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Identification of Cannabis plantations using hyperspectral technology
Article in Israel Journal of Plant Sciences · December 2012
DOI: 10.1560/IJPS.60.1-2.77
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Israel Journal of Plant Sciences
Vol. 60
DOI: 10.1560/IJPS.60.1-2.77
2012
pp. 77–83
Identification of Cannabis plantations using hyperspectral technology
Ilan Azaria,a Naftali Goldschleger,b and Eyal Ben-Dora
Department of Geography and Human Environment, P.O. Box 39040, Tel Aviv University, Tel Aviv 69989, Israel
b
Soil Erosion Research Station, Ministry of Agriculture, Emek-Hefer 40250, Israel
a
(Received 10 October 2011; accepted in revised form 7 January 2012)
Honoring Anatoly Gitelson on the occasion of his 70th birthday
Abstract
Drug use, mainly of Cannabis, has dramatically increased over the past two decades,
calling for efficient drug monitoring and prevention tools. This study evaluates the
use of ground-based hyperspectral detection for surveying and mapping Cannabis
cultivations. The ability to identify Cannabis plants at high spectral resolution using a ground-based hyperspectral detector (imaging spectroscopy sensor) outdoors
was measured using the AISA Eagle hyperspectral detector at 400–1000 nm wavelengths, at a distance of 75 m. Analysis of the measured data by image-processing
and statistical variation revealed that the spectral characteristics of Cannabis are
unique only within a wavelength range of 500–750 nm. It is important to notice that
variation was tested only with two species, and background was unique. Error in
classification (false alarm) was found between Cannabis canopy and citrus canopy:
15% of Citrus was classified as Cannabis.
Keywords: hyperspectral remote sensing, field spectrometer, spectral resolution,
spatial resolution, Cannabis
Introduction
Drug use, mainly of Cannabis, has dramatically increased over the past two decades. Cannabis remains
the most widely used illicit substance in the world.
Globally, the number of people who used Cannabis at
least once in 2008 was estimated at between 129 and
191 million, or between 2.9% and 4.3% of the world
population aged 15–64 (World Drug Report 2007).
Drug prevention calls for accurate and updated information on Cannabis fields, and the demand for monitoring
and detection tools that can cover large areas of drugoriented plants (e.g., Cannabis) has increased accordingly. In this research, a ground hyperspectral camera
was used to assess the appropriateness of hyperspectral
technology for this task.
The main aim of our research was to develop hyperspectral methodology that will make it possible
to discriminate Cannabis crops from the surrounding
vegetation. This methodology was based on theoretical
knowledge acquired from studies analyzing the spectral
properties of plants, and on comprehensive measurement of Cannabis canopy reflectance. The hypothesis
was that the spectral properties of Cannabis leaves differ from those of other vegetation’s leaves according to
the chemical and geometrical properties of this plant.
Hyperspectral remote sensing for plant discrimination
Kalacska et al. (2007a) made use of hyperspectral
remote sensing technologies to map lianas growing
in the tropical forests. That study involved measurements using an Analytical Spectral Devices (ASD) field
spectrometer and a hyperspectral image with a spatial
*Author to whom correspondence should be addressed.
E-mail: ilan@sensingis.com
© 2012 Science From Israel / LPPltd., Jerusalem
78
resolution of 1 m and 210 spectral channels between
400 and 2500 nm. Kalacska et al. (2007b) used those
hyperspectral images to map the ecological variety of
plant life in tropical forests and to identify “ecological fingerprints”, by employing models that determine
relationships between ground biomass data and various
hyperspectral indices. That study indicated the necessity
of collecting samples in different seasons (spectro-temporal domain) to obtain a reliable ecological fingerprint.
In addition, they found that the best separation method
is wavelet decomposition.
A study conducted in California by Underwood et al.
(2003) showed that invasive habitats can be separated
from endemic plant life by hyperspectral means. Similar findings were obtained by Asner et al. (2008), who
studied plant habitats in the Hawaiian Islands. Mapping
was performed with the 224-channel airborne visible
infrared imaging spectrometer (AVIRIS). Plant-life
characterization was carried out in a number of phases,
including atmospheric correction and image-processing
of the data to obtain reflectance values. Specifically, they
used an atmospheric-correction model, minimum noise
fraction (MNF) sorting to reduce imaging noise, principal component analysis (PCA) statistical processing to
separate the various plant groups, and spectral assortment using continuum removal. Selected wavelengths
were in spectral ranges that allowed water-absorption
identification. MNF assortment was determined to be
the most successful method for isolating the various
groups.
