Geo-information Science and Remote Sensing
Thesis Report GIRS-2023-04
Canopy temperature distribution in vineyards by analysing
UAV 3D point clouds fused with thermal information
Koen Exterkate
April 18, 2023
1023802
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Canopy temperature distribution in vineyards by analysing
UAV 3D point clouds fused with thermal information
Koen Exterkate
Registration number: 1023802
Supervisors:
Dr. ir. Lammert Kooistra
Dr. Sergio Vélez Martín
MSc Maria Del Mar Ariza Sentís
A thesis submitted in partial fulfilment of the degree of Master of Science
at Wageningen University and Research,
The Netherlands.
April 18, 2023
Wageningen, The Netherlands
Thesis code number: GRS-80421
Thesis Report: GIRS-2023-04
Wageningen University and Research
Laboratory of Geo-Information Science and Remote Sensing
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Acknowledgment
First of all, I would like to express my gratitude towards my supervisors, Sergio Vélez Martín and Mar
Ariza Sentís for ensuring high-quality and high-resolution drone data, detailed feedback, and guidance
throughout the thesis process. Lammert Kooistra for his feedback on the structure and style of my
writings. And the INF group for allowing me to work with the hard- and software needed for the
processing during the data analysis. Also, big thanks to my colleagues that provided me with handy
writing tips and feedback during the thesis ring session. And finally, thanks to friends and family that
gave me advice and motivation to go forward.
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Abstract
Multispectral and thermal imagery from UAVs is widely used in viticulture for monitoring water status,
irrigation management, water availability fluctuations, and disease detection. However, 3D
applications in these fields remain scarce. The goal of this study is to develop a method to merge
thermal images with multispectral 3D point clouds representing a vineyard and evaluate the
temperature distribution of the entire canopy per row and in individual grape vines, for healthy and
Botrytis cinerea-affected vines. This method applies SfM to build dense point clouds including
temperature, NDVI, and NDRE of a vineyard. These are merged into a single point cloud for a
predefined vineyard section, where rows and plants are extracted using ground truth locations.
Canopy temperature is analysed by examining visualizations and relationships between point cloud
attributes. The advantages of using 3D point clouds over orthomosaics are also evaluated. Vegetation
point clouds displayed significant temperature differences along the plant height (p < 0.01).
Temperature distribution varied on the East and West canopy sides with the greatest differences
observed in the morning. Moderate to strong correlations were found between absolute height and
temperature in the midday and afternoon (Pearson’s correlation coefficient: 0.42 and 0.35,
respectively). Analysis of individual vines at various daytimes showed strong correlations between
temperature and VIs in both the grape section and the entire plant. The highest correlation was found
for rNDVI = -0.73 and rNDRE = -0.76 for the entire plant, and rNDVI = -0.54 and rNDRE = -0.64 in the
grape section (0.5-1.0 meter). Correlations between point density and temperature were up to r = 0.72. This thesis proposed a method for combining thermal and multispectral point cloud data, which
provides a more accurate assessment of the temperature distribution in vineyard canopies than
conventional orthomosaics. The fact that temperature distribution is heterogeneous across different
height sections and sides of the canopy is helpful for decision-making in viticulture. Furthermore, it is
valuable to extract temperature, NDVI, NDRE, and gap fractions for individual vines as they can provide
insight into deviations regarding temperature and vegetative content. This information is useful for
monitoring the energy balance, stress, damage, and disease in viticulture. In addition, the NDVI and
NDRE could serve as cost-effective temperature indicators for individual vines, offering an alternative
to thermal equipment by using higher-resolution multispectral sensors.
Keywords: Point cloud fusion, SfM, UAV, Canopy temperature distribution, viticulture, thermal
imagery, multispectral imagery
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Table of contents
Acknowledgment..................................................................................................................................... 4
Abstract ................................................................................................................................................... 5
List of figures ........................................................................................................................................... 8
List of tables .......................................................................................................................................... 11
List of abbreviations .............................................................................................................................. 12
1
2
Introduction................................................................................................................................... 13
1.1
Background............................................................................................................................ 13
1.2
Scientific gap.......................................................................................................................... 14
1.3
Overall research aim and research questions ....................................................................... 14
1.3.1
General aim ................................................................................................................... 14
1.3.2
Research questions........................................................................................................ 15
RQ1 - Literature review ................................................................................................................. 16
2.1
3
2.1.1
Vineyard’s water status, irrigation management, and variability ................................. 16
2.1.2
Disease detection and monitoring ................................................................................ 16
2.1.3
Fusion of thermal imagery and point clouds in viticulture ........................................... 16
2.2
Point cloud applications in vineyards .................................................................................... 17
2.3
Thermal point cloud generation in agriculture and forestry................................................. 17
2.4
Point cloud enhancement outside viticulture ....................................................................... 18
2.4.1
Civil engineering and architecture ................................................................................ 18
2.4.2
Landscape monitoring ................................................................................................... 18
Data, materials, and methods ....................................................................................................... 19
3.1
Research design and data...................................................................................................... 19
3.1.1
Study area...................................................................................................................... 19
3.1.2
Data acquisition ............................................................................................................. 20
3.1.3
Flight information .......................................................................................................... 20
3.2
4
2D thermal imagery in vineyards .......................................................................................... 16
Workflow ............................................................................................................................... 20
3.2.1
Structure ........................................................................................................................ 20
3.2.2
Pre-processing - Point cloud generation ....................................................................... 21
3.2.3
Point cloud Fusion ......................................................................................................... 23
3.2.4
Temperature extraction along vine rows ...................................................................... 24
3.2.5
Temperature extraction in individual vines .................................................................. 27
3.2.6
Comparison between a point cloud and orthomosaic .................................................. 29
Results ........................................................................................................................................... 31
4.1
RQ2 - Canopy temperature distribution in vine rows ........................................................... 31
6
5
4.1.1
Temperature vs height sections .................................................................................... 32
4.1.2
Temperature deviations per side .................................................................................. 34
4.1.3
Temperature deviations vs daytime .............................................................................. 35
4.1.4
Temperature vs location ............................................................................................... 35
4.2
RQ3 – Temperature distribution in individual vines ............................................................. 38
4.3
RQ4 – Benefits of using point clouds over orthomosaics...................................................... 45
Discussion ...................................................................................................................................... 47
5.1
Canopy temperature distribution in vine rows ..................................................................... 47
5.2
Canopy temperature distribution in individual grapevines .................................................. 48
5.3
Benefits of an extra dimension ............................................................................................. 49
5.4
Limitations ............................................................................................................................. 50
6
Conclusion ..................................................................................................................................... 52
7
Recommendations ........................................................................................................................ 54
8
References ..................................................................................................................................... 55
7
List of figures
FIGURE 1: LOCATION OF THE VINEYARD INSIDE THE IBERIAN PENINSULA (TOP RIGHT). MULTISPECTRAL AND
THERMAL DATA ARE GATHERED FROM PARCEL B9 DATING FROM THE 12TH OF JULY UNTIL THE 9TH OF
SEPTEMBER 2022 (3 WEEKS PER FIELD) (BOTTOM). THE DATA ACQUISITION RESULTED IN FOUR SETS OF
IMAGES. THE BOTTOM IMAGE ALSO DISPLAYS THE LOCATIONS OF PLANTS AFFECTED BY THE BOTRYTIS
CINEREA FUNGUS (BLACK DOTS). ................................................................................................................. 19
FIGURE 2: SCHEMATIC SIMPLIFIED WORKFLOW. THIS IMAGE SHOWS THE SCHEMATIC WORKFLOW DURING THE
PRE-PROCESSING (PURPLE) WHERE THE GCPS AND THE IMAGES ARE TAKEN AS INPUT FOR THE CREATION
OF DENSE POINT CLOUDS, AND THE ELEVATION MODELS ARE LATER USED IN RQ2 AND RQ4. THE DENSE
POINT CLOUD IS THEN USED FOR THE EXTRACTION OF THE EASTERN AND WESTERN SIDES OF THE ROWS
IN RQ2 USING THE TRUNK LOCATIONS (RED). THE SAME EXTRACTION METHOD IS USED FOR THE
EXTRACTION OF HEALTHY AND INFECTED VINES IN RQ3 (ORANGE). LAST, THE 2D REPRESENTATION
OBTAINED FROM THE CHM AND THE MULTISPECTRAL ORTHOMOSAICS ARE COMPARED TO THE 3D
REPRESENTATIONS REGARDING ROWS 1,2, AND 3. .................................................................................... 21
FIGURE 3: SCHEMATIC OVERVIEW FOR POINT CLOUD GENERATION USING THE SOFTWARE AGISOFT
METASHAPE (VERSION 1.8.4). THE GCPS AND THE IMAGES FROM AL BANDS ARE TAKEN AS INPUT. AFTER
REFLECTANCE CALIBRATION AND THE MANUAL UPDATING OF GCP LOCATIONS, THE CAMERA ALIGNMENT
AND OPTIMIZATION ARE PERFORMED. FOLLOWED BY BUILDING THE DENSE POINT CLOUD, AND
CLASSIFICATION OF BOTH GROUND AND VEGETATION POINTS. THE BAND CALCULATION IS DONE USING
THE RASTER TRANSFORM TO OBTAIN THE VALUES FOR NDRI, NDRE, AND TEMPERATURE. ...................... 23
FIGURE 4: SCHEMATIC OVERVIEW FOR THE POINT CLOUD FUSION. THIS PROCESS DESCRIBES THE FUSION OF
THE FOUR INPUT CLOUDS (OBTAINED DURING PRE-PROCESSING IN AGISOFT METASHAPE) CONTAINING
MULTISPECTRAL ATTRIBUTES (NDVI, NDRE, TEMPERATURE) AND A CLASS ATTRIBUTE INTO ONE POINT
CLOUD. ......................................................................................................................................................... 24
FIGURE 5: VISUALIZATION OF THE NORMALIZATION PROCESS. THE LEFT IMAGE DISPLAYS THE ABSOLUTE
HEIGHT OF INDIVIDUAL POINTS. AFTER NORMALIZATION, THE HEIGHT RELATIVE TO THE TERRAIN IS
CALCULATED (RIGHT IMAGE) ....................................................................................................................... 25
FIGURE 6: THE VEGETATION IN THE NORMALIZED POINT CLOUD (LEFT) IS EXTRACTED USING THE THRESHOLDS
FOR THE NDVI (0.5), NDRE (0.25), AND THE NORMALIZED HEIGHT (0.5 METERS) TO EXCLUDE ALL NONVEGETATION POINTS (RIGHT). ..................................................................................................................... 26
FIGURE 7: A SCHEMATIC OVERVIEW OF THE WORKFLOW FOR TEMPERATURE EXTRACTION ALONG VINE ROWS.
