Forum Geografi, 31(1), 2017; DOI: 10.23917/forgeo.v31i1.3500
The Relationship between the Mixed Pixel Spectral Value of Landsat 8 OLI Data and LAPAN Surveillance Aircraft (LSA) Aerial-Photo Data
*)
Corresponding author (e-mail: nurwita.mustika@lapan.go.id)
Received: 07 March 2017 / Accepted: 07 June 2017 / Published: 01 July
2017
Abstract
Medium-resolution
satellite data such as data from Landsat has a lot of potential for a mixed
pixel (mixel) to occur. Indonesian land use is diverse,
especially in urban areas, which causes a high potential for mixels in the Landsat pixel size of 30 x 30m, based on the
actual conditions. Multispectral aerial photo data from LAPAN Surveillance
Aircraft (LSA) with a spatial resolution reaching 58cm can display objects in
more detail in these sizes. The purpose of this research is to study mixels in Landsat 8 Operational Land Imager (OLI) data,
with multispectral data from an LSA as a complement to the Landsat 8 OLI data.
The method proposed in this study is a visual interpretation using the
object-based image analysis (OBIA) method for the classification of land cover,
testing the validity of the sample to be used in the research, and then using
the normalized difference vegetation index (NDVI) to see the relationship
between the vegetation index of the LSA data and the Landsat 8 OLI data. The
results showed that the regression equation obtained from the regression
between the NDVI of the Landsat 8 OLI data and the NDVI of the LSA with a
significance of less than 0.05 is y = 0.732x - 0102 with a value of R2
= 0.887. Through these results we can conclude that the NDVI values for both sets
of data are related to one another.
Keywords: mixed pixel, aerial remote sensing, LSA, Landsat, OBIA, NDVI.
Abstrak
Data satelit beresolusi sedang seperti data dari Landsat memiliki banyak potensi untuk terjadinya piksel campuran (mixel). Penggunaan lahan di Indonesia beragam, terutama di daerah perkotaan, yang menyebabkan potensi mixel tinggi
pada ukuran piksel Landsat 30 x 30m, berdasarkan
kondisi sebenarnya. Data foto udara multispektral
dari LAPAN Surveillance Aircraft (LSA) dengan resolusi spasial mencapai 58cm dapat menampilkan objek secara lebih
rinci dalam ukuran ini. Tujuan
dari penelitian ini adalah untuk
mempelajari mixel dalam data Landsat 8 Operational Land Imager (OLI), dengan data multispektral dari LSA sebagai pelengkap data OLI Landsat 8. Metode
yang diusulkan dalam penelitian ini adalah interpretasi visual dengan menggunakan metode analisis citra berbasis objek (OBIA) untuk klasifikasi tutupan lahan, menguji validitas sampel yang akan digunakan dalam penelitian, dan kemudian menggunakan
normalized difference vegetation index ( NDVI) untuk melihat hubungan antara indeks vegetasi
dari data LSA dengan data
Landsat 8 OLI. Hasilnya menunjukkan
bahwa persamaan regresi yang diperoleh dari regresi antara
NDVI data OLI Landsat 8 dan NDVI untuk
LSA dengan signifikansi kurang dari 0,05
adalah y = 0,732x - 0102 dengan
nilai R2 = 0,887. Melalui
hasil ini, kita dapat menyimpulkan
bahwa nilai NDVI untuk kedua kumpulan
data saling terkait satu sama
lain.
Kata kunci: piksel campuran, penginderaan jauh udara, LSA, Landsat, OBIA,
NDVI.
Introduction
The LAPAN Surveillance Aircraft
or LSA is an ultra-light aircraft developed by LAPAN, which provides remote
sensing as one of its functions. It is capable of acquiring high-resolution
imagery in which the objects can be seen very clearly. It helps to produce
detailed-scale spatial information, which is currently required to meet the
needs of many sectors, such as infrastructure development for both rural and
urban developments, disaster mitigation and agricultural monitoring (Kushardono et
al., 2014). Along with these increasing needs, an aerial vehicle
that has flexibility in terms of time, flying height and location when performing
data acquisition is indispensable. With such a capability, it has much
potential to be explored, such as becoming the complement of or comparison to
remote sensing satellite data with lower spatial resolution (for example, Landsat)
because the objects presented in LSA data may not be visible on Landsat data.
Landsat imagery data is popular data
and has been widely used for various kinds of applications, including in
research by Sholihah et al. (2016), which finds that drought
monitoring of agricultural land can be conducted by observing the vegetation
health index (VHI). Landsat 8 Operational Land Imager (OLI) has been assessed by
the users for looking at the area of hydrothermal alteration in a copper mine. It
uses the existing thermal infrared (TIR) band and this data was able to be used
to do lithological mapping (Pour and Hashim,
2015). The integrated use of Landsat and LIDAR to map forest structure and biomass was
done by Zald et
al. (2016) in the area of Saskatchewan, Canada,
which gained a moderate to high level of accuracy in the model (the value of R2= 0.42 to 0.69). The use of
remote sensing data with different resolutions, namely Landsat and Ikonos, has also been applied to determine soil erosion (Wang et al., 2011).
