IJERD – International Journal of Environmental and Rural Development (2021) 12-2

Research article
erd

Relationship between NDVI and Canopy Cover Sensed bySmall UAV under Different Ground Resolution
KE ZHANG
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan

HIROMU OKAZAWA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: h1okazaw@nodai.ac.jp

YURI YAMAZAKI
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan

KIICHIRO HAYASHI
Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya, JapanOSAMU TSUJI
Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Japan
Received 16 January 2021

Accepted 14 October 2021(*Corresponding Author)

Abstract Canopy Cover (CC) is a significant indicator of crop development and estimationof the evapotranspiration volume of crop leaves within crop simulation models. During thelast three decades, monitoring CC for crops using Normalized Difference Vegetation Index(NDVI) obtained from satellite sensors has been studied worldwide. However, a few studies
have estimated the CC of crops using NDVI by UAVs. One of UAV imagery’s crucial
advantages is a high resolution of less than 0.10 m, while the resolution of satellite imagery
is usually larger than 10 m. Now that the UAV has become a popular method in agriculturescience, it is necessary to prove the interchangeability of UAV and satellite imagery ofmonitoring CC. In this study, small UAVs took RGB and multispectral images of theexperimental peanuts field in Hokkaido. Orthomosaic and reflectance map of the field wereconstructed using the UAV imagery and then were obtained CC and NDVI values with GIS
software. CC was calculated as the green canopy area ratio, extracted from the orthomosaic
using a GIS supervised classification tool. CC was compared with NDVI values undervarious resolutions of 0.50 m, 1.0 m, 2.5 m, 5.0 m, and 10 m. The NDVI showed a highly
correlated linear relationship with CC under each ground resolution from 0.10 m to 10 m (R2led a range of 0.88** to 0.94**). The shapes of NDVI and CC’s regression equations closely
resembled each other, with the slopes of 1.06 to 1.11 and the intercepts of 0.247 to 0.250,
respectively. From the result of ANCOVA, the UAV imagery resolution has no significant
impact on NDVI and CC’s relationship. Although more irrelevant factors, such as soil andmulching seat, got included within one pixel of the images, the regression equations stayed
the same with the increased ground resolution.
Keywords UAV, NDVI, canopy cover, ground resolution

INTRODUCTION
Canopy cover (CC) is a relatively easily measured property that is an indicator of crop growth andan important parameter in crop simulation models, such as the Aqua Crop (FAO, 2012) model. Whensimulating crop development, Aqua Crop model describes the development expansion of the CC
using the percentage area of green canopy cover. According to the Food and Agriculture Organization(FAO), CC is defined as the ratio between the soil surface covered by the green canopy cover over
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the ground surface (FAO, 2012). CC value ranges from 0, when there is bare soil, to 1, where thevegetation canopy fully covered the ground. The crop characteristic decides the maximum value ofCC. Aqua Crop model describes the development of the canopy between generation and the momentmaximum CC is reached through a logistic type equation. Then, at the end of the season, when thesenescence starts, the CC value declines (Steduto et al., 2009). Accurate and efficient CC estimation
would allow improved scheduling and allocation of irrigation water (Bausch, 1995).
On the other hand, during the last three decades, Vegetation indices (VIs) have been extensively
used to trace and monitor vegetation conditions such as health, growth levels, and water or nutrient
stress (Silleo et al., 2006). Previous studies have shown that various spectral calculated from visible
and near-infrared reflectance data are linearly related to the value of CC (Purevdor et al., 1998).
Healthy canopies of green vegetation have a very distinct interaction with certain portions of theelectromagnetic spectrum. In the visible region, chlorophyll causes strong absorption of energy,
primarily for use in photosynthesis. This absorption peaks in the red and blue region s of the visible
spectrum, while the green region is reflected by chlorophyll, thus leading to the green color of mostleaves. This strong contrast between the reflectance of red and near-infrared regions of theelectromagnet spectrum has been used to develop the Normalized Difference Vegetation Index(NDVI), a VI that has been widely used in the agricultural remote sensing field (Silleos et al., 2006).
Region scale NDVI data could be obtained using high-resolution satellite sensors such as NOAAAVHRR, Terra MODIS, Landsat TM. This kind of traditional remote sensing data has been used tomake better crop management, monitor the growing stress, and estimate the yield (Hirata, 2009).
Furthermore, for the last decade, the research methodology and data analysis techniques fromtraditional remote sensing have been used to process aerial images with much higher spatial andtemporal resolutions taken by Unmanned Aerial Vehicles (UAVs). This rapid development of remotesensing and precision agriculture provide aerial imagery with various resolution.
However, a few studies have estimated CC with UAV-obtained NDVI. It is still unclear if UAVbased NDVI has the same linear regression relationship with CC as satellite-based NDVI. To apply
both satellite-sensed and UAV-sensed CC data to crop simulation models such as Aqua Crop, it isnecessary to unravel whether the relationship between NDVI and CC remains the same under
different ground resolutions. Therefore, This paper processed the UAV-sensed data into different
resolutions, and compared the regression relationships between NDVI and CC.
METHODOLOGY
Study Site
This study was conducted in the experimental field of the Obihiro University of Agriculture andVeterinary Medicine located in Obihiro City, Hokkaido, Japan (143.1709-143.1747°N, 42.869842.8671°E; Fig.1). The experimental field has a total area of 3.2 ha (200 m×160 m), separated into
multiple sectors planted with various experimental crops. The peanuts sector inside the experimental
field was selected as the study site to compare NDVI with CC. The peanuts usually have relatively
spreading forms of about 30-50 cm high with long branches that grow close to the ground. This plant
form makes peanut a good objective to study the relationship between NDVI and CC. Previous study
shows that the NDVI tends to perform poor correlation to CC when the Leaf Area Index (LAI) value
is high. In other words, if the leaf density under the vegetation canopy varies, the NDVI value could
be different even though the CC value stays the same (Purevdorj et al., 1998). This kind of variationof LAI is relatively unapparent in peanuts because peanuts tend to grow with a horizonal pattern
rather than a vertical pattern. The peanut field area is 1600m2 (40 m×40 m), with a plant density of
7.7 plant/m2. The surface of the planting area was covered by white mulch sheets made by plasticfilms, because insolation was important during the early stage of growth to ensure the basic
vegetation growth of peanuts in low temperature region such as Hokkaido.