THE POINT CLOUDS CONTAINING VEGETATION POINTS ONLY AND THE GROUND TRUTH LOCATIONS OF
ALL VINE TRUNKS ARE USED AS INPUT. THE FIRST STEP IS PREPARING THE DATA TO BE USED FOR TERRAIN
RASTERIZATION AND NORMALIZATION. THEN, THE VEGETATION IS ISOLATED, AND THE VINE ROWS ARE
EXTRACTED USING THE TRUNK LOCATIONS OF THE VINES IN THE FOCUS AREA. AFTERWARD, A PLANE IS
FIT IN CLOUDCOMPARE TO SEPARATE THE EAST AND WEST SIDE OF THE INDIVIDUAL ROWS NEEDED FOR
THE TEMPERATURE ANALYSIS ...................................................................................................................... 26
FIGURE 8: EXAMPLE OF THE PLANE FITTING PROCEDURE IN CLOUDCOMPARE. THE GREEN PLANE OUTLINED IN
YELLOW IS FIT VERTICALLY THROUGH THE CENTRE OF THE VINE ROWS. THE DISTANCE OF EACH POINT TO
THE PLANE IS CALCULATED AND DISPLAYED IN RED (NEGATIVE DISTANCES TOWARDS THE PLANE;
WESTERN SIDE) AND BLUE (POSITIVE DISTANCES TOWARDS THE PLANE; EASTERN SIDE). ........................ 27
FIGURE 9: LONGITUDE AND LATITUDE VALUES OF THE VINE TREE STEMS IN THE XY PLANE. THE ORIENTATION
M OF THE VINE ROWS IS CALCULATED BY A LINEAR REGRESSION FIT. THE DIRECTIONAL COEFFICIENT OF
THIS FIT IS USED TO PREDICT THE SECOND POINT NEEDED FOR EXTRACTING INDIVIDUAL BOTRYTIS
PLANTS. ........................................................................................................................................................ 28
FIGURE 10: SCHEMATIC WORKFLOW FOR EXTRACTING INDIVIDUAL PLANTS, BOTH HEALTHY PLANTS AND
PLANTS INFECTED BY THE BOTRYTIS DISEASE. THIS FIGURE SHOWS TWO WORKFLOWS. THE FIRST ONE
(TOP) IS FOR THE EXTRACTION OF GRAPE VINES INFECTED WITH BOTRYTIS. THIS METHOD IS SIMILAR TO
THE WORKFLOW FOR EXTRACTING THE VINE ROWS, EXCEPT FOR THE EXTRACTION OF CIRCULAR
FEATURES FOR THE RASTERIZATION AND NORMALIZATION OF THE POINT CLOUD. AFTER EXCLUDING
POINTS CORRESPONDING TO NON-VEGETATION THE PLANT COORDINATES ARE ESTIMATED, AND THE
PLANTS ARE EXTRACTED FOR TEMPERATURE ANALYSIS. THE SECOND WORKFLOW (BOTTOM) USES THE
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VINE TRUNK LOCATION OF HEALTHY PLANTS TO EXTRACT THE INDIVIDUAL PLANTS FOR TEMPERATURE
ANALYSIS. RASTERIZATION, NORMALIZATION, AND THE FILTERING OF VEGETATION ARE NOT NEEDED
HERE AS THIS HAS ALREADY BEEN DONE DURING THE PROCESSING FOR RQ2. .......................................... 29
FIGURE 11: SCHEMATIC OVERVIEW OF THE WORKFLOW FOLLOWED FOR RQ4. THE POINT CLOUDS
REPRESENTING THE CROSS-SECTION, ORTHOMOSAICS, AND THE CHM ARE USED AS INPUT. FIRST, THE
ORTHOMOSAICS AND THE CHM ARE STACKED AFTER WHICH THE PIXELS CORRESPONDING TO
VEGETATION CAN BE FILTERED. ................................................................................................................... 30
FIGURE 12: BOXPLOTS OF THE VINEYARD ROWS’ TEMPERATURE (IN °C) PER HEIGHT SECTION IN THE MORNING
(LEFT), MIDDAY (MIDDLE), AND AFTERNOON (RIGHT). ............................................................................... 32
FIGURE 13: HEIGHT PROFILE OF THE VINEYARD ROWS IN THE MORNING DISPLAYING THE MAXIMUM, MEAN,
AND MINIMUM TEMPERATURE FOR EACH 0.1 M INTERVAL. ...................................................................... 33
FIGURE 14: RELATIONSHIPS BETWEEN THE NORMALIZED HEIGHT AND THE TEMPERATURE FOR THE VINEYARD
ROWS IN THE MORNING, MIDDAY, AND AFTERNOON. THE LINEAR RELATIONSHIP IS SPECIFIED BY
PEARSON’S CORRELATION COEFFICIENT (RIGHT TOP CORNER). THE RMSD GIVES THE ABILITY TO FIT THE
DATA FOR THE THREE CURVES. THIS FIGURE INDICATES THAT THE DEGREE OF NON-LINEARITY DOES NOT
INFLUENCE THE ABILITY OF THE MODEL TO FIT THE DATA, AS THE RSMD DECREASE FOR HIGHER
LINEARITY CAN BE IGNORED. ....................................................................................................................... 34
FIGURE 15: BOXPLOTS OF THE TEMPERATURE PER SIDE OF THE THREE VINEYARD ROWS IN THE MORNING
(LEFT), MIDDAY (MIDDLE), AND AFTERNOON (RIGHT). THE COLOURS CORRESPOND TO THE ROWS IN THE
VINEYARD (ROW 1: ORANGE, ROW 2: RED, ROW 3: YELLOW)..................................................................... 35
FIGURE 16: BOXPLOTS OF THE ROWS’ TEMPERATURE VERSUS THE DAYPARTS IN EACH HEIGHT SECTION. THE
BOXES ARE COLOURED BASED ON THE MIDDAY (ORANGE), MIDDAY (RED), AND AFTERNOON (YELLOW).35
FIGURE 17: VISUALIZATION OF THE CROSS-SECTION OF THE EASTERN AND WESTERN SIDES OF ROW 1 DURING
THE MORNING, MIDDAY, AND AFTERNOON. THE VEGETATION TEMPERATURE IS COLORIZED RELATIVE TO
THE MEAN TEMPERATURE RESULTING IN RED (WARMER THAN THE MEAN TEMPERATURE) AND BLUE
(COOLER THAN THE MEAN TEMPERATURE. ................................................................................................. 37
FIGURE 18: ABSOLUTE (REFERENCE) HEIGHT OF ROW 1. THIS FIGURE INDICATES THE RELIEF OF THE TERRAIN IN
ROW 1. ......................................................................................................................................................... 38
FIGURE 19: CORRELATIONS BETWEEN THE ABSOLUTE HEIGHT AND THE TEMPERATURE OF ROWS 1, 2, AND 3 IN
THE MORNING (LEFT), MIDDAY (MIDDLE), AND AFTERNOON (RIGHT). ...................................................... 38
FIGURE 20: THE VALUES FOR THE TEMPERATURE, NDVI, AND NDRE ARE PLOTTED RELATIVE TO THEIR MEAN.
THE FRONT, SIDE, AND TOP VIEWS OF A HEALTHY PLANT IN THE MIDDAY ARE PLOTTED TO SHOW HOW
THE TEMPERATURE, NDVI, AND NDRE BEHAVE PER DIMENSION. ............................................................... 39
FIGURE 21: FRONT, SIDE, AND TOP VIEW OF A HEALTHY PLANT IN THE MORNING. THE TEMPERATURE AND
NDRE ARE COLORIZED RELATIVE TO THE MEAN DISPLAYED. THIS FIGURE SHOWS THAT THE TEMPERATURE
AND THE NDRE IN THE MORNING BEHAVE DIFFERENTLY COMPARED TO THE MIDDAY AND AFTERNOON.
...................................................................................................................................................................... 39
FIGURE 22: SCATTERPLOTS DISPLAYING THE RELATIONSHIPS BETWEEN THE NDVI AND NDRE VALUES AND
TEMPERATURE DURING MORNING, MIDDAY, AND AFTERNOON PERIODS ARE PRESENTED, INCLUDING
THEIR 0.95 CONFIDENCE INTERVAL AND THE RMSD. ALSO, THE CORRELATION COEFFICIENTS ARE GIVEN,
REVEALING THAT TEMPERATURE DEVIATIONS IN THE MORNING LEAD TO CORRESPONDING DEVIATIONS
IN CORRELATION COEFFICIENTS DURING THIS SPECIFIC PERIOD OF THE DAY. THE RMSD GIVES THE ABILITY
OF THE MODEL TO FIT THE DATA. ................................................................................................................ 40
FIGURE 23: THIS FIGURE DEPICTS A SCATTER PLOT ANALYSIS OF THE ASSOCIATION BETWEEN NDVI AND NDRE
VALUES AND THE TEMPERATURE OF THE GRAPE SECTION AT DIFFERENT TIMES OF THE DAY, INCLUDING
THE 0.95 CONFIDENCE INTERVAL AND THE RMSD. THE CORRESPONDING CORRELATION COEFFICIENTS
ARE PRESENTED TO ILLUSTRATE THE STRENGTH AND DIRECTION OF THE RELATIONSHIPS AND THE RMSD
GIVES INFORMATION ABOUT THE ACCURACY OF THE LINEAR MODEL FITTING THE DATA. NOTABLY, THE
MORNING BEHAVIOUR OF THE GRAPE SECTION APPEARS TO EXERT A DISCERNIBLE INFLUENCE ON THE
CORRELATION COEFFICIENT DURING THAT SPECIFIC TEMPORAL INTERVAL. .............................................. 41
FIGURE 24: THIS FIGURE SHOWCASES THE FRONT, SIDE, AND TOP PERSPECTIVES OF A HEALTHY PLANT DURING
THE MIDDAY, WITH TEMPERATURE AND NDRE, DEPICTED IN RELATION TO OPEN VEGETATION. THIS
9
VISUALIZATION REVEALS THAT OPEN VEGETATION DISPLAYS A HIGHER TEMPERATURE AND LOWER NDRE
VALUES. ........................................................................................................................................................ 41
FIGURE 25: THIS SCATTERPLOT ILLUSTRATES THE RELATIONSHIP BETWEEN THE MEAN POINT DENSITY PER
PLANT AND THE MEAN TEMPERATURE INCLUDING THEIR 0.95 CONFIDENCE INTERVAL, WITH
CORRELATION COEFFICIENTS PROVIDED TO INDICATE THE STRENGTH AND DIRECTION OF THE
RELATIONSHIP. THE GRAPHS SHOW THAT THE STRENGTH OF THE CORRELATION IS HIGHER DURING
MIDDAY COMPARED TO MORNING AND AFTERNOON PERIODS................................................................. 42
FIGURE 26: THIS FIGURE ILLUSTRATES A SCATTERPLOT PRESENTING THE MEAN TEMPERATURE AND THE MEAN
NDRE FOR HEALTHY VINES (YELLOW) AND VINES AFFLICTED BY THE BOTRYTIS FUNGUS (GREEN) DURING
MIDDAY AND AFTERNOON PERIODS, BOTH FOR THE GRAPE SECTION AND THE ENTIRE PLANT. THE LINEAR
FIT LINES SHOW THE DIRECTION OF THE RELATIONSHIP FOR HEALTHY OR INFECTED VINES. NOTABLY,
THERE IS AN OVERLAP IN TEMPERATURE AND NDRE VALUES FOR HEALTHY AND INFECTED VEGETATION.
...................................................................................................................................................................... 43
FIGURE 27: THIS FIGURE ILLUSTRATES BOXPLOTS REPRESENTING THE MEAN VALUES OF TEMPERATURE, NDVI,
AND NDRE FOR BOTH HEALTHY VEGETATION AND VEGETATION AFFECTED BY THE BOTRYTIS FUNGUS
DURING THE MIDDAY AND AFTERNOON. THE BOXPLOTS ARE PRESENTED FOR THE ENTIRE PLANT AS WELL
AS THE SECTION IN WHICH GRAPES ARE LOCATED. ..................................................................................... 44
FIGURE 28: BOXPLOTS OF THE MEAN POINT DENSITY (A MEASURE OF THE DENSENESS OF THE VEGETATION) IN
HEALTHY AND BOTRYTIS-INFECTED PLANTS IN ENTIRE PLANTS AND THE GRAPE SECTION FOR BOTH THE
MIDDAY AND AFTERNOON........................................................................................................................... 44
FIGURE 29: THERMAL ORTHOMOSAICS (A AND B) COMPARED TO A THERMAL POINT CLOUD. (A) SHOWS THE
EXTENT OF THE VINEYARD. (B) SHOWS THE ZONE WHERE INTERPOLATION OCCURS BETWEEN GROUND
AND VEGETATION POINTS. THE POINT CLOUD (C) SHOWS THE DIRECT TRANSITION BETWEEN
VEGETATION AND GROUND POINTS. ........................................................................................................... 45
FIGURE 30: THIS FIGURE COMPARES THE VARIABLES: TEMPERATURE, NDVI, AND NDRE. OBTAINED FROM
ORTHOMOSAICS (2D) AND POINT CLOUDS (3D). ANALYSIS OF THE MEAN, MEDIAN, AND DISTRIBUTIONAL
SHIFTS BETWEEN THE TWO DATASETS REVEALS THE IMPACT OF RASTERIZATION AND ASSOCIATED
INFORMATION CHANGES WHEN TRANSFORMING POINT CLOUD DATA INTO A 2D FORMAT. ................... 46
FIGURE 31: IMAGE SHOWING THE OVERLAP OF THE SHADOW CASTED BY THE TREES POSITIONED AT THE EAST
SIDE OF THE VINEYARD WITH ROW 3. RESULTING IN DIFFERENT TEMPERATURE BEHAVIOUR DURING THE
MORNING. .................................................................................................................................................... 51
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List of tables
TABLE 1: BAND NAMES, NUMBERS, CENTRE WAVELENGTH, AND BANDWIDTH OF THE MICASENSE ALTUM-PT
SENSOR. ........................................................................................................................................................ 20
TABLE 2: MORNING TEMPERATURES (IN °C) FOR VARIOUS PARTS OF THE GRAPE CANOPY IN A VINEYARD,
SEPARATED BY ROWS AND SIDES. PROVIDES MINIMUM, MEAN, AND MAXIMUM TEMPERATURES FOR
THE GRAPE CLUSTERS (0.5-1.0M), SECOND CANOPY PART (1.0-1.5M), AND THIRD CANOPY PART (<1.5M)
...................................................................................................................................................................... 31
TABLE 3: MIDDAY TEMPERATURES (IN °C) FOR VARIOUS PARTS OF THE GRAPE CANOPY IN A VINEYARD,
SEPARATED BY ROWS AND SIDES. PROVIDES MINIMUM, MEAN, AND MAXIMUM TEMPERATURES FOR
THE GRAPE CLUSTERS (0.5-1.0M), SECOND CANOPY PART (1.0-1.5M), AND THIRD CANOPY PART (<1.5M)
...................................................................................................................................................................... 31
TABLE 4: AFTERNOON TEMPERATURES (IN °C) FOR VARIOUS PARTS OF THE GRAPE CANOPY IN A VINEYARD,
SEPARATED BY ROWS AND SIDES. PROVIDES MINIMUM, MEAN, AND MAXIMUM TEMPERATURES FOR
THE GRAPE CLUSTERS (0.5-1.0M), SECOND CANOPY PART (1.0-1.5M), AND THIRD CANOPY PART (<1.5M)
...................................................................................................................................................................... 31
TABLE 5: P-VALUES OF WELCH TWO-SAMPLE T-TEST TO DETERMINE THE SIGNIFICANCE OF THE DIFFERENCES
BETWEEN THE MEANS OF THE CANOPY SECTIONS. A VALUE LOWER THAN 0.05 CORRESPONDS TO A
SIGNIFICANT DIFFERENCE BETWEEN THE MEAN OF THE TWO GROUPS. THE NUMBERS 1,2, AND 3
CORRESPOND TO THE CANOPY SECTIONS WHICH ARE RESPECTIVELY THE GRAPE SECTION, THE SECOND
CANOPY SECTION, AND THE THIRD CANOPY SECTION. ............................................................................... 32
TABLE 6: TABLES DISPLAYING THE P-VALUES OBTAINED FROM TWO-SAMPLE T-TESTS FOR THE TEMPERATURE
AND NDRE IN THE GRAPE SECTION AS WELL AS THE ENTIRE PLANT DURING MIDDAY AND AFTERNOON. 42
11
List of abbreviations
UAV
3D, 2D
CT
SfM
VI
PC
RGB
NIR
TIR, LWIR
NDVI
NDRE
LiDAR
LAI
GCP
DTM
DSM
CHM
LAS
RMSD
PLY
Unmanned Arial Vehicle
Three-, two Dimensional
Canopy Temperature
Structure from Motion
Vegetation Index
Point Cloud
Red, Green, Blue
Near InfraRed
Thermal-, Long Wave InfraRed
Normalised Difference Vegetation Index
Normalised Difference Red Edge index
Light Detection And Ranging
Leaf Area Index
Ground Control Point
Digital Terrain Model
Digital Surface Model
Canopy Height Model
LASer file format
Root-Mean-Square-Deviation
Polygon File Format
12
1 Introduction
1.1 Background
The recent development of unmanned aerial vehicles (UAVs) for scientific purposes has led to gamechanging results in environmental remote sensing regarding vegetation and agricultural applications
(Sagan et al., 2019). The benefits of using sensors mounted on UAVs are the acquisition of higher
spatial resolutions compared to prevailing satellite imagery (Messina & Modica, 2020), and better
control of temporal resolutions (Tang & Shao, 2015) as missions can be planned at the desired time.
