Relative value of radar and optical data for land cover/use mapping: Peru example

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Relative value of radar and optical data for land
cover/use mapping: Peru example
Barry Haack & Ron Mahabir
To cite this article: Barry Haack & Ron Mahabir (2017): Relative value of radar and optical data
for land cover/use mapping: Peru example, International Journal of Image and Data Fusion, DOI:
10.1080/19479832.2017.1398188
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RESEARCH ARTICLE
Relative value of radar and optical data for land cover/use
mapping: Peru example
Barry Haack and Ron Mahabir
Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA
ABSTRACT
This study determined using divergence measures the best indivi-
dual and combinations of various numbers of bands for six land
cover/use classes around the city of Arequipa, Peru. A 15 band
data stack consisting of PALSAR L-band dual-polarised radar,
Landsat optical data, as well as six variance texture measures
extracted from the PALSAR images, was used in this study.
Spectral signatures were obtained for each class for the diver-
gence examination. The band having the highest separability
was the Landsat visible red band followed by the two largest
window PALSAR texture measures. The best three band combina-
tion included three very different data types, Landsat visible red,
near infrared and the PALSAR HH variance texture from a
17 × 17 pixel window. There was no need based upon the diver-
gence values to use more than five bands for classification.
ARTICLE HISTORY
Received 16 May 2017
Accepted 20 October 2017
KEYWORDS
Separability; divergence;
PALSAR; Landsat;
dual-polarisation;
sensor integration
1. Introduction
The last several decades have witnessed a drastic increase in the number of applications
and types of analysis that consume spaceborne remote sensing images as a primary
source of data. This has been in part driven by the continual deployment of spaceborne
systems and their sensors, and their ability to capture a wider spectrum of information
with increased spatial, spectral, radiometric and temporal resolution. As a result, the
deployment of satellites into space is now recognised as a lucrative industry. For
example, a recent report by the Space Industry Association (2016) stated that as of
2015, global revenues coming from the satellite industry amounted to USD $208.3
billion. Such revenues (in billions of US dollars) can further be divided into satellite
manufacturing ($16.6), launch industry ($5.4), ground equipment ($58.9) and satellite
services ($127.4). The amount of revenue generated by the satellite industry is not
surprising given that the use of satellite images has grown from mainly being used as
a reconnaissance tool, privileged to only few, to that of playing a major role in
supporting the monitoring of earth resources. This, in addition to the increasing number
of satellite image providers making their images freely available, along with the
increased commercialisation of fine spatial resolution satellite imagery by the private
sector, has greatly expanded the number of satellite image users.
CONTACT Ron Mahabir rmahabir@gmu.edu Department of Geography and Geoinformation Science, George
Mason University, Fairfax, VA, USA
INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION, 2017
https://doi.org/10.1080/19479832.2017.1398188
© 2017 Informa UK Limited, trading as Taylor & Francis Group
Downloadedby[100.36.116.186]at04:4616November2017

When it comes to the mapping of features that cover large areas, for example state,
national, regional or even global scales, remote sensing has typically been relied on as a
primary source of data for mapping and validation purposes. One such application is the
mapping of land cover/use (LCLU) features. LCLU plays an important role in informing
policy decisions for a wide range of applications, including, planning infrastructure,
reducing pollution and expanding food production (Idol et al. 2015a). Up-to-date
information on LCLU is also important for achieving goal 15 of the SDG, which is ‘to
protect, restore and promote sustainable use of terrestrial ecosystems, sustainably
manage forests, combat desertification, and halt and reverse land degradation and
halt biodiversity loss’ (United Nations 2017). Yet, for many places around the world,
obtaining up-to-date information on LCLU continues to be a challenge.
Traditionally, LCLU relied upon the use of images captured from multispectral sensors
onboard spaceborne systems such as the Landsat missions. While such information has
been extensively used for mapping LCLU (e.g. Homer et al. 2007, Griffiths et al. 2014), the
wavelengths of these systems are unable to penetrate clouds and haze conditions. This
presents an issue for regions around the world where persistent cloud cover (e.g. tropical
and high latitude areas) and atmospheric disturbances (e.g. pollution) are typical.
Furthermore, optical sensors such as the Landsat-8 Operational Land Imager passively
collect land surface information, which means that they rely on the Sun’s energy.
Most spaceborne radar sensors actively collect land surface information at much
longer wavelengths, which overcome the previous mentioned issues with optical sen-
sors (Idol et al. 2015b). Other radar systems (e.g. Bistatic Synthetic Aperture Radar)
usually dissociate receiver and antennae to two separate spaceborne systems (Tan
et al. 2005). Compared to multispectral imagery, radar data are much more complex
to process and extract information from, along with such imagery being much less
intuitive to interpret (Richards 2013). These factors, in addition to the limited availability
of spaceborne radar data in comparison to multispectral data, have led to the much
lower usage of radar data in LCLU mapping applications.
One consideration when using radar data for LCLU mapping is which polarisation
channel/band to use. Traditionally, only one band was used to collect radar information,
which limited the amount of backscatter being received at the sensor. In this case, a
single polarisation was transmitted and a single polarisation was received, producing an
image that was either horizontal–horizontal (HH), or vertical–vertical (VV) polarisation.
Newer quad-polarisation systems can capture all four polarisations (HH, HV, VH and VV),
permitting more surface scattering to be imaged. This is important since each polarisa-
tion can be used to capture different physical properties of land surface features (Haack
and Mahabir 2017). Such information can work in tandem with optical imagery to
improve the accuracy of LCLU maps. As a result, various studies have explored the
fusion of optical and radar imagery and have shown improved LCLU mapping accuracies
compared to using each type of data on its own (e.g. Amarsaikhan et al. 2012, Pereira
et al. 2013, Idol et al. 2015a, Xiao et al. 2016).
However, as highlighted in a recent review by Joshi et al. (2016), few studies have
explored the fusion of optical and radar data for LCLU mapping (50 studies between
1996 and 2016). That study also showed that the geographical distribution of such
research has been highly skewed. For example, 17 of the 50 studies were in Europe, this
was in comparison to only 6 studies located in South America (5 in Brazil and 1 study in
2 B. HAACK AND R. MAHABIR

