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Remote sensing and spatial statistical
techniques for modelling Ommatissus
lybicus (Hemiptera: Tropiduchidae)
habitat and population densities
Khalifa M. Al-Kindi
1
, Paul Kwan
1
, Nigel R. Andrew
2
and Mitchell Welch
1
1School of Science and Technology, University of New England, Armidale, NSW, Australia
2Centre for Excellence for Behavioural and Physiological Ecology, University of New England,
Armidale, NSW, Australia
ABSTRACT
In order to understand the distribution and prevalence of Ommatissus lybicus
(Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns
and predict their future spread, comprehensive and detailed information on the
environmental, climatic, and agricultural practices are essential. The spatial
analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help
detect and model spatial links and correlations between the presence, absence and
density of O. lybicus in response to climatic, environmental, and human factors. The
main objective of this paper is to review remote sensing and relevant analytical
techniques that can be applied in mapping and modelling the habitat and
population density of O. lybicus. An exhaustive search of related literature revealed
that there are very limited studies linking location-based infestation levels of pests
like the O. lybicus with climatic, environmental, and human practice related
variables. This review also highlights the accumulated knowledge and addresses the
gaps in this area of research. Furthermore, it makes recommendations for future
studies, and gives suggestions on monitoring and surveillance methods in designing
both local and regional level integrated pest management strategies of palm tree and
other affected cultivated crops.
Subjects Agricultural Science, Entomology, Environmental Sciences, Plant Science,
Coupled Natural and Human Systems
Keywords Remote sensing, Dubas bug, Ommatissus lybicus, Spatial statistics
INTRODUCTION
Remote sensing (RS) is a powerful technology that has been applied in precision
agriculture applications (Shah et al., 2013). Remotely sensed data can be used in mapping
tools to classify crops and examine their health and viability. They can also be used for
monitoring farming practices and to measure soil moisture across a wide area instead of at
discrete point locations that are inherent to ground measurement (Atzberger, 2013). Based
on these spatial differences, variable rate application of chemicals such as fertilisers or
pesticides can be made. Remote sensing information can further be used to establish
sub-field management zones, providing a less expensive yet finer resolution option than
grid sampling.
How to cite this article Al-Kindi et al. (2017), Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus
(Hemiptera: Tropiduchidae) habitat and population densities. PeerJ 5:e3752; DOI 10.7717/peerj.3752
Submitted 23 May 2017
Accepted 8 August 2017
Published 31 August 2017
Corresponding author
Khalifa M. Al-Kindi,
kalkindi@myune.edu.au
Academic editor
Robert Costanza
Additional Information and
Declarations can be found on
page 24
DOI 10.7717/peerj.3752
Copyright
2017 Al-Kindi et al.
Distributed under
Creative Commons CC-BY 4.0
Although RS technologies are more widely used in other industries, their potential for
profitable use by farmers is less frequently studied. As examples in agriculture, RS
technologies have been used successfully for monitoring and mapping water stress,
crop quality and growth, wetland, water quality, phosphorus and nitrogen deficiencies
in vegetation, as well as detecting and predicting insect infestations (e.g., O. lybicus)
(Al-Kindi et al., 2017a;Gooshbor et al., 2016;Lamb & Brown, 2001;Riley, 1989) and plant
diseases (Neteler et al., 2011).
Background
The date palm, Phoenix dactylifera Linnaeus, is an important economic resource in the
Sultanate of Oman. Plant-parasitic nematodes, associated with date palm trees in Oman
and in most other Arab countries, can reduce their economic yields (El-Juhany, 2010).
A variety of insect pests can cause major damages to this crop through production losses
and plant death (Abdullah, Lorca & Jansson, 2010;Al-Khatri, 2004;Blumberg, 2008;
El-Shafie, 2012;Howard, 2001). One such species, Ommatissus lybicus de Bergevin 1930
(Hemiptera: Tropiduchidae), which is known more commonly as the Dubas bug (DB),
has been identified as a major economic threat, and is presently affecting palm growth
yield in Oman (Al-Yahyai, 2006). Indeed, the DB has been identified as one of the primary
reasons for the decline in date production in Oman (Al-Yahyai & Al-Khanjari, 2008;Al-
Zadjali, Abd-Allah & El-Haidari, 2006;Mamoon, Wright & Dobson, 2016). It is also a
principal pest of date palms in many locations throughout the Middle East, East and
North Africa, (Klein & Venezian, 1985;Mifsud et al., 2010). The DB is believed to have
been introduced into the Tigris-Euphrates River Valley, from there it has spread to other
zones in recent decades (Blumberg, 2008;El-Haidari, Mohammed & Daoud, 1968).
The DB is a sap feeding insect; both adults and nymphs suck the sap from date palms,
thereby causing chlorosis (removal of photosynthetic cells and yellowing of fronds).
Prolonged high infestation level will result in the flagging and destruction of palm
plantations (Al-Khatri, 2004;Howard, 2001;Hussain, 1963;Mahmoudi et al., 2015;Mokhtar
& Al Nabhani, 2010;Shah et al., 2013). There is also an indirect effect whereby honeydew
secretions produced by the DB can promote the growth of black sooty mould on the foliage
and consequently a reduction in the photosynthetic rates of date palms (Blumberg, 2008;
Mokhtar & Al-Mjeini, 1999;Shah et al., 2012). Nymphs pass through five growth instars
(Hussain, 1963;Shah et al., 2012), with adult female DB reaching 5–6 mm and the males 3–
3.5 mm in length (Aldryhim, 2004;Mokhtar & Al Nabhani, 2010). Their colour is yellowish-
green while the main distinguishing feature between males and females is the presence of
spots on females; males have a more tapered abdomen and larger wings relative to the
abdomen (Al-Azawi, 1986;Al-Mahmooli, Deadman & Al-Wahabi, 2005;Elwan & Al-Tamimi,
1999;Hussein & Ali, 1996;Jasim & Al-Zubaidy, 2010;Kaszab, Wittmer & Buttiker, 1979;
Khalaf et al., 2012;Mokhtar & Al Nabhani, 2010;Thacker, Al-Mahmooli & Deadman, 2003).
Study area
The Sultanate of Oman, which covers an area of 309,500 km
2
, extends from 1640′Nto
2620′N, and 5150′Eto59
40′E. It occupies the south-eastern corner of the Arabian
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 2/36
Peninsula (Fig. 1). It has 3,165 km of coastline, extending from the Strait of Hormuz in the
north to the border with the Republic of Yemen in the South. The coastline faces onto
three different water bodies, namely the Arabian Sea, the Persian Gulf (also known as
Arabian Gulf), and the Gulf of Oman.
To the west, Oman is bordered by the United Arab Emirates and the Kingdom of Saudi
Arabia. Mountainous areas account for 15% of the land area, while desert plains and
sandy areas cover 74%, agro-biodiversity areas cover 8%, and the coastal zone covers 3%,
respectively (Luedeling & Buerkert, 2008). The location of Oman provides favourable
conditions for agriculture, with land under agricultural use accounting for 8% of the
territory and the economic output accounting for 14.6% of the GDP in 2008. According
to the 2004–2005 soil survey conducted by the Ministry of Food and Agricultural (MFA),
22,230 km
2
(equivalent 2.223 million ha) is optimal for agricultural activity, which
represents ∼7.5% of the country’s land area. Approximately 728.2 km
2
(∼72,820 ha) of
the country is irrigated using the Falaj irrigation system, where local springs or wadis
(streams) underflow areas are cultivated with palm trees, banana, limes, alfalfa, and
vegetables (Gebauer et al., 2007).