Furthermore, UAVs can be operated at low altitudes minimizing sensor interference caused by clouds.
UAVs offer spatial resolution up to a degree of decimetres to centimetres. Sensor platforms with higher
spatial resolutions store more spatial information per area. Images containing higher spatial
information show more heterogeneity (Weiss & Baret, 2017), making image analysis more accurate.
The use of UAVs has proven to be convenient in agricultural practices. For instance, the monitoring of
wheat trials, or quantitative remote sensing of orchards and vineyards (Turner et al., 2014).
Furthermore, the employment of UAVs and remote sensing sensors is associated with non-destructive
ways to extract spatial information as soils and vegetation are not physically touched or disturbed
(Torres-Sánchez et al., 2021).
Sensors that capture multispectral and thermal imagery in vineyards are useful for acquiring crop
parameters and characteristics for predicting the yield and quality of grapes. Parameters and
characteristics are for instance vegetation indices, plant composition (pH, acidity, sugar content),
temperature (Bonilla et al., 2013), and humidity in grapevines (Xue et al., 2008). Therefore, remotely
sensed imagery is a handy tool for crop monitoring in vineyards. Well-known applications for crop
monitoring in vineyards are; the determination of water stress (Prueger et al., 2019), weed control
(Sassu et al., 2021), the creation of 3D point clouds for canopy characterization (Torres-Sánchez et al.,
2021), canopy temperature distribution using 2D imagery (Zhang et al., 2019), and disease detection
(Kerkech et al., 2020).
Canopy temperature (CT) is an important and interesting factor to extract from vineyards. The general
temperature in grapevines influences biological processes in the grapevines and the berries. Higher
temperatures shorten the phenological cycle maturation of the grapevine and increase the risk of
diseases. This is caused by dense canopies due to faster canopy development (Keller, 2020). Also, leaf
temperature is a crucial plant parameter because it is a key component of the physiological regulation
process that ensures adequate levels of photosynthesis and transpiration (Yandún Narváez et al.,
2016). This process depends on a variety of factors, including the soil's water status, the atmosphere's
conditions, and the structure of the trees. Also monitoring temperature is important as it is an indicator
of water stress. Normally, excess heat is transferred to ambient air through convection, re-radiation,
and transpiration through the leaves (Keller, 2020). In drier conditions, less transpiration occurs
through the leaves, and the plant temperature rises (Zhang et al., 2019). Also, temperature and UV
radiation influence the spread of mildew and the bunch rot infection. Higher temperatures inhibit
mildew from spreading across the vineyard, reducing bunch rot infections as exposed fruit dries more
quickly after rain (Keller, 2020). Especially the bunch rot infection named Botrytis cinerea is known to
cause quality reduction of grape berries in moderate climate zones (Jacometti et al., 2010). Botrytis
bunch rot is one of the most frequent fungus infections in viticulture, affecting the vegetative tissues
in grape vines (Vélez et al., 2023). Conditions favourable for the Botrytis fungus are temperatures
between 1-30 degrees Celsius under high relative humidity (at least 90 percent) (Keller, 2020).
Therefore, monitoring canopy temperature is critical in viticulture.
13
Grape clusters can be found in the lower part of the canopy, and commonly under or among the leaves
(Carmona et al., 1995; Keller, 2020), making cluster detection more difficult. Leaf occlusion is a wellknown challenge in agriculture and forestry remote sensing applications. For instance, in fruit
detection, leaf occlusion leads to notable accuracy loss in fruit detection algorithms (Gongal et al.,
2015). In vineyards, leaf removal is a solution several studies use to increase the identification accuracy
of grape clusters and to get a better understanding of berry clusters (Font et al., 2015; Torres-Sánchez
et al., 2021). However, few winegrowers use leaf removal for fruit detection or field-based cluster
assessment. Since leaf removal is labour intensive. Therefore, fusing thermal images with 3D point
clouds obtained from structure from motion (SfM) using drone images could help perceive the
temperature of the canopy parts that contain the grape clusters. The cluster temperature can be used
to obtain information about the state of the grape clusters and their future quality. The process of data
fusion is beneficial since the combination of multiple sources (in the scope of this thesis; multispectral
and thermal imagery/point clouds) allows the improvement of information. This could express itself in
information that is less expensive to obtain, has a higher quality, or contains more information relevant
to the subject (Castanedo, 2013). The use of thermal images for 3D point cloud construction comes
with an important issue. First of all, the resolution of thermal images is quite coarse relative to
multispectral resolutions. This impacts the structure from motion algorithm negatively as fewer key
points can be detected leading to less accurate 3D point clouds (Hou et al., 2022).
1.2 Scientific gap
In literature, little information is available regarding the 3D application of thermal imagery in
vineyards. Although thermal imaging is used in numerous studies, very few of them include methods
for thermal imagery in a 3D manner. Many methods are developed for applications like, plant
parameters, water stress indicators, plant phenotyping, disease detection, and application in precision
agriculture for yield, biomass, and quality estimation. Since these methods are two-dimensional,
methods are mostly based on thermal orthomosaics and nadir images, they lack information in the
third dimension. In the scope of this thesis, the third dimension is important since grape clusters are
found in the bottom part of the canopy (0.5-1.0m). So, for temperature extraction at different canopy
heights, the third dimension is essential.
In the scope of this research, the domain of viticulture lacks two things. First, fusing thermal imagery
with point clouds. Secondly, constructing a thermal point cloud and working with it. Therefore, to
extract the temperature from image fusion between thermal imagery and 3D point clouds, it is
interesting to learn from literature and methods outside the scope of viticulture. Plausible fields of
interest are agriculture, forestry, architecture, civil engineering, and environmental monitoring.
Especially the fields of architecture and civil engineering are more developed regarding the
enhancement of 3D point clouds with thermal data.
This thesis reviews existing 3D thermal methodologies that are currently employed in non-viticulture
fields and subsequently applies this knowledge to the field of viticulture.
1.3 Overall research aim and research questions
1.3.1 General aim
Develop and evaluate a method to fuse thermal images with multispectral 3D point clouds of a
vineyard. Use this method to acquire the temperature of the canopy as a whole (upper and lower
canopy), and extract temperature in individual grape vines along the vertical dimension (height relative
14
to the terrain) in healthy grapevines as well as grapevines affected by Botrytis cinerea. The thermal
information is used to describe aspects regarding the state of the grapes and their future quality.
1.3.2 Research questions
1.
What methods to fuse thermal imagery with 3D point clouds are already available in viticulture,
and what can be learned from literature in other domains (agriculture, forestry, architecture/civil
engineering)?
2.
How is the canopy temperature distributed (both vertically and horizontally) over the vineyard,
particularly in the lower region where the grapes grow?
3.
How does the temperature of individual canopy parts differ along the height of the plants in healthy
plants as well as Botrytis-infected plants?
4.
What are the advantages/disadvantages of using thermal information in vineyards from a 3D point
of view?
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2 RQ1 - Literature review
This literature review starts with a brief review of the application of thermal imaging in vineyards.
Hereafter literature will be reviewed about methods to fuse thermal imagery with 3D point clouds in
various subjects. As the field of viticulture regarding the fusion of point clouds has gotten little
attention in research examples from literature in other domains will be used as well.
2.1 2D thermal imagery in vineyards
Thermal imagery has emerged as a useful tool regarding viticulture, with numerous articles exploring
the applications of TIR in vineyards. Since the temperature is an important variable in physiological
processes impacting the growth and stress of grapevines as well as the development of serval diseases.
2.1.1 Vineyard’s water status, irrigation management, and variability
Thermal imaging has been widely used in vineyards to explore the water status and irrigation
management of grapevines, which has a significant impact on yield and quality (Gutiérrez et al., 2018).
Irrigation practices are typically based on the water status of the vineyards, and advancements in this
area have led to the development of indicators such as the crop water stress index (CWSI) for mapping
water stress (Bellvert et al., 2014, 2015). Additionally, researchers have studied the temperature
changes caused by fluctuations in water availability due to seasonal variability in plant water status
(Baluja et al., 2012; Santesteban et al., 2017). For example, Bellvert et al. (2015) employed highresolution thermal infrared (TIR) imagery to evaluate the CWSI in grapevines, while Diago et al. (2022)
analysed multispectral imagery, environmental data, and thermography to describe the water status
in vineyards.
2.1.2 Disease detection and monitoring
Thermal imagery has become an essential tool in precision viticulture for the early detection and
monitoring of diseases in grapevines. Pests and diseases can adversely affect the yield and quality of
grape berries (Stoll et al., 2008) making their early detection crucial. Fungi such as Botrytis cinerea can
cause bunch rot (Walker et al., 2011), while Powdery Mildew, which attacks grapevine leaves, is one
of the most common and persistently harmful infections (Cohen et al., 2022; Keller, 2020). Pathogens
can influence stomatal transpiration, which is closely related to plant temperature (Mahlein, 2016). As
such, thermal radiation emitted from vines can be used to detect and monitor pests and diseases ZiaKhan et al. (2022). Zia-Khan et al. (2022) found that an increase of 3.2 degrees Celsius in leaf
temperature occurred before symptoms of Downy Mildew, a common grapevine disease, appeared,
demonstrating the potential of thermal imaging for early detection, and monitoring of diseases in
grapevines.
2.1.3 Fusion of thermal imagery and point clouds in viticulture
Although all these studies mentioned above describe their thermal imaging applications in detail, none
mention the use of thermal remote sensing in a photogrammetric way. In literature, 2D thermal
imaging and processing are mainstream. Few articles are available describing photogrammetric/threedimensional approaches for thermal images in Vineyards. Some studies describe the use of 3D point
clouds obtained by UAVs. However, in most studies, point clouds from UAV or LiDAR are used for
purposes outside the scope of this thesis, and primarily the visible and near-infrared part of the
16
spectrum is covered regarding viticulture. To be more specific, Herrero-Huerta et al. (2015) use
photogrammetry to create an automatic 3D bunch model to estimate yield in vineyards based on RGB
images. Weiss & Baret (2017) use only RGB imagery to create 3D point clouds to describe the 3D
macrostructure of a vineyard. Furthermore, Pagliai et al. (2022) compare methods based on 3D point
clouds to assess canopy size in viticulture. The previous article introduces the use of multispectral
sensors to build 3D point clouds but does not go into depth. Therefore, the rest of this literature review
is devoted to literature about methods that use the combination of thermal and multispectral imagery
in a 3D matter. Hereby including the limited amount of literature in the domain of viticulture, and
richer domains like agriculture, forestry, civil engineering, architecture, and environmental monitoring.
2.2 Point cloud applications in vineyards
Numerous studies have investigated the use of Unmanned Aerial Vehicles (UAVs) for generating 3D
point clouds in vineyards. Comba et al. (2018) use an unsupervised learning algorithm to identify
vineyards and assess vine row features from a point cloud, while Comba et al. (2020) utilized a UAV
point cloud to estimate Leaf Area Index (LAI) by extracting crop parameters. Weiss & Baret (2017)
obtained a 2D height distribution of the vineyard from a terrain altitude extracted from an RGBcoloured dense point cloud. In addition, 3D point clouds created by Structure-from-Motion (SfM)
techniques using an RGB sensor can provide canopy information (Mesas-Carrascosa et al., 2020).
Multi-spectral and photogrammetric algorithms utilizing 3D information can also enhance
evapotranspiration models and generate accurate LAI maps, canopy estimation, height, volume, and
surface area based on point cloud data (Aboutalebi et al., 2019). All these studies range in a variety of
applications for three-dimensional point clouds in vineyards. Most of them include RGB, some use
multispectral images to use for certain colour and vegetation indices, or assessment of the size, shape,
and structure of the canopy. Using thermal images for the processing of 3D point clouds to be used in
vineyards is rare. One study explores the combination of a point cloud with thermal imagery. (Comba
et al. (2019) make use of data fusion between 3D point cloud crop models, 2D multispectral aerial
imagery, and aerial thermal imagery to enhance the classification of vines in several vigour classes.
2.3 Thermal point cloud generation in agriculture and forestry
In the field of agriculture and forestry, more studies have investigated and used thermal point clouds
for achieving their research objectives. The main ways to generate dense thermal point clouds is by
using LiDAR or SfM. A general approach is to combine the LiDAR point cloud and thermal imagery by
using methods that fuse the two datasets. (Tsoulias et al., 2022) use LiDAR and thermal imagery which
are calibrated together using an active board target that gives each point in the point cloud the
corresponding thermal value. This is used in a study towards temperature monitoring on the fruit
surfaces of apples during their maturation to identify thresholds that could cause sunburn. A similar
approach is described by Yandún Narváez et al. (2016) where LiDAR range readings are combined with
thermal imagery to reconstruct a 3D thermal point cloud for the ground-based 3D characterization of
fruit trees. Even more popular is the same method, but in combination with the SfM algorithm. Several
examples in literature can be found to construct 3D thermal point clouds using RGB or multispectral
images. Whereafter the thermal information is projected on the RGB/multispectral point cloud (Comba
et al., 2019; Hou et al., 2022; Javadnejad et al., 2020; Zhu et al., 2021). Another less prevailing method
is described by Jurado et al. (2022), which reviews applications of image fusion in 3D scenarios
regarding agriculture and forestry. In their research, they address the 3D reconstruction from
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multispectral and thermal imagery individually using SfM, whereafter the temperature of points in the
dense cloud is extracted.