Landsat data has been used for surface-water monitoring in Australia by Mueller
et al. (2016)
following the extreme floods in 2011, which used two decades worth of data and developed
standard algorithms based on medium-resolution satellite data for surface water.Landsat data has also been used to evaluate changes
in land use/ land cover, which is correlated with the pattern formed by the
surface temperature (Lv and Zhou, 2011).
Landsat ortho has also been used to validate the
model for mapping the wetland area of Lampung province, and the level of
accuracy reached 90.6% (Parsa et al., 2014).
The greatest diversity of land
cover occurs in urban areas, where the cities are areas with the high physical
development of the region as a result of an increasing population (Widodo et al.,
2015). The dynamics of the rapid physical development of the city (Mikovits et al.,
2014) affects traffic, and one of the affected aspects is the urban road
network density (Xing et
al., 2013), which also adds to the diversity of land cover in the city.
Good urban planning for land use affects the quality of the city, and contains
both the complexity of land use and the human activity on it to create a
sustainable urban development (Vorontsova
et al., 2016).
Such diversity of urban land
cover affect to the high possibility of mixture pixel in low resolution imagery.
The city as a region of land cover is very diverse; as an example, Bandung, on
Green Open Space, just has diversity of land cover, such as areas of trees, mixed
garden, farms, bushes/shrubs, open land and paddy fields (Narulita et al.,
2016). For this, a study of mixed pixel (mixel) will be needed for further analysis. At Kuningan Regency,
West Java, the land cover changes causing narrow boundaries of land cover for the
growth of residential areas that are initially homogeneous, consisting of mixed
garden or natural forests (Nasihin et al., 2016). Indonesia is a
country with a high prospective housing construction market with the regulation
and affordability (Monkkonen, 2013).
On the other hand, another area
of Indonesia with high diversity of land use/ land cover is the tourist area,
including the Gili Matra Island
in Lombok, West Nusa Tenggara; this incorporates land use/ land cover such as shoal
beaches, sand beaches, salty lakes, mangrove areas, mixed forest, plantations,
bare areas, non-built-up areas, settlement areas and tourist accommodation areas.
Land conversion into the built-up areas here occurred very fast because of the
major influence of the ease of access (Kurniawan et al.,
2016).
Landsat data with a resolution of
30m, which includes an area with a high diversity of land cover, has potential
for mixels, which actually consist of several
objects. The number of mixels will be higher for imagery data with a coarse resolution (Danoedoro, 2009). The ability of higher-resolution
imagery data to display objects in an image well makes the number of pure
pixels greater than for low resolution imagery, which tends to have a lot of mixels (Hoyano and
Komatsu, 1988).
Mixture can occur in a pixel in
various forms, including a boundary between two or more areas of the mapping
unit, parts of pixels in the form of a line, parts of pixels in the form of an
object and intergrade or changing from one object to another object gradually (Fisher, 1997). Small training data sets containing mixels were used in research using the
support vector machine (SVM) method of classification by Foody and Mathur (2006) and obtained an
overall accuracy of 92%, which did not differ significantly with the
conventional method; therefore, it could be a good alternative, especially in instances
where there is difficulty in collecting training data, but is not a replacement
for the conventional method. Referring to the research by Hoyano
and Komatsu (1988) that divides pixels in several
types, including pure pixels, and mixel A and B. Mixel A is composed of several categories and does not have
a separate section, while mixel B is a mixel containing several classes/categories and has a
separate section.
The use of very-high-resolution remotely
sensed data, such as Worldview-2, for the detection of changes in land use (for
example, grazing) with Landsat-8 OLI being used as the pre-data will obtain
higher accuracy because more specific changes will be detected. It is because
the diverse objects will be detected in Worldview-2, while those objects will
only be displayed as the same pixel in Landsat-8 OLI (Tarantino et al. 2016).
In other research using an object-based method, the separation of objects has
been conducted via three methods: the first is based on Landsat 5 Thematic
Mapper (TM) and Satellite Pour l'Observation de la Terre (SPOT) 5 data, the second is only
based on SPOT 5 data and the last is only base on Landsat 5 TM data. Of the
three methods, the optimum segment result obtained is based on Landsat 5TM and
SPOT 5 and only from SPOT 5.By looking at the optimum results, it is concluded
that using the SPOT 5 data with higher spatial resolution can improve the
accuracy from 72.35% to 82.94%. With the increasing accuracy of the result, it
is known that high-spatial-resolution data can be used to be a complement of Landsat
data (Sun et al.,
2014). Study of mixel is important for measuring
the accuracy of satellite imagery.
The band on the Tetracam multispectral camera used by the LSA is able to
detect vegetation well (Tetracam
Inc., 2011). Aerial remote sensing, using an aerial vehicle called an
LSA that carried a payload in the form of a Tetracam
camera with a spatial resolution of 58cm, has been employed further to see the
quality of single-object vegetation in urban areas using the object-based image
analysis (OBIA) method and a vegetation index (Sari and Kushardono, 2016), and reached an accuracy of 88%. Besides
being used to detect vegetation, LSA multispectral data is capable of displaying
objects clearly in the coastal regions (Arifin
et al., 2015). LSA
multispectral data with a high resolution is a complement to Landsat data,
which has a lower resolution to show the estimation of mixel
occurring in the Landsat data.
