Perbandingan Metode Soil Adjusted Vegetation Index (SAVI) dan Forest Canopy Density (FCD) untuk Identifikasi Tutupan Vegetasi (Kasus; Area Pembuatan Jalan Baru Singaraja-Mengwi)
(1) Universitas Pendidikan Ganesha
(2) Universitas Pendidikan Ganesha
Abstract
This research uses Landsat 8 OLI/TIRS image which objective to determine the accuracy level of SAVI method and FCD model in the identification of vegetation cover. It is done as an effort to assist in determining the right method of monitoring the change of vegetation cover in the forest area. Therefore, this research compares the vegetation index of Soil Adjusted Vegetation Index (SAVI) because it is able to suppress the background of the soil so that the vegetation cover is able to be displayed according to the conditions in the field. While the FCD model uses four variables such as; Advanced Vegetation Index (AVI), Bare Soil Index (BI), Shadow Index (SI), and thermal index using the Split-Windows Algorithm (SWA) method. Comparison results between SAVI and FCD models indicate that the higher accuracy of SAVI is 84% and FCD model is only 82%. It is possible because the limited use of research areas that show SAVI is superior due to heterogeneous conditions and it approaches the conditions in the field than the FCD model that is more group and only able to be realized in three classes. Based on the results, it was concluded that the vegetation index can be used in monitoring the limited area of research but it is also not absolute because it is possible that FCD model is better.
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Ariyani, R., & B.S., S. H. M. (2016). Transformasi Forest Canopy Density Dan Second Modified Soil Adjusted Vegetation Index Untuk Monitoring Degradasi Hutan Lindung Dan Taman Nasional Di Sarolangun Jambi. Jurnal Bumi Indonesia, 5(3).
Ashaari, F., Kamal, M., & Dirgahayu, D. (2018). Comparison of Model Accuracy in Tree Canopy Density Estimation Using Single Band, Vegetation Indices and Forest Canopy Density (Fcd) Based on Landsat-8 Imagery (Case Study: Peat Swamp Forest in Riau Province). International Journal of Remote Sensing and Earth Sciences (IJReSES), 15(1), 81. https://doi.org/10.30536/j.ijreses.2018.v15.a2845
Azizi, Z., Najafi, A., & Sohrabi, H. (2008). Forest Canopy Density Estimating , Using Satellite Images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII(Part B8), 1127–1130.
Balipost. (2019). Bangun Shortcut 7 hingga 10, Pembebasan Lahan Mulai Dilakukan. Www.Balipost.Com. https://www.balipost.com/news/2019/10/24/90778/Bangun-Shortcut-7-hingga-10,...html
Boehm, H. D. V., Siegert, F., & Liews, S. C. (2002). Remote Sensing and Aerial Survey of Vegetation Cover Change in Lowland Peat Swamp of Central Kalimantan during the 1997 and 2002 Fires. Proceeding of the International Symposium on Land Management and Biodiversity in Southeast Asia.
Chavez, J. (1988). Animproved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24, 159–279.
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B
Danoedoro, P. (2012). Pengantar Penginderaan Jauh Digital. Andi Offset.
Department of the Interior U.S. Geological Survey. (2016). Landsat 8 Data Users Handbook. In Nasa (Vol. 8, Issue June). https://landsat.usgs.gov/documents/Landsat8DataUsersHandbook.pdf
Grainger, A. (1993). Rates of Deforestation in the Humid Tropics: Estimates and Measurements. The Geographical Journal, 159(1), 33–44.