2.4 Point cloud enhancement outside viticulture
Outside agriculture, literature about thermal point clouds and the enhancement of 3D point clouds
with thermal data is more numerous. Besides agriculture and forestry, the use of thermal point clouds
can be found in the construction industry and architecture(Dlesk & Vach, 2019; Ramón et al., 2022;
Wang & Kim, 2019) and environmental monitoring (Grechi et al., 2021; Guilbert et al., 2020). In the
sections below some approaches are highlighted.
2.4.1 Civil engineering and architecture
In the domain of civil engineering, literature presents three approaches to fuse thermal imagery and
multispectral imagery, mainly used for thermal inspection of buildings to detect problems regarding
construction and heat leakage (Dlesk & Vach, 2019). Image matching (2D-2D), is a method where
thermal imagery is matched with multispectral imagery. Fusion of 2D thermal images and 3D point
clouds (2D-3D) and the fusion of 3D thermal point clouds and 3D higher resolution point clouds (3D3D) (Hou et al., 2022; Lin et al., 2019). 2D-2D image matching is used in the work performed by Dlesk
& Vach (2019). In their paper, they present a method to capture thermal images of a building, process
them using the SfM method, and generate a point cloud in which the points are co-registered with
temperature information. Hou et al. (2022) use image-to-model matching (2D-3D) in their experiment
toward the performance of data fusion between thermal images and RGB point clouds under different
experimental conditions. Their approach is similar to the approaches mentioned before, namely the
registration of 2D images to create a point cloud. However, they project the thermal values of
corresponding pixels on the point cloud. Lin et al. (2019) perform the model-to-model approach (3D 3D) by using thermal image data to evaluate the insulation quality of buildings and to detect heat
leakages as well. Their approach uses a set of algorithms that perform a course and fine registration
to register the RGB and thermal point clouds.
2.4.2 Landscape monitoring
In literature, 3D reconstruction of landscapes that includes thermal imagery is used for several
applications. Thermal imaging of rock formations in landslide-prone areas can yield valuable insights
into fracture identification, water infiltration, and the detection of debris covers and discontinuity
networks, by analysing thermal anomalies (Melis et al., 2020). Although the fusion of thermal imagery
and 3D point clouds in this domain in not conventional, some studies make use of it. For instance,
Guilbert et al. (2020) use a reflex and TIR camera carried by UAV to create a visible and thermal point
cloud. Both clouds are fused together using a python code that associates the thermal scaler on the
visible cloud using a closest point algorithm. They perform this method to derive information about
the Vaches Noires landslide in Normandy, France. Also(Grechi et al., 2021) perform separate point
cloud generation and co-register the thermal point cloud and the high-resolution RGB cloud to merge
the thermal attributes with the RGB point cloud. These authors monitor jointed rock masses using
thermal and RGB photogrammetry, to obtain more information about geo-mechanical processes in
rock masses.
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3 Data, materials, and methods
3.1 Research design and data
3.1.1 Study area
The vineyard of interest is owned by 'Bodegas Terras Gauda, S.A.' and belongs to the "Rias Baixas AOP"
(Appellation of Origin). It is situated in "Tomiño, Pontevedra," Galicia, Spain (X: 516989.02, Y:
4644806.53; ETRS89 / UTM zone 29N; Figure 1). The information was gathered in a field that has Vitis
vinifera cv., Loureiro. The grape plants were trained in vertical shoot placement (VSP), grafted on a
196.17C rootstock with resistance to active limestone, and planted in 1990 with a NE-SW orientation
and 2.5 x 3 meters between plants and rows, respectively. The vineyard was operated under the AOP
procedure and their applicable laws, and cover crops were grown by naturally occurring plant species.
Data from the entire vineyard is available for data analysis (Vélez et al., 2023). The focus is downscaled
toward specific parts of the vineyard to optimize computer resources. As the amount of UAV images
taken is quite rich (around three terabytes), only vineyard B9 (red area in Figure 1) is used in the data
analysis. Moreover, the imagery acquired over vineyard B9 has allowed to produce point clouds that
take up to 8 gigabytes, making it necessary to only use three rows of B9 (white text in Figure 1). The
blue-lined area in Figure 1 shows the focus area consisting of three vine rows. For vine trunks in these
rows, the exact location is measured during the fieldwork. Three datasets are used for data processing
containing images taken in the morning, midday, and afternoon of the 14th and 15th of July 2022.
Figure 1: Location of the Vineyard inside the Iberian Peninsula (top right). Multispectral and Thermal data
are gathered from parcel B9 dating from the 12th of July until the 9th of September 2022 (3 weeks per
field) (bottom). The data acquisition resulted in four sets of images. The bottom image also displays the
locations of plants affected by the Botrytis cinerea fungus (black dots).
19
3.1.2 Data acquisition
The UAV data was collected by DJI M300 RTK (DJI, 2022). This drone is a powerful industrial platform
with an advanced flight control system, a six-direction detection and positioning system, automatic
obstacle detection, and is integrated with an FPV camera (first-person view). Data acquisition was
conducted using the Micasense Altum-PT (AgEagle Sensor Systems Inc., Wichita, Kansas, USA) whose
specifications are shown in Table 1 and include multispectral, panchromatic, and thermal sensors. The
multispectral and the panchromatic imager both have a pixel size of 3.45 μm, and the thermal sensor
has a pixel size of 12 μm. The multispectral, panchromatic, and thermal imagers have respectively a
resolution of 2064 x 1544 (3.2 MP per multispectral band), 4112 x 3008 (12 MP panchromatic band),
and 320 x 256. Their aspect ratios are 4:3, 6:5, and 5:4. The imagers come with sensor sizes of 7.12 x
5.33 mm, 14.18 x 10.37 mm, and 3.84 x 3.07 mm and focal lengths of respectively 8mm, 16.3 mm and
4.5 mm (MicaSense, 2022).
Before harvest, ground truth data on the plants' sanitary status was acquired. Botrytis cinerea has
developed in twelve plants (Figure 1). To obtain the location of these plants, an RTK GNSS was used
(Figure 1).
Table 1: Band names, numbers, Centre wavelength, and bandwidth of the Micasense Altum-PT
sensor.
Band name
Blue
Green
Red
Near-infrared
Red edge
Panchromatic
LWIR (thermal)
Band number
1
2
3
5
4
6
7
Centre
475 nm
560 nm
668 nm
842 nm
717 nm
634.5 nm
10.5 μm
Bandwidth
32 nm
27 nm
16 nm
57 nm
12 nm
463 nm
6 μm
3.1.3 Flight information
The data is gathered flying at a height of 15, 20, and 30 meters above ground level (AGL). The speed of
the drone was 2 meters per second, and it took the drone 10 to 30 minutes to complete the flight plan
depending on the mission length. Furthermore, tilt angles of 0 and 30 degrees were used. Overall, the
acquired image overlap was around 70-80%.
3.2 Workflow
3.2.1 Structure
This section explains the steps taken in the entire process, starting with the pre-processing in Agisoft
Metashape (version 1.8.4, (Agisoft LLC, 2023)) and CloudCompare (version 2.11.3, (CloudCompare,
2023)), followed up by the processing done for RQ2, 3 and 4. Figure 2 shows the simplified workflow
used during the pre-processing and the RQs. The following sections will show the individual parts of
Figure 2 in more detail, hereby also providing more advanced flow charts and the software used for
completion.
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Figure 2: Schematic simplified workflow. This image shows the schematic workflow during the preprocessing (purple) where the GCPs and the images are taken as input for the creation of dense point
clouds, and the elevation models are later used in RQ2 and RQ4. The dense point cloud is then used
for the extraction of the Eastern and Western sides of the rows in RQ2 using the trunk locations
(red). The same extraction method is used for the extraction of healthy and infected vines in RQ3
(orange). Last, the 2D representation obtained from the CHM and the multispectral orthomosaics are
compared to the 3D representations regarding rows 1,2, and 3.
3.2.2 Pre-processing - Point cloud generation
3.2.2.1 Reflectance calibration
The first step after importing drone images and GCPs is reflectance calibration (Figure 3), which is also
known as radiometric calibration. This is done for all bands except for the thermal band, as this band
is calibrated by the sensor itself. The radiometric calibration determines the models and parameters
used for the calculation of the radiance captured by the sensor from the digital numbers found in each
image (Honkavaara et al., 2009). Especially when multispectral sensors are used for quantitative
research, they first need to be radiometrically calibrated, which uses both solar and atmospheric
variables by turning the digital number into a unit of scene reflectance that can be used in multispectral
calculations (e.g. calculation of vegetation indices) (Guo et al., 2019).
3.2.2.2 Camera Alignment
In the second step of Figure 3, the positions of the cameras are estimated using aerial triangulation
and bundle block adjustment. This process finds and matches points in all input images, which are
saved in so-called tie points. The tie points represent the connection between the photos, which helps
21
determine the 3D positions (Agisoft LLC, 2023). The accuracy of the image alignment is user dependent,
which could affect the quality and processing times of the output negatively. This research is based on
the highest accuracy, meaning that the software Agisoft Metashape works with images that are
upscaled by a factor of four (Agisoft LLC, 2023). In case of accuracy, this setting is favourable as it yields
the best parameters for camera alignment. However, this setting has some downsides as the process
take becomes more time-consuming and gets computationally more intensive (Barbasiewicz et al.,
2018).
3.2.2.3 Ground control points and camera optimization
Figure 3 shows the use of GCPs (Ground Control Points). Using GCPs, which are spread all around the
field, is important for ensuring reliable accuracy in the 3D models created using Agisoft Metashape.
The geometrical precision as well as the georeferencing accuracy improve with the use of accurate
GCPs. When including GCPs the software is better capable of matching the real-world coordinates
system, allowing more precise scaling in the models (Agisoft LLC, 2023). A study by Nagendran et al.
2018 shows that the total error when using GCPs is significantly lower than the same process without
GCPs. Also, the resolution of the outputs slightly increases (an increase of 0.22 centimetre per pixel for
a flight height of 50 metres (Nagendran et al., 2018)). After importing the GCPs the camera
optimization is run as the GCP coordinates are often more accurate than the coordinates captured by
the drone’s GPS. Therefore, using the GCP data improve accurate and more reliable optimization
outputs (Agisoft LLC, 2023).
3.2.2.4 Build dense cloud
The following step displayed in Figure 3 is building the dense point cloud based on both the optimized
camera positions and the vineyard images. The quality of the point cloud is again dependent on the
quality specified by the user. The quality setting of the point clouds is set to high. This means that the
images are downscaled by a factor four to ensure reasonable processing times. In terms of accuracy
and reliability, the best setting is ultra-high, this setting uses the original photo sizes(Agisoft LLC, 2023),
but due to processing times and high computational power, this setting has been avoided. The dept
filtering parameter is set to mild as the vineyard exhibits small details between vines. Furthermore,
the boxes for the re-usage of depth maps and the calculation of the point colours are checked.
3.2.2.5 Classification
The point cloud generated in the previous step is classified into two categories. Ground points and
vegetation. Mainly the ground points are important later during processing, as they are used to
compute the digital terrain model (DTM) needed for the determination of the normalized height
(height relative to the terrain) in section 3.2.4.2 (Figure 7; Figure 9). In RQ4 (section 3.2.6) the DTM
and DSM are needed to compute the CHM. This is done by subtracting the DTM from the DSM (Figure
2). Basically, the CHM is the 2D representation of the normalised height.
3.2.2.6 Calculate point cloud attributes
After building the digital terrain models and orthomosaics several vegetation indices are calculated by
performing mathematical calculations on specific bands. For this research, the NDVI (Equation 1)
(Rouse et al., 1974), NDRE (Equation 2) (Barnes et al., 2000), and Temperature (Equation 3) are
calculated for all points in the point cloud. Among literature, the NDVI is the most used vegetation
22
index in vineyards (Giovos et al., 2021), as this VI has proven to be helpful when investigating the health
and growth of vegetation as it is a measure of the chlorophyll content in plants. The NDRE can be used
for the same purposes. Although the difference is that this VI is more sensitive to changes in chlorophyll
content and leaf structure, making it better suitable for the detection of plant health and stress. The
following formulas are used to determine the values for the three attributes. Afterward, the NDVI,
NDRE, Temperature, and classification attributes are exported as .PLY files. As this format allows easier
data transfer to CloudCompare.
𝑁𝐼𝑅 − 𝑅𝐸𝐷
𝑁𝐼𝑅 + 𝑅𝐸𝐷
(1)
𝑁𝐼𝑅 − 𝑅𝑒𝑑𝐸𝑑𝑔𝑒
𝑁𝐼𝑅 + 𝑅𝑒𝑑𝐸𝑑𝑔𝑒
(2)
𝑁𝐷𝑉𝐼 =
𝑁𝐷𝑅𝐸 =
𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 (°𝐶) =
𝐿𝑊𝐼𝑅
− 273,15
100
(3)
Figure 3: Schematic overview for point cloud generation using the software Agisoft Metashape
(version 1.8.4). The GCPs and the images from al bands are taken as input. After reflectance
calibration and the manual updating of GCP locations, the camera alignment and optimization are
performed. Followed by building the dense point cloud, and classification of both ground and
vegetation points. The band calculation is done using the raster transform to obtain the values for
NDRI, NDRE, and temperature.
3.2.3 Point cloud Fusion
Figure 4 shows the outputs created in the former steps are merged in CloudCompare using the
Interpolate from another entity option (CloudCompare, 2023). Although the name of this option refers
to interpolation between clouds this is not the case when using the nearest neighbour method while
using one neighbour. Since the four point clouds are identical, the attributes will be directly transferred
to the other point clouds as scalar fields, since the geometry of the point cloud for all four point clouds
is the same. This process is repeated three times to transfer all attributes to one point cloud. Hereafter,
one point cloud remains enclosing four scalar fields. The point cloud is saved as a .las file because the
LidR package in R handles this format relatively easily and has many ‘ready to use’ functions regarding
point cloud processing.
