Estimating Biomass and Leaf Area with LiDAR

The provided document contains a collection of studies related to estimating biomass and leaf area in various agricultural contexts. The research focuses on utilizing technologies such as terrestrial LiDAR, high-resolution stereo images from unmanned aerial vehicles (UAVs), and airborne LiDAR to estimate biomass components of crops like maize and wheat. Additionally, it explores the use of hyperspectral EO-1 Hyperion and multispectral IKONOS satellite imagery in crop biomass estimation. The studies also touch upon agricultural biomass burning trace gas and black carbon emission ratios. This document is essential for students and researchers in the field of remote sensing and agriculture, providing valuable insights into the application of advanced technologies in biomass estimation.

Running head: CALCULATION OF BIOMASS FROM FIVE DIFFERENT CROPS
OBTAINED IN THE UNITED KINGDOM
Calculation of biomass from five different crops obtained in the United Kingdom
Name of the student
Name of the University
Author note

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CALCULATION OF BIOMASS FROM FIVE DIFFERENT CROPS OBTAINED IN THE
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Executive summary
This paper discusses about the calculation involved in estimating the biomass content from five
different crops. The chosen country is Australia. This paper includes a discussion of the
associated literature related to the topic. This paper has also discussed about a methodology
section which is aimed at the collection of information from the crops. Moreover, the laboratory
analysis of the data involved is also shown in the paper in the form of steps to be adopted.

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Table of Contents
1.0 Introduction................................................................................................................................3
1.1 Background of the study........................................................................................................3
1.2 Aims and objectives...............................................................................................................4
1.3 Research questions and hypothesis........................................................................................4
1.4 Outcomes...............................................................................................................................5
1.5 Summary................................................................................................................................5
2.0 Literature review........................................................................................................................6
2.1 Crops......................................................................................................................................6
2.2 Biomass estimation................................................................................................................7
2.3 Biomass estimation from the crops........................................................................................9
3.0 Research methodology.............................................................................................................12
3.1 Introduction..........................................................................................................................12
3.2 Biomass calculation methods..............................................................................................12
3.3 Field sampling procedure and equipment needed...............................................................13
3.4 Laboratory analysis..............................................................................................................14
3.5 Timeline...............................................................................................................................14
4.0 References................................................................................................................................16

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CALCULATION OF BIOMASS FROM FIVE DIFFERENT CROPS OBTAINED IN THE
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1.0 Introduction
This section of the paper will discuss mainly on the background research, aims and
objectives, research questions, outcomes and summary of this section.
1.1 Background of the study
Biomass is termed as a resource which is gathered by the burning of woods and various
other organic matters. Although the burning of fuels releases carbon dioxide emissions, the
utilization of biomass is termed as renewable resource. The utilization of renewable resources
has been adopted in various places in the United Kingdom as well. The utilization of biomass
can be adopted for converting into bio-energy which in turn will generate electricity (Marshall &
Thenkabail, 2015). This is termed as a renewable energy generation. These types of energy are
also used in place of other conventional energy sources. The utilization of the biomass resource
can also be used to generate heat for various reasons and can also be used in the form of bio-fuel.
The constituents of plant based biomass are the lignin or cellulose while wastage and animal
wastes can also be used for obtaining energy from it.
It has been stated that 50 percent of the power generated in the United Kingdom comes
from various amounts of low-carbon based sources. Although the utilization of biomass
resources for burning processes leads to the formation of a large amount of gaseous emissions,
the government of UK thinks of it as a renewable source of energy (Tilly et al., 2016). A major
power station of the United Kingdom has switched their requirements from the utilization of coal
to biomass. Many other power station of The United Kingdom has reinstated their business
operations from the utilization of coal based power plant to biomass based.

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1.2 Aims and objectives
This section of the paper discusses about the research aims and objectives.
Research aims
The aims of this research are to calculate the amount of biomass from five different
crops. These crops are to be considered from the country UK. This research also aims to identify
the efficiency of the biomass resources.
Research objectives
 To select five different crops from the country of UK
 To estimate the biomass count from these five crops
 To successfully evaluate the insights obtained
1.3 Research questions and hypothesis
This section presents various research based questions and hypothesis to be followed by
the study.
Research questions
1. What are the different crops found in UK to be used for this research?
2. What type of data can be collected from these crops?
3. What insights are obtained from such data collection methods?
Research hypothesis
This paper utilizes the involvement of statistical data that will be utilized in making the
analysis. Moreover, the crops involved will be selected from the United Kingdom.

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1.4 Outcomes
By conducting such research analysis, the researcher tries to evaluate the biomass count
from plant resources. The research method will utilize the collection of data from five different
crops. After collecting the information, this paper will make an analysis of the data involved.
1.5 Summary
Thus, it can be summarized that this section of the research paper is mainly involved in
analyzing the biomass collection from five different crops. These crops will be selected as they
are to be found from the United Kingdom.

