Sains Malaysiana 46(4)(2017):
529–535
http://dx.doi.org/10.17576/jsm-2017-4604-03
Soil Erosion Assessment
in Tasik Chini Catchment using Remote Sensing and GIS Techniques
(Penilaian Hakisan Tanih
di LembanganTasik Chini menggunakan Teknik Pengesanan Jarak Jauh
dan GIS)
MUHAMMAD
RENDANA*,
SAHIBIN
ABDUL
RAHIM,
WAN
MOHD
RAZI
IDRIS,
TUKIMAT
LIHAN
& ZULFAHMI ALI RAHMAN
School of Environmental
& Natural Resource Sciences, Faculty of Science and Technology
Universiti Kebangsaan
Malaysian, 43600 Bangi, Selangor Darul Ehsan, Malaysia
Received: 27 February
2016/Accepted: 8 September 2016
ABSTRACT
Over many years, forested
land transformation into urban, agriculture and mining areas within
Tasik Chini Catchment become more intense. These activities have
negatively affected the catchment through soil erosion and increased
the amount of sediments that deposited into the lake. Hence, the
present study aimed to estimate soil erosion risk within Tasik Chini
Catchment integrating the Revised Universal Soil Loss Equation (RUSLE) model and remotely sensed
geospatial data. The multispectral imagery from LANDSAT 8
was used to provide up to date information on land cover within
the catchment. The result shows the majority of Tasik Chini Catchment
is classified at very low class (< 10 ton ha−1 yr−1)
about 4835.34 ha (92.38%), followed by the low class (10-50 ton
ha−1 yr−1)
with total area of 175.47 ha (3.35%), moderate high class (50-100
ton ha−1 yr−1)
with total area of 65.11 ha (1.24%), high class (100-150 ton ha−1 yr−1)
with total area of 38.37 ha (0.73%) and very high class (> 150
ton ha−1 yr−1)
with total area of 120.04 ha (2.30%). Tasik Chini Catchment is very
susceptible to soil erosion especially on northwest and southeast
regions, where the main sources of soil loss come from the agricultural,
new settlements and mining activities. To conclude, the estimation
of soil erosion model using remotely sensed data can be used to
build sustainable development strategy within Tasik Chini Catchment
in the future.
Keywords: LANDSAT 8; NDVI; RUSLE;
soil loss; Tasik Chini Catchment
ABSTRAK
Dalam tempoh masa yang
lama, transformasi kawasan hutan di Lembangan Tasik Chini kepada
kawasan-kawasan bandar, pertanian dan lombong menjadi lebih giat.
Aktiviti-aktiviti ini telah memberi kesan kepada kawasan lembangan
tersebut melalui hakisan tanih dan meningkatkan jumlah sedimen yang
masuk ke dalam tasik. Oleh itu, kajian ini bertujuan untuk meramalkan
risiko hakisan tanih di kawasan Lembangan Tasik Chini menggunakan
penggabungan model Revised Universal Soil Loss Equation (RUSLE) dan data georeruang penginderaan
jauh. Imej multispektral daripada LANDSAT 8
digunakan untuk memperoleh maklumat terkini mengenai litupan tanah
dalam lembangan. Hasil menunjukkan bahawa kebanyakan kawasan di
Lembangan Tasik Chini dikelaskan kepada sangat rendah (< 10 ton
ha−1 yr−1)
sekitar 4835.34 ha (92.38%), diikuti oleh rendah (10-50 ton ha−1 yr−1)
sekitar 175.47 ha (3.35%), sederhana tinggi (50-100 ton ha−1 yr−1)
sekitar 65.11 ha (1.24%), tinggi (100-150 ton ha−1 yr−1)
sekitar 38.37 ha (0.73%) dan kelas sangat tinggi (> 150 ton ha−1 yr−1)
sekitar 120.04 ha (2.30%). Lembangan Tasik Chini sangat kritikal
kepada hakisan tanih terutama di kawasan-kawasan barat laut dan
tenggara, dengan punca utama kehilangan tanih tersebut berasal daripada
aktiviti-aktiviti pertanian, perbandaran dan perlombongan. Kesimpulannya,
peramalan model hakisan tanih menggunakan data penginderaan jauh
dapat digunakan bagi membina strategi pembangunan yang mampan di
Lembangan Tasik Chini pada masa hadapan.
Kata kunci: Kehilangan tanih; LANDSAT
8; lembangan Tasik Chini; NDVI; RUSLE
REFERENCES
Abdulla,
H.H. 1966. A study of development of Podzol profiles in Dovey Forest.
Ph.D. Thesis, University of Wales, Aberystwyth (Unpublished).
Ahmet,
K. 2010. Estimation of C factor for soil erosion modeling using
NDVI in Buyukcekmece catchment. Ozean Journal of Applied Sciences
3: 77-85.
Avery,
B.W. & Bascomb, C.L. 1982. Soil survey laboratory methods. Harpenden:
Soil survey technical monograph No. 6.
Barzani,
M.G., Sahibin, A.R., Ekhwan, M.T., Idris, W.M.R., Tukimat, L., Zulfahmi,
A.R., Azman, H. & Norhadilla, H. 2013. Flux of nutrients and
heavy metals from the Melai River sub-catchment into Lake Chini,
Pekan, Pahang, Malaysia. Environmental Earth Sciences 68:
889-897.
Bhattarai,
R. & Dutta, D. 2007. Estimation of soil erosion and sediments
yield using GIS at catchment scale. Water Resources Management
21: 1635-1647.
Colombo,
S., Hanley, N. & Calatrava, J. 2005. Designing policy for reducing
the off-farm effect of soil erosion using choice experiments. Journal
of Agricultural Economics 56: 81-95.