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Figure 4: Schematic overview for the point cloud fusion. This process describes the fusion of the four
input clouds (obtained during pre-processing in Agisoft Metashape) containing multispectral
attributes (NDVI, NDRE, Temperature) and a class attribute into one point cloud.
3.2.4 Temperature extraction along vine rows
3.2.4.1 Prepare data
The first step before processing the point cloud obtained from the pre-processing in Agisoft Metashape
and CloudCompare is data preparation (Figure 7). The point cloud holding the four attributes first
needs to be prepared for further processing. This includes checking whether the right coordinate
system is used. As mentioned before the vineyard is located in the west of Spain. Therefore, the
coordinate system UTM zone 29N is used. This is important since all images, GCPs, trunks- and Botrytis
locations are all measured using this coordinate system.
Furthermore, The LidR package has its way of organizing and working with .las clouds. Some functions
use the built-in attributes for LAS point clouds (i.e., Classification, Intensity, ReturnNumber, etc.).
Therefore, the attribute class needs to be transferred to the built-in attribute ‘Classification’.
Otherwise, the LidR function will not work for the data (e.g., the rasterize terrain function to create a
DTM needed for normalizing the height dimension of the point cloud is based on the built-in attribute
Classification).
3.2.4.2 Normalize point cloud
The normalization of a point cloud is based on the height relative to the terrain. Therefore, the point
cloud is used to produce a DTM (Figure 7). This algorithm interpolates all the points classified as ground
points during the processing in Agisoft Metashape (section 3.2.2.5; Figure 3). The resolution of the
DTM depends on the resolution setting provided by the user. To reinsure a terrain model that is
accurate enough for the normalization of the point cloud, the resolution is specified as 0.01 (1x1 cm;
10.000 cells per squared meter). Which is relatively high compared to the point cloud densities of
around 1700 points per squared meter. This setting ensures few points are taken together during the
DTM rasterization while not exceeding the computer's processing capacity. For normalization,
equation 4 is used by the normalize height function provided by the lidR package.
𝑍𝑛(𝑃𝐶) = 𝑍𝑎(𝑃𝐶) − 𝑍𝑑𝑡𝑚(𝑅)
(4)
24
Where,
Zn is the Normalised height (meters)
Za is the Absolute height (meters)
Zdtm is the Absolute height DTM (meters)
(…) is the dataset type (PC: Point Cloud) (R: Raster)
Figure 5: Visualization of the normalization process. The left image displays the absolute height of
individual points. After normalization, the height relative to the terrain is calculated (right image)
The DTM is subtracted from the photogrammetric point cloud, resulting in a normalized point cloud
where the ground is at zero meters (Roussel et al., 2020; Roussel & Auty, 2022). Therefore, each point
in the point cloud indicates the height relative to the terrain.
3.2.4.3 Extract vegetation
The next step after normalization is reducing the point cloud by all points that are of no interest for
further processing (filter vegetation in Figure 7). As the main goal is to extract temperature in the
canopy of vine rows and individual plants the ground points, trunks, and branches are superfluous
from this point onward. Therefore, thresholds based on height, NDVI, and NDRE values are used to
exclude the points that are not related to vegetation. The threshold for NDVI has been set to 0.5 and
higher, indicating high, vigorous, and abundant vegetation (Hashim et al., 2019; Marquet et al., 2023;
Zhang et al., 2022). To continue, the value for NDRE is set to 0.25 and higher indicating non-stressed
vegetation based on the crop cover (Barnes et al., 2000). At last, the normalized height is used to
remove points that do not represent vegetation. Excluding all points below 0.5 meters will
predominantly filter out points corresponding with soil, trunks, and standalone branches. Figure 6
illustrates the extraction of the vegetation cloud from the normalised cloud.
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Figure 6: The vegetation in the normalized point cloud (left) is extracted using the thresholds for the
NDVI (0.5), NDRE (0.25), and the normalized height (0.5 meters) to exclude all non-vegetation
points (right).
3.2.4.4 Height profile
A data frame is obtained containing the basic temperature-related statistics of the point cloud. The
height component of the point clouds is segmented into 10-cm intervals, and statistics are gathered
for each interval to gain an understanding of temperature distribution across the canopy. This step is
visualised in Figure 7 as ‘Create Height Profile’.
3.2.4.5 Vine row extraction
The next step in Figure 7 is the extraction of vine rows. The trunk coordinates of three rows in the
vineyards have been retrieved as ground truth during the field work. The coordinates are used to
automatically extract these rows to ensure lower processing times as this excludes about 70% of the
other rows in the vineyard. For this process, the coordinates of both the first and last vine in the row
are used to create a transect, using 1.5 meters as the width parameter. This method creates for each
row a new point cloud.
3.2.4.6 Cross sections
To understand temperature change in the canopy caused by differentiating solar angles the individual
vine rows are cut vertically through the centre (‘Fit plane’ in Figure 7). To achieve this a vertical plane
is fit through the centre along the vine row (yellow line Figure 8). CloudCompare offers an option to
calculate the distance of every point toward the plane. The distance is used to divide the East (positive
distances) and the West (negative distances) sides of the canopy. In Figure 8 the positive distances to
the plane are visualized as blue and the negative distances as red. For three different datasets taken
on the 14th and 15th of august, a cross-section is created. The images of these three datasets were
taken during the morning, midday, and afternoon.
Figure 7: a schematic overview of the workflow for temperature extraction along vine rows. The point
clouds containing vegetation points only and the ground truth locations of all vine trunks are used
26
as input. The first step is preparing the data to be used for terrain rasterization and normalization.
Then, the vegetation is isolated, and the vine rows are extracted using the trunk locations of the
vines in the focus area. Afterward, a plane is fit in CloudCompare to separate the East and West side
of the individual rows needed for the temperature analysis
Figure 8: Example of the plane fitting procedure in CloudCompare. The green plane outlined in yellow
is fit vertically through the centre of the vine rows. The distance of each point to the plane is
calculated and displayed in red (negative distances towards the plane; Western side) and blue
(positive distances towards the plane; Eastern side).
3.2.5 Temperature extraction in individual vines
3.2.5.1 Individual vine extraction
The third research question is based on temperature analysis of individual grape vines canopies.
Therefore, temperature information is needed about individual plants. The trunk coordinates are used
to automatically clip transects. The lidR transect function uses two points to clip between. Therefore,
transects are clipped using the consecutive points in the data frame. The result is a directory containing
point cloud files representing individual grape vines for further temperature analysis.
3.2.5.2 Botrytis vine extraction
The foundation of the workflow for extracting the vines infected with the Botrytis cinerea fungus is
similar to the extraction of individual healthy vines as described in the previous section. Differences in
the workflow are the creation of circular areas around the infected plant using their measured
locations (to ensure sufficient ground points needed for interpolation of the DTM). Another difference
is the absence of a second coordinate to create a transect. Therefore, a method has been used to
estimate the position of the second plant coordinate. The first coordinate Is known and corresponds
to the trunk position. The second coordinate is estimated based on 1) the orientation of the vine rows,
and 2) the positioning of the grape vines in the vineyard. Equations 5 and 6 are used to estimate the
second x and y coordinate for individual plant extraction in Botrytis vines. Where the slope m is the
mean slope between all trunk locations derived by fitting a line using linear regression in the XY plane
(top view) of the focus area (Figure 9).
𝑥 = 𝑥0 ±
𝑑
(5)
√1 + 𝑚2
27
𝑦 = 𝑚(𝑥 − 𝑥0) + 𝑦0
(6)
Where,
x = x coordinate of estimated Botrytis point
y = y coordinate of estimated Botrytis point
x0 = x coordinate of measured Botrytis point
y0 = y coordinate of measured Botrytis point
d = length of the plant (2.5 meters between plant trunks)
m = orientation of the vineyard (slope of lines through the XY plane of the trunk locations (Figure
9)
Figure 9: Longitude and latitude values of the vine tree stems in the XY plane. The orientation m of
the vine rows is calculated by a linear regression fit. The directional coefficient of this fit is used to
predict the second point needed for extracting individual Botrytis plants.
28
Figure 10: Schematic workflow for extracting individual plants, both healthy plants and plants
infected by the Botrytis disease. This figure shows two workflows. The first one (top) is for the
extraction of grape vines infected with Botrytis. This method is similar to the workflow for extracting
the vine rows, except for the extraction of circular features for the rasterization and normalization of
the point cloud. After excluding points corresponding to non-vegetation the plant coordinates are
estimated, and the plants are extracted for temperature analysis. The second workflow (bottom)
uses the vine trunk location of healthy plants to extract the individual plants for temperature analysis.
Rasterization, normalization, and the filtering of vegetation are not needed here as this has already
been done during the processing for RQ2.
3.2.6 Comparison between a point cloud and orthomosaic
RQ4 is based on the analysis between the 3D and 2D representations of the three rows in the focus
area depicted in Figure 1. Figure 11 shows the workflow used to create the datasets to be used for this
comparison. The inputs used are the cross-sectional point clouds obtained from the workflow
described in section 3.2.4.6. The other inputs are the orthomosaics and the CHM created during the
pre-processing in Agisoft Metashape (section 3.2.2 ). First, to compare the attributes related to the
vegetation, rasters have to be created that include pixels representing vegetation only. This is done
following the same procedure as the filter vegetation step displayed in Figure 7 and Figure 10. Namely,
using the threshold for the NDVI (>0.5), NDRE (>0.25), and the CHM values (>0.5). In two-dimensional
applications, this works slightly different as pixels are encountered instead of points. So, all pixels that
do not comply with the rules specified by the threshold are set to NA (Not Available, also known as
‘missing values’), which leaves vegetation pixels only. Afterwards, basic statistics are calculated and
analysed to visualize the differences between the point clouds and the rasters for all attributes (NDVI,
NDRE, and Temperature).
29
Figure 11: Schematic overview of the workflow followed for RQ4. The point clouds representing the
cross-section, orthomosaics, and the CHM are used as input. First, the orthomosaics and the CHM
are stacked after which the pixels corresponding to vegetation can be filtered.
30
4 Results
4.1 RQ2 - Canopy temperature distribution in vine rows
The workflow RQ2 resulted in eighteen LAS point clouds, each representing the vegetation in the east
or west part of a row in the vineyard in the morning, midday, or afternoon. Table 2 represents the
basic temperature statistics of the cross section per part of the day. The analysis of the temperature
per cross-section points out that the temperature in the vineyard deviates in three ways. First, the
temperature decreases per normalized height section (height relative to the terrain). Secondly, it can
be observed that the temperature between the east and west sides of point clouds deviates. Thirdly,
the temperature varies during the day. The next section will discuss these findings in more detail.
Table 2: Morning temperatures (in °C) for various parts of the grape canopy in a vineyard, separated by rows
and sides. Provides minimum, mean, and maximum temperatures for the grape clusters (0.5-1.0m), second
canopy part (1.0-1.5m), and third canopy part (<1.5m)
Morning
Row
Side
Grape
cluster
temperature: (0.5-1.0m)
Min
Mean
Max
1
East
26.03
30.97
39.18
25.84
30.35
40.81
25.70
29.00
40.66
West
25.63
28.82
36.54
25.45
28.48
36.85
25.06
28.09
36.11
East
26.23
30.11
38.85
26.01
29.43
38.33
25.68
28.63
36.65
West
26.03
28.76
35.78
25.84
28.39
35.41
25.67
28.13
36.39
East
24.90
28.13
36.30
24.62
28.10
37.96
24.29
27.50
35.10
West
25.03
27.52
33.06
24.64
27.45
36.31
24.32
27.14
35.04
2
3
Temperature 2nd canopy part
(1.0-1.5m)
Min
Mean
Max
Mean temperature 3rd
canopy part (<1.5 m)
Min
Mean
Max
Table 3: Midday temperatures (in °C) for various parts of the grape canopy in a vineyard, separated by rows and
sides. Provides minimum, mean, and maximum temperatures for the grape clusters (0.5-1.0m), second canopy
part (1.0-1.5m), and third canopy part (<1.5m)
Midday
Row
Side
Grape cluster temperature:
(0.5-1.0m)
Min
Mean
Max
1
East
34.30
39.93
53.65
32.58
38.49
54.80
32.39
37.21
54.72
West
34.34
38.73
50.33
32.90
38.18
53.69
32.97
36.82
53.87
East
34.93
40.03
53.05
32.94
39.47
54.88
32.65
38.33
55.27
West
34.02
38.26
50.87
32.73
38.08
53.61
32.58
37.46
54.14
East
34.77
39.24
53.01
33.08
40.05
57.14
33.14
38.84
56.74
West
34.04
38.63
51.18
33.10
38.26
54.35
33.01
37.55
53.20
2
3
Temperature 2nd canopy part
(1.0-1.5m)
Min
Mean
Max
Mean temperature 3rd
canopy part (<1.5 m)
Min
Mean
Max
Table 4: Afternoon temperatures (in °C) for various parts of the grape canopy in a vineyard, separated by rows
and sides. Provides minimum, mean, and maximum temperatures for the grape clusters (0.5-1.0m), second
canopy part (1.0-1.5m), and third canopy part (<1.5m)
Afternoon
Row
Side
Grape cluster temperature:
(0.5-1.0m)
Min
Mean
Max
1
East
32.72
37.05
45.67
31.89
36.68
48.80
31.59
35.58
47.04
West
32.59
37.67
47.57
31.91
37.19
49.21
31.81
35.54
48.62
East
32.93
36.99
45.54
32.18
36.92
45.50
32.21
36.29
48.35
2
Temperature 2nd canopy part
(1.0-1.5m)
Min
Mean
Max
Mean temperature 3rd
canopy part (<1.5 m)
Min
Mean
Max
31
3
West
33.45
37.90
46.64
32.42
37.75
47.90
32.19
36.19
48.62
East
32.61
36.91
43.67
32.39
36.90
44.62
31.75
36.30
47.21
West
33.60
38.22
48.42
32.70
37.64
50.72
31.40
36.07
46.71
4.1.1 Temperature vs height sections
To get a nice overview of how the temperature is distributed across the three canopy sections the
cross sections (represented in Table 2) are split based on the part of the day. To simplify the results of
the tables for identifying temperature differences in the canopy, both the rows and sides have been
merged. Figure 12 is the result of this analysis. It shows boxplots of the temperature versus the canopy
sections in the morning, midday, and afternoon. Figure 12 shows a slight decrease in the median and
mean temperature per canopy section in the morning, midday, and afternoon. A two-sample t-test
proves that the means of the first and second, and second and third canopy sections are significantly
different Table 5). This means that the grape section’s temperature is higher on average than the
higher-located canopy sections. Not only do the mean and median temperatures show this decrease,
but also the boxes representing 50 percent of the data points in between the first and third quantiles
show this decrease.