Figure 1 Maps of the study area, including: (A) topography and location of Oman, with the study
area outlined by the black rectangle; (B) elevation change within the study area; and (C)
distribution of date palm plantations in the study area (Esri ArcGIS 10.3).
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 3/36
Oman has an arid climate, receiving less than 100 mm of rainfall per year; however, the
mountainous parts of the country receive higher precipitation levels (Kwa r te ng, D o rvl o &
Vijaya Kumar, 2009). As the dependent variable, DB infestations occur where palm trees are
concentrated; therefore, in this study we focused on northern Oman (2650′Nto22
26′N,
and 5550′Eto59
50E) which experiences high infestations (Fig. 1)(Al-Kindi et al., 2017b).
Dubas bugs are active on leaflets, rachis, fruiting bunches, and spines during different
stages of growth of date palm trees. These infestations are capable of causing up to 50%
crop loss during a heavy infestation (Shah et al., 2013). Studies of insect pests of the
date tree palm indicated more than 54 arthropods species insects connected with date
plantations. Nevertheless, DB and red weevil (RPW) Rhynchophorus ferrugineus Oliver,
and lesser moth, are considered major economically significant pests affecting growth and
yield of date palm trees (Al-Zadjali, Abd-Allah & El-Haidari, 2006).
Biology and life history
The biology of this species has been investigated in a number of studies (Al-Azawi, 1986;
Arbabtafti et al., 2014;Hussain, 1963;Jasim & Al-Zubaidy, 2010;Klein & Venezian, 1985;
Payandeh & Dehghan, 2011;Shah et al., 2012). The DB produces two generations annually,
including the spring and autumn generations (Blumberg, 2008;Hussain, 1963). In the
spring generation, eggs start hatching from February to April, after which nymphs pass
through five instars to become adults in approximately 6–7 weeks. The eggs aestivate
during the hot season (i.e., summer) until the autumn generation, when they start
hatching from late August to the last week of October. A nymph takes about 6 weeks to
develop into an adult, which then lives for about 12 weeks. Females lay between 100 and
130 eggs (Elwan & Al-Tamimi, 1999;Mokhtar & Al Nabhani, 2010). The female DB lays
her eggs by inserting them into holes in the tissue of the date palm frond at the end of each
generation. The eggs remain dormant for about three months. When they hatch, the
resulting nymphs remain on the fronds of the same tree, feeding on the sap, and defecating
large amounts of honeydew, which eventually covers the palm fronds and promotes the
growth of black sooty mould (Zamani, Aminaee & Khaniki, 2013).
In extreme cases, the sooty mould that develops from the honeydew secretions can
block the stomata of the leaves, causing partial or complete suffocation of the date palm,
which in turn reduces its yield. The honeydew secretion also makes the dates unpalatable
(Aminaee, Zare & Assari, 2010;El-Juhany, 2010;Gassouma, 2004;Mamoon, Wright &
Dobson, 2016). The eggs of DB are sensitive to temperature. In summer, the eggs can hatch
within 18–21 days, but in winter they may take up to 170 days to hatch (Blumberg, 2008).
The developmental time of DBs eggs has been studied at three different temperatures,
17.6, 27.5, and 32.4 C in Oman (Al-Khatri, 2011). The results showed that a temperature
of 27.5 C appeared to be the optimal temperature for the biological activities of this
species (Al-Khatri, 2011). At 35 C, the biological processes of the pest are disrupted, thus
leading to high mortality, particularly in females (Bagheri et al., 2016;Bedford et al., 2015).
Investigations into the population and the fluctuation in spatial distribution (Khalaf &
Khudhair, 2015) of the two DB generations in the Bam region of Iran showed that this pest
has an aggregated spatial distribution pattern (Payandeh, Kamali & Fathipour, 2010).
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 4/36
Seasonal activities effected by climate change showed that nymphs were dynamic from
April to May in the first generation and August to October in the second generation.
In the first and second generations, the adults are active from May to June and from
September to November, respectively. Earlier work (Blumberg, 2008) reported that
temperature exposure below 0 C adversely affects the ability of adults to survive. The
DB avoids direct sunlight (Klein & Venezian, 1985;Shah et al., 2013), and it is spread
via the transfer of infested offshoots as well as by wind or wind dust (Hassan, 2014;
Jasim & Al-Zubaidy, 2010).
Biological control
Some research has also been conducted on the natural biological control of the DB,
such as using predators and parasites. Early results showed a variety of natural predators
that could be used as biological control agents, among these being Aprostocetus sp.,
Oligosita sp., and Runcinia sp. (Cammell & Knight, 1992). Furthermore, Hussain, 1963
reported that the eggs of the DB could be parasitised by a small Chalcidoid, while the
nymphs and adults were preyed upon by the larvae of the lace wing (Chrysopa carnea
Steph.). Hussain also stated that three adult species of Coccinellids prey on nymphs and
adults of the DB. However, further study is needed to determine the distributions of these
natural enemies in Oman and their effectiveness in controlling DB populations. Some
measure of success was also achieved with pathogenic bacteria as microbiological control
agents (Khudhair, Alrubeai & Khalaf, 2016), although their toxicological aspects require
further research in order to assess the safety of their implementation at a large scale
(Cannon, 1998).
Chemical control
Given the significant economic impact of this pest, research into effective management
strategies demands high priority. Several insecticides have been evaluated for DB control
in Oman since 1962 (Table 1) with Sumi-Alpha-5 EC being effective as a ground spray and
KarateÒ2 ULV, TrebonÒ30 ULV, and Sumicombi 50ÒULV achieving some measure of
success as aerial sprays. Karate-ZeonÒwas also found to be very effective as it gave 100%
reduction in numbers of DB instars and adults, between three and seven days after
application. However, the use of this particular pesticide is restricted due to its side effects
such as irritation (Al-Yahyai & Khan, 2015). In Israel, systemic carbamates such as
aldicarb and butocarboxim have been successful, while in Iraq dichlorvos (DDVP)
injected directly into the infected palms were also successful in suppressing the pest
population (Blumberg, 2008). Nonetheless, this method of control is expensive with
negative environmental impacts on non-target species (particularly natural enemies of
DB) as well as on human health.
Research has shown that some pesticide residues persist in the fruit up to 60 days after
application (Al-Samarrie & Akela, 2011). In addition, chemical control has achieved
limited successes and DB continues to pose a major challenge to Omani agriculture.
More information about the biological and chemical control and their impacts can be
found in literature (Shifley et al., 2014;Thacker, Al-Mahmooli & Deadman, 2003).
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 5/36
Research opportunities
A number of opportunities exist for research into the biology and ecology of this species in
order to gain a thorough understanding of its life cycle and its distribution. The climatic
factors that influence its survival and distribution also merit study because such
information may be useful in determining current and future potential distributions,
particularly in light of climate change.
In a review of the effects of climate change on pest populations, an earlier report
(Cammell & Knight, 1992) suggested that increases in mean global temperatures, as well as
changes in rainfall and wind patterns, could have profound impacts on the population
dynamics, abundance, and distribution of insect pests of agricultural crops. More
recent research has supported this finding (Bale et al., 2002;Cannon, 1998;Cook, 2008;
Shifley et al., 2014;Tobin, Parry & Aukema, 2014). A key issue in ecology and conservation
is the mapping of pest species distributions as this information can be used to devise more
effective management strategies for their control.
Mapping of DB infestations is important for developing predictive models that give
the probability of occurrence, spatial distributions and densities under different
environmental, meteorological, anthropogenic and resource availability conditions.
