IJERD – International Journal of Environmental and Rural Development (2022) 13-2

Research article
erdComparison of Crop Surface Models and 3D Point Clouds
by UAV Imagery on Estimating Plant Height and Biomass
Volume of Pasture Grass
KE ZHANG
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan

AYAKO SEKIYAMA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: 45620002@nodai.ac.jp

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 Material and Systems for Sustainable, Nagoya University, Nagoya, JapanOSAMU TSUJI
Zukosha Co., Ltd, Obihiro, Japan

MASAHIRO AKIMOTO
Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Japan
Received 8 February 2022

Accepted 8 August 2022(*Corresponding Author)

Abstract Plant biomass is considered an important parameter for crop management andyield estimation, especially for grassland. Aerial photogrammetric techniques have beenused for vegetation data gathering of the areas of dense vegetation fields with high researchinterest. Recent advances in computer vision include structure from motion and multi-view
stereopsis (SfM-MVS) techniques, which can derive 3D data such as digital surface models(DSMs) and orthomosaic from overlapping photography taken from multiple angles. The
difference between the DSMs of a planted field and the digital terrain model (DTM) has
been referred to crop surface model (CSM). Ever since SfM-MVS has been adopted toderive plant height (PH) and above-ground biomass using CSMs at 2013, this method has
become the most explored and verified approach to simulate the structure of crops all over
the world. However, the complexity of crop structure is thought to be not well represented
in DSMs because the DSMs have only one Z value at each 2D pixel. Besides, lacking a
DTM representing the bare ground is another problem when adopting the CSM method. On
the other hand, the 3D point cloud where DSMs are derived from UAV may provide thestructure information in a faster and more detailed way. This research tested the capabilityof 3D point cloud in estimating plant height and biomass volume of pasture grass, andcompared the results with CSMs. UAV photography were conducted at the experimental
field of Obihiro University of Agriculture and veterinary Medicine, Hokkaido, Japan, 2019.
The biomass volume estimated by DSM and point cloud have no significant difference,
showing that DSM and point cloud have the same performance at estimating biomass
volume of grass. In the case that only the simple value data is required, the point cloud data
is recommended.
Keywords biomass volume, CSM, DSM, plant height, point cloud, UAVⒸ ISERD