Table 5: p-values of welch two-sample t-test to determine the significance of the differences between
the means of the canopy sections. A value lower than 0.05 corresponds to a significant difference
between the mean of the two groups. The numbers 1,2, and 3 correspond to the canopy sections
which are respectively the grape section, the second canopy section, and the third canopy section.
Morning p-values
Midday p-values
Afternoon p-values
Section
1
2
3
Section
1
2
3
Section
1
2
3
1
x
x
x
1
x
x
x
1
x
x
x
2
p < 2.2e-16
x
x
2
p = 3.4e-6
x
x
2
p < 2.2e-16
x
x
3
p < 2.2e-16
p < 2.2e-16
3
p < 2.2e-16
p < 2.2e-16
3
p < 2.2e-16
p < 2.2e-16
x
x
x
Figure 12: Boxplots of the vineyard rows’ temperature (in °C) per height section in the morning (left), midday
(middle), and afternoon (right).
Evidence of a relationship between the normalized height and the temperature in the point clouds can
also be found in the height profiles created during the processing for research question 2. Figure 13 is
an example of a height profile created from the data retrieved in the morning. It is visible that the min,
32
mean, and max temperatures have decreasing trends when going up in height (height intervals in
Figure 13). These min, mean, and max temperatures start deviating significantly when the height
passes the two-meter mark. This is caused by too few observations per height interval leading to
divergent values. Therefore, height sections higher than two meters are not considered as these might
represent unplausible statistics.
Figure 13 in combination with the boxplots in Figure 12 and the other boxplots in the following sections
shows information about the distribution of the data. The boxplots in Figure 12 make clear that the
distribution of the temperature data per cross-section is slightly skewed to the right. This can be
observed by the mean and median in the temperature. First, they are more located in the region with
lower temperatures. Secondly, the fact that in most boxplots the mean is located above the median
points to a right-skewed distribution. This simply means that the values corresponding to lower
temperatures are denser than the high temperatures.
Height profile Morning
Figure 13: Height profile of the vineyard rows in the morning displaying the maximum, mean, and minimum
temperature for each 0.1 m interval.
The information in Figure 12 and Figure 13 suggests a relationship between the height of a point in the
point cloud and its temperature. Therefore, a linear model is fitted to the data and Pearson’s
coefficient is calculated to retrieve information about the relationship between the height and
temperature of the points corresponding to vegetation. Figure 14 shows the correlations between
temperature and height. The outcome of the relationship in the morning, midday, and afternoon
shows a deviation in Pearson’s correlation coefficient. The relationship between the normalized height
and temperature can be described as weak to moderate in the morning, negligible to weak in the
midday, and moderate in the afternoon (Akoglu, 2018). Subsequently, a high Root Mean Squared
Deviation is observed (RMSD) for all curves meaning that in this case, the linear, quadratic as well as
polynomial functions do not quite fit the data. Also, increasing the non-linearity of the model has an
ignorable effect on the RMSD of the models.
33
Figure 14: Relationships between the normalized height and the temperature for the vineyard rows in the
morning, midday, and afternoon. The linear relationship is specified by Pearson’s correlation coefficient (right top
corner). The RMSD gives the ability to fit the data for the three curves. This figure indicates that the degree of
non-linearity does not influence the ability of the model to fit the data, as the RSMD decrease for higher linearity
can be ignored.
4.1.2 Temperature deviations per side
The data in Table 2(morning) shows that the mean temperature on the East side of the canopy is higher
than on the west side. The mean temperature of the east side at a height of 0.5 to 1.0 meters (grape
clusters) for rows 1, 2, and 3 are respectively 2.15, 1.35, and 0.61 degrees warmer. In the afternoon,
the east side of the vine rows is still warmer since the sun has given its radiation mostly to the east
side of the canopy during this day. This is what can be observed for all rows during the midday. The
mean temperature of the east side of these rows is respectively 1.20, 1.77, and 0.66 degrees warmer
than the west side. In the afternoon, the sun’s position shifts westward allowing the west side of the
vegetation to receive more radiation. Table 4 perfectly shows this principle as all three western canopy
sides have higher mean temperature values for the three height sections. The temperature of the
canopy section containing the grapes for the west side of the three rows is respectively 0.62, 0.91, and
1.31 warmer than the east side. The differences in the east and west parts of the canopy per daypart
are visualized in Figure 15. This figure shows the temperature deviations between the east and west
sides of the canopy for all three rows. To simplify the figure the height sections are not considered and
therefore the temperature values per side are averaged based on all these height sections. Figure 12
shows that the largest differences between the east and west sides of the canopy are found in the
morning. For all rows in the morning, the east side is cooler than the west side. Remarkable is that the
differences between east and west for rows 2 and 3 are less present, indicating that something
happens with the temperature in the second and third rows of the vineyard. Figure 12 shows that the
temperature differences per row in the midday are getting smaller and the temperature gets more
evenly distributed across the east and west sides of the canopy.
34
Figure 15: Boxplots of the temperature per side of the three vineyard rows in the morning (left), midday
(middle), and afternoon (right). The colours correspond to the rows in the vineyard (row 1: orange, row 2: red,
row 3: yellow).
4.1.3 Temperature deviations vs daytime
Table 2, as well as Figure 16, display the temperature variation during the day. The temperature
generally rises from the morning to midday caused by the sun reaches its position approximately
perpendicular to the vegetation. During midday, the temperatures will be highest and decrease when
the sun’s angle decreases while moving westward (afternoon). This principle is visible in Figure 16.
Figure 16: Boxplots of the rows’ temperature versus the dayparts in each height section. The boxes are coloured
based on the midday (orange), midday (red), and afternoon (yellow).
4.1.4 Temperature vs location
During the day, the temperature along the vine rows changes in both the X and Y direction as well as
in the Z direction. In this section, the most western row (row 1 in Figure 1) is used as an example.
Figure 17 is created based on the normalized and reference height of the vine row against the xcoordinates resulting in cross sections of the east and west sides of the rows. Thereafter the points are
colorized relative to the mean temperature of that specific row side, allowing us to see temperature
35
change in the row for the morning, midday, and afternoon. The cross-sections representing the midday
and afternoon are quite similar regarding the temperature. The northern part in these rows is colder
than the mean temperature (blue vegetation) and the southern part is warmer than the mean
temperature (red parts). The cross-section corresponding to the morning deviates from this pattern
since the northern part, especially the eastern lower canopy section, is warmer. This cross-section also
shows cooler parts in the south of the row.
36
Figure 17: Visualization of the cross-section of the eastern and western sides of row 1 during the morning, midday, and afternoon. The vegetation temperature is colorized
relative to the mean temperature resulting in red (warmer than the mean temperature) and blue (cooler than the mean temperature.
37
The colder northern parts and the warmer southern parts during the midday and afternoon correspond
to the lower and higher section of the vineyard (Figure 18). This suggests a relationship between the
temperature and the absolute height of the points in the point cloud.
Figure 18: Absolute (reference) height of row 1. This figure indicates the relief of the terrain in row 1.
Looking at the correlations between absolute height and the temperature for all dayparts (Figure 19)
it is indeed visible that the midday and afternoon shows moderate to strong correlations values and
the morning has aberrant behaviour with a negligible to weak negative correlation (Akoglu, 2018). The
correlation displayed in Figure 19 indicates that higher temperatures can be found in higher parts of
the vine rows, especially during the midday and afternoon as here the highest correlation coefficients
are found.
Figure 19: Correlations between the absolute height and the temperature of rows 1, 2, and 3 in the morning
(left), midday (middle), and afternoon (right).
4.2 RQ3 – Temperature distribution in individual vines
The workflow described in the method section for RQ3 resulted in three times 117 LAS clouds. The
three of them, correspond to the morning, midday, and afternoon and consist of purely healthy
vegetation. Also, two times ten LAS point clouds represent the plants invested with the fungus Botrytis
during the midday and afternoon. All these individual plants and their variables have been stored in a
data frame. These variables are the mean temperature, mean NDVI, mean NDRE, mean density
(denseness), and health status.
Figure 20 is a visualization of a healthy plant in the midday. All sides (front, side, and top) are plotted,
and colorized by the temperature relative to the mean temperature of the cloud. The same is done
using the NDVI and NDRE. In most of the vines, a spatial pattern can be observed for the temperature,
NDRE, and NDVI. The edges of the plants show higher temperatures and lower values for both
vegetation indices, whereas the bluer spots correspond to denser vegetation, which shows lower
temperatures and higher NDVI values.
38
Figure 20: The values for the temperature, NDVI, and NDRE are plotted relative to their mean. The front, side,
and top views of a healthy plant in the midday are plotted to show how the temperature, NDVI, and NDRE behave
per dimension.
However, this is not entirely valid throughout the day. During the morning, the patterns slightly
deviate, as the west side clearly shows higher temperatures and lower NDRE values. While the shadow
side shows about the same NDRE values as the sun side but no temperature increase (Figure 21).
Figure 21: Front, side, and top view of a healthy plant in the morning. The temperature and NDRE are colorized
relative to the mean displayed. This figure shows that the temperature and the NDRE in the morning behave
differently compared to the midday and afternoon.
39
Figure 20 indicates that there are relationships between temperature and the vegetation indices at
plant level during the morning, midday, and afternoon. However, this relationship deviates during the
day, as the morning shows deviating results in temperature and VI values. Figure 22 shows the
relationship between the mean of the two variables per plant and its strength by providing Pearson’s
correlation coefficient. Also, the RMSD is given, providing information about the accuracy of the linear
model in predicting the values temperature from the NDVI and NDRE values. Here is visible that the
morning indeed shows less similarity in the variable behaviour with a lower Pearson’s correlation
coefficient in the morning for both vegetation indices (rNDVI = -0.44 and rNDRE = -0.42). The NDVI and NDRE
in the midday and afternoon show a stronger correlation with respectively -0.61, -0.73, and -0.73, 0.76. Although Pearson’s correlation coefficient appears lower in the morning, it is valid to speak of a
moderate to strong relationship. The strength of the relationships between temperature and the VIs
in the midday and afternoon can be described as strong to very strong (Akoglu, 2018). Although the
linear correlation in the morning is weaker than during the rest of the day. The RMSD indicates that
the accuracy of the model fitting the data in the morning is more accurate than during the midday and
afternoon (RMSDNDVI: 0.92, RMSDNDRE: 0.93 in the morning versus RMSDNDVI: 1.93, RMSDNDRE: 1.68 in
the midday and RMSDNDVI: 1.46, RMSDNDRE: 1.41 in the afternoon.
Figure 22: Scatterplots displaying the relationships between the NDVI and NDRE values and temperature during
morning, midday, and afternoon periods are presented, including their 0.95 confidence interval and the RMSD.
Also, the correlation coefficients are given, revealing that temperature deviations in the morning lead to
corresponding deviations in correlation coefficients during this specific period of the day. The RMSD gives the
ability of the model to fit the data.
Similar relationships have been found for the canopy sections that contain the grape clusters Figure
23. The relationships between both the vegetation indices and the temperature both get weaker than
the relationships for the entire plants since the correlation coefficient decreases for all day parts.
Another observation is the increase of the RMSD compared to the values displayed in Figure 22. This
means that in the grape section (Figure 23), the model fits the data poorer than the fit in the entire
plant (Figure 22). Although Pearson’s correlation for the grape section in the morning, midday, and
afternoon is lower, they are also related to the vegetation indices. This means that points in the point
cloud with higher temperatures often correspond to lower NDVI and NDRE values. Figure 23 shows
the relationships between the vegetation indices and the temperature for the three dayparts. The
40
error margins for the linear fit in the grape section increase compared to the linear fit of the entire
plant because fewer points are used for the computation of the mean temperature, and mean VIs.
Figure 23: This figure depicts a scatter plot analysis of the association between NDVI and NDRE values and the
temperature of the grape section at different times of the day, including the 0.95 confidence interval and the
RMSD. The corresponding correlation coefficients are presented to illustrate the strength and direction of the
relationships and the RMSD gives information about the accuracy of the linear model fitting the data. Notably,
the morning behaviour of the grape section appears to exert a discernible influence on the correlation coefficient
during that specific temporal interval.
Another variable that influences the temperature in vines is the amount of consecutive vegetation.
This is displayed in Figure 24. In general, the temperature is higher in points that have no close
neighbours. Dense consecutive vegetation is often displayed as blue, indicating temperatures lower
than the mean temperature at a certain time during the day (Figure 20). When the holes in the canopy
are large enough the temperature of the vegetation increases. Figure 24 is an example of this theory.
Also, the values for the vegetation indices increase around the gaps.
Figure 24: This figure showcases the front, side, and top perspectives of a healthy plant during the midday, with
temperature and NDRE, depicted in relation to open vegetation. This visualization reveals that open vegetation
displays a higher temperature and lower NDRE values.
41
The point density inside the plants provides information about the denseness of the vegetation. As the
points in the point cloud are filtered based on the thresholds mentioned in section 3.2.4.3, the
remaining points correspond to vegetation. Therefore, the point density can be used to observe the
behaviour of the temperature in points corresponding to vegetation. Figure 25 is proof of a negative
correlation between the point density and the temperature. During the morning and afternoon, the
relationship is weaker than during midday. This relationship shows the denser the vegetation the
cooler it is. Therefore, inconsecutive vegetation is more likely to warm up than the average
temperature of the vine tree.
