 |
 |
 |
 |
 |
 |
Sains Malaysiana 52(5)(2023):
http://doi.org/10.17576/jsm-2023-5205-02
Keupayaan Aplikasi Indeks Spektrum dalam Penentuan Perubahan Pantai (Applicability of Spectral Indices in Determination of Coastal Changes)
SARAVANAKKUMAR
NACHIMUTHU & MUZZNEENA AHMAD MUSTAPHA*
Jabatan Sains Bumi dan Alam Sekitar, Fakulti Sains dan Teknologi, Universiti Kebangsaan Malaysia, 43600 UKM Bangi,
Selangor Darul Ehsan, Malaysia
Received: 17 June 2022/Accepted:
5 May 2023
Abstrak
Pantai penting dalam
menyediakan pelbagai perkhidmatan ekosistem. Garis pantai berubah secara
dinamik dan analisis perubahan garis pantai berupaya dilakukan oleh teknologi
penderiaan jauh dan GIS. Tujuan kajian ini adalah mengukur keupayaan indeks
spektrum seperti Modified Normalized Difference Water Index (MNDWI), Normalized
Difference Vegetation Index (NDVI) dan Soil Adjusted Vegetation Index (SAVI) dalam membezakan litupan tanah serta penentuan perubahan garis pantai di
pantai barat, Johor antara tahun 2000 dan 2020. Penyelidikan ini dijalankan
dengan analisis data imej satelit Landsat 7 ETM+ (2000) dan Landsat 8 OLI/TIRS
(2020) menggunakan perisian ERDAS dan ArcGIS. Imej indeks spektrum dijana bagi
penentuan garis pantai melalui pengelasan OTSU. Tindan lapis imej dibuat bagi
menentukan perubahan garis pantai. Penggunaan indeks spektrum dalam kajian ini
menunjukkan bahawa ketiga-tiga indeks spektrum tersebut mampu membezakan air
dan darat dengan berkesan di sepanjang pantai barat Johor. MNDWI didapati mempunyai
ketepatan keseluruhan 99.00% (2000) dan 97.50% (2020) dan nilai Kappa yang
paling tinggi bagi kedua-dua imej satelit Landsat, 0.98 (2000) dan 0.95
(2020). Indeks NDVI dan SAVI mempunyai
ketepatan yang sama iaitu 95.00% (2000) dan 96.50% (2020) dan nilai Kappa sama
sebanyak 0.90 (2000) dan 0.93 (2020). Pantai barat, Johor telah mengalami pengurangan pantai sebanyak 583.48
hektar dan penambahan 846.85 hektar. Pengurangan yang lebih tinggi diperhatikan
di sepanjang pantai Batu Pahat dan Pontian manakala garis pantai di pantai
utara Pontian menunjukkan jumlah penambahan yang sangat tinggi. Kajian ini
dapat memanfaatkan pihak berkepentingan dengan memberi status perubahan garis
pantai terkini untuk mengambil langkah yang berkesan bagi pembangunan dan pengurusan
pantai.
Kata kunci: Indeks spektrum; Landsat; pantai barat Johor; perubahan garis pantai
Abstract
Coast is
essential in providing wide range of ecosystem services. Shorelines change
dynamically, and analysing shoreline changes can be conducted with Remote
sensing and GIS technologies. This study aims to measure applicability of
spectral indices of Modified Normalized Difference Water Index (MNDWI),
Normalised Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation
Index (SAVI) to distinguish land cover classes and determine coastline changes
on west coast of Johor between 2000 and 2020. This study analysed satellite
image data obtained from Landsat 7 ETM+ (2000) and Landsat 8 OLI/TIRS (2020)
using ERDAS and ArcGIS software. Spectral index images were generated for
shoreline determination through OTSU classification. Image overlays are created
to determine shoreline changes. The use of spectral indices showed that the
three spectral indexes could effectively distinguish water and land along west
coast of Johor. MNDWI had an overall accuracy of 99.00% (2000) and 97.50%
(2020) and highest Kappa value for both Landsat satellite images, 0.98 (2000)
and 0.95 (2020). The NDVI and SAVI indices have the same accuracy of 95.00%
(2000) and 96.50% (2020) and Kappa value of 0.90 (2000) and 0.93 (2020). The
west coast of Johor has experienced a reduction of 583.48 hectares of coastline
and accretion of 846.85 hectares. Higher reduction was observed along Batu Pahat and Pontian coasts,
while the shoreline on the north shore of Pontian showed a very high amount of
accretion. This study can benefit stakeholders by giving the status of the
latest coastline changes in implementing effective coastal development and
management measures.