Ecuador). Evidently, there is a pressing need for the expansion of research examining the
fusion of optical and radar data in other unexplored areas. Such research will not only
assist in better understanding the potential benefits of radar as it applies to LCLU
mapping applications but also serve as a baseline for the design of the next generation
of earth observation systems for LCLU monitoring at regional and global scales.
While the fusion of optical and radar data has certainly led to improvements in LCLU
mapping accuracies, this has also resulted in the use of large multisensor data stacks.
Thus, a new challenge has emerged wherein the determination of what bands should be
used for analysis and mapping has become an issue. Prior to the availability of free
sources of remote sensing data, such as the Landsat missions, the persistence of clouds
making some images unusable and the cost of fine resolution imagery, these factors all
assisted in limiting the amount of image bands used for remote sensing applications.
Some researchers limit the amount of data to two or even three bands from a single
sensor based on familiarity or the availability of data (Frate et al. 2008, Al-Tahir et al.
2009). However, this has resulted in missed opportunities for understanding the bio-
physical properties of different LCLU features as observed from using multiple bands
and from different sensors.
The determination of the most suitable image bands also has importance in the visual
analysis of remote sensing imagery, still one of the most widely used approaches in
digital image analysis. This often requires the reduction in the number of bands to 3 to
be used for creating a red, green, blue (RGB) true or false colour image composite. Such
spectral three band combinations are widely used to highlight various LCLU features
such as soil, agriculture and urban areas (Harris Geospatial 2013). Further, if only three
image bands were to be considered, this would mean that the best RGB image would be
one of six different RGB combinations of these three bands, that is, an image can be
either RGB, RBG, GRB, GBR, BRG or an BGR composite. However, as the number of image
bands increases, the number of RGB combinations increases factorial. In such cases, the
manual determination of the best three band combination for visual analysis becomes
very challenging.
Besides visual analysis, the determination of what image bands to be used in analysis
can be helpful in other ways. One such benefit is the removal of redundant data, which
leads to shortened processing times and improvements in feature detection (Landgrebe
et al. 2001, Richards 2013, Li et al. 2014). Also, as the number of image bands increases,
the number of observations required to train remote sensing classifiers increases expo-
nentially, a paradox better known as the Hughes’ Phenomenon (Hughes 1968). For
features that usually occupy very small areas in relation to the full extent of the study
area being examined, such skewed distributions may have impacts on their classification
and mapping. In fact, recent work by Haack and Mahabir (2017) suggests that some
remote sensing classification algorithms, beyond a certain optimum number of bands,
may lead to degradation in classification accuracy. That study showed that no more than
six bands were needed for a viable classification using a maximum likelihood classifier.
Similarly, Le Bris et al. (2014) showed that only five to six image bands were needed for
LCLU classification when using a support vector machine. The results of these studies
and others are no doubt partly linked to the properties of the LCLU features being
investigated. Such properties include the number of classes to be classified, the spectral
separability within and among classes, the preprocessing steps involved in preparing the
INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION 3

image data prior to analysis and morphological characteristics of features being mapped
in relation to their environment. Moreover, knowledge of what image bands are useful
for various applications plays an important role in the design and deployment of future
spaceborne sensors. In this case, image bands that are less likely to be used may be
discontinued and their resources used to capture other wavelengths in the electromag-
netic spectrum.
The need to determine what image bands to be used prior to image classification has led to
the development of several statistical approaches to be used for this purpose. These
approaches can be divided into two main groups: feature extraction and feature selection.
Feature extraction approaches transform a set of image bands onto a lower dimensional space
producing a smaller number of bands. In this case, a statistical relationship, usually linear,
among images bands (e.g. variance) is used to remove unnecessary or unwanted data.
However, as in the case of principal component analysis (PCA – Pearson 1901), the resulting
image bands are not always intuitive to interpret. Feature selection approaches, on the other
hand, result in the selection of a subset of the original bands (Deliot and Kervella 2010). One
such approach is the use of various spectral separability measures, which include distance
measures such as divergence, transformed divergence, Bhattacharyya distance and Jeffries–
Matusita distance. Excellent reviews of these measures can be found in Swain and Davis (1978),
Latty and Hoffer (1981) and Swain et al. (1981), with studies having significant impact in this
area including work by Swain and King (1973), Goodenough et al. (1978) and Mausel et al.
(1990) just to name a few.
While studies using different methods to reduce the number of image bands for
classifying remote sensing imagery have been ongoing, they have mainly concentrated in
developed countries. At these locations, data are usually much more available and acces-
sible in comparison to developing countries. This is important, since as Mahabir et al. (2016)
suggests, developing countries are experiencing much more rapid changes due to increas-
ing urbanisation, political and civil unrest occurring in many such places. Also, while one
may argue that large-scale mapping initiatives exist that overcome such data poverty issues
in the developing world (e.g. Bontemps et al. 2011, Jun et al. 2014), such studies use
nomenclature that may not always be suitable for some locations due to unique cultural
and socio-economic factors impacting the specific landscapes. Such factors in turn may lead
to some separability measures being more suitable for some areas compared to others. This
is important since the use of one measure that may be unsuitable in other locations may
impair our understanding of the underlying LCLU processes taking place at those locations.
The aim of this study was to assess the suitability of various spectral separability
measures to determine the most suitable image band and band combinations to be
used prior to remote sensing LCLU image classification. The most suitable bands were
further analysed with respect to their spectral and spatial properties and linked to the
underlying characteristics of the LCLU. This is useful since many studies that use
separability measures do so without regard to understanding the underlying character-
istics of those features that make them more or less separable in remote sensing
imagery. Without such knowledge, it is difficult to determine whether separability
measures selected for specific applications are suitable, especially when working with
different types of data, as is the case in this study. Moreover, the application of such
measures in the context of developing countries may provide useful information in
prioritising the collection of data for different application areas. The spectral separability

