Comparing of Land Change Modeler and Geomod Modeling for the Assessment of Deforestation (Case Study: Forest Area at Poso Regency, Central Sulawesi Province)

( Vol-4,Issue-8,August 2018 )

Author(s): Irmadi Nahib, Turmudi, Rizka Windiastuti, Jaka Suryanta, Ratna S Dewi, Sri Lestari

Total View : 1221
Downloads : 166
Page No: 597-607
DOI: 10.22161/ijaems.4.8.4

Keywords:

LULC model, LCM, Geomod, deforestation, REDD.

Abstract:

The forest destruction, climate change and global warming can reduce an indirect forest benefit because forest is the largest carbon sink and it plays a very important role in global carbon cycle. To support reducing emissions from deforestation and forest degradation (REDD+) program, there is a need to understand the characteristics of existing Land Use/Cover Change (LUCC) modules. The aims of this study are 1) to calculate the rate of deforestation at Poso Regency; and 2) to compare the performance of LCM and GM for simulating baseline deforestation of multiple transitions based on model structure and predictive accuracy. The data used in this study are : 1) Indonesia tophographic map scale 1; 50.000, produced by Geospatial Information Agency (BIG), 2) Landcover maps (1990, 2000, and 2011) which were collected from the Director General of Forestry Planning, Ministry of Environment and Forestry. Meanwhile independent variables (environmental variables) such as : distance from the edge of the forest, the distance from roads, the distance from streams, the distance from settlement, elevation and slope. Landcover changes analysis was assessed by using Idrisi Terrset software and Geomod software. Landcover maps from 1990 and 2000 were used to simulate land-cover of 2011. The resulting maps were compared with an observed land-cover map of 2011. The predictive accuracy of multiple transition modeling was calculated by using Relative Operating Characteristics (ROC). The results show that the deforestation on the period of 1990-2011 reached 19,944 ha (3.55 %) or the rate of deforestation 949 ha year1. Based on the model structure and predictive accuracy comparisons, the LCM was more suitable than the GM for the asssement of deforestation.

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