Maps such as the DB hazard map can be used to devise an integrated palm management
(IPM) plan, thus enhancing capacity and educating farmers on how to apply IPM for the
control of this pest.
Mapping DBs are also beneficial in terms of better planning of date palm orchard
locations, better design and management of farms, what cultivars to plant, distance
between palms, irrigations, pesticides, fertilisations, etc. (Bouyer et al., 2010). There will
also be savings in the cost of monitoring since RS based techniques developed as part of
this study can provide a more efficient and cost-effective means for large scale monitoring
of infestations and observation of stress levels on date palm trees.
The aim of this review is to highlight technological advances in the fields of RS (i.e.,
by aircraft or a satellite platform) and spatial statistical techniques that can be used to
Table 1 Major pesticides used in Dubas bug management in Oman.
Brand names Active ingredients Chemical group
Dubaklin Dintefurn 10% ULV Neonicotinoid
DECIS Deltamethrin 12.5% ULV Synthetic pyrethroid
Sumicombi-Alpha Fenitothion %24.5 + esfenvalerate %0.5 ULV Organophosphate + pyrethorid
Trebon Etofenprox %20 EC Non-ester pyrethroid
Sumi-Alpha Esfenvalerate %0.5% EC Synthetic pyrethroid
Kingbo Oxymstrin %0.2 & 0.6 SL Botanical
Actellic Pirimiphos-methy1 %50 EC Organophosphate
Pyrethrum Pyrethrums %50 EC Botanical
Sumi-Mix Fenitrothion 25% + fenpropathrin %2.5 EC Organophosphate + pyrethorid
1-Green Angulation A: %1 W/V Botanical
Karate-Zeon Lambda-cyhalothrin %10 CS Synthetic pyrethyroids
Fytomax Azadirachtin %1 ULV Botanical
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 6/36
significantly enhance our ability to detect and characterise physical and biological stresses
on several plant species. In particular, advanced RS and spatial statistical techniques need
to be developed and implemented for the surveillance and control of DB adults and
nymphs over large spatial scales. In turn, this will greatly assist plant protection service
(PPS) projects, integrated pest management technology (IPMT) programs and farmers in
protecting their palm tree orchards by adopting timely preventative actions.
REMOTE SENSING DATA
Data requirements for crop management
It is important to collect data regarding crops and soil and to identify the changes that
occur in the field to achieve precise crop management in the agricultural sector. Pinter
et al. (2003) Data are needed on the conditions that are stable across seasons (e.g., crop
type, soil fertility), differing during the seasons (e.g., pest attacks, water quality and
quantity, nutrient contents, moisture, temperature), and on factors that contribute to
crop yield variability (Hall, Skakun & Arsenault, 2006;Jadhav & Patil, 2014). This data is
valuable for determining the unique phenological cycles of agricultural crops in different
geographic regions (Jensen, 2000;Abdullah & Umer, 2004;Acharya & Thapa, 2015).
A good example of this are date palms. Typically, date palm trees are 7–10 m tall with
crowns 2–4 m in diameter, and the trees are normally spaced 3–5 m apart. The understory
of date palm plantations might include banana palms, mango trees, acacia bushes,
vegetable crops, grain crops, forage crops. The reflectance characteristics of a date palm
area are often driven by the density and health of the understory vegetation (Harris, 2003).
It can be difficult to use small pixel data to study date palm areas with little or no
understory vegetation because the small pixel effects may make it difficult to identify
infestations (e.g., where date palms are infested between mountains and dry rivers) given
the tree spacing and density of leaves and branches. Studies like Hussain (1963) and
Mahmoudi et al. (2015) have revealed that heavy infestations occur mostly along valleys.
Additionally, the characteristics of the understory vegetation may dominate the
contribution of spectral responses rather than the tree vegetation themselves.
Optical remote sensing data
The vital feature of RS is the detection of radiant energy emitted by various objects.
The energy detected might be in the form of acoustic energy (sound) or electromagnetic
energy (visible light, infrared heat, ultraviolet, and microwaves). Remote sensing
technology deployed from ground, air, or space-based platforms is capable of providing
detailed spectral, spatial, and temporal information on vegetation health and
is particularly useful for crop yield estimation applications (Justice et al., 2002).
Temporal resolution of remote sensing data
The temporal resolution of remote sensing data is important for commercial monitoring
or management projects. The commercial Landsat and SPOT have revisit intervals of
16 and 26 days, respectively. The IKONOS revisit times range from 1 to 3 days. On the
other hand, airborne (aircraft-mounted) sensors are more amenable to user defined
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 7/36
re-visitation. The capacity of high temporal resolution RS technology has been exploited
for monitoring seasonal vegetation variations, over wide areas is the estimation of net
primary production and deciding time boundary conditions for crop yield modelling
(Hatfield & Pinter, 1993;Marx et al., 2010;Reynolds, Reynolds & Riley, 2009;Reynolds &
Riley, 1997). We believe temporal RS data can be used to study seasonal DB infestations
because there are two generations, namely spring and autumn.
Longer term temporal images (e.g., covering a 15-year period) can be used to classify
and to determine the directions and speed of spread of DB infestations. This approach
can also be applied to historical images to obtain as much information as possible on
selected areas. Change detection can also be performed to quantify the degree of variation
in the infestation levels that is needed to occur before the change can be detected by
satellite technology. This is important for the development of a management and
surveillance strategy for DB.
Spatial resolution of remote sensing data
Spatial resolution is measured in terms of the smallest target on the ground. The number
of available image-forming pixels in the sensor and its distance from the ground
contribute to determining the pixel-size on the ground and the overall image footprint
allowing low and high spatial resolution data on insect pests like DB (Kerr & Ostrovsky,
2003). Depending on the goals of a study, technology with an appropriate spatial
resolution should be chosen. For example, certain Landsat data sets have spatial resolution
of 30 m while certain SPOT data sets have spatial resolution of 20 m in each dimension.
If it is a large scale study (e.g., large orchard), Landsat imagery at a 30 m resolution
may be sufficient (White & Roy, 2015).
However, if the study is for small orchards surrounding the mountains where several
types of plantations are present, high resolution data would be needed. High resolution
imagery products are available, such as SPOT’s panchromatic 10 m resolution data sets
and Landsat’s multispectral scanner 20 m resolution imagery, Wolter, Townsend &
Sturtevant (2009). Furthermore, very high resolution imagery are available, including
QuickBird’s 2.15 m resolution images or the National Agricultural Imagery Programme’s
(NAIP’s) 1m resolution orthophotographs (Boryan et al., 2011).
More recently, high resolution satellite imagery from IKONOS, which consists of 4 m
resolution multispectral imagery, have become available; but the costs for obtaining such
data remain a significant impediment to their widespread use. These high resolution
images can be used to classify and map the spatial distribution and infestation levels of
DB. Very high resolution data collected with unmanned aerial vehicle (UAV)-based
remote sensing technology can be used for detecting and mapping of plant diseases and
infestations such as those due to DB (Colomina & Molina, 2014;Kattenborn et al., 2014;
Loper, 1992;Nebiker et al., 2008;Sperlich et al., 2014;Zhang & Kovacs, 2012).
Spectral resolution of remote sensing data
Spectral resolution is typically defined as the number of bands of the electromagnetic
spectrum that are sensed by the RS device. A very important aspect of spectral resolution
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 8/36
is the width of the bands. Different band-widths have been employed extensively in
multispectral imagery applications (Zwiggelaar, 1998), and these data often cover an
entire colour or colours such as, the red and blue bands of the spectrum. Multispectral
systems commonly obtain data for 3–7 bands in a single observation such as in the visible
and near-infrared (NIR) regions of the electromagnetic spectrum (dos Santos et al., 2016).