measures evaluated in this study (discussed further in Section 3) were specifically chosen
because they are available in a wide range of remote sensing software such as Erdas
Imagine (Hexagon Geospatial 2017) and PCI Geomatica (PCI Geomatics 2017), as well as
in several open source packages such as the R suite (R Core Team 2017). As a result, this
study, or parts of this study, can be replicated or modified to meet the needs of local or
national stakeholders in the selected study area, as well as in other countries.
This study also evaluates the impact that feature extraction prior to the application of
spectral separability has on the selection the optimum number of image bands for LCLU
classification. The study area used is a site in Arequipa, Peru, which presented an
interesting landscape as discussed further in Section 2. Following this, the data and
methods are described, and the results and conclusions discussed.
2. Study area and data
The site selected for this analysis was Arequipa, Peru. Figure 1 is a Landsat Thematic
Mapper (TM) image for the Arequipa study area. Arequipa is about 100 km from the
South Pacific Ocean (Google Earth 2017) at an elevation of 2350 m (Holmgren et al.
2001). It is a west coast desert with a very hot and dry climate. The average temperature
Figure 1. Arequipa subscene. Landsat Thematic Mapper 12 October 2008. TM bands 2, 3, 4 in BGR.
Subscene width approximately 6.5 km.

during the daytime is 22°C, which drops to about half this temperature at night. The
average precipitation in Arequipa is 104 mm (Carpio and Fath 2011), with the highest
precipitation of about 40 mm occurring in February (Weather and Climate 2016).
Arequipa is the second largest city in Peru with a population of about 1.3 million people
in 2012 (Knoema 2017). The city is very industrialised with irrigated agriculture on the
periphery, including, corn, potatoes and other vegetables (Carpio and Fath 2011).
As shown in the subscene in Figure 1, the study area is dominated by extensive urban
features, with considerable texture. The airport, centre of Figure 1, is dominated in all
directions by urban areas, which also contains some small parks shown with more pro-
nounced rectangular geometric shape and with much smoother texture compared to urban
areas. These parks typically occur in residential neighbourhoods, which is a common feature
of planned neighbourhoods in this region. Some larger park areas occur aswell. The
commercial and industrial areas have visibly larger buildings and coarser texture, especially
to the southwest of the airport. The dominant landform features in the study area are the
Andes Mountains, an extension of which can be seen to the north of the city. To the north of
the airport is a large area of bare soil with similar tones to urban areas, but with much
smoother texture. There is also a very highly reflective salt flat area in the southernpart of
the image. Also visible in the southern portion of the subscene, there are many agriculture
fields, almost all of which are irrigated and are visible as large rectangular patchworks
composed of different tones in Figure 1.
This study utilised both radar and optical data. PALSAR L-band dual-polarisation radar
imagery were collected on 16 October 2008. These data were used to derive 3 variance
texture measures at window sizes of 5 × 5, 11 × 11 and 17 × 17, and for each polarisation
band. The decision to use variance texture was based upon previous research, which
suggested that this measure produces adequate LCLU mapping accuracies (Idol et al.
2015a). Also, following previous work by Idol et al. (2017), the radar data were not
despeckled prior to the extraction of texture since that study showed that this can lead to
lower classification performance. Optical imagery were acquired from the Landsat 5 TM
sensor on 12 October 2009. The acquisition date of the optical data is 1 year after the radar
data. It is often difficult to acquire imagery from different sensors at the same time. This is
due to several reasons including different satellite orbits and in the case of optical imagery
over the study area, large amounts of cloud cover. However, the same season for both data
sets was used and it is unlikely that most LCLU features would have changed significantly
over the 1 year period with the exception of the agricultural classes. The TM imagery had
seven spectral bands from the visible to the thermal infrared regions of the electromagnetic
spectrum. The TM spatial resolution was 30 m for all bands except the thermal band which
was 120 m. This resulted in a 15 band data stack: 2 bands from the original dual polarised
radar, 6 radar derived variance texture measures and 7 bands from TM.
Both the radar and optical data were co-registered and the optical imagery resampled
to the 12.5 m spatial resolution of the original PALSAR data using the nearest neighbour
algorithm. The decision to resample to the spatial resolution of the finest resolution data
was done in order to preserve, as much as possible, some of the radar texture that
would have been smoothened if the original radar imagery were resampled to the 30-m
resolution of the optical imagery. The original TM data were in 8 bit radiometric
resolution and the PALSAR in 16 bit. The combination of different radiometric resolu-
tions was of concern. The basic question is should all the data be rescaled to the same