Deng,
J.S., Wang, K., Deng, Y.H. & Qi, G.J. 2008. PCA-based land-use
change detection and analysis using multitemporal and multisensor
satellite data. International Journal of Remote Sensing 29:
4823-4838.
Efe,
R., Ekinci, D. & Curebel, I. 2008. Erosion analysis of Fındıklı
Creek catchment (NW of Turkey) using GIS based on RUSLE (3d) method.
Fresenius Environmental Bulletin 17: 586-576.
Jensen,
J.R. 2005. Introductory Digital Image Processing: A Remote Sensing
Perspective. New Jersey: Pearson Prentice Hall.
Jose,
C.S. & Das, D.C. 1982. Geomorphic prediction models for sediments
production rate and intensive priorities of catchments in Mayurakshi
catchment. Proceedings of the International Symposium on Hydrological
Aspects of Mountainous Catchment, University of Roorkee.
Karaburun,
A. 2009. Estimating potential erosion risks in Corlu using the GIS-based
RUSLE Method. Fresenius Environmental Bulletin 18: 1692-1700.
Kirkby,
M.J. 1980. Soil Erosion: Soil Loss Estimation. New York:
John Wiley & Sons.
Kothyari,
U.C. & Jain, S.K. 1997. Sediment yield estimation using GIS.
Hydrological Science Journal 42: 833-843.
Lee,
G.S. & Lee, H.S. 2006. Scaling effect for estimating soil loss
in the RUSLE model using remotely sensed geospatial data in Korea.
Hydrologycal Earth System Sciences Discussion 3: 135-157.
Lin,
C.Y., Lin, W.T. & Chou, W.C. 2002. Soil erosion prediction and
sediment yield estimation: the Taiwan experience. Soil and Tillage
Research 68: 143-152.
Ministry
of Natural Resources and Environment Malaysia. 2010. Preparation
of Design Guides For Erosion and Sediment Control in Malaysia. Kuala
Lumpur: Department of Irrigation and Drainage Malaysia.
Morgan,
R.P.C. 2005. Soil Erosion and Conservation. 3rd ed. Australia:
Blackwell Publishing Company.
Parveen,
R. & Kumar, U. 2012. Integrated approach of universal soil loss
equation (USLE) and geographical information system (GIS) for soil
loss risk assessment in upper South Koel Basin, Jharkhand. Journal
of Geographic Information System 4: 588-596.
Renard,
K.G., Foster, G.R., Weesies, G.A., McCool, D.K. & Yoder, D.C.
1997. Predicting Soil Erosion by Water: A Guide to Conservation
Planning with the Revised Universal Soil Loss Equation (RUSLE).
Washington: US Department of Agriculture.
Roose,
E.J. 1977. Application of the universal soil loss equation of Wischmeier
and Smith in West Africa. In Soil Conservation and Management
in the Humid Tropics, edited by Greenland, D.J. & Lal, R.
London: John Wiley & Sons.
Sujaul,
I.M., Muhd Barzani, G., Ismail, B.S., Sahibin, A.R. & Mohd Ekhwan,
T. 2012. Estimation of the rate of soil erosion in the Tasik Chini
Catchment, Malaysia using the RUSLE Model integrated with the GIS.
Australian Journal of Basic and Applied Sciences 6: 286-296.
Serra,
P., Pons, X. & Sauri, D. 2008. Land-cover and land-use change
in a Mediterranean landscape: A spatial analysis of driving forces
integrating biophysical and human factors. Applied Geography
28: 189-209.
Tew,
K. 1999. Production of Malaysian Soil Erodibility Nomograph in
Relation to Soil Erosion Issues. VT Soil Erosion Research and
Consultancy.
van
der Knijff, J.M., Jones, R.J.A. & Montanarella, L. 2000. Soil
Erosion Risk Assessment in Europe. Office for Official Publications
of the European Communities.
van
Leeuwen, W.J.D. & Sammons, G. 2004. Vegetation dynamics and
soil erosion modeling using remotely sensed data (MODIS) and GIS.
10th Biennial USDA Forest Service Remote Sensing Applications
Conference, Salt Lake City, US Department of Agriculture Forest
Service Remote Sensing Applications Center.
Wang,
G., Wente, S., Gertner, G.Z. & Anderson, A. 2002. Improvement
in mapping vegetation cover factor for the universal soil loss equation
by geostatistical methods with Landsat Thematic Mapper images. International
Journal of Remote Sensing 23: 3649-3667.
Wetlands
International Asia Pacific. 1998. The Ecological Assessment of
Chini Lake, Pahang, Peninsular Malaysia: An Evaluation of Its Conservation
Value and Environmental Improvement Requirements. Kuala Lumpur:
WIAP.
Williams,
J.R. 1975. Sediments routing for agricultural catchments. Water
Resources Bulletin 11: 965-974.
Wischmeier,
W.H. 1975. Estimating the soil loss equation’s cover and management
factor for undisturbed areas, in present and prospective technology
for predicting sediment yields and sources. Proceedings Sediment-Yields
Workshop, U.S. Department.
Wischmeier, W.H. &
Smith, D.D. 1965. Predicting rainfall-erosion losses from cropland
east of Rocky Mountains: Guide for selection of practices for soil
and water conservation. Agricultural handbook 282. United States:
US Department of Agriculture.
Yuan, F. 2008. Land-cover
change and environmental impact analysis in the Greater Mankato
area of Minnesota using remote sensing and GIS modeling. International
Journal of Remote Sensing 29: 1169-1184.
*Corresponding
author; email: mrendana02@gmail.com
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