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INTRODUCTION
Proper planting of biomass fuel plant has become one of the main challenges of carbon neutral. In
recent years, attention has been paid to the use of pasture grass as biofuel. Gramineae grass has avigorous regenerative power and can be cut multiple times a year. Some species can becontinuously cultivated for more than five years (Nakagawa et al., 2009). Furthermore, pasturegrasses can adapt to most kinds of ground surface environment and provide high yields where theedible crops can hardly grow, which avoids the competition between biofuel and food production.
At the site of grassland management, plant height and biomass production monitoring during
the growing stage is one of the most important measurements. In the last decade, with theadvancement in new platforms such as unmanned aerial vehicles (UAVs), methods based onremote sensing for biomass production estimation are gathering popularity. As a result of the fusionof UAV remote sensing and digital photogrammetry technology, a flexible and automatic approachof progressing aerial imagery has been developed, which is known as the structure from motion(SfM) technology. Based on the multiple overlapping images, characteristic feature points aredetected of which the three-dimensional (3D) coordinated are reconstructed during the bundle
adjustment progress afterwards. After the bundle adjustment, a detailed scene geometry made by asparse point cloud is built and all pixels are used in this step to reconstruct finer scene details.
Based on this sparse point cloud, the dense point cloud, orthomosaic and the digital surface model(DSM) are exported. As an intermediate production to construct the DSM, the characteristic of thedense point cloud is that it is not filtered, meaning that it contains all the outliers and noise point(Agisoft LLC, 2013). On the other hand, DSM is exported in a common image format such as *.tif
with a particular coordinate system, with the pixel size of more than one centimeter, meaning thatone pixel represents the mean value of all 3D points inside it. Furthermore, filters such as noise
filter and surface smoothing filter are applied to the DSM, which make the DSM unable torepresent the detail of the small features on the ground precisely, such as the leaves of plants(Cubero-Castan et al., 2018).
The difference between the DSMs of a planted field and the digital terrain model (DTM) has
been referred to crop surface model (CSM) (Hoffmeister et al., 2010). Ever since Bendig et al.
firstly adopted SfM-MVS to derive plant height (PH) and above-ground biomass using CSMs at
2013, this method has become the most explored and verified approach to simulate the structure ofcrops all over the world. Most studies showed that the plant height estimated by CSMs tend to belower than the plant height measured on the ground, because of the average and smooth surface of
CSMs (Bendig et al., 2014). Based on this background, there is thought to be a possibility thatpoint clouds can represent the plant height of crops more accurately than CSMs.
However, there is no study yet discussing the difference at the performance on estimating
plant height and above-ground biomass volume of pasture grass. The objectives of this study are 1)
to compare the estimation results of plant height and biomass volume obtained by point clouds andCSMs respectively, and 2) discuss the characteristics of each of them on representing threedimensional structure of the crop.
METHODOLOGY
The study site was a grass field inside the experimental field of Obihiro University of Agriculture
and Veterinary Medicine, Obihiro City, Hokkaido, Japan. The specie of the pasture grass was reedcanary grass. The surveys by UAV were conducted weekly from 31st May to 3 rd September 2019(15 times in total), with Phantom 4 Pro (DJI). Before the UAV flights, seven ground control points(GCPs), of which position information was measured using RTK-GNSS (HiperV, TOPCON), weresettled all over the experimental field. The flights were carried out automatically by Pix4D Capture(Pix4D). During each flight, the flying height was 50 m above ground. Both the top-overlap and theside-overlap rate were 80%. The ground sampling distance (GSD) of the raw aerial imagery was
1.32 cm. Ground surveys in order to obtain the plant height of the grass were conducted from 17 thJuly to 3rd September (totally 8 times), on the same dates as the UAV surveys. During each time ofⒸ ISERD

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the ground surveys, six measuring points were settled all around the grass field and marked withmarking tapes which could be seen at the aerial imagery. Sampling of these six measure points wasmade during every time of the ground survey. A self-made plant height measure was used to obtain
the optimal height value of the grasses. According to Bendig et al. (2014), “the PHCSM represents
the mean plant height of all pixels in a pixel. As a result, not only the top of the plant, for example
the ears, is measured, but also the lower parts, like the leaves. Consequently, the detail of PHCSM ishigher than PHM, because PHCSM contains more than on pixel per plant and, the method of the
PHM reference measurements in the field should be discussed.” Based on this opinion, which ismost reasonable, the method of method to measure plant height with tape measure or staff ruler,which has been used in many studies, can defiantly not obtain the obtain value standing for theplant height of a certain the field. In this research, a self-made plant height measure was used toobtain the reference plant height. A sliceable plastic plate (10 cm × 20 cm) was used to determine
the proper height of the grass canopy. When measuring, the plate was sliced down from above,
until every part of its bottom was touched by the grass leaves. The leaves should be naturally
curved instead of being forced bending when the plat has stopped. This method can not only help
determine the optimal canopy position, but also help the observer to read the scale efficiently. The
height of the grass is usually lower than one meter, meaning that the investigator has to squat down
or gravel down to the ground in order to look at the canopy from a horizontal direction.
Fig. 1 shows the workflow of the data processing. The RGB imagery obtained by UAV wasprogressed using Pix4D mapper (ver 4.6.5, Pix4D) to construct the dense point clouds, orthomosaic,
and DSMs, of which coordinate was corrected by GCP calibration. By identifying the measuring
points at the orthomosaic, the coordinates of the measuring points were extracted using ArcGIS Pro(ver 2.4.1, Esri). After inputting the coordinates into the Pix4D mapper again, the location of themeasuring points was marked at the dense point clouds. The plant height obtained from point
clouds (PHPC) was then calculated by subtraction the altitude of the ground surface from thealtitude of the grass surface. On the other hand, CSMs were made with ArcGIS Pro by subtractionof the DSM of the field without plants from the DSMs with plants. The plant height obtained from
CSMs (PHCSM), which was in other words the CSM value of each measuring point, was thenextracted using ArcGIS Pro. Then, both PHPC and PHCSM were compared to the measured value ofplant height (PHM) to evaluate the accuracy of point clouds and CSMs to estimate the plant heightof grasses. Finally, the above-ground biomass volume (BV) of the whole grass field instead of theparticular measuring points was extracted from point clouds and CSMs (BVPC, BVCSM),
respectively, and compared to each other to unravel the characteristics of point clouds and CSMs
on estimating above-ground biomass volume of pasture grass. The calculation of BVCSM used thegeo-metry function of ArcGIS, and the calculation of BVPC used the Volume Tool of Pix4D
mapper.