radiometric resolution and if so, what impact might this rescaling may have on selecting
the best bands and band combinations. To resolve this issue, areas of interest (AOI)
polygons for the same area were obtained for the combined 8 and 16 bit 15 band data
stack. The suitability of this heterogeneous radiometric scale combination was evaluated
using separability measures, the results of which were compared to a similar 15 band
data stack where all data were rescaled to 8 bit. The results were identical in separability
values in terms of ranking by band. Therefore, the radiometric resolution of all data was
consistently set at 8 bit for analysis.
The 15 bands for this study are listed in Table 1 while Table 2 contains various first- and
second-order statistics for each individual band. There are some interesting and, at the same
time, some uncertain components of the subscene statistics in Table 2. Band 1, the TM visible
blue has a higher minimum value, 58, than most other bands. This is most likely because the
data have not been compensated for atmospheric issues, so this band includes additive values
from scattering within the atmosphere. The thermal band 6 has a very high mean value and
low standard deviation, which is typical of thermal data. Band 5, the first mid infrared band,
also has a high mean as a result of a surface area with considerable dry bare soil and urban
features such as rooftops and roads, which reflect highly in these wavelengths. Most of the TM
bands have similar standard deviations. Bands 8–15, which are the original radar and derived
texture values, are stretched across the full8 bit range,0to255,from their rescaling from 16bit.
The means and standard deviations for the two original PALSAR bands, 8 and 9, are
very similar. The texture bands, 10–15, have relatively high means and standard deviations
indicating that there is a considerable amount of texture as would be expected from
urban landscapes. There are also areas of low texture, which would increase the standard
deviation, which is understandable as the subscene includes bare soil and agricultural
areas. The texture means and standard deviations also increase with window sizes.
3. Methodology
This study consists of two components. First, spectral signatures were extracted for
various LCLU features using AOI polygons. The LCLU features examined were bare soil,
residential, salt flat, industrial and two locations for agriculture. These LCLU classes are
Table 1. Arequipa, Peru image bands.
Band Sensor Band Date Description
1 Landsat TM 1 12 October 2009 Visible blue
2 Landsat TM 2 12 October 2009 Visible green
3 Landsat TM 3 12 October 2009 Visible red
4 Landsat TM 4 12 October 2009 Near infrared
5 Landsat TM 5 12 October 2009 MIR-1
6 Landsat TM 6 12 October 2009 Thermal IR
7 Landsat TM 7 12 October 2009 MIR-2
8 PALSAR HH 16 October 2008 Horizontal–horizontal
9 PALSAR HV 16 October 2008 Horizontal–vertical
10 PALSAR Texture HH 16 October 2008 Variance 5 × 5
11 PALSAR Texture HV 16 October 2008 Variance 5 × 5

generalised and limited in number, and as shown in Joshi et al. (2016); such broad
categories are not unusual in LCLU mapping studies that integrate both radar and
optical data. In the case of this research, the use of these broad LCLU classes was
used to first obtain a generalised understanding of LCLU distribution in the study
area. Based upon the results of this study, a more detailed classification using more
LCLU classes, for example different crop types, may be incorporated. Only one signature
was extracted for each LCLU to eliminate the confusion of low spectral separability
between the same cover types to permit interclass comparisons. As large an AOI as
possible was created for each LCLU feature. However, for several classes, this was not
possible due to the distribution of these classes within the study area. The AOI pixel
counts were bare soil (8971), residential (12,287), salt flat (780), industrial (4020), agri-
culture-1 (406) and agriculture-2 (193).
Table 3 contains the spectral signatures for the various LCLU classes. The values for the
TM bands are quite reasonable. There are also significant differences in mean values
between the classes in all TM bands. The salt flat has the highest mean values in all bands.
The two agricultural classes have very similar patterns in mean values across all TM bands
but the second signature has consistently higher values. The visible blue band has
Table 2. Band mean and standard deviations for Arequipa data stack.
Band Minimum Maximum Mean Standard deviation
1 58 255 114 20.4
2 25 162 63 12.8
3 26 198 80 20.0
4 30 186 89 17.9
5 30 255 125 29.0
6 137 197 166 9.7
7 16 245 80 21.8
8 1 255 17 17.1
9 1 255 17 12.4
10 1 255 47 67.3
11 1 255 49 57.5
12 1 255 67 75.8
13 1 255 70 63.9
14 1 255 79 79.5
15 1 255 82 66.1
Table 3. Arequipa Peru class signatures (mean/standard deviation).
Band Bare soil Residential Salt flat Industrial Agriculture-1 Agriculture-2
1 TM blue 118/4.4 121/7.4 159/7.4 143/13.0 73/5.7 89/3.9
2 TM green 67/2.8 66/4.8 101/5.5 84/8.9 38/3.4 52/2.7
3 TM red 88/3.5 84/6.8 145/8.7 115/14.2 37/6.0 53/4.3
4 TM NIR 82/2.9 77/6.4 146/8.4 110/15.1 141/14.1 154/12.3
5 TM MIR 1 130/6.2 114/9.5 251/8.0 181/28.3 84/11.0 125/7.7
6 TM Thermal 185/4.5 166/3.9 170/3.3 168/3.1 146/4.5 151/1.6
7 TM MIR 2 85/3.8 79/6.6 168/11.7 121/17.9 34/6.9 51/4.3
8 PALSAR HH 6/2.5 18/9.0 7/4.6 13/10.5 11/3.9 17/7.7
9 PALSAR HV 6/3.2 27/12.6 6/3.2 16/12.4 10/5.7 20/12.9
10 HH texture 5 × 5 5/4.1 43/43.1 12/11.6 38/50.4 14/19.4 59/58.7
11 HV texture 5 × 5 7/12.2 86/57.3 6/6.9 68/73.1 18/23.8 96/82.1
12 HH texture 11 × 11 5/3.6 59/46.5 14/6.5 57/55.3 21/16.2 145/81.4
13 HV texture 11 × 11 8/10.1 117/47.2 8/4.6 106/74.0 31/21.0 149/68.7
14 HH texture 17 × 17 6/3.2 66/46.0 15/4.7 72/59.4 37/45.9 214/50.7
15 HV texture 17 × 17 8/8.0 132/39.1 8/3.0 131/69.0 37/14.1 188/59.3