A previous study of mixel, as mentioned before, has already explained the forms
of mixel, such as parts of pixels as a line, an object,
or an intergrade (which is changing from one object to another object gradually),
and another study divides pixel into several types, namely pure pixels, mixel A (which does not have a separate section) and mixel B (which does have a separate section) (Fisher, 1997; Hoyano
and Komatsu, 1988), but this study has not completed a stage to determine the
relationship of the spectral value between lower-spatial-resolution data and
higher-spatial-resolution data. The purpose of this study is to observe the
relationship between the mixel spectral value of
Landsat 8 OLI data and LSA aerial-photo data.
Research Methods
Materials
The data used in this study is
multispectral image data acquired by the Aeronautic Technology Center’s LSA on 19 September 2014 (Figure
1a), which includes the urban areas of Indramayu,
West Java and its surroundings, with a spatial resolution of up to 58cm (Table 1).The image data that has been acquired has been
processed into mosaics and ortho corrected by the Remote
Sensing Application Centre (Kushardono et al., 2014). The other data
used is Landsat 8 OLI data acquired on 22 September 2014 (Figure
1b) for the same area. The Landsat data selected is for the closest period to the acquisition of the
LSA data and has low cloud cover.
Imagery |
Bands |
Wavelength (micrometres) |
Resolution (metres) |
Landsat 8 Operational Land Imager (OLI) and
Thermal Infrared Sensor (TIRS) |
Band 1 – Ultra Blue (coastal/aerosol) |
0.43 – 0.45 |
30 |
Band 2 – Blue |
0.45 – 0.51 |
30 |
|
Band 3 – Green |
0.53 – 0.59 |
30 |
|
Band 4 – Red |
0.64 – 0.67 |
30 |
|
Band 5 – Near Infrared (NIR) |
0.85 – 0.88 |
30 |
|
Band 6 – Shortwave Infrared (SWIR) 1 |
1.57 – 1.65 |
30 |
|
Band 7 – Shortwave Infrared (SWIR) 2 |
2.11 – 2.29 |
30 |
|
Band 8 – Panchromatic |
0.50 – 0.68 |
15 |
|
Band 9 – Cirrus |
1.36 – 1.38 |
30 |
|
Band 10 – Thermal Infrared (TIRS) 1 |
10.60 – 11.19 |
100 * (30) |
|
Band 11 – Thermal Infrared (TIRS) 2 |
11.50 – 12.51 |
100 * (30) |
|
Tetracam
Agricultural Digital Camera (ADC) (approx. equal to Thematic Mapper 2,3,4) |
Green |
0.52-0.60 |
0.58 |
Red |
0.63-0.69 |
||
Near Infrared (NIR) |
0.76-0.90 |
Source:Tetracam Inc.with
modification, US Geological Survey with modification
* TIRS bands are acquired at 100 m resolution, but are
resampled to 30 m in delivered data product.
Digital Image Processing and Analysis
This
research mainly used Landsat 8 OLI data and LSA aerial-photo data for the
digital analysis. Figure 2 is the flowchart of the
relationship between the mixel spectral values of
Landsat 8 OLI data and LSA aerial-photo data, which is further explained in the
description of each research stage, such as land cover extraction, atmospheric
correction, construct fishnet, sampling, validity assessment of the sample,
transformation to NDVI and regression analysis between the NDVI of Landsat and
the NDVI of LSA.
Land Cover Extraction
The extraction of objects used an OBIA method
consisting of a segmentation and classification process. The segmentation was
done using a multiresolution
segmentation algorithm. The multiresolution segmentation has already been used on
aerial-photo data and has provided a good result (Sari and Kushardono, 2015). After conducting the segmentation
stage, classification was performed using several nearest-neighbour feature
spaces (Trimble Doc., 2014):
Mean
of layer values
where:
Pv is set
of pixels of an image object v,
Pv ={(x,y) :(x,y) ∈v},
#Pv
is total number of
pixels contained in Pv,
(x,y) is image layer value
at pixel (x,y),
Ckmin is the darkest possible intensity value of
layer k,
Ckmax is the brightest possible intensity value of
layer k,
k is the mean intensity of layer k.
Mean
of brightness
………………….….…………………………(2)
where:
is the brightness
weight of layer k,
is the mean intensity
of layer k of an image object v,
ckmin is the darkest
possible intensity value of layer k,
ckmax is the brightest
possible intensity value of layer k.
Max
Diff
………………………………………………………(3)
where:
i, j is image layers,
is brightness,
is mean intensity of
layer i
is mean intensity of
layer j,
ckmax is brightest
possible intensity value of layer k,
KB is layers with
positive brightness weight,
KB ={k
∈ K : wk=1},
wk is layer weight.