Figure 25: This scatterplot illustrates the relationship between the mean point density per plant and the mean
temperature including their 0.95 confidence interval, with correlation coefficients provided to indicate the strength
and direction of the relationship. The graphs show that the strength of the correlation is higher during midday
compared to morning and afternoon periods.
The datasets in the midday and afternoon also contained points corresponding to plants infected with
the Botrytis disease. As the morning dataset only covered the three rows, no information could be
gathered on the health status of the botrytis-infected plants. The workflow for the extraction of plants
infested with the Botrytis disease led to the following results.
The NDRE is used to detect differences between healthy and sick vegetation. Figure 26 shows that the
healthy vines for both the grape section and the entire plant in the midday and afternoon have similar
behaviour regarding the temperature. In all figures, a negative relationship is observed between the
temperature and the NDRE. The vines infected with the fungus show a slight deviation from the healthy
line, however, the points are overlapping with the points corresponding with healthy vegetation. Table
6 shows the p-values for the two-sample t-tests to check the significance between the means of the
temperature and the NDRE for healthy plants and plants infected with Botrytis. All the t-tests resulting
in the H0 hypothesis to be true: (no significant difference between the means of the temperature as
well as the NDRE). Except for the NDRE at plant level during the midday, here a significant difference
between the healthy and Botrytis group is found.
Table 6: Tables displaying the p-values obtained from two-sample t-tests for the Temperature and
NDRE in the grape section as well as the entire plant during midday and afternoon.
p-Values in the Grape section
Time
Temperature NDRE
Midday
0.7
0.78
Afternoon 0.08
0.42
p-Values in the entire plant
Time
Temperature NDRE
Midday
0.19
0.03
Afternoon 0.68
0.49
42
Figure 26: This figure illustrates a scatterplot presenting the mean temperature and the mean NDRE for healthy
vines (yellow) and vines afflicted by the Botrytis fungus (green) during midday and afternoon periods, both for
the grape section and the entire plant. The linear fit lines show the direction of the relationship for healthy or
infected vines. Notably, there is an overlap in temperature and NDRE values for healthy and infected vegetation.
Assessing the basic statistics of data representing healthy and botrytis-infected vines in the midday
and afternoon for the entire vines as well as the grape section led to the following boxplots (Figure
27). The mean and median temperature for the entire plant, as well as the grape section in the
afternoon, show the same pattern, both are higher in vegetation infected with Botrytis and lower in
healthy vegetation. The grape section in the midday shows the same pattern with a higher median and
mean. The mean temperature in entire plants during the midday show high deviation in the dataset as
the quantiles range and the difference between min and max is larger than for the rest of the boxplots
in Figure 27A and 27B. The pattern of the boxplots for the NDVI (Figures 27C and 27D) shows the
inverse of the temperature (Figures 27A and 27B, which stresses the relationship mentioned before
between the vegetation indices and the temperature. These patterns show lower mean and median
NDVI values in botrytis-infected vines during the midday and afternoon in the entire plant as well as in
the grape section. The NDRE in the grape section (Figure 27F) displays the same behaviour as the NVDI
(Figure 27D) with lower mean and median values. The medians of the NDRE boxplots in Figure 27E also
show the same pattern as the NDVI, however, the distribution of the data is different with wider boxes
for the botrytis-infected vines in both the midday and afternoon and almost the same mean values
insinuating little difference in healthy and Botrytis infected vegetation.
43
Figure 27: This figure illustrates boxplots representing the mean values of temperature, NDVI, and NDRE for
both healthy vegetation and vegetation affected by the Botrytis fungus during the midday and afternoon. The
boxplots are presented for the entire plant as well as the section in which grapes are located.
Point density is a measure of the denseness of the vegetation. Figure 28 shows the mean point density
in entire vines and the grape sections. The boxplots suggest that the botrytis-infected vines generally
have more vegetation as the mean and median during the midday and afternoon are higher. The first
three Botrytis boxplots have boxes, which represent 50% of the data points, and are higher located
than healthy vegetation. A statistical test pointed out that for the entire plants in both the midday and
afternoon the means of the point densities are not equal (midday p = 0.0003, afternoon p = 0.02). The
Botrytis boxplot in Figure 28D shows very deviant behaviour as the space between the first and third
quantile is much lower than in Figures 28A and 28B. A plausible explanation could be the small number
of Botrytis samples.
Figure 28: Boxplots of the mean point density (a measure of the denseness of the vegetation) in healthy and
botrytis-infected plants in entire plants and the grape section for both the midday and afternoon.
44
4.3 RQ4 – Benefits of using point clouds over orthomosaics
Simply said, the benefit of using a point cloud instead of two-dimensional representations of reality is
that a point cloud stores more information. The third dimension of a point cloud offers information
about the height of a scene. For example, orthomosaics representing a vineyard can only be visualized
and processed as a flattened view of the scene. Using a point cloud on the other hand offers realitybased visualization and processing. An example is the creation of cross-sectional point clouds
(described in sections 4.1.2 and 3.2.4.6), which gives lots of information by using the Z-coordinate.
These cross-sections provide a realistic view of the vines, which allows us to explore (spatial)
relationships in three dimensions instead of two. In these cross-sections, the grapes grow in the
bottom part of the canopy. Obtaining the temperature in this part of the canopy is therefore essential
for wine managers to assess the quality and health of the grapes. In 2D applications, this is not possible
as only the top of the canopy is registered by the sensors. Using structure from motion allows for the
inclusion of the Z-coordinate and therefore the height of the plant including the canopy part that
includes the grapes.
For application in vineyards involving thermal images, the third dimension can be particularly
important. This thesis uses thresholds (height, NDVI, and NDRE) to filter out points in the point cloud
corresponding to vegetation. First of all, a two-dimensional application like an orthomosaics cannot be
filtered on a height value without using an elevation model, which is often derived from a point cloud.
Secondly, for two-dimensional applications, this process is harder as no points, but raster cells are
encountered. Each cell contains a generalized value based on cell resolution. This can lead to
interpolation between vegetation points as well as ground points in the same cell. When this happens
the temperature in the edges of the vegetation in the orthomosaics is not as accurate as the point
cloud. Figure 29A and 29B displays this principle through the transition zones at the edges of the
vegetation. The absence of interpolation between vegetation and ground points results in the
transition not being visible in the point cloud depicted in Figure 29C, corresponding to specific thermal
values.
Figure 29: Thermal orthomosaics (A and B) compared to a thermal point cloud. (A) shows the extent of the
vineyard. (B) shows the zone where interpolation occurs between ground and vegetation points. The point cloud
(C) shows the direct transition between vegetation and ground points.
45
To continue, the fact that orthomosaics lack information in the Z direction has an impact on the quality
of the output. As this thesis is only interested in the temperature of the vegetation, the ground points
need to be excluded. In point clouds, this can be done very accurately by using the normalized height,
NDVI, and NDRE thresholds. Where the normalized height excludes all points lower than 0.5 metres,
which is convenient because the ground in between rows can also contain vegetation. To achieve the
same output in two dimensions some steps must be taken that reduce information. The elevation
models are estimations based on certain algorithms that in the end produce cells containing several
point cloud points. Comparing the temperature, NDVI, and NDRE in the point cloud as well as the
raster. Clear differences can be seen between both. Figure 30 shows the variables mentioned above
grouped by the dimensionality of the input. Visible is that the aggregation of multiple points in raster
cells results in overall higher temperatures and lower NDVI and NDRE values, the plots show clear
differences in mean and median temperatures. Furthermore, a shift in the boxes that represent the
first and third quantiles can be observed indicating deviating values between the two and threedimensional outputs.
Figure 30: This figure compares the variables: temperature, NDVI, and NDRE. Obtained from orthomosaics
(2D) and point clouds (3D). Analysis of the mean, median, and distributional shifts between the two datasets
reveals the impact of rasterization and associated information changes when transforming point cloud data into
a 2D format.
46
5 Discussion
5.1 Canopy temperature distribution in vine rows
Analysis of the point clouds obtained from the workflow in RQ2 showed that the temperature in the
canopy deviates in three ways. The canopy temperature inside vine rows changes per height section,
per side (east and west), and at last the temperature of the canopy regarding the absolute height. The
following section will discuss these deviations in canopy temperature per row in the vineyard.
First, when relating the canopy height sections to the canopy temperature, a significant difference
between means per height section was observed during the morning, midday, and afternoon (p < 0.05;
Figure 12, Table 5). For all rows, the median and mean temperature decreased per height section
Figure 12. This indicates that the mean temperature in the berry clusters during the entire day is
warmer than in the higher-located canopy sections. The observed decreasing trend in temperature
from morning to afternoon across all canopy height sections can be attributed to the process of
transpiration. Transpiration plays an important role in cooling both the leaf surface and the entire
canopy by supporting the exchange of latent heat, with the top regions of the canopy experiencing
more effective heat transfer, resulting in lower temperatures at the canopy's uppermost parts, as
observed in studies by Prueger et al. 2019 and Still et al. (2021). Also, short-wave radiation from direct
sunlight heats the berries, branches, stems, and older vegetation located in the lower canopy more
than the top of the canopy. Figure 14 shows the relationship between the normalized height and the
canopy temperature. Also, Pearson’s correlation is given together with the RMSD for a linear,
quadratic, and third-degree polynomial function. Some remarkable points are worth discussing. For
example, the RMSD is lower during the morning and higher during the midday and afternoon. This
means that the model is not a good fit for the data. This might be caused by the fact that entire rows
in the vineyard are used. Important to note is that on the scale of the vineyard, spatial deviation could
lead to different temperatures, hence the dispersion in these scatterplots. To reduce the impact of
spatial variations, it could be more interesting to determine the relationships at plant level instead of
row level.
Secondly, investigation of the temperature distribution in the east and west side of the canopy resulted
in clear differences inside each vine row, indicating that the canopy temperature has no uniform
distribution. This can be explained by the orientation of the vineyard and the sun's position during the
day. As visible in Figure 15, especially in the morning, the difference between the east and west side is
large. As the vineyard is oriented in a NE-SW position the east side of the canopy receives more shortwave solar radiation resulting in higher observed temperatures. The midday and afternoon showed
fewer differences in temperature between the east and west due to the altitude of the sun at these
times. In the afternoon, the west side of the canopy starts receiving more direct solar radiation leading
to higher temperatures on that side of the canopy.
Thirdly, the analysis showed that the normalised height in the vine rows is correlated to the
temperature. Pearson’s correlation coefficient was calculated for the row’s normalized height and
temperature in the morning, midday, and afternoon. These coefficients pointed out decent to strong
relationships during the midday and afternoon. This means that during these parts of the day, a higher
temperature could be found at higher parts of the vineyard and vice versa. However, another factor
that plays a vital role in the temperature distribution across vine rows is the slope orientation. For
instance, Figure 17 visualizes the canopy temperatures on both sides of the canopy for row 1. The fact
that the northeast side is warmer during the morning relative to the mean temperature, can be
explained with the help of Figure 18. This figure shows that this part of the vineyard is sloped facing
the northeast and will therefore receive more short-wave radiation during the morning. This also
47
explains the deflecting behaviour of the correlation plot during the morning (Figure 19), as lower parts
of the vineyard seem to have higher temperatures due to the sun's position and the row’s orientation
towards the sun (northeast part of the row). As the sun changes its position to a higher altitude and
azimuth angles the same part is covered in more shadow than during the morning leading to lower
relative temperature in the midday and afternoon. The same process warms up the southwest part of
the row during the midday and afternoon as this part of the row is more flattened making it easier to
capture sunlight, hence the positive correlations.
5.2 Canopy temperature distribution in individual grapevines
The same method to visualize the row temperatures relative to the mean temperature has been used
to visually analyse individual extracted vines. The result pointed out more visible spatial patterns for
plants during the midday and afternoon compared to the morning. Observed is that both the edges of
the canopy during the midday and afternoon displayed higher values in terms of temperature and
lower regarding the NDVI and NDRE. The morning deviates due to warmer temperatures in the west
side of the canopy caused by increased absorption of solar radiation. Therefore, correlations between
the temperature and both the Vis are very strong in the morning and afternoon. The dataset
representing the morning logically showed less pronounced correlations due to higher temperatures
in the west side of the canopy. Similar but weaker decreasing trends are observed for the grape section
temperature (0.5-1.0 meter) and the Vis. These lower correlations compared to the entire plant could
be explained by the lower number of detected points in the lower parts of the canopy. For the
processing of the images, only nadir images have been used leading to a greater number of key points
detected in the top of the canopy. This could explain the higher error margins and lower correlations
in the grape section of the canopy. NDRE and temperature seem to be more correlated than NDVI and
temperature. This makes sense, because of the band composition of both VI’s. The NDVI uses the red
band which is highly absorbed in the top canopy whereas the NDRE uses the NIR band which is less
absorbed by the top canopy and therefore measures deeper into the vegetation resulting in
measurement with higher accuracy (Davidson et al., 2022) For that reason, the NDRE is also used more
in applications regarding disease detection and the observance of the change in canopies during the
late growth stages of crops (Thilakarathna & Raizada, 2018). At the same time, it has been observed
that the RMSD for the NDVI and NDRE is lowest during the morning, and higher during the midday and
afternoon. This is a bit counterintuitive as the correlation coefficient during the morning is lower than
for the midday and afternoon. However, when taking a closer look at the temperature ranges in the
morning, midday, and afternoon the temperature ranges respectively 26-31, 34-46, and 34-45 degrees
Celsius. As the temperature range in the morning is lower compared to the other two day parts, it
seems logical that for this day part the RMSD is indeed lower.