relatively high average values in all classes. This is primarily because of the additive effect
of the atmosphere. The first mid-infrared band, band 5, has the largest variation in mean
values from 84 for the agricultural-1 class to 251 for the salt flat. Not surprisingly, the
industrial class has the largest standard deviations because of the variation in features
including different roofing materials, as well as roads and open areas. As would be
expected there is little variation of the mean thermal band values, band 6, which is also
supported by the low standard deviations for this band. The slightly lower band 6 values
for the agricultural classes are likely due to the agricultural land being irrigated.
The radar and radar texture responses are more complicated. The backscatter values
for the original PALSAR, bands 8 and 9, for most classes are quite low. The mean values
for the HV band are slightly higher than the values for the HH band. As expected, the
residential and industrial classes have much larger standard deviations. The second
agricultural class also has higher standard deviations in the original PALSAR data
especially for the HV band, which may be due to direct scattering on recently irrigated
fields. For some classes, their low mean and standard deviation values may make them
more separable amongst the other classes. For example, the bare soil and salt flat LCLU
classes have much lower mean and standard deviation values than the other classes. It is
somewhat surprising that the residential LCLU class has higher mean backscatter than
the industrial. This may be due to features in the study area such as parking lots
between the buildings in the industrial areas, which would lead to reduced mean
backscatter.
The variance texture means vary much more than the original PALSAR backscatter
values. In most cases, the larger window sizes have higher mean texture values and the
HV values are higher than those of the HH band. As expected, the mean texture values
for bare soil and salt flat are very low and those for residential and industrial very high.
The texture values for the second agricultural class are also very high. One concern for
the physically smaller classes is that the larger texture windows may have been influ-
enced by surrounding edge features. Based simply upon the means and standard
deviations, it is apparent that the texture values will provide considerable separability
for some classes.
The second component of this study was to use spectral separability measures to
determine the best single band and band combinations for different numbers of bands.
Four measures, divergence, transformed divergence, Euclidian and Jeffries–Matusita
distance, were applied to the 15 band data stack. Besides divergence, all other measures
became saturated at maximum values for multiple combinations of the same number of
bands, thus providing little effective information. Singh et al. (1999) reported a similar
pattern of saturation with transformed divergence for broad LCLU classes in Northern
India as the number of image bands increased. However, this result was not tested in the
present study and only the divergence measure was used. An additional study might
compare these separability measures in greater depth to better understand why they
became saturated.
Divergence is calculated from the means and covariance matrices of each spectral
class. It is a measure of statistical distance between class or site pairs of interest and
provides information on their spectral separability. This separability is an indirect esti-
mate of the likelihood of correct classifications between groups of different band
combinations (Swain et al. 1981). Divergence is an estimate of information usually

obtained by the time-consuming and expensive process of actual classification and
accuracy evaluations. The formula for divergence is (Swain and Davis 1978):
Dij ¼
1
2
tr½ Covi À Covj
À Á
CovÀ1
i À CovÀ1
j

þ
1
2
tr
!
CovÀ1
i À CovÀ1
j

μi À μj
À Á
μi À μjÞT
i
(1)
where i and j are the spectral signatures of the two classes to be being compared, Cov
and μ are the covariance matrix and mean vectors for classes i and j. Finally, tr and T are
the trace and transposition functions. To assess the divergence separability for more
than 2 classes, 6 in this study, the average divergence between class pairs was used. The
formula for deriving the average divergence between class pairs follows (Richards 2013):
divavg ¼
Xm
i¼1
Xm
j¼1
p wið Þ p wj
À Á
Divi;j (2)
where p wið Þ and p wj
À Á
are the prior class probabilities for class i and class j, m is the
number of classes and Divi; j the values between those classes. The average divergence
values between class pairs were studied and related to the properties of surface features
and their spectral signatures in order to gain a better understanding as to why some
classes are more or less separable in remote sensing imagery.
Average divergence was also used to determine the best bands for LCLU classifica-
tion. This was done for the best single band, then the best two bands, and progressing
for an increasing number of bands, best three, best four etc. In this case, the best N
bands were selected by applying Equation 2 to all combinations of N bands. For
example, if four bands were used, the best three bands would be one of four band
combinations: bands 1, 2 and 3, bands 1, 2 and 4, bands 1, 3 and 4 and band 2, 3 and 4,
with the best three band combinations having the highest divergence value. The best
band and band combinations were determined based on the average divergence value
for all 15 class pairs of spectral signatures. It should be noted that the order in which
image bands were evaluated has no impact on the overall divergence value based on
Equations 1 and 2. In addition to creating a data stack of Landsat and PALSAR data for
this study, an additional examination was carried out to determine the relative value of
PCA. PCA is one of the most widely employed data reduction or transformation methods
in remote sensing as well as in many other disciplines. PCA transforms the original
image to new axes composed of linear combinations of existing bands. This transforma-
tion method is useful in that it reduces data redundancy between bands as well as
accentuates the most important data in the derived components (Balaji and Sumathi
2014). The unique spectral content within the first two derived PCA bands can often
account for as much as 93% of the variability contained within the original bands
(Muchoney and Haack 1994, Campbell and Wynne 2011). However, these results can
vary from one application to the next. PCA was run on the 15 band data stack to create
six band components. These components contained almost 95% of the variance con-
tained within the original data, with only very small increments in variance with the
inclusion of the remaining components.
An initial effort made to use the original 8-bit PCA bands without stretching failed as
there were several spectral signature bands with mean values of 0, which the divergence