Vrindts et al. (2002) showed the possibility of separating seven families of weeds from corn and beet crop
plantations. Their images were obtained with a SPECIM
hyperspectral ground-based camera over the spectral
range of 400–1000 nm. That study was carried out under laboratory conditions at a distance of 70 cm from
the object. The most significant statistical sorting was
based on eight wavelengths situated in specific spectral
regions associated with the biochemical components
of the leaf. Despite the good results (94% separation
capacity between corn and weeds), it was concluded
that artificial and imbalanced lighting decreases sorting
capacity.
Cannabis cultivation characterization via remote
sensing
Multispectral remote sensing
A study conducted by the United Nations’s Office on
Drug and Crime (UNODC) in Morocco used remotesensing methodologies to map Cannabis fields. In that
project, data were extracted only from multispectral satellite imagery and based on ground validation. The process is complex, and requires a great deal of manpower
Israel Journal of Plant Sciences 60
2012
(Morocco Cannabis Survey, United Nations, 2003).
Spectroscopy and hyperspectral remote sensing
In California, spectral discrimination of the Cannabis plant from other plants using field spectrometry
was studied (Daughtry and Walthall, 1998). In the first
phase, spectral measurements were taken from Cannabis leaves and from other plants co-existing in the
same habitat. Measurements showed a significant disparity between the various leaves at the 550 nm (green
spectrum) and 720 nm (IR spectrum) wavelengths. Of
the wide range of plants measured, the lowest variance
was found between weeds and the Cannabis plant. The
second phase examined specific soil-related influences
on the reflectance spectrum of Cannabis leaf-tops in the
same region. Walthall and Daughtry (2001) examined
spectral reflectance signatures of Cannabis leaves and
canopies using laboratory, field, and airborne (AISA)
systems. They found that the spectral signature of Cannabis contrasts with other landscape signatures. The
spectral bands that included the most spectral separation
for Cannabis from other cover types were at 780, 800,
850, 880, and 900 nm using a Mahalanobis classifier.
Materials and methods
Data acquisition
Cannabis crops were grown in the botanical garden of
Tel Aviv University under optimal irrigation and humidity conditions (with special permission from the Israeli Police Department and the Department of Health).
Plants were grown in pots on a peat–tuff substrate.
Cultivation was conducted in a naturally lit greenhouse,
and no fertilizers were used. Seed germination began
in mid-May, and two months later the first flowers appeared. Hyperspectral measurements were conducted at
this phenological stage.
Remote-sensing instrument: Ground-based
hyperspectral camera
Spectral variance was gauged using a ground-based hyperspectral camera. Spatial variance between Cannabis
canopy, citrus, weeds, and green grass was examined
in unique resolutions, and the canopies of three species
dominate the measurement. The spectral separation
capacity was measured under full field conditions using
only the AISA EAGLE camera. This phase was performed in two corresponding experiments: Acquisition
from a 30-m high building, at an aerial distance of 60
to 75 m taken at 1130 h on 4 Apr 2008. Incidence angle
68º and sun angle was 67º. Pixel size was calculated to
be less than the size of one leaf, and the canopy of each
species was sampled randomly 25 times.
79
Pre-processing of spectral information
The digital number (DN) recorded by the imaging sensor for each spectral band was converted to radiance
and then to reflectance units according to the supervised
vicarious calibration (SVC) method. This method relies
on calibrating imaging values in accordance with target
reflection values (black and white bodies objects), gathered in the imaging area using field spectrometry. The
targets chosen for calibration are black nets of varying
density: 25%, 50%, 75%, and white (Brook and BenDor, 2011). This method decreases errors in the reflectance properties of each canopy species. High spectral
resolution data obtained from the AISA EAGLE sensor
are characterized by sensor noise and external factors
such as diffuse reflectance that modify the spectral
properties of the leaves and the canopy’s reflectance.
Moving average and Savitzky-Golay (Vaiphasa, 2006)
algorithms were used to smooth the hyper-spectral cube
obtained from ground acquisition.
