Agus Suharyanto, Ery Suhartanto, Pudyono Pudyono


Critical land classification can be analyzed using combination between Top Soil Thickness - Land erosion method, and BRLT methods. Both methods are needed soil erosion data as one of input data. The soil erosion data can be analyzed using USLE and MUSLE methods. The combination of two critical land analyses methods with input soil erosion data from two analyses methods will be produced four combinations of critical land classification. In this research, four of the critical land classification and two soil erosion classification will be analyzed using GIS. The best method to classify critical land will be investigated in this research. The best classified critical land is the classified critical land data is nearest with the field condition.
Percentage of vegetation cover (PVC) is one of the most important input data in the critical land classification analysis using BRLKT method. This data have 50% weight. PVC condition is classified into five categories i.e. very good, good, fair, poor, and very poor. Each category have score 5, 4, 3, 2, 1 respectively. To analyze this PVC classification, NDVI generated from satellite remote sensing data is used in this research. From the four methods of land critical classification analyses used in this research, critical land classified using BRLKT method with input soil erosion analyzed using method is produced the critical land classification nearest with the critical land condition in the field.

Keywords: Critical land, Land erosion, GIS, Satellite Remote Sensing Data, NDVI

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Anonymous. 1998. Guidance book for detail engineering design compilation of land rehabilitation and soil conservation in watershed. Ministry of Forestry Indonesia (in Indonesian). p. 50-110.

Ghanshyam, D.A.S. 2004. Hydrology and soil conservation engineering, Prentice Hall of India. p. 81-203.

Harianto. 2007. River basin development and its impact to sediment balance in the basin case study: the Brantas river basin Indonesia. Second International Workshop on Water and Sediment Management, JasaTirta I Public Corporation. Indonesia. p.25- 33.

Hardjowigeno, S.W. 2001.Land suitability and land use plan. Soil Science Department, Bogor Agriculture Institute. Bogor-Indonesia. p.110-155.

Mather, P.M. 1987. Computer processing of remotely sensed images. Chichester. p.76-144.

Morawitz, D.F., T.M. Blewett, A. Cohen and M. Alberti. 2006. Using NDVI to asses vegetative land cover change in central puget sound. Journal of Environmental Monitoring and Assessment. Springer 114: 85-106.

Nakagawa, H., Y. Satofuka, S. Oishi, Y. Muto, T. Sayama, K. Takara and R.H. Sharma. 2007. Rainfall and sediment run off in the Lesti river basin, Tributary of the Brantas river, Indonesia. Brantas River Workshop in Malang, Indonesia. p.45-51.

Ozcan, A.U., G. Erpul, M. Basaran and H. E. Erdogan. 2008. Use of USLE/GIS techno-logy integrated with geostatistics to assess soil erosion risk in different land use of Indagi mountain pass Çangkiri, Turkey. Journal of Environment and Geology. Springer. 53: 1731-1741.

Park S.D., K.S. Lee and S.S. Shin. 2012. Statistical soil erosion model for Burnt mountain areas in Korea – RUSLE Approach. J. of Hydrologic Engineering 17(2): 292-304.

Prakash A.K., I.V. Muralikrishna, P.K. Mishra and R.V.R.K. Chalam. 2007. Deciding alternative land use option in watershed using GIS, Journal of Irrigation and Drainage Engineering. 133 (2): 162-174.


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