Vol16 No.10: 2215-2231

【Title】Assessment of causes and future deforestation in the mountainous tropical forest of Timor Island, Indonesia

【Author】PUJIONO Eko^1,3; SADONO Ronggo¹*; HARTONO²; IMRON Muhammad Ali¹

【Addresses】1 Faculty of Forestry, Universitas Gadjah Mada, Jl. Agro No. 1, Yogyakarta 55281, Indonesia; 2 Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Jl. Kaliurang, Yogyakarta 55281, Indonesia; 3 Environment and Forestry Research and Development Institute of Kupang, Ministry of Environment and Forestry, Jl. Alfons Nisnoni No. 7B, Kupang 85119, Indonesia

【Corresponding author】SADONO Ronggo

【Citation】Pujiono E, Sadono R, Hartono, et al. (2019) Assessment of causes and future deforestation in the mountainous tropical forest of Timor Island, Indonesia. Journal of Mountain Science 16(10). https://doi.org/10.1007/s11629-019-5480-1

【DOI】https://doi.org/10.1007/s11629-019-5480-1

【Abstract】The Mutis–Timau Forest Complex, one of the remaining mountainous tropical forest areas in Timor Island, eastern Indonesia that covers an area of 31,984 ha, tends to decrease gradually. Efforts to secure mountain forest functions and counteract the negative impact of declining forest areas are often constrained by data uncertainty on factors contributing to deforestation. For this reason, this study attempts to develop models of deforestation and predict future deforestation in the Mutis–Timau Forest Complex. We constructed models of deforestation that describe the relationship between deforestation and factors contributing to deforestation using spatial statistical models. In this model, we used the deforestation data for the1987–2017 periodobtained from a previous study as dependent variables and the potential causes of deforestation generated from Geographic Information System spatial analysis as independent variables. Using the probability of deforestation derived from the model, we predicted future deforestation under two different scenarios, namely, business-as-usual (as the reference scenario) and reducing emission from deforestation and forest degradation. Our findings showed that a positive relationship exists between probability of deforestation, distance to the settlement, and population density variables, whereas a negative relationship exists between likelihood of deforestation, elevation, slope, distance to the road, distance to the savanna, and forest management unit variables. During the 2017–2030 period, under the business-as-usual scenario, the Mutis–Timau Forest Complex will lose 1327.65 ha in forest area with an annual deforestation rate of 0.54%. Meanwhile, under the reducing emission from deforestation and forest degradation scenario, the overall forest loss was estimated to be 1237.11 ha with an annual deforestation rate of 0.50%. The predicted area of avoided deforestation in 2017–2030 under the reducing emission from deforestation and forest degradation scenario was 90.54 ha. Such data and information are important for the Mutis–Timau Forest Complex authority in prioritizing actions for combating deforestation and designing appropriate forest-related policies and supporting data for reducing emission from deforestation and forest degradation programme or other incentive schemes in reducing deforestation.

【Keywords】Mountainous tropical forest; Deforestation; Spatial statistical model; Geographic information system; Reducing emission from deforestation and forest degradation