Fig. 1 Workflow of data analysisⒸ ISERD

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RESULTS
1. Time Series of the Plant Height Estimated by Point Clouds and CSMs
Fig. 2 shows the Time series of measured value of plant height (PHM), plant height obtained frompoint clouds (PHPC) and plant height obtained from crop surface model (PHCSM). From 17th July to
3rd September, all the three time series lines remained approximately parallel to each other.
However, the line of PHCSM stayed lower than the reference line all the time, while the PHPC line
was almost laying over the reference line. This indicated that both CSMs and point clouds could
reflect the growth trend of grass, while there was a constant difference existing between the plantheight estimated by CSMs and the reference value.

Fig. 2 Time series of PHM, PHPC and PHCSM
2. Accuracy of Plant Height Estimation by CSMs
Fig. 3 shows the relationship between PHM and the estimated plant height by CSMS (PHCSM). The
number of samples was 48 (6 samples × 8 times of ground survey). The regression coefficient wasclose to 1, which suggests the regression line was nearly parallel to the 1:1 line. On the other hand,
the intercept was approximately 10, suggesting there was a difference of about 10 cm between PHM
and PHCSM, which stayed stable during the whole survey period since the regression coefficient wasnear to 1. The R2 and RMSE were 0.97** and 14 cm, respectively, also sowing that PHCSM has thesame changing trend, however a stable difference with PHM. These showed that CSM can represent
the changing trend of the plant height of pasture grasses, but has a relatively low accuracy on
estimating the value of plant height.

Fig. 3 Relationship between PHM and PHCSM

Fig. 4 CSM map for grass field (2019/7/24)

On the other hand, as shown at Fig. 4, the CSM map could show the growth unevenness
clearly by the estimated plant height within ArcGIS.
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3. Accuracy of Plant Height Estimation by Point Clouds
Fig. 5 shows the relationship between the measured value of plant height (PHM) and the estimatedplant height by point clouds (PHPC). The regression coefficient and intercept of the regression
equation were 1.04 and -0.16, respectively, making the regression line extremely close to the 1:1
line. The coefficient of determination (R2) and the RMSE were 0.99** and 3 cm, respectively. This
result showed that point clouds can estimate plant height of pasture grass with extremely highaccuracy with little need for calibration.