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2.0 Literature review
This section of the paper is concerned with the discussion of the associated literature
which is related to the topic. The various aspects to be considered for this research is also
depicted in this section of the paper.
2.1 Crops
For the utilization of biomass energy from the plant based resources, the adoption of
energy crops are a pre-requisite. There are three different types of energy crops which are
broadly adopted for the type of resources obtained from them. The first type of crop is the
agricultural energy crops which include oil crops, sugar crops and starch crops (Kross et al.,
2015). These comprises of wheat, barley, oat, potato and sugar beet. The second type of crop is
the non-weedy crops which mainly includes hemp and rye. The third type of crop is the short
rotation based energy crops which consists of eucalyptus, sycamore and poplar.

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Figure 1: Life-cycle of an energy crop
(Source: Li et al., 2015, pp 495)
There are many other types of crops which are grown in the United Kingdom but these
crops are considered mainly due to their energy harvesting capabilities. These crops can remain
in situ conservation for a range of 7 to 25 years. In addition, these crops have much deeper
rooting characteristics. Moreover, their growth rate is very rapid which in turn produces much
high amount of yield (Bendig et al., 2015). Furthermore, the fertilizer input and the pesticide
input is also very low which helps in better productivity of the crops. The harvesting of these
crops takes place in the winter season and in the early spring season. These crops are also
supposed to reach greater heights and form dense plantations.
For the research involved in this paper, the utilization of five different crops is required.
These five crops are wheat, barley, oat, potato and sugar beet. These crops will be collected and
the biomass content will be calculated. These crops will be stored in plastic bags which can be
re-sealed whenever required (Dong et al., 2016). These will then be kept in a cool condition and
the weighing of the crops will be done which will then be dried. If the sample plant is not being
weighed for measuring fresh weights, they will be transported in wet ice and will be weighed
within the next 24 hours.
2.2 Biomass estimation
There are various methods which can be utilized in the context of estimating biomass
content of crops. These are mainly used to find out the amount of resources that can be obtained
for obtaining the necessary requirements. The vegetation will be measured by considering the
quadrate frame of measurement. In this case, the delineation of the areas are required using the

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help of these types of frames (Ledo et al., 2018). The measurement of multiple areas from such
quadrates is to be considered which will help in collecting the samples as required from the
whole plantation involved.
Although the need for calculating the biomass is a necessary consideration in case of any
plantation, the most common method of calculating the biomass is by using the harvest based
method. This involves removing the biomass and then drying it in the oven. This is used to
remove the water content from the plants involved which is then weighed. The water content of
plants can vary depending on the time of estimation (Fu et al., 2014). The water content varies
during the day and during the night. In addition, the seasonal basis is also responsible for
changing the water weight. This is the reason for drying the samples for collecting weights.
Another consideration to b adopted is the presence of the plants with respect to ground level.
For considering the biomass concentration, some plants have higher density above the
ground level while some others have low density in that region (Eitel et al., 2014). For various
processes involved in researches, the utilization of above ground measurements are more
preferred than the below ground measurements. This is mainly because there are various
problems associated in collecting information and data associated from below ground
measurements.
The use of various equations will be adopted for calculating the biomass of the crops
involved. With reference to the larger plantations, the calculation of biomass is based on the
various sections of them. These sections are mainly calculated by considering the stem, branch,
foliage and fruits (Jannoura et al., 2015). In each of the plantation involved, there are difference
calculation based metrics which are used for determining the biomass present.

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Stem biomass=0.0478∗(DBH2∗H)0.8665
B ranch biomass=0.061∗(DBH2∗H)0.8905
Foliage biomass=0.2650∗(DBH2∗H)0.4701
Fruit biomass=0.0342∗(DBH2∗H )0.5779
In case of these above equations, the DBH is termed as the diameter of the breast height
which is used to denote the main diameter involved. The H symbol stands for height which
denotes the height of the concerned sample. Using these equations, the calculations of biomass is
made.
Figure 2: Mass content
(Source: Tilly, Aasen & Bareth, 2015, pp 11450)
2.3 Biomass estimation from the crops
The estimation of the biomass and its assessment processes is comprised of two different
stages. The first stage is the power plant design stage and the other is the power plant re-

