Conference PaperPDF Available

Empirical Algorithm Modeling for Estimating Total Suspended Solid Concentration Using In-situ Data and Atmospheric Corrected Landsat 8, Case Study: Gili Iyang's Waters

Authors:

Abstract and Figures

TSS is one of frequently used parameter for monitoring seawater quality. Traditional measurement of TSS requiring laborious laboratory work, which is often expensive and time consuming. In this research, remote sensing technology used for estimating TSS concentrations over Gili Iyang's waters by a high accuracy algorithm models were developed. The in situ data of TSS concentration were taken on October 15, 2015 (6 stations) and Landsat 8 data that has been atmospheric corrected using FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube) were used. From the analysis, algorithm model produced determination coefficient (R 2) of 0.661. The estimated TSS concentration by using the developed algorithm model ranged from 12.50 to 16.97 g/m 3 .
Content may be subject to copyright.
The 2nd Internasional Seminar on Science and Technology (ISST) for Sustainable Infrastructure
Empowering Research and Technology for Sustainable Infrastructure – 2nd August 2016
Geometics Engineering 379
Empirical Algorithm Modeling for
Estimating Total Suspended Solid
Concentration Using In-situ Data and
Atmospheric Corrected Landsat 8, Case
Study: Gili Iyang’s Waters
Nia Kurniadin
Postgraduate Program, Department of
Geomatics Engineering, Institut Teknologi
Sepuluh Nopember
nia.kurniadin14@mhs.geodesy.its.ac.id
Lalu Muhamad Jaelani
Department of Geomatics Engineering,
Institut Teknologi Sepuluh Nopember
lmjaelani@geodesy.its.ac.id
Abstract- TSS is one of frequently used
parameter for monitoring seawater quality.
Traditional measurement of TSS requiring
laborious laboratory work, which is often
expensive and time consuming. In this research,
remote sensing technology used for estimating
TSS concentrations over Gili Iyang’s waters by
a high accuracy algorithm models were
developed. The in situ data of TSS concentration
were taken on October 15, 2015 (6 stations) and
Landsat 8 data that has been atmospheric
corrected using FLAASH (Fast Line-of-sight
Atmospheric Analysis of Spectral Hypercube)
were used. From the analysis, algorithm model
produced determination coefficient (R2) of
0.661. The estimated TSS concentration by using
the developed algorithm model ranged from
12.50 to 16.97 g/m3.
Index Terms - Empirical algorithm, FLAASH,
Landsat 8, TSS
INTRODUCTION
TSS is one of the important indexes for
evaluation of water quality. Traditional
measurement of TSS is spend a lot of time and
exhausting, sampling is often not enough to
measure all water bodies. Fortunately, remote
sensing can offer attentively, repetitive,
consistent, effective of cost and comprehensive
spatial and temporal views [1][2][3]. Remote
sensing is the science of studying the information
retrieval about the Earth's surface, land and sea,
from the image obtained from a distance. This
method is usually dependent on the measurement
of electromagnetic energy reflected or emitted by
the object being observed [4]. Physical parameter
retrieval algorithms and the accurate atmospheric
correction algorithm are the two factors which
influence the accuracy of estimated data derived
from remote sensing [5], [6]. The goal of this
research is to develop accurate algorithm to
estimate TSS concentration using remote sensing
reflectance of Landsat 8 over Gili Iyang’s waters.
METHODS
The data in this research were collected from
Gili Iyang’s waters, Dungkek, Sumenep, East
Java Province. A small island in the southeast of
Madura. Geographically located in 114.15o to
114.19o E and 6.96o to 7.01o S.
Landsat 8 image data with path/row of 117/065
and acquisition date on October 15, 2015 were
used. By using atmospheric correction FLAASH
model from image processing software, the
Landsat 8 image data were corrected from
atmospherically effect. The equation is as follow:
a
e
e
e
L
S
B
S
A
L
11 (1)
where:
is the pixel surface reflectance,
e is
an average surface reflectance for the pixel and a
surrounding region, S is the spherical albedo of
the atmosphere, La is the radiance back scattered
by the atmosphere, A and B are coefficients that
depend on atmospheric and geometric condition
but not on the surface.
While in-situ data of seawater samples (6
samples) collected at the same acquisition date of
Landsat 8 image data. Furthermore, laboratory
work was required for analyzing the water
samples to calculate TSS concentration.