Fig. 5 Relationship between PHM and PHPC

Fig. 6 Comparison of BVPC and BVCSM

4. Comparison of the Above-ground Biomass Volume Estimated by Point Clouds and CSMs
Fig. 6 shows the comparison of BVPC and BVCSM of the whole field during the whole survey period
from 31th May to 3rd September. The BVCSM and BVPC of 13th June, 19th June, 26th June, 7th August,and 14th August showed minus value because the DSM used as the bare ground surface had
included the remaining grass after the reaping. The result of t-test showed that there was no
significant difference between BVPC and BVCSM. This result showed that despite point cloud canestimate plant height of pasture grass with higher accuracy than CSM, it gives the same value ofabove-ground biomass volume estimation with CSMs.
DISCUSSION
Despite the reference plant height was measured by an improved method and was as accurate as
possible, there was still a 13 cm RMSE of the PHCSM of the grassland. It is necessary to realize that
there is a problem estimating when estimating plant height using CSMs, which is caused not by thehuman error or the environmental factors such as the wind, but by the characteristics of the DSM
itself. There is a fact that should not be ignored that the airborne laser scanning or the aerial digital
photogrammetry were aiming at generating DEMs standing for the basic topographic shape at thefirst place. The key point of DEM generation using aerial imagery was never about the bumpy
terrain or the above-ground objects, but about the smooth terrain. This fact is so important, because
it resulted in a critical characteristic of the dense matching algorithm such as SfM-MVS, which are
the denoising filter and the smooth filter. Both of these filters can improve the accuracy and quality
when constructing terrain models or large-scaled buildings. However, the disadvantage of using
these filters is that the small-scaled or low-heighted objects may be smoothed, for example, theeleave of crop plants. As long as DSM is still raster data, which represents the ground objects withcertain-sized pixels, it has a limit on estimating plant height of crops, because there are spaces
between the crop leaves. This is thought to be the inherent weakness of apply remote sensing
photogrammetry to agriculture.
Compared with CSMs, point clouds showed much higher accuracy on estimating plant heightof grass field. The reason of the high accuracy is considered to be because no smooth filter andⒸ ISERD141

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pixel averaging algorithm has been conducted when constructing point clouds. While CSM shows
the average height of all the objects of a certain area (for example, one pixel), point cloud shows
the particular height of one certain object (for example, the leaf of grass). In other words, point
cloud reflects a more detailed height distribution data of the grass field. Therefore, it is consideredthat point cloud is more qualified than CSM to estimate grass field plant height.
However, on the other hand, when estimating above-ground biomass volume, no significant
difference was found between the results obtained by CSMs and point clouds. It is because whencalculating the total value of the grass above-ground volume with point cloud, not only the dense
points of the canopy that were included, but also the lower parts between the grass leaves,including the ground surface. As the result, point cloud yields the same value as CSM on grassabove-ground biomass volume estimation. The spaces between the leaves, which has been found anerror factor and should be excluded when estimating plant height, is no longer an error factor whenestimating above-ground biomass volume and should be considered in order to increase theaccuracy. This led to a conclusion that point cloud is more qualified on estimating plant height ofgrass than CSM, but has the same accuracy as CSM on estimating above-ground biomass volume.
There have been mainly two purposes for remote sensing at agriculture field, monitoring thecurrent status of the crop land, and predicting the final yield of the crops before harvesting. Before
UAV was popularized, the former purpose could not be fully achieved, because neither satellite
imagery nor laser scanning was properly suit for the purpose. The ground resolution of satellite
imagery, which is usually larger than one meter, is too large for a single crop land. The laser
scanning conducted by ground survey is both time and labor consuming, making it almost notrealistic for the whole crop field. This is exactly why UAV is so crucial on applying remote sensing
to the real agriculture sites. It is more precise than satellite imagery, and more efficient than laser
scanning, making it perfectly suit the purpose of current status monitoring of crop lands. For
decades, remote sensing has found difficulties on benefiting the farmers directly. With UAV
becoming a trustable platform of remote sensing, it is finally possible to help farmers make better
management of their own crop land, by not only the traditional CSM data, but also the precise
dense point cloud data.
CONCLUSION
In this study, the abilities of dense point clouds and CSMs on estimating plant height and aboveground biomass volume for pasture grass have been validated and compared based on multipletime surveys. Plant height was monitored by dense point cloud with very high accuracy (RMSE =3.5 cm), while the plant height monitored by CSMs was consistently lower than the reference value.
On the other hand, no significant difference exists between the above-ground biomass volume
estimated by dense point cloud and CSMs. These results show that dense point cloud has anadvantage on reflecting current status of crops, while has the same accuracy with CSMs on
predicting above-ground biomass volume. When the purpose of the UAV survey is to monitor the
precise status of the crops or identify the lodging area, point cloud is a better choice of data; while
when the purpose is estimating the biomass volume of the whole field with little requirement ofdetails, DSM data provides the estimated value with both accuracy and efficiency.
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