Multispectral imagery, however, lacks the sensitivity to detect subtle changes in tree
canopy reflectance that are caused by physiologic stress from insects or pathogens
(Lawrence & Labus, 2003).
Dakshinamurti et al. (1971) found that multispectral photography is useful for clearly
differentiating between coconut plantations and other crops such as jack fruit, mangoes,
and bananas in India. Another relevant study, Leckie et al. (2004), used multispectral data
for detecting and assessing trees infested with Phellinus weirii which causes Laminated
root rot disease. Other work (Stephens, Havlicek & Dakshinamurti, 1971) has shown that
multispectral photography can be used to clearly distinguish between many types of fruit
orchards and crops.
Hyperspectral imagery tends to have much narrower band widths, with several to many
bands within a single colour of the spectrum (Jadhav & Patil, 2014). These might include
the visible (VIS), NIR, mid-infrared (MIR), and thermal infrared portions. In the visible
portion of the electromagnetic spectrum (400–700 nm), the reflectance of healthy green
vegetation is relatively low because of the strong absorption of light by the pigments in
plant leaves (Apan, Datt & Kelly, 2005;Shafri & Hamdan, 2009;Teke et al., 2013). If there is
a reduction in pigments (e.g., chlorophyll) due to pests, the reflectance in the affected
spectral region will increase (Carter & Knapp, 2001;Prabhakar et al., 2011). A past study
(Vigier et al., 2004) reported that reflectance in the red wavelengths (e.g., 675–685 nm)
dominated most of detection data for Sclerotinia spp. stem rot infections in soybeans.
Over approximately 700–1,300 nm (the NIR portion), the reflectance of healthy
vegetation is very high. Damages caused by DB infestations in the form of black sooty
mould on palm tree leaves and understory vegetation that is promoted by bug excrement
causes overall reflectance in the NIR region to be lower than expected. The new
hyperspectral RS technology could be used to develop early (pre-visual) detection
methods for DB infestations.
Colour-infrared technology with supporting hyperspectral reflectance data could be
used to identify specific trees and fronds of date palm trees that have been infested
with DB. These methods can be used to monitor changes in infestation levels according
to honeydew, which is converted to sooty mould on the fronds during high levels of
infestation. Honeydew secretion is a good indicator of DB feeding activity (Al-Abbasi,
1988). The indirect assessments of the insect populations can be carried out by measuring
the amounts of honeydew caused by the insects (Southwood, 1978). Additionally, airborne
visible/infrared imaging spectrometer (AVIRIS) can be used to determine the extent and
severity of DB infestation damage in different areas.
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 9/36
Radar data
For many years, airborne technology has been employed in agricultural operations.
Nevertheless, space-borne synthetic aperture radar (SAR) technology such as those of the
Advanced Land Observing satellite; TerraSAR-X and Phased Array L-band have become
available since the 2000s (Ortiz, Breidenbach & Ka
¨ndler, 2013). Multiple radar sensors can
work autonomously to detect solar radiation variation, but dissimilar optical sensors from
which spectral reflectance measurements are taken are affected differently by variation in
the solar emission. Radar technology has found limited applications in regional studies
because of its high costs, the narrow swath widths, and limited extent of coverage
(Feng et al., 2003).
The data can be extracted routinely by using the existing network of weather radars,
and it can be used to alert growers that local crops are at heightened risk (Westbrook &
Isard, 1999;Drake, 2002). Such information can then be used for fine tuning pest
management practices such as pesticide applications, and could potentially reduce
pesticide use by nearly 50% and lessen the overall impact of toxic chemicals on the
environment (Dupont, Campanella & Seal, 2000), as well as on the natural enemies of
these insect pests. Table 2 shows example applications of different remote sensing
technologies used to detect change in vegetation.
Spectroscopic analysis
Fluorescence spectroscopy (FS) is a type of spectroscopic method by which fluorescence is
measured of an object of interest following excitation by rays of light. Fluorescence has
been used for vegetation research to monitor stress levels and physiological states in
plants. There are two types of fluorescence. The first is blue-green fluorescence in the
∼400–600 nm range and the second type is chlorophyll fluorescence in the ∼650–800 nm
Table 2 Example applications of the use of remote sensing technologies to detect change in vegetation.
Satellite and aircraft sensor Spatial resolution Biophysical variables for vegetation
Landsat 7 (ETM+) 15 m Panchromatic (Pan) bands; 30 m in the sex VIS,
NIR, IR, and shortwave (SWIR) infrared bands; and
60 m in the thermal infrared bands
Designed to monitor seasonal and small-scale
processes on a global scale such as cycles of
vegetation and agriculture
Landsat 8 (OLI) 15 m pan bands; 30 m in the sex VIS, NIR, SWIR1,
SWIR2; and 30 m in the cirrus bands
ASTER 15 m in the VIS and NIR range, 30 m in the shortwave
infrared band
Land cover classification and change detection
NOAA (AVHRR) 1.1 km spatial resolution Large-area land cover and vegetation mapping
SPOT 5 and 2.5 m in single-band, and 10 m in multiband Land cover and agricultural
GeoEye/IKONOS Panchromatic at 1 m resolution and multispectral at
4 m resolution and colour images at 1 m
Pigments
Canopy structure
Biomass derive from vegetation indices
Leaf index
Vegetation stress
Absorbed photosynthetically active radiation
Evaporations
Digital Globe’s/QuickBird Panchromatic with 61 cm resolution and
multispectral images with 2.44 m resolution and
colour images with 70 cm
RADAR (SAR) 3 m resolution
LIDAR 0.5–2 m resolution and vertical accuracy of less than
15 cm
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 10/36
range. Fluorescence spectroscopy can be used to monitor nutrient deficiencies,
environmental conditions based on stress levels, infestations, and plant diseases. In fact, it
can be used to monitor fruit quality, photosynthetic activity, tissue stress, and infestations
in many types of crops (Karoui & Blecker, 2011;Tremblay, Wang & Cerovic, 2012).
Remote Sensing is a powerful technique for visualising, diagnosing, and quantifying
plant responses to stress like temperature, drought, salinity, flooding, and mineral toxicity.
Approaches can range from the use of simple combinations of thermal and reflectance
sensor data to visible reflectance and fluorescence data. In particular, combined
fluorescence reflectance and thermal imaging sensor data can be used for quick
investigations of vegetation stress (Lenk et al., 2007).
Solar radiation and the humid-thermal ratio
Biological systems are highly dependent on two most important climatic factors, namely
temperature and precipitation. Temperature is influenced by solar radiation and thermal
emissions, while precipitation controls the dry or wet conditions (humidity) associated
with plant growth. These factors are especially important in regions where extreme
temperatures and humidity conditions are prevalent and large fluctuations exist
throughout the seasons as such conditions can predispose plants to insect pests and
diseases. In this regard, solar radiation models can be used to investigate insect
Figure 2 A diagram showing the design and use of solar radiation models to analyse the relationship
between Dubas bug infestation levels and positional solar radiation.
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 11 /36
infestations. Solar radiation models can be applied to calculate the potential solar
radiation at a chosen location over a 12-month period.
The results from solar radiation studies can then be used to find correlations with
different infestation levels to examine if solar radiation plays a determinant role in different
infestation levels (see Fig. 2). Solar radiation can also be used to study the presence/absence
and density of animals, plants diseases and infestations such as those caused by DB.