The strong correlations between NDVI/NDRE and temperature imply that multispectral sensors
capable of capturing NDVI and NDRE could be used as a less expensive option to thermal sensors in
certain uses that require data on plant level. This is especially important for people or organizations
that cannot afford a thermal monitor. Multispectral instruments could provide valuable insights into
temperature-related phenomena, such as heat stress, by utilizing the connection between NDVI/NDRE
and temperature.
Another factor that influences the temperature distribution in individual plants is the gap fraction in
the plants. Inconsecutive vegetation allows more short-wave radiation to penetrate the canopy and
more exposure to ambient air (with is often higher than the canopy temperature in well-watered vines
(Van Zyl, 2017)) leading to increased temperatures surrounding the gap. This is shown in Figure 24.
48
Strong correlations between the mean point density and the temperature confirm this theory,
especially during the midday with Pearson’s correlation coefficient of -0.72. The morning and
afternoon show less present correlations. However, this could be caused by the deflective character
of the morning discussed in the former section, and flight height during the acquisition of the images.
The flight altitude during the afternoon was 30 meters, compared to 15 meters in the morning and 20
meters in the midday. This could also explain the higher error margins in Figure 25 for the morning and
afternoon. Taking a closer look at the mean point densities in Figure 25 you find different densities for
each day part. The point densities of the individual plants in the morning midday and afternoon show
respectively up to 4000, 6000, and 1500, suggesting that for higher flight altitudes it is harder to
estimate the gap fraction in the canopy.
Comparing healthy vegetation and vegetation infected with the fungus Botrytis gave less pronounced
results. For this matter, the NDRE was used in the hope that it would give a better insight into canopy
temperature change for the fungus-infested plant. This, however, cannot be said from the analysis
displayed in Figure 26. In these visualisations is observed that for both the grape sections as well as
the entire plants no clear difference can be found between the two groups. For the vines infested with
the fungus, a steeper trend is obtained. However, these Botrytis observations overlap with the cluster
that corresponds with healthy vegetation. Interestingly, it can be observed that in the entire plant, the
clusters seem to be more overlapping than the grape sections, where it is found that the Botrytis points
show slightly less overlap between healthy and Botrytis clusters. These results might not be truly
reliable as data is used from only ten plants that were known to have the Botrytis disease. On the other
hand, the patterns displayed in Figure 27 seem to show some similar patterns when looking into the
mean temperature, NDVI, and NDRE for the entire plant and the grape sections on their own. But
again, to find the true values more Botrytis-infected vines must be analysed.
Canopy density analysis in healthy and Botrytis-infested vines resulted in remarkable outputs. Figure 28
shows that for both the datasets in the midday and afternoon, the point density (a measure of canopy
size) of the vines infested with the Botrytis fungus show on average higher point densities than healthy
vegetation. This means that the plants infected with Botrytis tend to contain denser canopies than
healthy vegetation. Normally, under more humid conditions Botrytis disease infects all green organs
of grapevines through wounds or dead plant material. Furthermore, it enters younger organs straight
through the cuticle and outer cell wall, both of which it damages, resulting in decreasing plant vigour
or necrosis of both berries and plant material (Keller, 2020; Walker et al., 2013). However, ValdésGómez et al. (2008) prove, in their study on grey mold incidence at harvest, that during dry conditions
Botrytis infections exclusively occur in the most vigorous plants. During the data acquisition in July, it
was indeed drier than normal. There was only a total of 4.4 mm precipitation with an average
temperature of 23 degrees Celsius, compared to the average precipitation of 41.1 mm and a
temperature of 20.4 degrees Celsius in July for the period between 1991 and 2020 (Meteostat,
Pontevedra, Spain). Studies on Botrytis infection in grapevine also explain that the fungus thrives best
in humid conditions (Elmer & Michailides, 2007; Keller, 2020; Vélez et al., 2023; Walker et al., 2011).
As higher humidities can be found in the denser canopies due to more transpiration of green
vegetation and less impact by wind, it makes sense that during this study the infections can be found
in the plants with higher point densities.
5.3 Benefits of an extra dimension
The use of two-dimensional representations for thermal data is currently dominant in literature, for
instance in research on water status, irrigation management, variability, and disease detection.
49
However, this study has produced and used thermal data three-dimensionally, including the height
dimension, which shows to be beneficial to the accuracy related to the temperature, NDVI, and NDRE.
Comparing both two- and three-dimensional representations has shown a difference in values, caused
by the mixing of both ground and vegetation points during the interpolation from point cloud points
to pixels. This stresses the importance of three-dimensionality toward the accuracy of end products
after processing.
5.4 Limitations
During the entire process, some limitations have been encountered impacting the quality and accuracy
of the outputs. This section will describe the limitations encountered during this thesis research.
During the generation of a point cloud from images, the quality of the point cloud is dependent on the
processing parameters specified by the user. The processing quality during the processing is set to
high, meaning that the algorithm generates a point cloud where the input images are downscaled by
a factor four. Compared to the ‘ultra-high’ quality option, some accuracy is already lost. Using the
‘ultra-high’ quality setting leads to a higher number of points in the dense point cloud as more points
corresponding to vegetation will be detected when using a higher quality. Since the algorithm can
detect more delicate details, such as small leaves and branches, and has a higher point density. Using
the ultra-high-quality processing option goes in hand with an increase in processing time and
computation resources. Therefore, it is important to find a balance in the desired level of accuracy
with the available resources when selecting the processing parameters for generating a point cloud.
The images taken during fieldwork contained both nadir images and oblique images. However, for the
processing only the nadir images have been used as using oblique images leads to some issues during
the alignment of these images. However, some studies have shown that oblique images are a good
addition to nadir images. Adding oblique images can increase the accuracy of the point cloud regarding
the number of points in the point cloud and improve the accuracy of measurements taken in the 3D
models (Vacca et al., 2017). When considering the canopy section that contains the grapes, oblique
images could have been a good addition since the oblique angles allow us to see more detail in the
lower canopy, which has a higher chance to be covered with vegetation in nadir images. This is
especially the case for the dataset taken in the afternoon, as the flying height was higher. Higher flying
height results in fewer points obtained in the dense point cloud. This principle is for instance shown in
Figure 25 and Figure 28. Here the mean point densities showed lower values for the afternoon and
much higher densities in the morning and midday, caused by the difference in flying altitude.
The absence of the Botrytis fungus in the vineyard is beneficial for the quality and quantity of the grape
yield. However, for studying and analysing what happens in vegetation infected with Botrytis disease
it is useful to have more infections. This year was a good year for the wine manager as only 12 plants
have been infected by Botrytis. The absence of adequate data influences the accuracy and the ability
to generalize the results. With limited data, it is more challenging to determine whether the observed
patterns for the temperature, NDVI, and NDRE, are representative of the entire population, or just
occurred by chance. To continue, insufficient data could also lead to bias, estimates, and low statistical
power, which can reduce the ability to detect and quantify the effect of Botrytis disease accurately,
therefore having a larger and more robust dataset is critical for accurate data analysis and drawing
meaningful conclusions.
Another point to be addressed is the fact that during the morning, a part of row 3 is covered by
shadows casted by trees at the east side of the vineyard. In Figure 15 this can be seen by the decrease
50
in differences between east and west during the morning. The figure shows that during the morning
larger temperature differences are found for rows 1 and 2, but lower temperature differences in row
3 due to the trees blocking direct sunlight. Figure 31 shows this principle.
Figure 31: Image showing the overlap of the shadow casted by the trees positioned at the east side
of the vineyard with row 3. Resulting in different temperature behaviour during the morning.
In vineyards, multiple factors influence the canopy temperature. These are not considered for this
study. For instance, the slope, wind direction, and cloudiness during the day. Vineyards planted on
slopes generally receive more solar energy compared to flat vineyards leading to different temperature
distribution in grapevine canopies (Strack et al., 2021). Although Figure 18 shows the relief in the
vineyard, no actual slopes have been calculated which could yield more information on the
temperature distribution of this vineyard. Also, prevailing wind direction and speed can severely
influence the canopy temperature. Higher windspeeds normally have a cooling effect on the canopy
temperature as it allows the canopy to transfer its heat to ambient air easier (Hunter et al., 2016). Also,
the prevailing wind direction could affect canopy temperature depending on the landscape. For
instance, the onshore wind coming from the Atlantic Ocean might supply colder air than offshore winds
coming from Spain’s mainland.
51
6 Conclusion
Literature outside the domain of viticulture describes several ways to construct and fuse point clouds
obtained from thermal and multispectral imagery. These methods consist of image-to-image matching
in combination with image-to-model or model-to-model matching. This thesis has shown that the
combination of all three methods can help fuse point clouds generated from multispectral and thermal
imagery for use in viticulture. Altogether, the combination of those methods has led to a suitable way
to extract canopy temperatures and allows to analyse of the canopy temperature distribution in
vineyards.
The canopy temperature influences the ripening process of the grapes and therefore the final quality.
Thus, analysing the temperature distribution through multispectral point clouds can come in handy for
vineyard managers. The fact that the temperature distribution varies depending on the height, time of
the day, and the canopy side, could help in updating irrigation schedules for precision viticulture.
Furthermore, the point cloud allows the identification of specific parts of vine rows that might need
extra attention caused by deviation in canopy temperature. For instance, pruning practices could be
adjusted on different sides of the canopy to ensure a better energy balance or harvest timing could be
more plant-specific depending on the temperature as grape ripening is influenced by the temperature.
In both the entire plant as well as in the section where the grapes grow, a strong correlation has been
found between the NDVI/NDRE and temperature. This is interesting since the use of thermal sensors
to derive the canopy temperature is not always feasible. To continue, thermal sensors are quite
expensive and the production of thermal point clouds from solely thermal images yields low
resolutions compared to other bands (RGB NIR, RedEdge, etc.). Using a cheaper multispectral sensor
for the processing of NDVI/NDRE point clouds, which gives higher resolution point clouds, could
indirectly provide information about the temperature in an individual plant.
Another element that affects temperature distribution in individual plants is the gap fraction in the
plants. The strong correlations between mean point density and gap percentage support this
hypothesis, particularly during the midday period. The morning and afternoon exhibit less prevalent
correlations, which could be due to the deflective nature of the morning described in the previous
part, as well as flying height during image acquisition. The mean point density is a useful tool for
explaining temperature changes in the canopy. As lower point densities (possible gaps in the canopy)
correspond to higher temperatures. This is useful for the remote localization of stressed or damaged
plants and the effects it has on the surrounding vegetation. Vineyard managers could use this
information to optimize the growing conditions by monitoring the mean point density and the
temperature for instance to give additional attention and resources to plants in need.
Comparing healthy plants and plants infected with the fungus Botrytis has led to insignificant
differences between the temperature and NDRE in both groups, meaning that no real differences in
temperature and NDVI could be found. These findings may not be completely trustworthy because
data was gathered from only ten trees that were known to be infected with Botrytis. Therefore, in
future studies, more data is to be collected about vines infected with the Botrytis disease to help
improve disease detection in vineyards as the temperature and NDRE are proven to help detect and
analyse diseases in vineyards.
Comparing the point densities between the healthy and Botrytis group in individual plants, led to
significant differences. Here the canopy density of the Botrytis group is significantly larger than the
healthy group. This provides valuable information to be used for the detection of Botrytis in vineyards
during times with limited precipitation. This could aid vineyard managers as it allows them to monitor
52
Botrytis by considering canopy densities and take measures to prevent the further spread of the
disease and improve the overall health of the vineyard.
Finally, this thesis has shown the benefits of using point clouds over two-dimensional representations
for spatial data analysis and visualization. Point clouds provide more comprehensive and accurate
information, particularly in the vertical plane, which is critical for use in vineyards where the height of
the plant affects the temperature distribution. Furthermore, point clouds enable exact data filtering
and the removal of unwanted points, which is more difficult to accomplish with 2D techniques. The
contrast between point clouds and orthomosaics reveals that the latter frequently results in poorer
accuracy and information loss due to the rasterization process. As a result, point clouds should be
considered an important instrument for spatial research and decision-making in vineyards.
53
7 Recommendations
To further enhance the point cloud's accuracy, it is recommended to improve the quality setting during
pre-processing. This can be achieved by using advanced SfM techniques that do not downscale image
resolutions to save on computing resources, and by optimizing the parameter settings of the preprocessing software. High-quality pre-processing is crucial for obtaining accurate, denser, and reliable
point clouds. Secondly, implementing oblique images to increase the number of key points detected
in lower grape vine regions can enhance point cloud accuracy and denseness, especially the part where
the grapes grow (0.5-1 meter) is important as these areas are often blocked by the denser canopy top.
Oblique images provide a distinct perspective of the object, and they can capture details that might
not always be visible from the top view. This could increase the number of key points detected, leading
to more accurate point clouds. Thirdly, it is also recommended to use the lowest flight heights possible
during data acquisition. Especially important when analysing point clouds at plant level. This can help
with generating more dense point clouds, as the sensors can capture more points per unit area.
However, it is important to ensure that the flight heights are safe and comply with regulations.
Furthermore, reconsider that lower flight altitudes go in hand with more images and therefore demand
higher processing times.
The results obtained from this study regarding the temperature distribution in vines infected with the
Botrytis disease led to insignificant differences between the infected fines and the healthy vines. The
sparse number of Botrytis observations used in the analysis has a large influence on the generalization
of these results and therefore the reliability. Thus, recommended is that follow-up studies should focus
on the acquisition of more data related to the temperature distribution in vines affected by Botrytis
disease. This will could help researchers to better describe the relationship between the sickness
indicators (e.g., temperature, NDVI, and NDRE). Which eventually could contribute to the
improvement of accurate predictions and generalizations about Botrytis in vineyards.
Another recommendation is the use of sufficient computing power and storage for the processing of
point clouds. The generation of dense point clouds from images is a computationally intensive process
that requires high-quality machines. Moreover, storing point clouds takes up a significant amount of
storage space. Thus, aiming for denser point clouds and higher accuracies it is important to use
machines with adequate computing power and storage capacity.
54
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