Spectral data analysis
For each one of the species a spectral library composed
of 25 pure pixels (spectral sampling) was built. Spectral
data were reduced and smoothed to 6 nm resolution.
Using that data, three mathematical methods were used
to identify spectral variance between vegetation species
(canopy scale) represented by these spectral libraries.
PCA was used to reduce the spectral data to small matrices containing the most relevant information required
for discrimination. The first derivative was calculated
on the continuum-removed spectra in the red-edge spectral region (680–740 nm); continuum removal normalizes the reflectance spectra, allowing comparisons of
individual absorption features from a common baseline
(Mutanga and Skidmore, 2007). Standardized variation
and standard deviation from the mean of the Cannabis
spectral signature (leaf and canopy level) were used to
score similarity between spectral signals. These three
methods are complementary and allowed us to reduce
errors in the classification. The aim of this step was to
localize the narrow spectral region in which the variation is repeatable and stable, and determine spectral
regions where Cannabis is distinct from other plant
species.
it can be seen that variation between species is determined by three spectral regions: VIS (550–690 nm),
NIR (700–720 nm, red edge), and 800–900 nm. Factors
that contribute to these variations are: the biophysical
attributes of the leaves, the orientation of the leaves,
leaf structure, canopy structure, soil reflectance, illumination and viewing conditions, and water content in
the canopy (Asner, 1998). Walthall et al. (2001) found
the Cannabis canopy bands showing the most spectral
separation from other cover types to be 780, 800, 850,
880, and 900 nm. To more closely analyze the variation
between the Cannabis canopy and other cover types,
three methods were used: standardized variation from
the mean of the Cannabis canopy spectrum, first derivative of continuum removal of canopy reflectance, and
PCA.
Analyzing the spectral variation between Cannabis
canopy other species
Internal spectral variation of the Cannabis canopy
Figure 2 shows the tendency toward dispersion
around the mean and median spectral signature of the
Cannabis canopy between 450 and 950 nm. The spectral region with the largest variance was 740–950 nm,
with a standard deviation of between 0.035 and 0.068
in this region (Fig. 3). The large variation between 740
and 950 nm is explained by some authors as resulting
from photon scattering at the air–cell interfaces within
the leaf spongy mesophyll (Asner, 1998; Kuo-Wei et
al. 2005). In contrast, small variation was found in the
VIS and red-edge regions (690–740 nm). Asner (1998)
explained that the relatively stable optical properties of
the leaves at VIS wavelengths are due to biochemical
Results
Canopy spectral variance analysis from the AISA
EAGLE imaging detector
Looking at the post-processed reflectance spectral signature of Cannabis, citrus, and grass canopies (Fig. 1)
extracted from ground hyperspectral imagery (75 m),
Fig. 1. Mean of spectral signatures of Cannabis, citrus, and
green grass canopies obtained from the ground hyperspectral
AISA Eagle camera.
Azaria et al. / Identifying Cannabis using hyperspectral technology
80
characteristics resulting from the presence of biologically active pigments.
Fig. 2. Dispersion tendency around mean and median spectral
signature of Cannabis canopy.
Spectral variation between species
Figure 4 illustrates the dispersion around the standardized deviation of the mean canopy spectral signature. In the VIS region between 500 and 640 nm, both
green grass and citrus canopy Z-score (distance from
0) values are negative and far from the Cannabis mean
(Table 1). It is important to note that between 750 and
950 nm, the citrus canopy and Cannabis canopy are
similar (–1 < Z-score < 0). In contrast, between 750 and
950 nm, the green grass canopy is highly heterogeneous
relative to the Cannabis canopy (Z-score > 2).
PCA analysis of hyperspectral camera (75 m) data
In the PCA of hyperspectral data acquired from 75 m
(Fig. 5), PCA1 explains only 62% and PCA2 26% of
the variation, mostly due to the resolution of the target.
In this study, the size of the target was constant, so we
assume that environmental factors reduced the ability
to discriminate Cannabis from the other species. The
major regions contributing to the spectral variation between species were 520–580 and 705–720 nm (n = 50,
the entire series is represented).
First-derivative analysis of red-edge region (680–
740 nm)
Fig. 3. Standard deviation of Cannabis canopy spectral signature (n = 25).