method could not process and provided error messages. The Erdas software uses a
mean of 0 and unit value of 1 to standardise each PCA component. This may have
reduced the spectral dynamic range between some classes (Balaji and Sumathi 2014),
while compressing others. As a result, some class means may have tended towards 0,
leading to issues with the divergence function. One possible solution would be to derive
unstandardised PCA components and use these in divergence analysis, a topic that will
be investigated in a follow-up study. Subsequently, the 6 PCA component bands were
systematically stretched to their full 8-bit range, following which divergence analysis was
applied.
4. Results
4.1. Best single band
The best individual band based upon divergence analysis, providing the most separ-
ability between LCLU classes in the study area, was the TM visible red band 2 with an
average divergence value of 1962. Initially, this seems surprising; however, considering
the reflectance statistics for the various LCLU classes in this band as shown in Table 3,
the selection of this band is understandable. The two agricultural classes have low
means, the bare soil and residential moderate values and the salt flat and industrial
classes have very high means in this band. In addition, all class means have low standard
deviations making them much easier to separate in the TM red band compared to the
other image bands.
There is a range of divergence values between the 15 class pairs in the TM visible red
band, with values from 24 to 7262. The lowest divergence value of 24 is between the
bare soil and residential with average reflectance values from Table 2 of 88 and 84.
Other low separability classes are the two agricultural classes (237), and residential and
industrial (358), both of which are logical. The highest divergence of 7262 is between the
salt flat and first agricultural class with mean reflectance values of 145 and 37. Among
the other class pairs of high separability are salt flat and the agriculture-2 (4735), and
industrial and agricultural-1 (3806) LCLU classes. As expected, the high and low separ-
ability values are often intuitive based upon knowledge of the feature characteristics,
their appearance in Figure 1 and the LCLU class statistics in Table 3.
Table 4 contains all bands ranked by divergence values. The second and third most
useful bands are the PALSAR variance textures at the largest window of 17 × 17. There is
a considerable range of divergence values by band from a high of 1962 to a low of 7.
The two lowest bands are the two original PALSAR polarisations. Among the TM bands,
based upon subscene statistics in Table 3, it is understandable why the thermal band 6
has the lowest divergence value. The second most useful TM band is the second mid-
infrared band, band 7, followed closely by band 4, the near-infrared band. The least
useful TM bands are the mid-infrared-1 and visible blue bands.
Based upon the information provided in Table 4, it is clear that the usefulness of
texture increases with larger window sizes. This may be in part due to the broad
categories of LCLU coupled with the moderate spatial resolution of the imagery used
in this research. At fine spatial resolutions, and possibly for a more detailed LCLU
classification scheme, the increased variations between pixels tones may lead to

increased ambiguity. However, further testing would need to be done to prove or
disprove this hypothesis. Notwithstanding, the statistics in Table 3 generally assist in
understanding the observed pattern for texture bands. For those classes with
expected high texture, residential and industrial, larger texture window sizes have
higher means and little change in standard deviations, thus increasing separability.
Typically, this increase in divergence with larger window sizes would be expected to
be reversed at some point where continually increasing window size begins to be less
useful, this being a function of pixel resolution and the relative size of landscape
features being examined.
4.2. Best two bands
The best two bands were the TM visible red and the RADARSAT HH variance texture at
17 × 17 window size (3832), which were the two best single bands. Intuitively, this seems
reasonable as these two bands represent very diﬀerent types of remote sensing data by
wavelengths. Also, whereas the TM data are providing information on the spectral
properties of LCLU features, the derived texture measure is providing contextual infor-
mation on the spatial relationship between image pixels. The highest class pair separ-
ability for these two bands was between bare soil and agriculture-2 classes (17,266). The
lowest class pair separability was between bare soil and residential, as well as between
agriculture-1 and 2, both having a divergence value of 242.
4.3. Best three bands
The best three bands were the TM visible red and near-infrared bands with the PALSAR
HH texture window of 17 × 17 (4652). This combination of bands also generally follows
the pattern of very diﬀerent types of data being most useful. The best class pair
separability for these three bands was between bare soil and agriculture-2 (9313) and
the least separable were bare soil and residential (289). The best class separability
between bare soil and agriculture-2 LCLU classes is understandable, since as shown
Table 4. Arequipa divergence rank by single band.
Rank Band Divergence value
1 TM red 1962
2 HH texture 17 × 17 1829
3 HV texture 17 × 17 1255
4 TM MIR 2 963
5 HV texture 11 × 11 961
6 TM NIR 931
7 TM green 832
8 TM MIR 1 770
9 TM blue 625
10 HV texture 11 × 11 456
11 TM Thermal 106
12 HH texture 5 × 5 95
13 HV texture 5 × 5 86
14 HV 18
15 HH 7

