelengths, but this is highly unlikely.
 Spectralon is not perfectly clean, meaning that there is imprecision due to dust on the white
reference measurement device.
44


Accumulation of dust on the radiometric panel. It can change the reflection of the panel
colours over time.
A combination of blurriness, noise, image resolution cause and size of the radiometric panel
decrease the reliability of the extraction of the raw DN extraction of the radiometric panel of
the images acquired by eBee. At least one of these four elements need to change to enable
precise and accurate DN extraction. A solution to overcome the current problem might be to
use polyester calibration tarps, which are made for the purpose. Hruska et al. (2012) describe
how to use these tarps to calibrate the imagery of a hyperspectral UAV. These carps are
lightweight, big and foldable, which makes them ideal for field work purposes.
NDVI calculation
Because of the quality of the images of radiometric panel B, the transformation of the pixels of the
UAV images to reflectance values was done based on the one value of the panel. Precision of
calibration line could be decreased by this choice. Occasionally pixels appear to have higher values
than 1 (mostly caused by reflection of the sun in water pools or metal roofs of houses) or lower than
0. However, the NDVI is a normalized index. NDVI values derived from a hypothetical perfect
radiometric calibration would probably not deviate much compared to the NDVI values found in this
study. However, for this research it has still been chosen not to merge the NDVI values calculated
from the two image acquisition dates.
Geometric correction
For this research precise (with dGPS), but inaccurate coordinates are used. Since RMSE gives an
estimate of precision and accuracy together it provides little information. However, blurriness can
influence the manual geometric correction procedure. An error of 1-2 pixels should be taken into
consideration when points are located manually in blurry pictures.
6.2.2. Correlation NDVI and height of the crops
There is a strong relation between NDVI values derived from eBee imagery and plant height. Even
taking into consideration the difference in time between the day of plant height measurements and
the day of the UAV flight, most of the correlations score a r2 value higher than 80%. This again means
that the relationship can be used to calculate the height of the crop at a certain location using the
NDVI pixel values. The results are in line with Imran et al. (2013), who were able to link yields of
millet, sorghum and cotton to NDVI values derived from SPOT imagery. Millet and sorghum yield has
been estimated before using satellite (AVHRR) NDVI values (Bartholome, 1990), where also the effect
of the local ecological environment was acknowledged. This indicates that the potential of imagery of
higher resolution to monitor growth from above is very high. Also, with high resolution images, small
fields of smallholder farmers can be measured as well.
Comparing the NDVI values between crops, it can be seen that cotton and maize clearly show higher
values than millet and sorghum. This makes sense because millet and sorghum are harvested in
October and November (Kouyaté et al, 2000), while the harvest of cotton and maize already starts in
September. The difference in ‘greenness’ between the crops can be used for the construction of a
temporal profile of the season. The results in this study indicate that this is likely to be possible, since
the temporal profile of only two dates shows already differences between NDVI values of different
crop types.
Noteworthy is that, using the least square residuals method, now a linear line has been fitted, but for
a wider range of plant heights and NDVI values a logarithmic line would probably be better, because
of the NDVI’s saturation behaviour.
The ordinal classes for weed cover defined at the moment (<10%, 10-50%, >50%) are making it hard
to assess the relative influence of weed on NDVI values. At this moment these classes can be used as
45
quality control, but to a limited extent. Including more explanatory variables (related to crop growth)
will likely improve NDVI prediction.
6.2.3. eBee as a source of information
For West-Africa, this is the first research that studies the potential of monitoring crop growth using
UAV technology. Results indicate that the potential is high. It has been shown that plant height can
be calculated using NDVI pixel values. Assuming a relationship between crop height and yield/m2, it is
likely that NDVI values can be transformed to yield as well. When biomass data is available, the
biomass-NDVI relation can and should be investigated. Using the pixel resolution yield can then be
calculated directly from the eBee derived NDVI values. Yields of maize (e.g. Kuri et al., 2014; Scudiero
et al., 2014; Mo et al., 2005), cotton (e.g. Li et al., 2014; Imran et al., 2013), millet (Imran et al., 2013;