More information on the theory and technical aspects of solar radiation models can be
found in Bonan (1989),Dubayah & Rich (1995),Flint & Childs (1987),Geiger et al. (2002),
Hetrick et al. (1993),Kumar, Skidmore & Knowles (1997),Kirkpatrick & Nunez (1980),
Mazza et al. (2000),andSwift (1976).
The humid-thermal ratio (HTR) has successfully been used to develop and test
relationships between different plant infestations levels in varied climate conditions in
areas such as Australia, India, Europe, and North America. An HTR prototype has been
developed to simulate ecological conditions appropriate for the establishment and spread
of plant diseases in India (Jhorar et al., 1997). The HTR method has also been used to
evaluate the risk of the establishment and spread of Karnal in wheat, grown under a
variety of climatic conditions and in different areas (Mavi et al., 1992;Stansbury &
Pretorius, 2001;Workneh et al., 2008). This method has potential value in researching
insect pests and their associated diseases, which may allow for the prediction of
occurrence and non-occurrence under specific combinations of climate and weather
conditions.
VEGETATION
Image processing for vegetation
In order to detect changes, important information must be provided including spatial
distributions of change, change rates, change trajectories for different vegetation types,
and assessment of the accuracy of the change detection results. The three main steps
in implementing change detection are (1) image pre-processing, e.g., geometrical
rectification (GR), image registration (IR), minimum noise fraction (MNF) analysis,
radiometric, automorphic, and topographic correction (the latter is needed if the study
area is close to mountains) (Bagheri et al., 2016;Bishop & Colby, 2002;Civco, 1989;Teillet,
Guindon & Goodenough, 1982); (2) selection of optimal techniques to conduct the change
detection analysis; and (3) accuracy assessments (Datt et al., 2003;Lu et al., 2004;Lunetta
et al., 2006;Lyon et al., 1998;Song et al., 2001) (see Fig. 3).
Although the selection of appropriate change detection techniques is important for the
accuracy of change results, in practice, it might not be easy to select a suitable algorithm
for a specific change detection application. Some simple techniques can be used to
provide change and non-change information (e.g., image differencing). Other techniques
may be used to provide a complex matrix of change direction data such as that used
for post-classification comparisons (Lu et al., 2004). This review provides examples of
change detection methods that can be used to address DB infestations and their impacts
on date palm trees.
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Techniques and methods
Vegetation indices
Vegetation indexes (VIs) are used to compile data into a single number that quantifies
vegetation biomass and/or plant vigour for each pixel in a RS image. An index is
computed by using several spectral bands that are sensitive to plant biomass and vigour
(McFeeters, 1996). Such indices can be used to (1) specify the amount of vegetation (e.g.,
biomass, SAVI, the percentage of vegetation cover); (2) discriminate between soil and
vegetation; and (3) reduce atmospheric and topographic effects. However, variability in VI
data can arise from atmospheric effects, viewing and illumination angles, sensor
calibrations, errors in geometric registration, subpixel water and clouds, snow cover,
background materials, image compositing, and landscape topography (e.g., slope and
relief). For example, in sparsely vegetated areas, the reflectance of soil and sand are much
higher than the reflection of vegetation; so the detection of reflection from the vegetation
cover is difficult.
Difference vegetation index
The difference vegetation index (DVI) is the simplest vegetation index (DVI = NIR–Red).
DVI is sensitive to the amount of vegetation, and it can be used to distinguish between
Figure 3 Flowchart of an image processing methodology, which include three main steps for
implementing change detection research, namely: (1) image pre-processing work: geometrical
replication (GR), image registration (IR), minimum nose fraction (MNF) analysis, radiometric
correction (RC), atmospheric correction (AC), and topographic correction (TC); (2) selection of
optimal techniques to conduct the change detection; and (3) accuracy assessments to obtain final
maps.
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 13/36
soil and vegetation. However, it does consider the difference between reflectance and
radiance caused by the atmosphere and shadows (Jiang et al., 2006). Previous research
(Glenn et al., 2008) that used the utility of image differencing, image rationing, and the
vegetation index for detecting gypsy moth defoliation found that a difference of the
MSS7/MSS5 ratio was more useful for delineating defoliated areas than any single band-pair
difference.
Ratio-based vegetation indices
Ratio-based vegetation indices are also called the simple ratio (SR) or RVI (SR = NIR/Red).
The SR provides valuable information about vegetation biomass or leaf area index (LAI)
variations in high-biomass vegetation areas such as forests. It is also useful in low-biomass
situations, such as those containing soil, water, ice, etc., where the SR indicates the
amount of vegetation present. The SR is capable of reducing the effects of the atmosphere
and topography on the analysis results.
Normalised difference vegetation index
Normalised difference vegetation index (NDVI) are generally well-documented, quality-
controlled data sources that have been re-processed for many applications and problems.
It is possible to use the NDVI values to discriminate between dense forests, non-forested
areas, agricultural fields, and savannahs; however, distinguishing between forests with
different dominant species is not possible by using this type of RS data because several
assemblages of plant species can produce similar NDVI values or similar NDVI temporal
trends. Atmospheric conditions are another aspect that must be considered when using
the NDVI (Willers et al., 2012).
One study, Nageswara Rao et al. (2004), reported that bananas and coconuts have close
greenness profiles in mid-April, but have rather distinct greenness profiles in mid-March.
Another study Chavez & MacKinnon (1994) reported that red band image differencing
provided better change detection results for vegetation than red data when using the
NDVI in arid and semi-arid environments of south-western United States. The NDVI may
not be appropriate to use in dry areas, and caution is warranted for such applications.
Date palms trees are often planted in a regular grid pattern, as are olive trees and such trees
may be able to be easily distinguished with NDVI data.
Normalisation difference moisture index
The normalisation difference moisture index (NDMI) data can be used to determine the
threshold presence of pest infestations (green attack). Such data can also be potentially
used for deriving regional estimates of the year of stand death, for example, by using
Landsat data and decision tree analysis. However, there are limitations associated with
using the NDMI, which include difficulties in detecting low rates of infestation and the
need to add images from other dates (to achieve a higher temporal frequency) to quantify
the spectral response to insects such as the DB.
The application of a VI such as the NDVI and SAVI to multispectral satellite imagery
(blue, red, and NIR) has been shown to be useful to quantify variations in plant vigour,
make relative biomass predictions, assess yields and investigate the occurrences of pests
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 14/36
and disease attacks outbreaks (Plant, 2001). Landsat TM data can be used to assess both
plant age and LAI values by applying a number of indices such as the shadow index (SI),
bare soil index (BI), NDVI, and advanced vegetation index (AVI).
Transformation
Feature space transformation, which relates to band space, involves processing data
that are n-dimensions. It may be difficult to visualise these data because the feature
space (where nis roughly the number of bands). However, several mathematical
techniques are readily available to analyse the feature space; they include principal
components analysis (PCA), Kauth’s Tasseled Cap (KTC), perpendicular vegetation
index (PVI), leaf water content index (LWCI), SAVI, NDMI, atmospherically resistant
vegetation index (ARVI), aerosol free vegetation index (AFRI), global environmental
monitoring index (GEMI), and red-edge position (REP) determination (Eitel et al., 2011).
These techniques and many more can be used to find areas that contain plentiful spectral
information.
The PCA and the KTC transformations can be used for land cover change detection via
NIR reflectance or greenness data that can detect crop type changes between vegetation
and non-vegetation features (Gorczyca, Gong & Darzynkiewicz, 1993;Lu et al., 2004).
An earlier study (Rondeaux, Steven & Baret, 1996) found that SAVI, where the value Xwas
tuned to 0.16, easily out-performed all other indices when applied to agricultural surfaces.