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commissioning stage. The methods involved in both the stages of biomass estimation are same
but the objectives involved in them are totally different.
Estimating biomass at the power plant design stage:
In the power plant design stage, the method involves a detailed analysis and assessment
at the village level and the regional level. The regional level comprises of both the block and the
district involved. There are four main objectives of this design stage. The first objective is to
make a successful forecasting of its availability for considering its supply for a span of five
years. This step is followed by adopting the estimation of the pricings of the biomass resources
for the next five year period. The third objective is to apply for streamlining the process involved
which will help in providing biomass as a resource for the next time period of five years (Bendig
et al., 2014). The last step involved is validating the gasification technology which is based on
the use of biomass as a resource. This validation is considered in terms of calculating type of
technology to be used and the capacity of the storage services. For example, in case of a 30Kw
gasifier which is running daily for 10 hours, the calculation is done by considering the number of
days available. In case the number of days being 350, the total number of biomass to be
generated is 210 MT (30 * 10 * 350 * 2= 2, 10,000 Kg= 210 MT).
Estimating biomass at the power plant re-commissioning stage:
In this stage of re-commissioning power plant, the analysis and evaluation of the biomass
content is considered. This is done by considering both the level of villages and regional aspects
so that an effective evaluation can be carried out. These evaluations are done on a detailed basis
such that all the objectives and steps involved are met. The first step involved is to estimate the
availability of the supply of biomass based resources. The next step is similar to the above

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method which involves streamlining the process of biomass supply chain system which helps in
meeting the continuous demands of the associated parameters present in the power plant
(Hiloidhari, Das&Baruah, 2014). The next step involved is to estimate the final price of the
biomass resource which is based on its availability and the variations of the seasons involved in
the region. The last option involved in this process is to adopt various alternative solutions which
will help in facilitating biomass supplies by considering community participation based
alternatives. These types of alternatives include waste land based energy plantations and
integrations of energy plantations in association to the forest department.

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3.0 Research methodology
This section of the paper discusses about the various methods involved in doing the
research. Moreover, the processes involved in data collection and analysis is also discussed in
this section of the paper.
3.1 Introduction
This section of the research paper is basically involved in the collection of the biomass
from five different crops. The data pertaining to it are shown in a very logical manner. This
paper has also utilized the involvement of the positivism philosophy where the involvement of
scientific resources for the conduction of research is adopted. Moreover, for analyzing the
various data and information collected, the use of the deductive approach is utilized.
3.2 Biomass calculation methods
The first step involved is the collection of moisture content. This is done by following the
equation listed below. The final values involved in a mass/area consideration are assessed
according to the above-ground based considerations.
(Field Moist Weight−Dry Weight)=Weight of Moisture
(Weight of Moisture ❑
❑ Dry Weight of Plant Material ) x 100=MoistureContent (% )
The next step involves reporting the O horizons on the basis of the mass/volume or
mass/area basis. This process is then followed by calculating the kg/ha content by calculating the
grams plant material/250 cm^3. This O horizons mass is converted to soil bulk density which is
calculated by( Mass of O Horizon Material /Volume Sampled ). This is then converted to area with

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kg/ha units. When considering the root biomass, only the above ground resource is to be
considered without the litter.
Air Dry O Horizon ( g)x depth( cm) x Soil Bulk Densi ty (g/cc )100,000(cm2/ha)= Air Dry O Horizon kg /h
Figure 3: Biomass vs Volume
(Source: Greaves et al., 2015, pp 30)
3.3 Field sampling procedure and equipment needed
The various equipment needed for sampling the plant crops are clippers, stakes, plastic
bags, ice chest, weighing scale, oven for drying, desiccant and desiccators, documentation
papers, labels and spring scale. A calcium chloride-based pellet will be used as a desiccant which
will then be placed in a desiccators jar for absorbing moisture from it.
The area to be sampled will be considered in a 50*50 cm notes. This will be followed by
clipping vegetation to the surface of the soil. The next step involved is the separation of the crops
on the basis of species and live or dead factions. These separations will then be weighed and then

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inserted into the oven and re-weighed for measuring the gravimetric parameters. The surface
litters and O horizons are measured separately.
3.4 Laboratory analysis
The plant tissues will be placed in aluminum dishes or the paper bags which varies
according to the size of the sample. This will then be set in an oven with temperatures of 80
degrees. The specimen will then be dried for a span of 24 to 48 hours which will then be placed
in desiccators jar and then re-weighed for dry weight. In addition, the sample specimen will be
weighed for each interval of 4 to 8 hours to validate that no further water weight is lost. This
process must be done in a very careful manner so as to not char the material. In case of limited
availabilities of the oven space, the other specimens from the crops will be refrigerated up to a
week. The samples must not be allowed to decay which will result in a halt on the research
process. Furthermore, the lesser amount of succulent specimens can be kept at room temperature
to prevent them from decaying.
Water Content=FreshWeight – Dry Weight
Standing Biomass=Dry Weight (of above ground tissues)/Plot Area
3.5 Timeline
1st-2nd
week
3rd-4th
week
5th-6th
week
7th-8th
week
8th-9th
week
10th-
11th
week
12th-
13th
week
Selecting the
research topic
Getting approval
for topic from the
respected
authorities
Performing