Standard
deviation of layer values
…... (4)
where:
σk(v) is standard deviation of layer k of an image object v,
Pv is set of pixels of an image object v,
#Pv
is total number of pixels contained in Pv,
ck(x,y) is image layer value
at pixel (x,y),
(x,y) is pixel
coordinates,
ck range is data range of layer k,
ck range = ckmax – ckmin.
Density
………………………………………
(5)
where:
√#Pv
is diameter of a square object with #Pv pixels,
√VarX+VarY is diameter of the
ellipse.
Compactness
………………..…………………………………………(6)
where:
lv is length of an
image object v,
wv is width of an image
object v,
#Pv is total number of pixels contained in Pv.
Atmospheric correction
The atmospheric correction was conducted using the
Top of Atmosphere (ToA) correction method, which
includes the ToA reflectance and sun angle correction
(Rahayu and Candra, 2014). The
ToA reflectance correction phase converts the digital
number (DN) into a reflectance value.
…………………………………(7)
where:
is ToA planetary reflectance
(unitless), without sun angle correction,
is reflectance multiplicative scaling factor for
the band (REFLECTANCEW_MULT_BAND_N from the metadata),
is reflectance
additive scaling factor for the band (REFLECTANCE_ADD_BAND_N from the metadata),
is L1 pixel value in
DN.
Then the image is corrected for the sun angle to
eliminate the difference in DN values caused by the position of the sun.
…………………………………(8)
where:
is ToA planetary reflectance (unitless),
is solar elevation angle ,
is solar zenith angle.
Construct a fishnet
Fishnet construction was completed on Landsat data with
a pixel size of 30 × 30m. With this fishnet, the process to create a sample and
the analysis will be easier and more precise at the same place.
Sampling
The sample selection was conducted using the purposive sampling method that employs
specific criteria. A similar sampling method was completed by specifying the criteria
for the samples to be taken with a specific purpose. In several studies
conducted by Smith et al. (2013) and Topp et al. (2004), the
sample criteria are very specific for sampling the respondents. Furthermore, in
the study by Iryadi and Sadewo
(2015), land surface area samples are chosen based on
certain criteria, including vegetation cover, normalized difference vegetation
index (NDVI) class and geomorphology unit. In this case, the total number of
samples taken was 35. The selected samples in this study were determined based
on vegetation cover within a certain percentage ranging from 0%, which means a pure
pixel of non-vegetation land cover, up to 100%, which means it is a pure pixel
of vegetation. The number of samples taken was 35 due to those samples being
able to represent diverse vegetation cover percentages in the research area. A
representative sample of the entire area has a percentage of vegetation cover that
varies from 0% to 100%.
Validity assessment of the sample
A sample validity assessment was conducted for the
35 samples selected to undergo a normality test. The validity assessment was
conducted using
the vegetation area as the primary variable; this validity test was to find out
the distribution of samples based on the percentage of vegetation cover.
A normality test was conducted using the Shapiro-Wilk test because the sample
size was less than 50 (Shapiro and Wilk, 1965).
Transformation to NDVI
NDVI is one of the most commonly
used vegetation indexes, with values that are between -1 and 1. The Landsat 8
OLI data and LSA data transformation process to the NDVI value is conducted
using a formula that uses the NDVI value NIR band and the red band value in its
calculations (Mokarram et al., 2016). Theoretically, if the NDVI
approaches a value of 1, the existing vegetation in that area is greener and
denser; if it approaches a value of 0, it means that the vegetation is dry or
not green; and if the NDVI value is between 0 and -1, then it is land cover
that is not a type of vegetation (Haque
and Basak, 2017). The index can be used to detect changes in land
cover (Haque and Basak, 2017).
…………………………………………(9)
Regression analysis between the NDVI of Landsat and the NDVI of LSA
Regression analysis is a statistical process for
estimating the relationships among variables, focusing on the functional relationship
between a dependent variable and one or more independent variables (Rawlings et al.,
2001). Regression analysis was conducted to determine the relationship
between the vegetation index of the Landsat 8 OLI data and the vegetation index
of LSA data. The NDVI of the LSA data was calculated from the average pixel
value from each 30 x 30m fishnet grid to match the Landsat 8 OLI spatial
resolution.
Results and Discussion
A
segmentation process was conducted using a multiresolution segmentation algorithm
like in the previous research (Sari and Kushardono,
2015). Object extraction is performed on the LSA data after the object is
segmented into 797 objects. The classification process divides the objects into
three classes, water, built up area and vegetation, as shown in Figure 3.
The body of water is coloured blue, the built-up
area is coloured magenta and the vegetation is coloured. From Figure
3 we can see that the classification result shows the distribution of three
land cover classes based on LSA data and there are several class boundaries in
one pixel size 30 x 30m. In those adjacent areas, the potential occurrence of mixel is very high.
Based on Table
2, it can be observed that the combinations constructed from Landsat mixels include vegetation and built-up area, body of water
and vegetation, body of water and built-up area, and body of water, built-up
area and vegetation. In addition, the types of pure pixels consist of
vegetation, body of water and built-up area.