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Fig. 1 Location and details of the study site
Investigation and Analyses
The RGB imagery of the experimental field was taken by Phantom 4 Pro (DJI) on 31st July 2019,
and the reflectance imagery was made by a portable multispectral camera “Sequoia” (Parrot), which
was mounted on Inspire 1(DJI) on 1st August 2019. The flight data is shown in Table 1.
Table 1 Flight conditions
Equipment

Date

Phantom 4 Pro
Inspire 1 with Sequoia

31th July
1st August

Camera heightfrom ground (m)5040

Speed(m/s)
4.8
3.0Overlap (%)
TopSide
80
80
80
80

Resolution(cm/pixel)
1.3
5.9

NDVI and CC
After obtaining aerial imagery, RGB orthomosaic image and reflectance map of the experimental
field were generated with the photogrammetric software, Pix4D Mapper 4.6.4 (Pix4D). The RGB
orthomosaic image was used to separate the vegetation, soil and mulch films in ArcGIS Pro 2.3.0(Esri), with a supervised image classification tool. The result of classification is shown in Fig.3.
Based on the classified raster, the CC value of the peanuts field was calculated using Eq. 1.
𝐶𝐶 =𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛
𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 + 𝑆𝑜𝑖𝑙 + 𝑀𝑢𝑙𝑐ℎ 𝑓𝑖𝑙𝑚

(1)

Where “Vegetation,” “Soil,” and “Mulch film” mean the number of pixels covered by green
vegetation canopy, soil, and mulch films, respectively. To obtain the CC and NDVI values under
different ground resolutions, we divided the peanut field into squared grids with a side length of 0.5m, 1.0 m, 2.5 m, 5.0 m, and 10 m. As an example, the 1.0 m grids are shown in Fig.4. Based on thegeoreferenced reflectance map, the NDVI values were calculated by Eq. 2.
𝑁𝐷𝑉𝐼 =(2)𝑁𝐼𝑅 − 𝑅𝑒𝑑𝑁𝐼𝑅 + 𝑅𝑒𝑑

Where “NIR” and “Red” mean the reflectance volume of the near-infrared region and the redregion. The relationship between NDVI and CC values of each grid’s size was determined using aleast-squares fitting algorithm with python 3.7. An Analysis of Covariance (ANCOVA) was carriedout to testify if there is a significant difference between NDVI and CC relationships under different
ground resolutions. Finally, the accuracy of the regression equation was verified using RMSE.

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Fig. 3 Classification result (Left: RGB orthomosaic image; Right: Classified raster)Fig. 4 Example of squared grid dividing the peanuts field (Grid size: 1.0 m)
RESULTS AND DISCUSSION
Fig. 5a and Fig. 5b show the spatial distribution of NDVI and vegetation cover under the original
resolution of the peanuts field. NDVI tended to be high in the area covered by vegetation and low in
the area covered by soil or mulch films. This is because the vegetation surface has a lower reflectance
rate in the red region and a higher reflectance in the near-infrared region. In contrast, the soil and theplastic mulch films have similar reflectance in both regions. Besides, the mulch film has a lower
NDVI than soil because the PVC material has a relatively higher reflectance of red, and at themeantime, a lower reflectance of near infrared than soil (Corradini et al., 2019). The spatial
distribution of NDVI of the other grid sizes (0.5m, 1.0m, 2.5m, 5.0m, and 10m) showed the sametendency. The NDVI value varied from -0.47 to 0.68 under the original resolution, from -0.37 to 0.61
under 0.5 m grid size, from -0.29 to 0.40 under 1.0 m, grid size, from -0.25 to 0.24 under 2.5 m gridsize, from -0.10 to 0.13 under 5.0 m grid size, and from -0.05 to 0.08 under 10 m grid size. Unlike
the crops or forests with the vertical growth pattern, the maximum NDVI value of peanuts was lessthan 0.7. This is because peanuts have low-height, wide-expanding, and horizontal-trained canopies,
which cause relatively low LAI within the canopies. The CC values varied from 0.00 to 1.00 under
the original resolution, from 0.00 to 1.00 under 0.5 m grid size, from 0.00 to 0.74 under 1.0 m gridsize, from 0.03 to 0.53 under 2.5 m grid size, from 0.14 to 0.42 under 5.0 m grid size, and from 0.19
to 0.35 under 10 m grid size. The range of both NDVI and CC values decreased with grid sizes.