Table 3, the mean values for these classes for the two TM bands had very different
values. A similar pattern was also observed for the bare soil and agriculture-2 LCLU
classes in the HH texture at 17 × 17 window size. However, it was unclear as to why the
agriculture-2 class had such a high mean value in this texture band compared to the
other texture bands.
There was low separability between bare soil and residential. This was somewhat
surprising since intuitively, they would be expected to be similar in the TM data. In this
case, residential areas would typically be expected to have higher radar backscatter. The
original PALSAR bands in Table 3 indicate higher backscatter for residential; however,
their standard deviation values are also high, which would suggest low class separability.
The second best three band combination was the TM visible red and second mid-
infrared bands, together with the same PALSAR HH variance at 17 × 17 window
(4302). The third best three band combination was TM visible red and near-infrared
with the PALSAR HV variance texture at 17 × 17 window (3967). These best band
combinations are quite similar in the selected bands.
As previously mentioned in Section 1, the selection of the best three bands is very
important in remote sensing for creating colour composites for viewing any imagery and
also for manual thematic information extraction. However, the best three band combi-
nation for Arequipa was examined visually but the large texture window created a very
blocky image, which was not very useful as shown in Figure 2.
4.4. Best four, five and six bands
The best four bands from 1365 possible combinations were the TM visible red and near-
infrared bands together with two texture bands, HV at 11 × 11 window and HH at
17 × 17. The lack of utility of the original PALSAR bands is initially surprising. However,
from Table 3 for all LCLU classes, their means for bands 8 and 9, the original PALSAR, are
quite similar and have relatively high standard deviations for several classes reducing
class separability. The best five bands added the second TM mid-infrared band to the
best four bands. The best six bands added a second texture band, HH at 5 × 5 window,
to the best five bands.
4.5. All band combinations
The divergence values by number of bands follow an expected pattern of initially
increasing with the number of bands. The progression begins with the best individual
band at 1962 and increases to a peak at 6129 with seven bands. The pattern then
decreases to a low of 2655 with all 15 bands as presented in Figure 3. This Figure shows
that there is a negative effect with respect to the separation of LCLU classes with the
addition of too many bands, and for this data set, results suggest that there is no reason
to include more than five or six bands. The divergence value with the best five bands
was 5921, only marginally lower than the highest value at seven bands. This information
is an additional argument for the use of separability analysis. It not only indicates the
best bands but also the number of necessary bands for a viable classification.

Figure 2. Arequipa best three bands. Palsar HH variance from 17 × 17 window, TM visible red and
TM near infrared in RGB. Scene width approximately 6.5 km.
0
1000
2000
3000
4000
5000
6000
7000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Divergence
Bands
Figure 3. Arequipa divergence values by best combination for diﬀerent number of bands, horizontal
axis.

4.6. Principal components analysis (PCA)
Divergence analysis was also used to study the LCLU class separability between the six
derived PCA component bands, and using the same AOIs for LCLU classes that were
used with the 15 band TM, radar and radar-derived texture data stack. As would be
expected from PCA, the divergence values decreased by band from PCA band 1 to PCA
band 6 (425–51). Interestingly, when compared to the single band divergence values for
the original 15 bands in Table 4, these values are very low. The best PCA divergence is
less than the top 10 single band values. For the best PCA 1 divergence, the class pair
values are very understandable. The best divergence is between bare soil and the
second agriculture LCLU class, which are very different features (divergence of 3034).
The second best divergence, but at a much lower value of 935, is between bare soil and
residential. There are two class pairs with a divergence value of 0, which are bare soil
and salt flat, and residential and industrial, both of which would be expected based
upon their appearance and class signatures.
The best three PCA bands by divergence are 1, 3 and 6 with a value of 759. This is
somewhat surprising as initially one might expect the first three bands. However, the
different bands are related to varied surface features. This divergence value for three
bands is well below the best three from the non-PCA stacked approach, which was 4652
for the TM visible red and near-infrared bands with the PALSAR HH texture window of
17 × 17. Even if all six PCA bands were to be used, the average divergence was only 854.
There was no clear reason for the low divergence values derived from the PCA analysis.
Perhaps these values occurred as a result of the rescaling of the PCA components.
However, as stated before, without rescaling, the divergence algorithm would not work.
An extension of this study might be to do classifications and accuracy assessments with
the two sets of data, the 15-layer data stack and the unstretched PCA components and
to compare results.
5. Discussion and conclusions
The increasing availability of spaceborne remote sensing data over the last several
decades has led to a substantial increase in the number applications and users of
these data. One such application, LCLU mapping, has consistently relied on remote
sensing as a main source of mapping and validation data. Traditionally, LCLU maps
depended on information extracted from optical sensors; however, newer approaches to
extracting information now combine information retrieved from different parts of the
electromagnetic spectrum such as the microwave region. This has led to use of large
multisensor data stacks in the mapping of LCLU. While such multisensor integration has
led to improved LCLU mapping accuracies, new issues have emerged such as the need
to determine what image bands should be used for achieving maximum or even optimal
mapping accuracies. With few bands, the problem of what image bands to be used for
analysis may not be as challenging. However, as the number of bands increases, the
decision of what combination of bands to be used quickly becomes a factorial problem.
Such issues are only expected to be exacerbated with the continued growth, advance-
ment and the deployment of spaceborne sensors seeking to collect information at
increasingly finer spatial, temporal, spectral and radiometric resolutions. Moreover,