Others (Kaufman & Tanre, 1992;Leprieur, Kerr & Pichon, 1996) have concluded that
the GEMI and ARVI are less sensitive to atmosphere, but may be incapable of dealing with
variation in soil reflectance. More information about feature space transformation can
be found in Gebauer et al. (2007) and Luedeling & Buerkert (2008). According to
Darvishzadeh et al. (2008), REP is the most studied feature on vegetation spectral curve
because it is strongly correlated with foliar chlorophyll content and can be a sensitive
indicator of stress in vegetation.
Classification
The objective of image classification is to categorise all pixels in the imagery into one of
several land cover classes or themes. The categorised data can then be used to produce
thematic maps of land cover (e.g., vegetation type) based on remotely sensed data. Most
image processing techniques offers several methods to test hypotheses. The best-known
methods include supervised and unsupervised classification; however, these techniques
require ground reference data.
Maximum likelihood classification, for example, requires samples of pixels obtained
by field observations or aerial photography interpretations that are deemed to be
representative of specific land cover types. The maximum likelihood method relies on
the assumption that the populations from which these training samples are drawn
are multivariate–normal in their distributions. The traditional methods employ
classical image classification algorithms (e.g., k-means and ISODATA) for
unsupervised classification, and maximum likelihood classification for supervised
classification.
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Maximum likelihood classification algorithm
The maximum likelihood classification algorithm (or parametric information extraction)
is the most widely adopted parametric classification algorithm. However, it requires
normally distributed training data, especially for n(rarely the case) to compute the class
variance and covariance matrices. Another limitation is that it is difficult to integrate
non-image categorical data into a maximum likelihood classification. However, fuzzy
maximum likelihood classification algorithms are also available (Zhang & Foody, 2001).
Classification techniques
Supervised classification. The supervised classification methods can be used to select
representative samples for each land cover class in a digital image. Sample land classes are
more commonly called training sites. The image classification software uses the training
sites to identify the land cover classes in the entire image. The classification of land cover is
based on spectral signatures defined in the training set. The digital image classification
software determines the class based on what it resembles most in the training set. The
limitation on the use of supervised classification is that analysis is required to identify
areas on an image of known informational types and to create a training area (group of
pixels) from which the computer generates a statistics file (Mountrakis, Im & Ogole, 2011).
Unsupervised classification. The advantage of the use of unsupervised classification is
that all spectral variation in the image are captured and used to group the imagery data
into clusters. The major disadvantage is that it is difficult to completely label all the
clusters to produce the thematic map.
Combined and advanced methods. Many examples exist whereby the supervised and
unsupervised techniques were combined together in analyses. The associated advantages
and disadvantages can be found in Castellana, D’Addabbo & Pasquariello (2007) and
Pao & Sobajic (1992). However, the combined approach only slightly improves the ability
to create thematic maps when compared to using each technique separately. Moreover,
a large amount of effort has been devoted to developing advanced classification
approaches to improve our ability to create thematic maps from digital remotely sensed
imagery. One of the most recent advances has been the adoption of artificial neural
networks (ANNs) in the place of maximum likelihood classification (standard in most
RS software). This review only covers a few of the non-parametric techniques.
Artificial neural network. Fortunately, the ANN methods (non-parametric information
extraction) do not require normally distributed training data, and may be used to
integrate with virtually any type of spatially distributed data in classification. The
disadvantage of using ANN is that occasionally it is difficult to determine exactly how the
ANN came up with a certain assumption because such information is locked within
weights in a hidden layer or layers. The method has been used successfully for classifying
infestations, diseases/conditions of plants and the associated damage based on spectral
data (Cox, 2002;Liu, Wu & Huang, 2010;Pydipati, Burks & Lee, 2005). In recent years,
spectral mixture analysis, ANNs, GISs, and RS data have become important tools for
change detection applications.
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Artificial intelligence. Use of nonmetric information extraction or AI methods allows
the computer to analyse data perhaps better than people. The benefits of using AI for
image analysis involve the use of expert systems that place all the information contained
within an image in its proper context with ancillary data and then to extract valuable
information (Duda, Hart & Stork, 2001).
Classification and regression tree. Classification and regression tree is a non-parametric
algorithm that uses a set of training data to develop a hierarchical decision tree. The
decision tree is created by using a binary partitioning algorithm that selects the best
variable by which to split the data into separate categories at each level of the hierarchy.
Once the final tree is generated, it can be used to label all unknown pixels in the image.
This method is extremely robust and provides significantly better map accuracies than
those that have been achieved by using more basic approaches (Lawrence & Wright, 2001).
Support vector machines. Support vector machines are derived from the field of
statistical learning theory and have been used in the machine vision field for the last
10 years. These methods have been developed for use in creating thematic maps from
remotely sensed imagery. The SVM performs by projecting the training data using a kernel
function and this results in a data set that can then be linearly separated. The capability
to separate out the various informational classes in the imagery is a powerful advantage.
The use of SVM is relatively new, but it offers great potential for creating thematic
maps from digital imagery.
Several advanced techniques for classifying digital remotely sensed data involve the
extensive development and adoption of object-based image analysis (OBIA). Moreover,
advanced image classification techniques such as k-means, ISODATA, fuzzy ARTMP, fuzzy
multivariate cluster analysis, the WARD minimum variance technique, SOM, the artificial
neural classification algorithm (i.e., for the propagation of neural networks and
self-organising maps) and Bayesian analysis can be used (1) for the classification of
remotely sensed data; and (2) to delineate horticultural crops in satellite maps. The major
advantage of these techniques is their ability to generate a matrix of change information
and to reduce external impacts from the atmospheric and environmental differences
among the multi-temporal images. However, it may be difficult to select high quality and
sufficiently numerous training sets for image classification, in particular for important
historical image data classifications due to the lack of data (Lu, Moran & Batistella, 2003;
Lu & Weng, 2007;Lunetta et al., 2006;Monteiro, Souza & Barreto, 2003;Rogan, Franklin &
Roberts, 2002).
All these classifications are performed on a pixel-by-pixel basis. Therefore, given that a
pixel maps an arbitrary delineation of an area on the ground, any selected pixel may or
may not be representative of the vegetation/land cover of that area. In OBIA, unlabelled
pixels are grouped into meaningful polygons that are then classified as polygon pixels
(Blaschke, 2010;Dey, Zhang & Zhong, 2010;Haralick & Shapiro, 1985;Stafford, 2000).
Classified satellite imagery can also be used to extract palm crown data. The centre of
crowns can be isolated because they often remain green and are not as severely impacted
by the DB as the palm fronds. Densities of the DB tend to be highest outside of the crown
region. The removal of the centre and concentration on the outer parts of the vegetation
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can then lead to a higher probability of detecting the impacts of DB and categorising
the infestation levels accurately. The images can also be used by classification techniques
(e.g., unsupervised) to detect stages for which users do not have ground truth data.
Image segmentation techniques
Image segmentation techniques can be used to extract information on palm canopies.
The crown information can be used to calculate the density of palms per unit. This
information can then be applied as part of a GIS-based spatial analysis to answer questions
about whether infestation levels are linked to the density of palms or not. The crown
information could also be used to determine the random or systematic nature of farms.
This information can be further used in GIS-based analyses to answer questions
about whether or not randomly situated plants have a higher risk of infestation than
non-randomly situated plants. Such information would be useful for determining the
optimal row spacing. Research published in the literature suggests that those plantations
that have wide row spacing have a lesser likelihood of DB infestations (Ali & Hama, 2016).