Maselli et al., 2000), sorghum (e.g. Imran et al., 2013; Maselli et al., 2000; Rasmussen, 1997) and
even peanuts (Knudby, 2004) have already been estimated using satellite based NDVI. However, the
satellites used (mostly AVHRR) have often very low spatial resolution (>1km). eBee could be used to
predict yields at a much smaller scale. Constructing a temporal profile for automated crop type
differentiation is another quantitative research potential of the eBee as well.
The results show that eBee also provides information at a qualitative level, for e.g. data quality
control. It provides insights to which extent results at field / plot level could be explained by local
circumstances. Furthermore, landscape features and/or spatial patterns can be recognized from the
air, like field boundaries or ant nests. These visual inspections can be valuable for exploration of
possible sources of heterogeneity. Visual interpretations can be a helpful tool to get an overview of
the agricultural system or to provide answers to other research questions not included in the STARS
project.
In all cases, the STARS project should be aware of image quality issues. Pixel to pixel comparison will
not be precise, because of noise and blurriness. Taking NDVI averages of larger areas for yield or crop
height prediction can minimize this problem.
6.3. Future research perspectives
This research shows that crop growth is dependent on a combination of fertilization and local
environments. To gain more insight how much varying environments influence fertilization effects
extended data analysis should be conducted taking all experimental fields into account. The
relationship between plant height and NDVI is strong. Additional growth variables (e.g. LAI,
chlorophyll content, biomass etc..) could improve the NDVI relationship.
A logical following step is to monitor crop growth based on satellite imagery and to investigate how
this connects with the UAV-crop analyses. It can be assumed that this will be possible, since remote
sensing imagery has often proved to successfully predict yields for the crops used in this study, but
also for e.g. rice (e.g. Quarmby et al., 1993 ) or soybean (Prasad et al., 2006), which are cultivated in
the Sukumba region as well. This will give very important insights in what information satellite
imagery could provide and how feasible the set-up of a crop monitoring system from space imagery
is. This should not only focus on relationships between crop growth variables, but also to which
extent cloud cover in the growing season will be problematic for the acquisition of enough images.
At this moment crops are responding differently to fertilization. This could be due to limiting food
sources in the soil. It has often been proved that soil quality is influencing crop yield (e.g. Kumar and
Goh, 1999; Malhi et al., 2006; Wani et al., 1994; Maughan et al., 2009). Taking soil samples could give
insights in what minerals are limited in the soil and which fertilization treatment would be effective.
Untill now only field heterogeneity has been discussed only qualitatively, while it can be investigated
quantitatively as well. Kriging is a geo-statistical tool that can characterize spatial variation.
Information derived from nugget, sill and range could aid in crop recognition algorithms.
46
7. Conclusion
This study shows that application of fertilizers is improving crop growth in the Sukumba region, but
the relative impact of fertilization depends on the local environment of the fields as well. Results
show that NDVI values derived from UAV imagery are strongly related to plant height, indicating that
pixel values can be translated into crop height, meaning that potential for crop growth monitoring is
high. Temporal profiles indicate that differences in NDVI values between crop types could be used for
automated crop recognition. Additionally, UAV technology can serve as a tool for quality control and
for deriving qualitative information. However, pixel-to-pixel comparison is limited because of image
quality issues. Calibration tarps could be a solution for practical constraints of the radiometric
calibration. Improving image quality would increase reliability of crop yield prediction and allow
precise comparison of images acquired at different dates. Future research should include data of all
experimental fields to get more insight in the relative effect of the local environment on the
fertilization response of the crops. In addition to that, including other crop growth variables (e.g. LAI,
chlorophyll, biomass) would improve the linear NDVI-growth model. Temporal profile analysis should
be extended with NDVI derived from more dates to recognize seasonal growth pattern differences
between the crops.
47
8. Recommendations
Image issues are restraining precision and pixel-to-pixel comparison. Therefore, many recommenddations are aimed at improving the image quality.
Radiometric Calibration