while traditionally spaceborne sensors collected snap shots of the earth’s surface, more
recent technologies such as CubeSats are being manufactured at a fraction of the cost of
traditional satellites with the ability to do video imaging. This greatly expands the
amount of remote sensing data available to users.
This study examined the use of spectral separability measures, in particular, the
divergence measure, as one approach towards searching through large stacks of data
to determine what bands should be used for LCLU mapping. In addition, an effort was
made to link the results of divergence analysis to the spectral and spatial properties of
the LCLU. This was important to better understand the underlying characteristics of
LCLU that make some features more or less easy to separate in remote sensing imagery
compared to others. Further, the study area, Arequipa, Peru, presented an interesting
case study of a developing country where topography, such as mountainous terrain,
makes the use of remote sensing a valuable tool in capturing up-to-date information on
LCLU changes.
The results of the divergence analysis determined that the best single band,
based on a 15 band data stack composed of TM imagery, radar and derived radar
textures at different window sizes, was the TM visible red. This was followed by the
HH and HV texture bands extracted at the largest window size of 17 × 17. These
results corroborated with the class spectral signatures for the various LCLU classes
in Table 3, highlighting the fundamental role that basic statistics extracted from
remote sensing imagery continue to play in the selection on image bands for LCLU
mapping. Additional observations from this study include the limited value of the
two original PALSAR HH and HV polarisation bands. It was also observed that as
the window size used to extract texture increased from 5 × 5 to 17 × 17, the
divergence value also increased suggesting increased LCLU mapping accuracies.
An examination of the best combination of image bands to achieve maximum
mapping accuracies showed that bands composing of different data from different
sensors produced the best divergence values. This finding highlights the importance
for the continual experimentation of different types of data when trying to both
map and understand features in remote sensing imagery. Moreover, additional
analysis assessing the relative value of PCA determined that this approach per-
formed poorly when trying to separate the various LCLU classes. Of course, this
could be due to limitations with the data stack used or may be the way the PCA
combined the data prior to divergence analysis. Other factors, such as the inap-
propriate selection of components, may have also contributed to the poor perfor-
mance of PCA. Cheriyadat and Bruce (2003), for example, showed that the
components explaining the highest variance in the data, as use in this study, and
used for classification may not always yield the best classification results. Therefore,
further experimentation with the full complement and combinations of individual
principal components is needed to determine if such measures will lead to improved
LCLU class separability.
The results of this study also showed that divergence values as expected initially
increased as the number of bands increased. This indicates better separability and poten-
tially increased LCLU mapping accuracies. However, a maximum divergence value was
achieved at seven bands. This was only slightly higher than divergence values achieved
when using five or six bands and would suggest that no more than five or six bands are

needed for a viable classification. The six bands needed for a viable classification were the
TM visible red, near-infrared band, the second TM mid-infrared band, the radar texture
bands 11 × 11 HV, 5 × 5 HH and 17 × 17 HH. After peaking at seven bands, the divergence
values decreased with the successive introduction of more image bands. These results
provide a compelling argument for the use of separability measures such as divergence for
identification of the best bands for the mapping of features in large image data stacks.
Besides improvements in LCLU classification accuracy, greater selectivity and reduc-
tion of image bands are also expected to reduce processing time during analysis. This is
especially important when applying machine-learning algorithms such as a support
vector machine to classify imagery (Catak and Balaban 2013). Also, bands that are not
found to be of limited use can be substituted or removed from future similar analysis.
For example, in this study, the PALSAR HH and HV polarisation bands were found to
have limited use amongst the other data used. Such approaches can greatly benefit
remote sensing scientists and practitioners that may be overwhelmed with the increas-
ing amount of remote sensing data available to them. Knowing which sensors or derived
values are also likely to provide the best classification results may also reduce the cost
and effort of data acquisition. This could be as trivial as identifying specific image bands
or as part as larger multilevel framework. With respect to the latter, free and coarser
resolution imagery can be used to identify hotspots such as degrading forest areas,
followed by the purchase of finer resolution imagery over these specific areas to better
understand causative factors.
Along with reduced processing times and costs in acquiring data, the methodology
used to determine the optimal bands for classification in this study offers a simple
method that can be used to experiment with different types of data, and for different
applications. This is especially important for developing countries where issues such as
persistent cloud cover are a typical occurrence, and where the availability of up-to-date
information on LCLU and other spatial phenomena continues to be an ongoing chal-
lenge. Finally, the methodology and results of this study can be used to better under-
stand the underlying characteristics of the LULC in other areas. This provides an
important first step towards creating a comprehensive LCLU monitoring programme.
For example, knowledge of the spectral characteristics of agricultural fields in remote
sensing data can be used to monitor agricultural productivity (e.g. Chandna et al. 2012).
This information, combined with other information such as population expansion and
rates of forest decline, can be a powerful driver of sustainable growth in large cities such
as Arequipa. Future research will further investigate different data stacks and study
areas, along with comparing other separability measures and data compression techni-
ques. This also includes the use of fully polarimetric radar systems such as RADARSAT-2.
Moreover, these could be investigated with respect to the LCLU information captured at
different spatial scales and at different dates to determine the rates at which different
LCLU classes may be changing. Such efforts are expected to provide important quanti-
tative information that can be used for informing policy changes.
Acknowledgement
The Alaska Space Facility, under sponsorship from NASA, provided the PALSAR imagery for this
study.

Funding
The Alaska Space Facility, under sponsorship from NASA, provided the PALSAR imagery for this
study.
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