The row spacing data extracted from satellite imagery could thus be used to confirm the
relationship between row spacing and infestation levels.
Image fusion
Image fusion is a technology that merges two or more images of the same area collected by
different sensors or at different wavelengths. For example, merging a 2.5 m multispectral
image with a 0.7 m panchromatic image can be done to capitalise on the advantages
of both image sets. The panchromatic images have very good spatial resolution but lack
the multiband information that the 2.3 m multispectral image provides. Thus, the
advantage of using image fusion for change detection is that fusion can allow for both
high spatial and spectral resolutions, which will enable users to extract high quality land
cover/vegetation information (Boryan et al., 2011;Simone et al., 2002). Image fusion
techniques such as the HSV (hue, saturation, value), Brovey, Gram-Schmidt, and
principle components methods can be used to compare the accuracy and distortion levels
of images (e.g., 8-band Worldview images).
ACCURACY ASSESSMENT
Accuracy assessment is an important part of any classification algorithm process, and it
should be undertaken for every project because it is difficult to know how accurate a
classification is without an accuracy assessment. The accuracy of a classification is usually
assessed by comparing the classification with some reference data that is believed to
accurately reflect the true land-cover. Reference data may include ground truth data,
higher resolution satellite images and maps derived from aerial photographic
interpretations. However, in the case for all reference data, even ground truth data,
these data sets may also contain some inaccuracies. More information about accuracy
assessments can be found in Al-Kindi et al. (2017b),Congalton (2001),Foody (2002),
Gibbs et al. (2010),Hirano, Welch & Lang (2003),Huang et al. (2007), and Hughes,
McDowell & Marcus (2006).
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Positional accuracy methods can be used to provide an assessment of the differences
in distance among a sample of locations on the map and those same locations on a
reference data set. This same basic process can be used in assessing the thematic accuracy
of a map, and it involves a number of initial considerations such as taking into account the
sources of errors and the proper selection of classification systems (Congalton & Green,
2008). Determination of the thematic accuracy is more complicated than that of the
positional accuracy.
This is due to the size requirements for sampling thematic accuracy assessments,
which are larger than those for positional accuracy assessments. An error matrix technique
can be used to compute the thematic accuracy, and the error matrix can be generated by
using reference data and correct or incorrect designations; one can also use qualifiers such as
good, acceptable and poor to produce a fuzzy error matrix. Additionally, there are a number
of analysis techniques that can be performed using the error matrix, such as the Kappa
analysis. The Kappa analysis can be used to test statistically whether or not one error matrix
is significantly different than another (Goodchild, 1994).
MODELLING THE SPATIAL RELATIONSHIPS BETWEEN
INSECT INFESTATIONS AND THE ENVIRONMENTAL AND
CLIMATE FACTORS
While RS techniques focus on visual and pre-visual detection and mapping, spatial
analytical techniques can be used to evaluate correlations, identify important variables,
and develop predictive models. Spatial statistics functions and tools have made it possible
to implement state-of-the-art spatial autoregressive techniques to investigate many
research problems (e.g., insect pest) (Carrie
`re et al., 2006;Carruthers, 2003;Wulder et al.,
2006). Advances in spatial analytical techniques software, such as ArcInfoÒ, have greatly
reduced the time for estimating spatial parameters. For example, regression analysis allows
users to examine, model, and explore spatial relationships in order to better understand
the factors behind the observed spatial patterns. It also allows users to predict hypotheses
based on understanding of these factors. There are three main types of regressions,
namely, linear regression, local regression, and logistic regression (Liebhold, Rossi & Kemp,
1993;Wichmann & Ravn, 2001). Linear regression can be used to predict the values of y
from values of x
i
as follows:
y¼aþb1x1þb2x2þ::: þbnxn(1)
where yis the dependent variable, x
i
represents the independent variables i, and b
i
,:::,bn
are the regression coefficients. However, this requires several assumptions about the error,
or residuals, between the predicted values and the actual values (Miles & Shevlin, 2001).
Some errors are related to a normal distribution for a set of independent variables, while
others are related to the expected mean value of zero. Linear regression has been used to
model wildlife home ranges (Anderson et al., 2005) and soil moisture (Lookingbill &
Urban, 2004;Lema, Mendez & Blazquez, 1988). According to Harris et al. (2010), local
regression or geographically weighted regression (GWR) analysis can be used to predict
information for every known point in order to derive a local model. Moreover, parameters
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 19/36
for this method can include variations in space, thereby providing a basis for exploring
non-stationary spatial relationships. The logistic regression method can be applied to
model spatial relationships between features, such as when the dependent variable is
categorical (e.g., presence or absence data) and when the independent variables are
categorical, numeric, or both (Menard, 2002). The advantage of using the logistic
regression is that it does not require the same set of rigid assumptions as required by linear
regression.
Various studies have involved the use of autoregressive models to investigate the
relationships between insect infestations and factors that are based on environmental
information. Munar-Vivas, Morales-Osorio & Castan
˜eda-Sa
´nchez (2010) combined
environmental information, spatial data, and attribute data in GIS-based maps to assess
the impact of Moko disease on banana yields in Colombia. Specifically, they used a
regression model to investigate the relationship between infested areas and distances from
the Moko foci to cable-ways and drainage channels. Coops et al. (2006) studied the
associations among the likelihood of occurrence, forest structure and forest predisposition
variables using regression tree models. They found through modelling that location and
slope were the major factors driving variations in the probability of red tree outbreaks.
The GWR model has been used to detect high-risk infestations caused by mountain pine
beetle invasions of lodge-pole pine forests over large areas (Robertson et al., 2008).
It is important to start by using single variables to develop correlations before moving
to more complicated predictive models and regression analyses, where all factors are
incorporated to investigate which combination of factors is most conducive to the
survival and spread of insects or diseases. In our study, for instance, GWR could be used to
model the correlation between DB infestation and meteorological variables such as
humidity, rainfall, temperature, wind direction, and wind speed; GWR could also be
applied to model the correlations between DB infestations and environmental variables
including soil type, slope, aspect ratio, ecology, soil salinity, and solar radiation.
Additionally, autoregressive models could be used to investigate the relationships between
DB infestations and human practices such as irrigation, plantation systems, insecticide
use, and methods of spraying (Al-Kindi et al., 2017a).
Suitability model for detecting and investigating insect infestations
All of the methods used to study the relationships between dependent and independent
variables discussed previously are traditional statistical methods, which sometimes might
not reflect the complicated relationships between infestations and environmental factors.
In particular, ecological and geographical environments represent complex systems in
which individual elements interact to create complex behaviour, and consequently,
complex methods such as ANN, Cellular Automata (CA), and multi-agent systems (MAS)
may be better suited to study the relationships and conduct factor analyses in insect
infestation or disease detection research and to perform spread simulations (De Smith,
Goodchild & Longley, 2007).
Numerous suitability models have been proposed to identify locations that have a
particular set of characteristics.
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In Hernandez et al. (2006), the authors compared four different models (BIOCLIM,
GAPP, DOMIN, and MAXENT) and found that MAXENT was most capable for
producing useful results with small sample sizes and minimum species occurrences. These
models can also be used to identify areas that are susceptible to risks such as insect
infestations, based on conditions favoured by the species. For example, a relevant study
(Drees et al., 2010) used the habitat suitability selection method to model potential
conservation areas for a rare ground beetle species (using barcode index number or BIN).