No |
Combination |
1 |
Pure pixel of vegetation |
2 |
Pure pixel of a body of water |
3 |
Pure pixel of a built-up area |
4 |
Vegetation & built-up area |
5 |
Body of water & vegetation |
6 |
Body of water & built-up area |
7 |
Body of water & built-up area &
vegetation |
To
conduct further analysis, including sample selection and analysis, a fishnet was
constructed with a Landsat pixel size of 30 x 30m. Figure 3
shows the fishnet overlaid onto the classification result. The function of a fishnet/ grid is to make the analysis easier; this
was done in previous research, such as in a study in Haiti for the population census
area and for modelling in Lake Icaria (Deichmann et al.,
2001; Wang and Cui, 2005).
First,
the normality test was conducted on the first 35 samples that had been already
chosen by the purposive sampling for the regression test. Those 35 samples, as
mentioned before, were selected by choosing the areas with a certain percentage
of vegetation cover, starting from 0% and going up to 100%. The sampling
criteria fit with the index used for the regression test, namely NDVI, to resolve
the relationship between the Landsat 8 OLI and LSA data for the spectral value.
The sample distribution is also shown in Figure 4. The
first normality test result is shown in Table 3.
|
Kolmogorov-Smirnov |
Shapiro-Wilk |
||||
Statistic |
df |
Sig. |
Statistic |
Df |
Sig. |
|
Percentage
Vegetation |
.195 |
35 |
.002 |
.881 |
35 |
.001 |
For
this first sampling, the amounts from the sample with a certain percentage are
not normally distributed, as seen in the Table 3. The significance
value, which is less than 0.05, indicates that the samples did not meet the
normal criteria. This was confirmed by a histogram of the tested sample, which
shows that the percentage of vegetation (x-axis) has not been found on a normally
distributed frequency (y-axis).
Because the first sample group did not pass the
normality test, then another sample group was taken with same amount of samples
(35) as the previous sample group. Based on the first sampling experience, the
second sample selection was conducted by selecting more mixel
areas than the first sampling and more mixel areas than
pure pixel areas, as the purpose is to see the relationship of the spectral
values in a mixel area between the Landsat 8 OLI and
LSA data. The result of the normality test on the second sample test shown in Table 4 and Figure 4.
|
Kolmogorov-Smirnov |
Shapiro-Wilk |
||||
Statistic |
df |
Sig. |
Statistic |
df |
Sig. |
|
Shape_Area |
.089 |
31 |
.200* |
.967 |
35 |
.368 |
Table 3 shows that the Shapiro-Wilk test significance value
is more than 0.05; this indicates that the sample passes the normality test
requirement. This was confirmed by the histogram from that group of samples. As
the normality test requirement was passed, the analysis of the sample was
continued to the next stage, called a regression test. Figure 5 shows the distribution of first and second sample
locations. The first and second sample locations consist of 35 samples, which
were chosen using different methods, as mentioned previously.
The Landsat spectral data value
was transformed into a vegetation index and the result is shown in Figure 6, where the lowest value of NDVI is -0.076 and the
highest value is 0.606. An NDVI value that is close to 1 shows the vegetation
conditions are quite green and dense. When viewed from the Landsat composite in
Figure 2, the high NDVI values, which are green, are
located on the vegetation areas, while the low NDVI values (yellow through to
red) are located in mixel areas and non-vegetation
areas (e.g. water).
A transformation into the same
index was conducted on the LSA data (Figure 7), for
which the NDVI value ranges from -0.309 to 0.783. The low NDVI values, coloured
red, are located on non-vegetation areas/ built-up areas. Furthermore, the high
NDVI values, coloured green, are located in the vegetation land-cover areas. Table 5 shows the NDVI values for the Landsat 8 OLI and LSA
using the same 30 x 30m pixel size, and the NDVI values for the LSA data are
from the average of the 30 x 30m fishnet grid of the corresponding Landsat 8
OLI pixel.
NO. |
NDVI_L8 |
NDVI_LSA |
|
NO. |
NDVI_L8 |
NDVI_LSA |
1 |
0.167 |
0.018 |
|
21 |
0.257 |
0.099 |
2 |
0.334 |
0.137 |
|
22 |
0.136 |
0.170 |
3 |
0.404 |
0.198 |
|
23 |
0.416 |
0.203 |
4 |
0.481 |
0.234 |
|
24 |
0.233 |
0.085 |
5 |
0.379 |
0.234 |
|
25 |
0.147 |
0.145 |
6 |
0.356 |
0.135 |
|
26 |
0.192 |
0.047 |
7 |
0.250 |
0.025 |
|
27 |
0.259 |
0.165 |
8 |
0.351 |
0.113 |
|
28 |
0.222 |
0.079 |
9 |
0.465 |
0.253 |
|
29 |
0.348 |
0.180 |
10 |
0.279 |
0.105 |
|
30 |
0.423 |
0.245 |
11 |
0.418 |
0.195 |
|
31 |
0.170 |
0.0340 |
12 |
0.334 |
0.110 |
|
32 |
0.287 |
0.098 |
13 |
0.328 |
0.136 |
|
33 |
0.225 |
0.054 |
14 |
0.271 |
0.077 |
|
34 |
0.256 |
0.101 |
15 |
0.343 |
0.144 |
|
35 |
0.190 |
0.061 |
16 |
0.373 |
0.156 |
|
|||
17 |
0.230 |
0.184 |
|
|||
18 |
0.404 |
0.201 |
|
|||
19 |
0.364 |
0.164 |
|
|||
20 |
0.276 |
0.084 |
|
There are variance of NDVI values
between Landsat 8 OLI and LSA (Table 5). The LSA NDVI
values, which have a higher spatial resolution, have a lower NDVI value than
the Landsat 8 OLI NDVI, and there is a considerable difference between the values,
except in some areas that have a grey or green highlight in Table
5. In the grey-highlighted areas, the differences in the NDVI values are
not too large, while in the green-highlighted areas the NDVI value for Landsat
8 OLI is lower than the NDVI for the LSA data. The higher spatial resolution of
the LSA data means it is able to display objects with more detail and
precision. As the transformation to NDVI of this data was based on the average
value of all pixels in the LSA data fishnet with a size of 30 x 30 m to match the
Landsat pixel in the same position, this could lead to differences between the NDVI
values for the LSA and Landsat 8 OLI data, which could be caused by the detection
of other objects in the LSA data on the mixel areas.