Huete, A, Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213. https://doi.org/10.3390/rs4103201
Huete, Ar. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Kumar, J., Talwar, P., & A.P., K. (2015). Forest Canopy Density and ASTER DEM based Study for Dense Forest Investigation using Remote Sensing and GIS Techniques around East Singhbhum in Jharkhand, India. International Journal of Advanced Remote Sensing and GIS, 4(1), 1026–1032. https://doi.org/10.23953/cloud.ijarsg.96
Loi, D. T., Chou, T.-Y., & Fang, Y.-M. (2017). Integration of GIS and Remote Sensing for Evaluating Forest Canopy Density Index in Thai Nguyen Province, Vietnam. International Journal of Environmental Science and Development, 8(8), 539–542. https://doi.org/10.18178/ijesd.2017.8.8.1012
Mothi Kumar, K. E., Kumar, R., Kumar, P., Sattyam, Sihag, V., Partibha, Singh, K., Rani, S., Sharma, P., Hooda, R. S., & Singh, T. P. (2018). Forest canopy density assessment using high resolution LISS-4 data in Yamunanagar District, Haryana. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(5), 285–288. https://doi.org/10.5194/isprs-archives-XLII-5-285-2018
Muhammad, A., Prasetyo, L. B., & Kartono, A. P. (2014). PEMETAAN PERUBAHAN FOREST CANOPY DENSITY DI KPH KUNINGAN. Seminar Nasional Penginderaan Jauh, 652–661.
Nugraha, A. S.A., Gunawan, T., & Kamal, M. (2019). Comparison of Land Surface Temperature Derived from Landsat 7 ETM+ and Landsat 8 OLI/TIRS for Drought Monitoring. IOP Conference Series: Earth and Environmental Science, 313(1), 0–10. https://doi.org/10.1088/1755-1315/313/1/012041
Nugraha, A Sediyo Adi. (2016). Pemanfaatan Citra Penginderaan Jauh Multi-Tingkat Untuk Pemetaan Perubahan Kekeringan (Kasus di Provinsi Jawa Timur). Universitas Gadjah Mada.
Nugraha, A Sediyo Adi. (2019). Pemanfaatan Metode Split-Windows Algorithm ( SWA ) pada Landsat 8 Menggunakan Data Uap Air MODIS Terra (The Application of Split-Windows Algorithm (SWA) Methods on Landsat 8 Using Modis Terra Water Vapor). Geomatika, 25(1), 9–16. https://doi.org/http://doi.org/10.24895/JIG.2019.25-1.877
Nugraha, A Sediyo Adi, Gunawan, T., & Kamal, M. (2019). Downscaling land surface temperature on multi-scale image for drought monitoring. Sixth Geoinformation Science Symposium, November, 6. https://doi.org/10.1117/12.2544550
Rikimaru, A. (1999). The Concept of FCD Mapping Model and Semi-Expert System. FCD Mapper User’s Guide. 80.
Rikimaru, A., & Miyatake, S. (2009). Development of forest canopy density mapping and monitoring model using indices of vegetation, bare soil and shadow - Geospatial World. 1–5. http://www.geospatialworld.net/article/development-of-forest-canopy-density-mapping-and-monitoring-model-using-indices-of-vegetation-bare-soil-and-shadow/
Rikimaru, A., Roy, P. S., & Miyatake, S. (2002). Tropical forest cover density mapping. Tropical Ecology, 43(1), 39–47.
Septiani, R., Citra, I. P. A., & Nugraha, A. S. A. (2019). Perbandingan Metode Supervised Classification dan Unsupervised Classification terhadap Penutup Lahan di Kabupaten Buleleng. Jurnal Geografi : Media Informasi Pengembangan Dan Profesi Kegeografian, 16(2), 90–96. https://doi.org/10.15294/jg.v16i2.19777
Sobrino, J. A., Li, Z. L., Stoll, M. P., & Becker, F. (1996). Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with atsr data. International Journal of Remote Sensing, 17(11), 2089–2114. https://doi.org/10.1080/01431169608948760
Sukarna, R. M. (2008). Aplikasi Model Forest Canopy Density Citra Landsat 7 Etm Untuk Menentukan Indeks Luas Tajuk (Crown Area Index) Dan Kerapatan Tegakan (Stand Density) Hutan Rawa Gambut Di Das Sebangau Provinsi Kalimantan Tengah. Majalah Geografi Indonesia, 22(2), 1–21. https://doi.org/10.22146/mgi.15383
Supriatno, A., Ode, L., Jaya, M. G., & Harimudin, J. (2019). Pemanfaatan Model Forest Canopy Density ( FCD ) Untuk Analisis Perubahan Kerapatan Kanopi Hutan Lambusango Kabupaten Buton. Physical and Social Geography Research Journal, 1(2), 19–28.
Sutanto. (1987). Penginderaan Jauh Jilid II. Gadjah Mada University Press.
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