Keywords: Landsat; shoreline change; spectral index; west coast Johor
REFERENCES
Acharya, T.D., Subedi, A. & Lee, D.H. 2018.
Evaluation of water indices for surface water extraction in a Landsat 8 scene
of Nepal. Sensors 18(8): 1-15.
Adamu,
B., Tansey, K. & Ogutu, B. 2018. Remote sensing
for detection and monitoring of vegetation affected by oil spills. International
Journal of Remote Sensing 39(11): 3628-3645.
Azlan,
N.I. & Othman, R. 2009. Monitoring of mangrove area using remote sensing
toward shoreline protection. GIS Ostrava 1: 25-28.
http://gisak.vsb.cz/GIS_Ostrava/GIS_Ova_2009/sbornik/Lists/Papers/102.pdf
Duru,
U. 2017. Shoreline change assessment using multi-temporal satellite images: A
case study of Lake Sapanca, Nw Turkey. Environmental Monitoring and Assessment 189(8): 1-14.
Ehsan,
S., Ara Begum, R., Ghani Md Nor, N. & Nizam Abdul Maulud, K. 2019. Current and potential impacts of sea level
rise in the coastal areas of Malaysia. IOP Conference Series: Earth and
Environmental Science. hlm. 1-11.
Hamdi, A.E., Maryati, M. & Shafiq Hamdin, M. 2019.
The potential of nature tourism at Muar and Tangkak Districts, Johor, Malaysia. IOP Conference Series: Earth and Environmental Science. hlm. 1-12.
Hamzah, M.L., Amir, A.A., Maulud, K.N.A., Sharma, S., Mohd,
F.A., Selamat, S.N., Karim, O., Ariffin, E.H. & Begum, R.A. 2020.
Assessment of the mangrove forest changes along the Pahang Coast using remote
sensing and GIS technology. Journal of Sustainability Science and Management 15(5): 43-58.
Hassan, M.I. & Rahmat, N.H. 2016. The effect of
coastline changes to local community’s social-economic. ISPRS -
International Archives of the Photogrammetry, Remote Sensing and Spatial
Information Sciences XLII-4/W1. pp. 25-36.
Huete, A.R. 1988. A soil-adjusted vegetation index (Savi). Remote
Sensing of Environment 25(3): 295-309.
Kumar, M. 1988. World Geodetic System 1984: A modern
and accurate global reference frame. Marine Geodesy 12(2): 117-126.
Lechner, A.M., Foody, G.M. & Boyd, D.S. 2020.
Applications in remote sensing to forest ecology and management. One Earth 2(5): 405-412.
Li,
W., El-Askary, H., Qurban,
M.A., Li, J., ManiKandan, K.P. & Piechota, T. 2019. Using multi-indices approach to quantify
mangrove changes over the western Arabian Gulf along Saudi Arabia coast. Ecological
Indicators 102: 734-745.
Lynch, P., Blesius, L. & Hines, E. 2020.
Classification of urban area using multispectral indices for urban planning. Remote
Sensing 12(15): 2503.
Malaysia,
S.A. 2020. Impacts of Coastal Reclamation in Malaysia. Pulau Pinang: SAM’s Publication.
Maulud, K.N.A. & Rafar, R.M. 2015. Determination
the impact of sea level rise to shoreline changes using GIS. International
Conference on Space Science and Communication, IconSpace. hlm. 352-357.
McFeeters,
S.K. 1996. The use of the Normalized Difference Water Index (NDWI) in the
delineation of open water features. International Journal of Remote Sensing 17(7): 1425-1432.
McHugh, M.L. 2012. Interrater reliability: The Kappa
statistic. Biochemia Medica 22(3): 276-282.
Mohd Razali, S., Nuruddin, A.A. & Kamarudin, N.
2020. Mapping mangrove density for conservation of the Ramsar Site in
Peninsular Malaysia. International Journal of Conservation Science 11(1):
153-164.
Mustafa, M.T., Hassoon, K.I., Hussain, H.M. & Abd, M.H.