Developing empirical algorithms using
regression algorithm from measured-TSS
concentration as well as single-band, two-band
The 2
nd
Internasional Seminar on Science and Technology (ISST) for Sustainable Infrastructure
Empowering Research and Technology for Sustainable Infrastructure – 2
nd
August 2016
Geometics Engineering 380
ratios and combination of bands of remote sensing
reflectance (Rrs()) [7].
R
ESULT AND DISCUSSION
The empirical algorithm with the best
determination coefficient (R
2
= 0.661), two-band
ratio of
3
and
4
were used as presented on
Equation (2) and Figure 1:


b
RrsLog
RrsLog
aTSS
4
3
*
(2)
F
IGURE
3.
L
INIER REGRESSION FOR
TSS
ESTIMATION
T
ABLE
1.
C
OMPARISON BETWEEN MEASURED AND
ESTIMATED
TSS
Station
Number
Estimated
TSS
Measured
TSS
Sta_01 16.15 16.00
Sta_02 16.20 16.00
Sta_03 16.97 18.00
Sta_04 14.16 14.00
Sta_05 12.50 14.00
Sta_06 14.02 12.00
TSS concentration over Gili Iyang’s waters
was estimated using developed empirical
algorithm with remote sensing reflectance as pure
input. Estimated-TSS concentration in ranged
12.50 to 16.97 g/m
3
. The estimation of TSS and
in-situ TSS were similar (Table 1). It was
indicated that the developed empirical algorithm
applicable over Gili Iyang’s water. Distribution
map of TSS concentration was shown at Figure 4.
F
IGURE
4.
D
ISTRIBUTION MAP OF
TSS
FROM
L
ANDSAT
8
C
ONCLUSSION
The empirical algorithm to estimate TSS
concentration with coefficient determination of
0.661 were developed and implemented in Gili
Iyang’s waters. The estimated-TSS from Landsat
8 image produced a distribution map of TSS in
ranged 12.50 to 16.97 g/m
3
.
R
EFERENCES
[1] Y. Dingtian, P. Delu, Z. Xiaoyu, Z. Xiaofeng, H.
Xianqiang, and L. Shujing, “Retrieval of chlorophyll a
and suspended solid concentrations by hyperspectral
remote sensing in Taihu Lake, China,” Chinese J.
Oceanol. Limnol., vol. 24, no. 4, pp. 428–434, 2006.
[2] N. Laili, F. Arafah, L. M. Jaelani, L. Subehi, A.
Pamungkas, E. S. Koenhardono, and A. Sulisetyono,
“Development of Water Quality Parameter Retrieval
Algorithms for Estimating Total Suspended Solids and
Chlorophyll-a Concentration Using Landsat-8 Imagery
At Poteran Island Water,” ISPRS Ann. Photogramm.
Remote Sens. Spat. Inf. Sci., vol. II–2/W2, no. October,
pp. 55–62, 2015.
[3] R. Limehuwey and L. M. Jaelani, “Development
of Algorithm Model for Estimating Cholophyll-a
Concentration Using In-Situ Data and Atmospherically
Corrected Landsat-8 Image by 6SV, Case Study: Gili
Iyang’s Waters,” in Internasional Seminar of Basic
Science, 2016.
[4] James B. Campbell, Introduction to Remote
Sensing. New York: The Guilford Press, 1987.
[5] L. M. Jaelani, B. Matsushita, W. Yang, and T.
Fukushima, “An improved atmospheric correction
algorithm for applying MERIS data to very turbid
inland waters,” Int. J. Appl. Earth Obs. Geoinf., vol. 39,
pp. 128–141, 2015.
[6] L. M. Jaelani, R. Limehuwey, N. Kurniadin, A.
Pamungkas, E. S. Koenhardono, and A. Sulisetyono,
“Estimation of TSS and Chl - a Concentration from
Landsat 8 - OLI : The Effect of Atmosphere and
Retrieval Algorithm,” IPTEK, J. Technol. Sci., vol. 27,
no. 1, pp. 16–23, 2016.
[7] R. Aguirre-Gomez, Detection of Total
Suspended Sediments in the North Sea using AVHRR
and Ship Data,” Int. J. Remote Sens., vol. 21, no. 8, pp.
1583–1596, 2000.