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secondary data
collections in an
initial basis
Training of the
final draft for the
research proposal
Submission for the
research proposal
Studying the in-
depth literature of
the secondary
data
Preparing the
literature review
associated
Identification of
the participants
for conducting
primary research
Conducting
interview of the
participants
Arrangement of
ideas in data
collection
Data analysis and
evaluation
Interpreting and
discussing about
the data
Developing the
conclusion
Final revision of
the paper
Submitting the
research paper

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4.0 References
Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., &Bareth, G. (2014). Estimating
biomass of barley using crop surface models (CSMs) derived from UAV-based RGB
imaging. Remote Sensing, 6(11), 10395-10412.
Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., ...&Bareth, G. (2015).
Combining UAV-based plant height from crop surface models, visible, and near infrared
vegetation indices for biomass monitoring in barley. International Journal of Applied
Earth Observation and Geoinformation, 39, 79-87.
Dong, T., Liu, J., Qian, B., Zhao, T., Jing, Q., Geng, X., ...& Shang, J. (2016). Estimating winter
wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and
MODIS data. International Journal of Applied Earth Observation and
Geoinformation, 49, 63-74.
Eitel, J. U., Magney, T. S., Vierling, L. A., Brown, T. T., & Huggins, D. R. (2014). LiDAR
based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat
nitrogen status. Field Crops Research, 159, 21-32.
Fu, Y., Yang, G., Wang, J., Song, X., & Feng, H. (2014). Winter wheat biomass estimation
based on spectral indices, band depth analysis and partial least squares regression using
hyperspectral measurements. Computers and Electronics in Agriculture, 100, 51-59.
Greaves, H. E., Vierling, L. A., Eitel, J. U., Boelman, N. T., Magney, T. S., Prager, C. M., &
Griffin, K. L. (2015). Estimating aboveground biomass and leaf area of low-stature
Arctic shrubs with terrestrial LiDAR. Remote Sensing of Environment, 164, 26-35.

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UNITED KINGDOMS
Hiloidhari, M., Das, D., &Baruah, D. C. (2014). Bioenergy potential from crop residue biomass
in India. Renewable and sustainable energy reviews, 32, 504-512.
Jannoura, R., Brinkmann, K., Uteau, D., Bruns, C., &Joergensen, R. G. (2015). Monitoring of
crop biomass using true colour aerial photographs taken from a remote controlled
hexacopter. Biosystems Engineering, 129, 341-351.
Jin, X., Yang, G., Xu, X., Yang, H., Feng, H., Li, Z., ...& Zhao, C. (2015). Combined multi-
temporal optical and radar parameters for estimating LAI and biomass in winter wheat
using HJ and RADARSAR-2 data. Remote Sensing, 7(10), 13251-13272.
Kross, A., McNairn, H., Lapen, D., Sunohara, M., & Champagne, C. (2015). Assessment of
RapidEye vegetation indices for estimation of leaf area index and biomass in corn and
soybean crops. International Journal of Applied Earth Observation and
Geoinformation, 34, 235-248.
Ledo, A., Heathcote, R., Hastings, A., Smith, P., & Hillier, J. (2018). Perennial-GHG: A new
generic allometric model to estimate biomass accumulation and greenhouse gas
emissions in perennial food and bioenergy crops. Environmental Modelling &
Software, 102, 292-305.
Li, W., Niu, Z., Chen, H., Li, D., Wu, M., & Zhao, W. (2016). Remote estimation of canopy
height and aboveground biomass of maize using high-resolution stereo images from a
low-cost unmanned aerial vehicle system. Ecological indicators, 67, 637-648.

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Li, W., Niu, Z., Huang, N., Wang, C., Gao, S., & Wu, C. (2015). Airborne LiDAR technique for
estimating biomass components of maize: A case study in Zhangye City, Northwest
China. Ecological indicators, 57, 486-496.
Marshall, M., &Thenkabail, P. (2015). Advantage of hyperspectral EO-1 Hyperion over
multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS
vegetation indices in crop biomass estimation. ISPRS Journal of Photogrammetry and
Remote Sensing, 108, 205-218.
Tilly, N., Aasen, H., &Bareth, G. (2015). Fusion of plant height and vegetation indices for the
estimation of barley biomass. Remote Sensing, 7(9), 11449-11480.
Tilly, N., Hoffmeister, D., Cao, Q., Huang, S., Lenz-Wiedemann, V., Miao, Y., &Bareth, G.
(2014). Multitemporal crop surface models: accurate plant height measurement and
biomass estimation with terrestrial laser scanning in paddy rice. Journal of Applied
Remote Sensing, 8(1), 083671.
Zhang, T., Wooster, M. J., Green, D. C., & Main, B. (2015). New field-based agricultural
biomass burning trace gas, PM2. 5, and black carbon emission ratios and factors
measured in situ at crop residue fires in Eastern China. Atmospheric Environment, 121,
22-34.