The radiometric panels used at the moment are not ideal. They require much maintenance
(mostly painting), can break and it is not easy to carry them for distances. Also, the big
radiometric panel shows cracks between the wooden parts. Because paint is drying very fast,
it is hard to paint the wooden panels perfectly homogeneous. As mentioned in the
discussion, one or more calibration tarps would fit the field work in Sukumba very well,
because of the light weight and the possibility to fold them and move them around. Also,
they can be bought in several sizes. They can be bought at “Group 8 technology” (site:
http://www.group8tech.com/).

For the spectrometer measurements of the radiometric panel a fore-optic with a known field
of view is needed to be able to calculate the percentage reflected light as a function of
distance to the panel and – if it is not a Lambertian surface – measurement angle. This has
not been possible till now.

A new spectralon with lab certificate should be purchased. This tool is imperative to calculate
It would be good to ask the manufacturer how to clean the spectralon in case it becomes
dirty by dust.

Image resolution and radiometric panel size should be fine-tuned. With the current image I
estimate that the dimension of one square panel colour should be at least 10-12 times the
pixel size to be able to extract reliable DN’s. Test flights need to confirm this hypothesis.

More available eBee batteries are convenient in case a flights need to be repeated, because
of flight errors. Also, if we will fly lower, covering a smaller area per flight, more batteries are
not unnecessary luxury.
Image quality

Since the sensor-imposed sharpness is most likely caused by a fault in the fabrication of the
NIR sensor in the camera, it should be fixed at the manufacturer. This fix could significantly
improve image quality.

It is hypothesized that image stabilization of the camera and the camera’s focal length
settings can have an influence on the degree of blurriness of the pictures. Small experiments
should be conducted with more test flights to be able to come to a well-considered decision
which camera parameter settings provide the best quality of the images.

Another cause of differences in (location- or moment-imposed) sharpness could be due to
the roll, pitch and yaw of the image, due to the orientation of the eBee while it takes a
photo. This information (roll, pitch and yaw) can be derived by software, like Agisoft
Photoscan.

When blurry images are unavoidable, it might be interesting to explore the possibility to
select algorithm-based selection of images based on sharpness. In that case increasing the
48
longitudinal overlap would be a sensible step, since more pictures will be taken with higher
chance on enough sharp pictures. If the latitudinal overlap remains the same, it will not
influence the flight time of eBee. However, more pictures and an extra picture selection step
will reduce the speed of the post-processing speed. Perhaps a trade-off can be found
between number of pictures and processing speed.
Geometric correction - GCPs
It should be noted that these recommendations should have lower priority than the
recommendations for the radiometric correction.

The quality of the smaller units of the concrete crosses is low. Therefore, crosses are fragile,
which is problematic since they need to be stable and immobile structures over time. If more
GCPs need to be built in the future, higher quality units for the crosses are preferred.
Perhaps these can be requested and manufactured by the same mason in Koutiala for the
next season.

One should not build new GCPs on top of an ant nest to avoid ants moving sand on top of the
cross.

Geometric correction will improve with circular GCPs. However, feasibility of constructing
these should be assessed first.
Other issues

At this moment data quality control is barely performed or possible. The application
mInventaire does not perform data quality checks. Also, often there are were problems with
connecting to the server of Manobi. Collecting data with ODK collect should be genuinely
considered, since data quality checks can be implemented while the user is entering data.
This way, problems encountered now (6 measurements per quadrat and recordings of
unrealistic values) can easily be avoided. Furthermore, the server is easy to access and the
user-friendliness of the application is high, meaning that settings can easily be adapted to the
preferences of the user.

It is hard to assess the relative influence of weed cover on NDVI values with the ordinal
classes defined at the moment. This can be improved by estimating amount of weed cover
quantitatively. This will incorporate some level of subjectivism as well, but it will be more
accurate than it is at the moment.

CAN-EYE is a very useful tool, but it is a tedious job to process all images. On the old AMEDD
computers, one field was taking more or less one day of one person. Other software tools
could be considered, if available.
49
9. Literature
Bartholome, E. (1990). Estimation of APAR values from AVHRR NDVI for regional crop yield assessment in West
Africa. 10th Annual International Geoscience and Remote Sensing Symposium - IGARSS '90; College Park, MD, USA
(Conference paper). Code 14750: 587-590.
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51
Appendices

Appendix 1: Data Collection Protocols

Appendix 2: Crop development stage

Appendix 3: CAN-EYE processing of vertical photos

Appendix 4: Radiometric Calibration Documentation

Appendix 5: Cluster 1 (GeoEye, 1st of May)

Appendix 6: UAV flight checklist

Appendix 7: Description STARS ICRISAT West-Africa
52

Master Geo-Information Science Internship Report GRS

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