Conference Paper
Full-text available
The Sea Surface Temperature (SST) retrieval from satellites data has been available since 1980's both temporally and spatially. Some researchers have established SST inversion models depending on the correlation between the TM/ETM+ TIR radiance and the in-situ data. The objective of this research is to evaluate the performance of Landsat 8-estimated SST from 4 existing algorithms: Planck, Mono-Window Algorithm (MWA), Syariz and Split Window Algorithm (SWA) algorithms on 4 different tested areas: Eastern Bali, Bangkalan, Bombana and Poteran waters. Algorithm of Syariz dan SWA produced acceptable accuracy on all tested area with the NMAE ranged at 0.2-19.6% and 3.4-9.9% for Syariz and SWA, respectively. However, MWA and Planck produced NMAE larger than 30% on Bali and Poteran waters. Following the successful of SWA algorithm, the same algorithm was developed using insitu data collected on Poteran waters. The estimated SST by the developed algorithm produced acceptable accuracies on all tested water areas with the NMAE ranged from 0.401% to 16.630%. It was indicated that Syariz, SWA and the developed algorithms were applicable for SST retrieval on all tested waters
Conference Paper
Full-text available
Chlorophyll-a (Chl-a) is an important parameter for monitoring the quality of seawater. By implementing a high accuracy of algorithm model, the concentrations of chl-a could be estimated from satellite remote sensing data. In this research, algorithm models for estimating the concentration of chl-a over Gili Iyang's waters were developed. For the development purposes, the in situ data of chl-a were taken on October 15, 2015 at 08:53 to 10:37 am (6 stations) and Landsat-8 data that has been atmospherically corrected using Second Simulation of Satellite Signal in the Solar Spectrum Vector (6SV). The regression model for estimating chl-a produced a high with determination coefficient of 0.697. By applying the developed algorithm, the estimated of chl-a ranged from 114.158 to 147.379 mg/m3 .
Article
Full-text available
Total Suspended Sediment (TSS) and Chlorophyll-a (Chl-a) are globally knows as a key parameters for regular seawater monitoring. Considering the high temporal and spatial variation of water constituent, remote sensing technique is an efficient and accurate method for extracting water physical parameter. A high accurate estimated data derived from remote sensing depends on an accurate atmospheric correction algorithm and physical parameter retrieval algorithms. In this research, we evaluated the accuracy of atmospheric corrected product of NASA as well as develop algorithms for estimating TSS and Chl-a concentration over Poteran and Gili Iyang island water using Landsat-8 OLI data. The data used in this study was collected from Poteran’s waters (9 stations) on April 22, 2015 and Gili Iyang’s waters (six stations) on October 15, 2015. Low correlation between in situ and Landsat Rrs(λ) (R2= 0.106) indicated that atmospheric correction algorithm performed by NASA has a limitation. The TSS concentration retrieval algorithm produced acceptable accuracy both over Poteran’s Waters (RE of 4.60% and R2 of 0.628) and over Gili Iyang’s waters (RE of 14.82% and R2 of 0.345). Although the R2 lower than 0.5, the relative error was more accurate than the minimum requirement of 30%. Whereas, The Chl-a concentration retrieval algorithm produced acceptable result over Poteran (RE of 13.87% and R2 of 0.416) and failed over Gili Iyang’s waters (RE of 99.140 and R2 of 0.090). The low correlation between TSS or Chl-a measured and estimated TSS or Chl-a concentration were caused not only by performance of the developed TSS and Chl-a estimation retrieval algorithm but also the effect and accuracy of atmospheric corrected reflectance of Landsat product.
Conference Paper
Full-text available
The Landsat-8 satellite imagery is now highly developed compares to the former of Landsat projects. Both land and water area are possibly mapped using this satellite sensor. Considerable approaches have been made to obtain a more accurate method for extracting the information of water area from the images. It is difficult to generate an accurate water quality information from Landsat images by using some existing algorithm provided by researchers. Even though, those algorithms have been validated in some water area, but the dynamic changes and the specific characteristics of each area make it necessary to get them evaluated and validated over another water area. This paper aims to make a new algorithm by correlating the measured and estimated TSS and Chla concentration. We collected in-situ remote sensing reflectance, TSS and Chl-a concentration in 9 stations surrounding the Poteran islands as well as Landsat 8 data on the same acquisition time of April 22, 2015. The regression model for estimating TSS produced high accuracy with determination coefficient (R 2), NMAE and RMSE of 0.709; 9.67 % and 1.705 g/m 3 respectively. Whereas, Chla retrieval algorithm produced R 2 of 0.579; NMAE of 10.40% and RMSE of 51.946 mg/m 3. By implementing these algorithms to Landsat 8 image, the estimated water quality parameters over Poteran island water ranged from 9.480 to 15.801 g/m 3 and 238.546 to 346.627 mg/m 3 for TSS and Chl-a respectively.