2017. Using water indices (NDWI, MNDWI, NDMI, WRI and AWEI) to detect physical
and chemical parameters by apply remote sensing and GIS techniques. International
Journal of Research-Granthaalayah 5(10): 117-128.
Omar, H., Misman, M.A. & Musa, S. 2019. GIS and
remote sensing for mangroves mapping and monitoring. In Geographic
Information Systems and Science,disunting oleh Rocha, J. & Abrantes, P. IntechOpen.
https://doi.org/10.5772/intechopen.75243
Otsu, N. 1979. A threshold selection method from
gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1): 62-66.
Patel,
A.K., Ghosh, J.K. & Sayyad, S.U. 2020. Fractional abundances study of
macronutrients in soil using hyperspectral remote sensing. Geocarto International 37(2): 474-493.
Paul,
E.A. 2016. The nature and dynamics of soil organic matter: Plant inputs,
microbial transformations, and organic matter stabilization. Soil Biology
and Biochemistry 98: 109-126.
Rhyma, P.P., Norizah, K., Hamdan, O., Faridah-Hanum,
I. & Zulfa, A.W. 2020. Integration of normalised different vegetation index
and soil-adjusted vegetation index for mangrove vegetation delineation. Remote
Sensing Applications: Society and Environment 17: 1-14.
Rondeaux,
G., Steven, M. & Baret, F. 1996. Optimization of
soil-adjusted vegetation indices. Remote Sensing of Environment 55(2):
95-107.
Samanta, S. & Paul, S.K. 2016. Geospatial analysis
of shoreline and land use/land cover changes through remote sensing and GIS
techniques. Modeling Earth Systems and Environment 2(3): 1-8.
Sany,
S.B.T., Tajfard, M., Rezayi,
M., Rahman, M.A. & Hashim, R. 2019. The west
coast of Peninsular Malaysia. Dlm. World Seas: An
Environmental Evaluation, disunting oleh Sheppard, C. United Kingdom: Elsevier. hlm. 437-458.
Sarmin,
N., Hasmadi, I.M., Pakhriazad,
H. & Khairil, W. 2016. The DPSIR framework for
causes analysis of mangrove deforestation in Johor, Malaysia. Environmental
Nanotechnology, Monitoring & Management 6: 214-218.
Sun,
F., Sun, W., Chen, J. & Gong, P. 2012. Comparison and improvement of
methods for identifying waterbodies in remotely sensed imagery. International
Journal of Remote Sensing 33(21): 6854-6875.
Szabó, S., Gácsi, Z. & Balázs, B. 2016. Specific
features of NDVI, NDWI AND MNDWI as reflected in land cover categories. Landscape
& Environment 10(3-4): 194-202.
Toure, S., Diop, O., Kpalma, K. & Maiga, A.S.
2019. Shoreline detection using optical remote sensing: A review. ISPRS
International Journal of Geo-Information 8(2): 1-21.
U.S. Geological Survey. 2021. URL
https://waterdata.usgs.gov/nwis/.
Wicaksono, A. & Wicaksono, P. 2019. Geometric
accuracy assessment for shoreline derived from NDWI, MNDWI, and AWEI
transformation on various coastal physical typology in jepara regency using
Landsat 8 OLI imagery in 2018. Geo-planning Journal of Geomatics and
Planning 6(1): 55-72.
Xu, H. 2006. Modification of Normalised Difference Water
Index (NDWI) to enhance open water features in remotely sensed imagery. International
Journal of Remote Sensing 27(14): 3025-3033.
Xu, N. 2018. Detecting coastline change with all available
landsat data over 1986-2015: A case study for the state of Texas, USA. Atmosphere 9(3): 1-20.
Yatim, M.H., Norazlimi, N.A. & Abdul-Latiff,
M.A.B. 2019. Avifauna study of Tanjung Piai, Johor, Malaysia. IOP Conference
Series: Earth and Environmental Science, hlm. 1-7.
Yunus, M.Z.M., Omar, C.M. & Ismail, Z. 2016.
GIS-based assessment of mangrove response to shoreline change along the coast
of Kukup Island, Johor. ARPN Journal of Engineering and Applied Sciences 11(6): 3747-3754.
*Corresponding author; email: muzz@ukm.edu.my
|
|||
|