Specifically, they used five different data sets to identify several key habitat factors for
Carabus variolosus stress levels. A model was developed in Bone, Dragicevic & Roberts
(2005) by using fuzzy theory to identify areas of susceptibility to Dendroctonus ponderosae
Hopkins in Canada. However, spatial data have unique characteristics that can impact the
results of the model (Crooks & Castle, 2012).
Raster data models are often used for finding and rating suitable locations. The raster
overlay results are formatted in a single layer of suitable versus unsuitable cells, rather than
in a vector layer with many polygons and an attribute table, which contains the attribute
values for each of the polygons. There are two ways to create raster suitability layers.
The first approach is to query the individual sources to create the suitability layer. The
query can be used to create a suitability layer with two values, ‘1’ for cells meeting all
criteria of a suitable habitat, and ‘0’ for the others. Because the layer consists of only two
values, one indicating suitable and the other unsuitable cells, they are called binary
suitability layers. Binary processing however is not always necessary. Combined with other
evaluation models, suitability mapping can be achieved by overlaying directly or by post
processing the overlay results. Figure 4 shows a process that could be used to find suitable
location conditions (habitat) for insects such as DB by using a raster method overlay.
The uncertainty that results from geo-processing operations, demonstrates that
sophisticated spatial analysis cannot be achieved using traditional, deterministic
geoprocessing methods alone (Goodchild & Glennon, 2010;Zhang & Goodchild, 2002).
Figure 4 Schematic of the process that can be used to model the suitable location for Dubas bug
infestations.
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Fuzzy logic is a superset of Boolean logic and has the ability to handle uncertainty in data
that arises from vagueness instead of randomness alone (Li et al., 2010).
Fuzzy logic can be utilised to extract information from high resolution RS data and
combined with a raster-based spatial data to produce maps representing the spatial
variation of vulnerability to pests across a landscape (Zhang & Foody, 2001). This method
also allows for partial association with one or more classes, meaning that objects may be
represented by a value based on a membership function between ‘0’ and ‘1’ (Li & Zhao,
2007). The membership function of an element xbelonging to a fuzzy set A is computed by:
A:U!½0;1(2)
where Uis the universal set of x. The concept of fuzzy sets has also been employed for
defining the spatial and attributes characteristics of geographic objects (Burrough & Frank,
1996;Wang & Hall, 1996). The results of such analysis can be rendered directly into a
decision framework via maps, tables, and charts. The results can also be used in further
analyses or to provide additional understanding of the problem.
The challenge in any particular area of study is the geographical extent and the
resolution of analysis, which is determined by the phenomenon being modelled. To
achieve validity, researchers must ensure that they are using accurate and current data
whenever possible. If the data are from one’s own organisation, one can rely on data
quality controls that are in place. Data quality should be checked against alternate sources
if possible in order to ensure it meets the requirements of the analysis. Assessing the
quality of data will provide guidance to predicting what level of confidence can be
attributed to the result of the modelling work.
PROOF-OF-CONCEPT CASES
The first proof-of-concept case is published in Al-Kindi et al. (2017a). In this paper, we
analysed a set of IKONOS satellite images collected in 2015 on our study area (5 m spatial
resolution) by processing them using chosen image segmentation functions and extracted
density information of the palm canopies. The techniques used can be found in
‘Image Segmentation Techniques.’
Next, sample locations (i.e., GPS points) were identified in the satellite images by
examining their normalised different vegetation index (NDVI) values. NDVI served as a
surrogate measure of palm plantation density and homogeneity in the neighbourhood
surrounding an image pixel. The relevant techniques can be found in ‘Normalised
Difference Vegetation Index.’
In addition, spatial statistical techniques including GWR, Ordinary Least Squares and
Exploratory Regression (corresponding implementations included in ArcGISTM) were
applied to study the correlations between various human factors related to date palm
farming and the distribution density of the DB. These techniques have been reviewed in
‘Modelling the Spatial Relationships between Insect Infestations and the Environmental
and Climate Factors.’
The second proof-of-concept case is published in Al-Kindi et al. (2017b). In that paper,
we applied spatial statistical techniques to model spatiotemporal patterns of DB on date
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 22/36
palm in north of Oman. Data on the DB infestations and their impact were collected
through observations of palm trees from 2006 to 2015 by the Ministry of Agriculture and
Fisheries of the Sultanate of Oman. The techniques used can be found in ‘Modelling the
Spatial Relationships between Insect Infestations and the Environmental and Climate
Factors’ and ‘Data Requirements for Crop Management.’
CONCLUSION
In this review, a variety of spatial information technologies, including remote sensing and
spatial statistical methods, have been shown to be useful in areas of research involving
insect infestations worldwide. Environmental and climatic conditions are very important
in determining the distribution and survival of any species, including the DB, which is a
problematic pest in date palm plantations. We argue that most of the current research on
DB has focused on its ecology, biology, or control mechanisms only. There has been very
limited research linking the presence/absence, density, spatial, and temporal distributions
of DB with environmental, meteorological, and human practices that promote its
development, prevalence, and spread. Understanding the distribution and affinity of the
DB in terms of these variables and mapping of the data can play a key role in its control
and management, as well as resource allocation.
ABBREVIATIONS
AFRI aerosol free vegetation index
ANN artificial neural network
AI artificial intelligence
ASTER advanced space thermal emission and reflection radiometer
AVHRR advanced very high resolution radiometer
AVIRIS airborne visible/infrared imaging spectrometer
ALOS advanced land observing satellite
AC atmospheric correction
ARVI atmospherically resistant vegetation index
BIO bare soil index
CA cellular automata
CART classification and regression tree
CIR colour-infrared
DEM digital elevation model
DB Dubas bug
DVI different vegetation index
NDV normalised different vegetation
NDMI normalisation different moisture index
FS fluorescence spectroscopy
GIS geographical information systems
GEMI global environmental monitoring index
GR geometrical rectification
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 23/36
GWR geographically weighted regression
HTR humid-thermal ratio
IPM integrated pest management
IR image registration
LIDAR light detection and ranging
LAI leaf area index
LWC I leaf water content index
MIR mid-infrared
MODIS moderate resolution imaging spectroradiometer
MAS multi-agent system
MNF minimum noise fraction
MSS multi-spectral scanner
NAIP national agricultural imagery programme
NIR near-infrared
OBIA object-based image analysis
PCA principal components analysis
PVI perpendicular vegetation index
REPD red-edge position determination
RS remote sensing
RVI ratio vegetation Index
SAVI soil adjusted vegetation
SCI shadow canopy index
SPOT Satellite Probatoire l’Observation de la Terre
SVM support vector machines
TM thematic mapper
TC topographic correction
UAV unmanned aerial vehicle
VIS visible
VI vegetation indices
ACKNOWLEDGEMENTS
We thank the Unit of Cultivations Protection in the Ministry of Agriculture and Fisheries
of the Sultanate of Oman for providing the data on DB infestations in the study area.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
The authors received no funding for this work.
Competing Interests
Nigel R. Andrew is an Academic Editor for PeerJ.
Al-Kindi et al. (2017), PeerJ, DOI 10.7717/peerj.3752 24/36
Author Contributions
Khalifa M. Al-Kindi conceived and designed the experiments, performed the
experiments, analysed the data, wrote the paper, prepared figures and/or tables,
reviewed drafts of the paper.
Paul Kwan conceived and designed the experiments, analysed the data, wrote the paper,
reviewed drafts of the paper.
Nigel R. Andrew conceived and designed the experiments, analysed the data,
contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the
paper.
Mitchell Welch conceived and designed the experiments, analysed the data, contributed
reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper.
Data Availability
The following information was supplied regarding data availability:
The research in this article did not generate any data or code (literature review).
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