In the grey- and green-highlighted areas, which consist of mixel
that has a part that is a body of water, the value differences are not too large
and the Landsat 8 OLI data even has a lower value than the LSA data in the
green-highlighted area.
This phenomenon happened because of
the wavelength comparison between the Landsat 8 OLI and LSA data. The
wavelength of the LSA data for the green, red and NIR bands is approximately
equal to TM2, TM3 and TM4 (Tetracam,
2011) as seen in Table 1. From this table, it can
be observed that the NIR band for the Landsat 8 OLI data is narrower than for
the LSA data, which can help with water detection (Ke et al., 2015) and caused the anomalies
with the grey and green highlights, where the green highlight shows that the
NDVI of Landsat 8 OLI data is lower than for the LSA data, and the grey
highlight shows that difference in values are not significant. After performing
the normality test, the next stage was the regression test. The result of the
regression test between the NDVI values for the LSA and Landsat 8 OLI data is
shown in Figure 8.
The x-axis is the Landsat 8 OLI
NDVI and the y-axis is the LSA NDVI. With a significance value of less than
0.05, which means there is an error rate of less than 5%, the regression
equation is y = 0.520x – 0.023 with a coefficient of determination or R2 that is 0.552, which means
that the independent variables are able to explain variances of 55.2%. From the
graphic in Figure 9, there are some outliers from the sample distribution and those
outliers are four samples in the grey-highlighted area in Table 5.
Four samples of 35 (Figure 3) have a significant part in the body of water
object, so the NDVI values for those samples are less related to one another,
plus they are subject to the influence
of the NIR band that has been discussed before. The four samples are those with
sample numbers 17, 22, 25 and 27, which contain a part that is water. On the
second regression test, the four samples were removed, so only 31 samples were used,
but still there are two samples used that containing a part that is water,
namely samples 29 and 30. It is not defined that the relationship is only for
dry pixels, but the existence of water in a sample area influenced the comparison
between the NDVI of the Landsat 8 OLI data and the NDVI of the LSA data.
The second regression test show
that the sample distribution is more linear (Figure 9).
The same as for the first regression test, the x-axis is the Landsat 8 OLI NDVI
and the y-axis is the LSA NDVI. The regression equation is y = 0.732x – 0.102
with R2= 0.887. This means
that, with a significance of less than 0.05, this has a confidence level of
more than 95% and coefficient of determination reaches 0.887. This value
indicates that the independent variables are able to explain the variance to
88.7%. In other words, the NDVI values for the LSA data are associated with the
NDVI values of the Landsat 8 OLI data.
Conclusion
This study examined how the 30x
30m pixel size of Landsat 8 OLI compares with the LSA data that has a spatial
resolution of up to 58cm. Based on the research, it is shown that the mixel area on Landsat 8
OLI data is a mixture of several objects as seen from the LSA multispectral
data. The combination created from each Landsat 8 OLI mixel
includes vegetation and built-up areas, body of water and vegetation area, body
of water and built-up areas, and body of water, built-up areas and vegetation. Spectrally,
the regression equation obtained by the regression analysis between the NDVI of
the Landsat 8 OLI data and the NDVI of the LSA data, after the elimination of four
samples containing a part that is water that were distributed in a non-linear manner
(outliers), is y = 0.732x – 0.102
with a value of R2= 0.887 with
a significance of less than 0.05. Through these results, we can conclude that
the NDVI values on both sets of data are related to one another only for dry
land cover, which does not include an area that is part of a body of water.
Acknowledgement
The authors deliver their
gratitude to the Aeronautic Technology Center LAPAN
for the opportunity to use the LSA remote sensing data in this study and to Prof. Lilik Budi Prasetyo from Institut Pertanian Bogor, who was our resource person.