Figure 6 shows the first-derivative reflectance on continuum-removed spectra of Cannabis, citrus and green
grass canopies. The first-derivative peak for the Cannabis spectrum was located at 705 nm, at 715 nm for citrus
Fig. 4. Dispersion around the standardized deviation of mean canopy spectral signature.
Israel Journal of Plant Sciences 60
2012
81
leaves and at 720 nm for green grass. Figure 7 shows a
low standardized variation of the median signature from
the mean for Cannabis, green grass and citrus. The box
Table 1
Spectral variation range for each spectral region expressed as
Z-score values
Z-score
–1.5 < Z < –3.8
–1.5 < Z < –3.4
1.5 < Z < 5.3
–2 < Z < –4.6
–1.7 < Z < –2.3
Wavelength (nm)
Species
515–617
700–724
740–950
512–588
695–720
green grass
green grass
green grass
citrus
citrus
plot in Fig. 8 explains the variation in the location of the
first-derivative peak more clearly. Variation in the location of the maximum peak in the red edge is related to
nitrogen concentration (Mutanga and Skidmore, 2007).
Discussion
Hyperspectral remote sensing gives continuous spectral signatures of materials. For green vegetation, this
technology enables extracting information on several of
the plant’s chromophores. Chlorophyll absorbs strongly
in the blue (450 nm) and red (670 nm) regions, reflects
strongly in the NIR region (700–1300 nm), and shows
strong water absorption at around 1400 and 1900 nm
Fig. 5. PCA components 1 and 2, spectral libraries of foliage from Cannabis and citrus, retrieved by hyperspectral imaging at
75 m.
Fig. 6. First-derivative reflectance on continuum removed spectra of Cannabis, citrus and green grass canopies. Cannabis curve
shifts to short wavelengths.
Azaria et al. / Identifying Cannabis using hyperspectral technology
82
Fig. 7. Median variation around mean spectrum for citrus,
Cannabis, and green grass canopies.
Fig. 8. Box plot of first-derivative peak for Cannabis, citrus,
and green grass canopies. First derivative of citrus spectrum central tendency is exactly at 715 nm; n = 25 for each
species.
(Govender et al., 2007). Although these are common
spectral properties for all green vegetation, a small
variation exists in the biophysical and biochemical statuses of plants (Jacquemoud et al., 1996). Moreover, the
structure of the mesophyll layer, the external structure of
the leaves, and the plant’s architecture are major factors
determining spectral variation among species (Zhumar,
1999; Walthall and Daughtry, 2001). From the results
obtained in this study, it was possible to see variations
only in the VIS–NIR region (512–580 and 705–720 nm)
as compared to weeds and citrus.
Israel Journal of Plant Sciences 60
2012
The spectral variation in these specific narrow
wavebands can be explained by the physiological and
biochemical variations between the species examined
(Cannabis, citrus, and grass). In general, photosynthetic
activities are not similar between species, and this may
be the case here. In addition, ecological and genetic
factors can also contribute to the abovementioned variations (Martin et al., 2007). However, although a specific investigation of the physiological and biochemical
variations of Cannabis was beyond the scope of this
study, our results enabled discrimination between the
examined species.
Three mathematical tools were used to analyze the
variation between groups: first derivative, first derivative of the continuum removal, and PCA. These methods are commonly used to discriminate between groups
in a highly clustered environment. Although there are
other applicable methods, the success of the methods
used herein in discriminating between Cannabis, citrus,
and weeds suggests that the spectral information in the
VIS region is reliable and solid, and enables locating
errors and trends. For example, in Fig. 5, 15% of citrus
was classified as Cannabis, and Fig. 4 shows this similarity as a distance (Z-score) from the mean of the Cannabis spectrum. The origin of this error is explained by
similarity between Cannabis and citrus in the 720–950
nm spectral region. This region is characterized by internal scattering of the mesophyll layer and is not favorable
for classification, even if it is statistically significant. In
general, we can say that despite some restrictions in the
spectral discrimination of green vegetation across the
VIS–NIR–SWIR spectral region and the spectral similarity of Cannabis to the other plants tested, significant
information could be culled from the major chlorophyll
bands enabling spectral detection of Cannabis.
Acknowledgments
This research was funded by the Israel Anti-Drug Authority.
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