ANALISIS DEFORESTASI MENGGUNAKAN REGRESI LOGISTIK MODEL DI TAHURA SEKITAR TANJUNG PROVINSI JAMBI
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Accepted
30 October 2017
Published
1 December 2017
Abstract
Deforestation cause damage to the environment, climate change and greenhouse gas emissions. Deforestation in Tahura was caused by ilegall logging and forest fire. To reduce deforestation and greenhouse gas emissions, Tahura can participate in REDD+ scheme. REDD+ scheme is a scheme to reduce emissions from deforestation and forest degradation and provide incentives if it is able to reduce emissions. Related to REDD+ was required information on forest cover change (deforestation) and predictions. The method that used in the deforestation analysis, namely logistic regression. This study shows that forest cover in Tahura has changed from 2000 to 2015. Changes in forest cover into a bush is the biggest change with a percentage of 71%. Factors which is tested in logistic regression method i.e. the distance from the road, the distance from the river, the distance from the plantation and total population. Based on the prediction of deforestation, forest area that deforested amount of 103 ha in 2030.
References
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Arekhi, S., 2011. Modelling spatial pattern of deforestation using GIS and logistic regression: A case study of northern ilam forest, ilam province, Iran. African journal of biotechnology 10(72), pp.16236-16249.
Barber et al., 2014. Roads, deforestation, and mitigating effect of protected areas in the Amazon. Journal Biological conservation 177, pp. 203-209.
Chakravarty S. et al., 2012. Deforestation: Causes, Effect and Control and Strategis. Dalam: Global Perspectives on sustainable forest management. Editor Clement Akais Okia
[Dishut Provinsi Jambi] Dinas Kehutanan Provinsi Jambi, 2014. Taman Hutan Raya (Tahura) Sekitar Tanjung menjadi urusan pemerintahan Provinsi Jambi.
Ghebrezgabher, M.G., Yang, T., Wang, X., Khan, M., 2016. Extracting and analyze forest and woodland cover change in Eritrea based on landsat data using supervised classification. The Egyptian journal of Remote Sensing and Space Science. 19, pp. 37-47.
Hosmer, J.D.W., Lemeshow, S., 2000. Applied logistic regression. Second Edition. John Wiley & Sons, Canada.
Huang, Q. H., et al., 2007. Modelling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China. Enviromental Modelling & Assesment. 12(1). pp.55-61
[IPCC] Intergovernmental Panel on Climate Change. 2007. The Physical Science Basis. Contribution of Working Group I to the Fourth Assesment Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, USA.
Jaya, I.N.S., 2015. Analisis Citra Digital: Perspektif Penginderaan Jauh Untuk Pengelolaan dan Sumberdaya Alam. Fakultas Kehutanan, Institut Pertanian Bogor, Bogor.
Jensen, J.R., 1986. Introductory Digital Image Processing A Remote Sensing Perspective 2nd Edition. Prentice-Hall Inc., USA.
[Kemenhut] Kementerian Kehutanan. 2014. Statisitik Kementerian Kehutanan Tahun 2013. Kementerian Kehutanan, Jakarta.
Kissinger, et al., 2012. Drivers of deforestation dan forest degradatio n. A synthesis report for REDD+ policymakers. Lexeme Consulting, Vancouver Canada.
Kumar, R., et al., 2014. Forest Cover Dynamic Analysis and Prediction Modelling Using Logistic Regression Model. Ecol Indic. 45, pp. 444-455.
Landis, J., Koch, G., 1977. The Measurement of Observer Agreement for Categorical Data. Biometric. 33(1), pp. 159-174
Menard, S., 2002. Applied Logistic Regression Analysis: Sage University Series on Quantitative Applicatiobs in the Social Sciences. Volume 106. Thousand Oaks. Sage, California.
Nurjanah, S., et.al., 2013. Identifikasi Lokasi Penanaman Kembali Ramin (Gonystylus bancanus Kurz) di Hutan Rawa Gambut Sumate dan Kalimantan. Pusat Penelitian dan Pengembangan Koservasi dan Rehabilitasi, Badan Penelitian dan Pengembangan Kehutanan. Forda Press, Bogor.
O’brien, R.M., 2007. A Caution Regarding Rules of Thumb for Variance Inflation Factors. Uality & Quantity. 41 (5), pp. 673-690.
Prasetyo, L.B., et al., 2009. Spatial Model Approach on Deforestation of Java Island Indonesia. Journal of Integrated Field Science.
[Permenhut]. Peraturan Menteri Kehutanan Republik Indonesia Nomor: P.20/Menhut-II/2009. 2009. Tata Cara Pengurangan Emisi dari Deforestasi dan Degradasi Hutan (REDD).
Rosa, I.M.D, et.al., 2016. The Environmental Legacy of Modern Tropical Deforestation. Current Biology (26), pp. 2161-2166.
Satgas REDD+, 2013. Instrumen Pendanaan REDD+ di Indonesia.
Setiawan, H., 2015. Model spasial deforestasi di Kabupaten Konawe Utara dan Konawe Povinsi Sulawesi Tenggara. Thesis. Institut Pertanian Bogor, Bogor.
Tarigan, S. D., 2016. Land cover change and its impact on flooding frequency of Batanghari Watershed, Jambi Province, Indonesia. Procedia Environmental Sciences (22), pp. 386-392
Tritsh I., 2016. Population densities and deforestation in the Brazilian Amazon: New insights on the current human settlement patterns. Applied geography. 76, pp. 163-172.
[UNFCC] United Nation Framework Convention on Climate Change, 2010. Report of the conference of the parties on its sixteenth session, held in Cancun from 29 November to 10 Desember 2010. Part two: Action taken by the conference of the parties at its sixteenth session.
Wijaya, P.A., 2015. Model Spasial deforestasi Di Provinsi Jambi Periode 1990-2011. Thesis. Institut Pertanian Bogor, Bogor.
Wymas, M.S., V.S., Taylor, 2010. Modeling sosial and land-use/land cover change data to assess drivers of smallholder deforestation in Belize. Applied Geography. 30, pp. 329-342.
Authors
FebrianiI., PrasetyoL. B. and DharmawanA. H. (2017) “ANALISIS DEFORESTASI MENGGUNAKAN REGRESI LOGISTIK MODEL DI TAHURA SEKITAR TANJUNG PROVINSI JAMBI”, Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management). Bogor, ID, 7(3), pp. 195-203. doi: 10.29244/jpsl.7.3.195-203.
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