References
Arifin, S., Annas, A., Sari, N. M., Kushardono, D. (2015) Identifikasi
dan Interpretasi Visual
Citra Kamera Digital Multispektral
untuk Objek Wilayah Pesisir. Prosiding Seminar Nasional Penginderaan Jauh 2015, Bogor, Indonesia
Danoedoro, P. (2009) Penginderaan
Jauh untuk Inventarisasi Mangrove; Potensi, Keterbatasan dan Kebutuhan Data. Prosiding Workshop “Sinergi Survei dan Pemetaan Nasional
dalam Mendukung Pengelolaan Mangrove Berkelanjutan”,
BAKOSURTANAL Bogor 30 Juli 2009
Deichmann, U., Balk, D., & Yetman, G. (2001).
Transforming population data for interdisciplinary usages: from census to grid. Washington (DC): Center for International Earth Science Information Network, Pp 200(1)
Fisher, P. (1997) The
Pixel: A Snare and A Delusion. International
Journal of Remote Sensing, February 1997 doi: 10.1080/014311697219015
Foody, G. M., Mathur,
A. (2006) The Use of
Small Training Sets Containing Mixed Pixels for Accurate Hard Image
Classification: Training on Mixed Spectral Responsesfor
Classification by A SVM. Remote Sensing of
Environment 103 (2006) 179–189
Haque, M. I., Basak, R. (2017) Land Cover Change Detection Using GIS
and Remote Sensing Techniques: A Spatio-temporal
Study on Tanguar Haor, Sunamganj, Bangladesh. The
Egyptian Journal of Remote Sensing and Space Sciences (2017),
http://dx.doi.org/10.1016/j.ejrs.2016.12.003
Hoyano, A., Komatsu, Y. (1988) Influence of Mixels on
Land Cover Classification in Residential Areas Using Airborne MSS Data. ISPRS Archives – Volume XXVII Part B7, 1988
Iryadi, R., Sadewo, M. N. (2015) Influence the
Existence of the Bali Botanical Garden for Land Cover Change in Bedugul Basin Using Landsat Time Series. Procedia Environmental Sciences 24, pp. 158
– 164
Ke, Y., Im, J., Lee, J., Gong, H., Ryu, Y. (2015) Characteristics of Landsat 8 OLI-derived
NDVI by Comparison with Multiple Satellite Sensors and In-situ Observations. Remote Sensing of Environment 164, pp. 298–313
Kurniawan, F. Adrianto,
L., Bengen, D. G., Prasetyo,
L. B. (2016) Patterns of Landscape Change on Small Islands: A Case of Gili Matra Islands, Marine
Tourism Park, Indonesia. Procedia Social
and Behavioral Sciences 227, pp. 553 – 559
Kushardono, D., Annas, A., Maryanto,
A., Utama, A. A., Winanto (2014)
Pemanfaatan Data LSA (LAPAN Surveillance Aircraft) Untuk Mendukung Pemetaan Skala Rinci. Prosiding Pertemuan Ilmiah Tahunan XX MAPIN 2014,
Bogor
Lv, Zhi-qiang, Qi-gang Z. (2011) Utility of Landsat Image
in the Study of Land Cover and Land Surface Temperature Change. Procedia Environmental Sciences
, pp. 1287 – 1292
Marshall, A. D.,
Martin, R.R. (1992) Computer Vision, Models, and Inspection. World Scientific Series in Robotics and
Automated Systems Vol. 4
Mikovits, C., Rauch,
W., Kleidorfer, M. (2014) Dynamics in Urban
Development, Population Growth and Their Influences on Urban Water
Infrastructure. Procedia Engineering 70, pp. 1147 – 1156
Mokarram, M., Boloorani,
A. D., Hojati, M. (2016) Relationship between Land
Cover and Vegetation Indices. Case Study: Eghlid
Plain, Fars Province, Iran. European Journal of Geography 7( 2), pp. 48 - 60
Monkkonen, P. (2013) Urban Land-use Regulations
and Housing Markets in DevelopingCountries: Evidence
from Indonesia on the Importance of Enforcement. Land Use Policy 34, pp.255–264
Mueller, N.,
Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., Ip, A. (2016) Water Observations from Space: Mapping
Surface Water from 25 Years of Landsat Imagery across Australia. Remote Sensing of Environment 174, pp. 341–352
Narulita, S., Zain, A. F. M., Prasetyo, L. B. (2016)
Geographic Information System (GIS) Application on Urban Forest Development in
Bandung City. Procedia Environmental
Sciences 33, pp. 279-289
Nasihin, I., Prasetyo,
L. B., Kartono, A. P., Kosmaryandi,
N. (2016) Land Cover Change in Kuningan District during 1994 – 2015. Procedia Environmental Sciences 33, pp. 428 – 435
Parsa, I. M., Yudhatama, D., Harini,
S. (2014) Validasi Model Pemetaan
Lahan Sawah Menggunakan Teknik Segmentasi dan Klasifikasi Citra Landsat Ortho (Studi
Kasus Lampung). Pemanfaatan Citra Penginderaan Jauh
untuk Sumber Daya Wilayah Darat ISBN No : 978-602-14437-6-7
Pour, A. B., Hashim, M. (2015) Hydrothermal Alteration Mapping from
Landsat-8 Data, Sar Cheshmeh Copper Mining District, South-Eastern Islamic
Republic of Iran. Journal of Taibah University for Science 9, pp. 155–166
Rahayu, Candra, D. S. (2014) Koreksi
Radiometrik Citra Landsat-8 Kanal
Multispektral Menggunakan
Top of Atmosphere (ToA) untuk
Mendukung Klasifikasi Penutup Lahan. Prosiding Seminar Nasional Penginderaan Jauh 2014, Bogor
Rawlings, J. O., Pantula, S. G., Dickey, D. A. (2001) Applied Regression Analysis: A Research Tool.