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Fig. 6a to Fig. 6e show the correlation between NDVI and CC under each grid size. NDVI wasstrongly correlated with CC under the resolution of 0.5m, 1.0m, 2.5m, 5.0m, and10m (R2=0.88**,0.92**, 0.94**, 0.89**, and 0.93**, respectively). The shape of the linear regression line (y=ax+b)of NDVI and CC closely resembled each other; the slopes (a) such as 1.16, 1.11, 1.09, 1.08, and1.06, respectively, and the intercepts (b) such as 0.25, 0.25, 0.25, 0.25, and 0.25, respectively. The
incept values indicate that a grid with 25 percent of it covered by peanuts canopies has anapproximately NDVI value of 0. The slope values slightly decreased with the increasing grid size,
resulting in the grid’s NDVI values without any vegetation cover varying from 0.5m to 10m (-0.21,-0.22, 0.23, -0.23, and -0.24 respectively). This difference of slopes is considered to be caused by
two kinds of abnormal values of NDVI. One was the abnormally high NDVI value due to relatively
high LAI. The other one was the abnormally low NDVI value caused by the mulch sheets. These twokinds of abnormal NDVI became outliers when the grid size was small, because there were grids that
include only canopies or only plastic. However, they cancelled each out when the grid size was large,
so that the regression line was closer to the ideal state where there were simply vegetation and soil.

Fig. 6 Relationship between NDVI and CC
To testify the significance of each regression equation’s differences, an ANCOVA wasconducted with an Excel data analysis add-in, XLSTAT (ver 2020.5.1, Addinsoft), where CC as thedependent variable, while NDVI and ground resolution as the explanatory variables. The result isshown in Table 2. The p-value of all kinds of grid sizes except for 10.0 m was near 1.000, indicatingthat the grid sizes have no significant effect on NDVI and CC’s relationship.
Table 2 Model parameters of ANCOVA
FactorCoefficientIntercept
NDVI
Ground Resolution-0.5
Ground Resolution-1
Ground Resolution-3
Ground Resolution-5
Ground Resolution-10

24.775115.735-0.021
0.004
0.078
0.000
0.000

Standarddeviation
1.980
0.466
1.983
1.990
2.072
2.214
1.310

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Pr > |t|12.512248.448-0.011
0.002
0.038
0.000
0.000

<0.0001<0.0001
0.991
0.998
0.970
1.000
1.000

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Therefore, despite the slight difference between the regression equations of the five kinds ofgrid sizes, it is considered that the NDVI value of peanuts remains the same relationship with CC
under different ground resolution. Since the offset effect of two abnormal values of NDVI was mostremarkable at the 10 m grids, the equation derived from the data of 10m grid size was used to predict
CC using NDVI. The RMSE of CC estimation of each grid size from 0.50 m to 10 m was 0.081,0.089, 0.048, 0.025, 0.020, 0.014, respectively.
CONCLUSION
Herein, we analyzed the relationships between NDVI and CC values of peanuts in the experimental
field using UAV-sensed data under five kinds of ground resolutions. As a result, the NDVI showed ahighly correlated linear relationship with CC under each ground resolution. Slight differences ofslopes and intercepts were found between the regression equations, however this kind of difference
was found not significant due to an ANCOVA. The regression equation of 10 m grid size performeda moderate estimation accuracy of CC with the RMSE less than 0.01. This result demonstrated thatCC estimation tend to keep consistent despite of the change of resolution, suggesting the possibility
that both UAV-sensed and satellite-sensed NDVI could be used to estimate CC for the usage of crop
simulation models such as Aqua Crop.
ACKNOWLEDGEMENTS
We would like to express our gratitude to Dr. Masahiro AKIMOTO (Obihiro University ofAgriculture and Veterinary Medicine) for providing the experimental field. This research wassupported by JSPS KAKENHI (Grant Number 20K06351), the joint usage/research program of theInstitute of Materials and Systems for Sustainability (IMaSS) in Nagoya University, and the strategic
research project founded by Tokyo NODAI.
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