Article
Full-text available
Chlorophylla (chl-a) and suspended solid concentrations are two frequently used water quality parameters for monitoring a lake. Traditional measurement of chl-a and suspended solids, requiring laborious laboratory work, which is often expensive and time consuming. Hyperspectral remote-sensing measurement provides a fast and easy tool for estimating water trophic status.In situ hyperspectral data on March 7–8, July 6–7, September 20 and December 7–8, 2004 and the corresponding water chemical data were used to regress the algorithm of water quality parameters. Results showed that the peak of water leaving radiance around 700 nm (R 700) varied proportionally with chl-a concentration, and moved to infrared when algal bloom occurred. The reflectance ratio ofR 702/R 685 was well correlated with chl-a when water surface in no algal bloom case and the correlation coefficient was better if absorption of phycocyanin was considered. The reflectance ratioR 620/R 531 was highly correlated to the concentration of suspended solids. The relationship between suspended solids and other band groups were also compared. Secchi disk depth could be calculated by non-linear correlation with suspended solids concentration.
Article
Full-text available
This study reports on a combined investigation between in situ measurements of total suspended sediments, collected by ship, and remotely sensed data provided by the Advanced Very High Resolution Radiometer (AVHRR) during the winter-summer period of 1989. It is meant to be a case study stating the problems inherent to coastal waters and proposing a methodology to understand them. Ship and satellite data are compared in order to detect suspended sediments in case 2 waters in the North Sea through a linear regression analysis. The results show a wide range of coefficient of determination (R2) values. The highest values correspond to summer dates, while the lowest belonged to the winter and spring period. It was found that for the summer dates the relatively still atmospheric and water conditions were suitable for comparison with satellite data providing good values of R2. In winter and spring, unsettled sea water conditions complicated the comparison of the data. It can be concluded that the seasonal stratification of the water column during summer time allows a better correlation between in situ and remotely sensed data than the typically well-mixed waters during winter
Article
Atmospheric correction (AC) is a necessary process when quantitatively monitoring water quality parameters from satellite data. However, it is still a major challenge to carry out AC for turbid coastal and inland waters. In this study, we propose an improved AC algorithm named N-GWI (new standard Gordon and Wang’s algorithms with an iterative process and a bio-optical model) for applying MERIS data to very turbid inland waters (i.e., waters with a water-leaving reflectance at 864.8 nm between 0.001 and 0.01). The N-GWI algorithm incorporates three improvements to avoid certain invalid assumptions that limit the applicability of the existing algorithms in very turbid inland waters. First, the N-GWI uses a fixed aerosol type (coastal aerosol) but permits aerosol concentration to vary at each pixel; this improvement omits a complicated requirement for aerosol model selection based only on satellite data. Second, it shifts the reference band from 670 nm to 754 nm to validate the assumption that the total absorption coefficient at the reference band can be replaced by that of pure water, and thus can avoid the uncorrected estimation of the total absorption coefficient at the reference band in very turbid waters. Third, the N-GWI generates a semi-analytical relationship instead of an empirical one for estimation of the spectral slope of particle backscattering. Our analysis showed that the N-GWI improved the accuracy of atmospheric correction in two very turbid Asian lakes (Lake Kasumigaura, Japan and Lake Dianchi, China), with a normalized mean absolute error (NMAE) of less than 22% for wavelengths longer than 620 nm. However, the N-GWI exhibited poor performance in moderately turbid waters (the NMAE values were larger than 83.6% in the four American coastal waters). The applicability of the N-GWI, which includes both advantages and limitations, was discussed.
Book
A leading text for undergraduate- and graduate-level courses, this book introduces widely used forms of remote sensing imagery and their applications in plant sciences, hydrology, earth sciences, and land use analysis. The text provides comprehensive coverage of principal topics and serves as a framework for organizing the vast amount of remote sensing information available on the Web. Featuring case studies and review questions, the book's 4 sections and 21 chapters are carefully designed as independent units that instructors can select from as needed for their courses. Illustrations include 29 color plates and over 400 black-and-white figures. New to This Edition Reflects significant technological and methodological advances. Chapter on aerial photography now emphasizes digital rather than analog systems. Updated discussions of accuracy assessment, multitemporal change detection, and digital preprocessing. Links to recommended online videos and tutorials.
Article
This popular text introduces students to widely used forms of remote sensing imagery and their applications in plant sciences, hydrology, earth sciences, and land use analysis. Providing comprehensive coverage of principal topics in the field, the book's 4 sections and 21 chapters are carefully designed as independent units that instructors can select from as needed for their courses. Relevant case studies and review questions that reinforce the concepts presented in each chapter make this book essential reading for students in remote sensing. Illustrations include 28 color plates and nearly 400 black-and-white images and figures.