Springer Science & Business Media
Sari, N. M., Kushardono, D. (2015) Object Segmentation on UAV Photo Data
to Support the Provision of Rural Area Spatial Information. Forum Geografi Vol.
29 (1) July 2015 49-58
Sari, N. M., Kushardono, D. (2016) Quality Analysis of Single Tree
Object with OBIA and Vegetation Index from LAPAN Surveillance Aircraft
Multispectral Data in Urban Area. Journal
of Geomatics and Planning doi:
10.14710/geoplanning.3.2.93-106
Shapiro, S.S.,
Wilk, M. B. (1965) An Analysis of Variance Test for Normality (Complete
Samples). Biometrika 52 (3): 591-611 http://www.jstor.org/stable/2333709
Sholihah, R.I., H.T., Bambang, S., Diar,
S. I., La Ode, K., Selamet,
Manijo, R.P., Dyah. (2016) Identification
of Agricultural Drought Extent Based on Vegetation Health Indices of Landsat
Data: Case of Subang and Karawang,
Indonesia. Procedia Environmental Sciences 33 (2016)
14 – 20
Smith, A. R., Colombi, J. M., Wirthlin, J. R. (2013)
Rapid Development: A Content Analysis Comparison of Literature and Purposive
Sampling of Rapid Reaction Projects. Procedia
Computer Science 16, pp. 475 – 482
Sun, X., Du,
H., Han, N., Zhou, G., Lu, D., Ge, H., Xu, X., Liu, L. (2014) Synergistic Use
of Landsat TM and SPOT 5 imagery for Object-Based Forest Classification. Journal of Applied Remote Sensing Vol. 8
2014 083550-1 - 083550-15
Tarantino, C., Adamo, M., Lucas, R., Blonda, P. (2016) Detection of Changes in Semi-Natural
Grasslands by Cross Correlation Analysis with WorldView-2 Images and New
Landsat 8 Data. Remote Sensing of
Environment 175, pp. 65–72
Tetracam
Inc. (2011)
Agricultural Camera Digital User’s Guide. Tetracam
Inc. California, USA
Topp, L., Barker, B., Degenhardt, L. (2004) The
External Validity of Results Derived from Ecstasy Users Recruited Using
Purposive Sampling Strategies. Drug and
Alcohol Dependence 73, pp. 33–40
Trimble Documentation (2014) Ecognition
Developer Reference Book 9.0. Trimble Documentation München,
Germany
USGS (2016)
Landsat 8 Data Users Handbook. US Geological Survey Dept. of Interior South
Dakota, USA
Vorontsova, A. V., Vorontsova,
V. L., Salimgareev, D. V. (2016) The
Development of Urban Areas and Spaces with the Mixed Functional Use. Procedia Engineering 150, pp. 1996-2000
Wang, J.X., W.,
Jun, Z., Fuxia. (2011) Investigate Research of
Soil Erosion Base on Ikonos and Landsat Images in Jinning. Procedia
Engineering 15, pp. 1345 – 1349
Wang, X., Cui,
P. (2005) Support Soil Conservation Practices by Identifying Critical Erosion
Areas within an American Watershed Using the GIS-AGNPS Model. Journal of Spatial Hydrology Vol. 5 No.
2 31-44
Widodo, B., Lupyanto, R., Sulistiono, B., Harjito, D. A., Hamidin, J.,Hapsari, E., M., Yasin, C., Ellinda. (2015)
Analysis of Environmental Carrying Capacity for the Development of Sustainable
Settlement in Yogyakarta Urban Area. Procedia
Environmental Sciences 28 (2015) 519-527
Xing, Y.,
Liang, H., Xu, D. (2013) Sustainable Development Evaluation of Urban Traffic
System. Procedia Social and Behavioral Sciences 96, pp. 496 – 504
Zald, H.S.J., Michael A.W., Joanne C.W., Thomas H., Txomin
H., Geordie W.H., Nicholas C.C. (2016) Integrating Landsat pixel
composites and change metrics with lidar plots to
predictively map forest structure and above ground biomass in Saskatchewan,
Canada. Remote Sensing of Environment 176, pp. 188–201
© 2017 by the authors. Submitted for possible open
access publication under the terms and conditions of the Creative Commons
Attribution (CC-BY-NC-ND) license
(http://creativecommons.org/licenses/by/4.0/).
Article Metrics
Abstract view(s): 1801 time(s)Refbacks
- There are currently no refbacks.