2019Environment Development and SustainabilityOpen access

Examining forest cover change and deforestation drivers in Taunggyi District, Shan State, Myanmar

Prashanti Sharma, Rajesh Bahadur Thapa, Mir A. Matin

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Abstract

Myanmar has been experiencing a significant amount of deforestation and forest degradation in recent years. Being a developing country, people are heavily dependent on its forest for sustenance and livelihood. This study examines a methodology to identify potential drivers and their relative significance for deforestation. The study was tested in one of the districts but could be applied in other areas of the country. The forest and non-forest land cover maps from the Japan Aerospace Exploration Agency (JAXA) for the years 2008 and 2016 were used in the study. It was derived that 46.54% of study area is still covered with forest, but there has been a significant decrease in forest area by 7.29% between the years 2008 and 2016. We examined a number of spatially explicit potential drivers of deforestation such as infrastructure, elevation, slope, deforested land, and population. As informed prevention awareness of deforestation, we projected future forest conditions using a cellular automation modeling technique for the years 2020, 2025 and 2030. We found that major physical and socioeconomic driving factors of deforestation such as proximity to infrastructure (reservoirs and roads), certain elevation levels, slope, proximity to previously deforested area and population density are strongly associated with neighborhood deforestation. The future projection showed a decrease in forest area by 13.8% from 2016 to 2030. This work therefore provides crucial information on forest landscape for forest management in the district. The projective scenario of study area generated by the model highlights the need for forest conservation and planning while addressing the key drivers of deforestation, giving direction for future potential areas of REDD+ implementation in the region.

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Myanmar has been experiencing a significant amount of deforestation and forest degradation in recent years. Being a developing country, people are heavily dependent on its forest for sustenance and livelihood. This study examines a methodology to identify potential drivers and their relative significance for deforestation. The study was tested in one of the districts but could be applied in other areas of the country. The forest and non-forest land cover maps from the Japan Aerospace Exploration Agency (JAXA) for the years 2008 and 2016 were used in the study. It was derived that 46.54% of study area is still covered with forest, but there has been a significant decrease in forest area by 7.29% between the years 2008 and 2016. We examined a number of spatially explicit potential drivers of deforestation such as infrastructure, elevation, slope, deforested land, and population. As informed prevention awareness of deforestation, we projected future forest conditions using a cellular automation modeling technique for the years 2020, 2025 and 2030. We found that major physical and socioeconomic driving factors of deforestation such as proximity to infrastructure (reservoirs and roads), certain elevation levels, slope, proximity to previously deforested area and population density are strongly associated with neighborhood deforestation. The future projection showed a decrease in forest area by 13.8% from 2016 to 2030. This work therefore provides crucial information on forest landscape for forest management in the district. The projective scenario of study area generated by the model highlights the need for forest conservation and planning while addressing the key drivers of deforestation, giving direction for future potential areas of REDD+ implementation in the region.

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Available abstract

Myanmar has been experiencing a significant amount of deforestation and forest degradation in recent years. Being a developing country, people are heavily dependent on its forest for sustenance and livelihood. This study examines a methodology to identify potential drivers and their relative significance for deforestation. The study was tested in one of the districts but could be applied in other areas of the country. The forest and non-forest land cover maps from the Japan Aerospace Exploration Agency (JAXA) for the years 2008 and 2016 were used in the study. It was derived that 46.54% of study area is still covered with forest, but there has been a significant decrease in forest area by 7.29% between the years 2008 and 2016. We examined a number of spatially explicit potential drivers of deforestation such as infrastructure, elevation, slope, deforested land, and population. As informed prevention awareness of deforestation, we projected future forest conditions using a cellular automation modeling technique for the years 2020, 2025 and 2030. We found that major physical and socioeconomic driving factors of deforestation such as proximity to infrastructure (reservoirs and roads), certain elevation levels, slope, proximity to previously deforested area and population density are strongly associated with neighborhood deforestation. The future projection showed a decrease in forest area by 13.8% from 2016 to 2030. This work therefore provides crucial information on forest landscape for forest management in the district. The projective scenario of study area generated by the model highlights the need for forest conservation and planning while addressing the key drivers of deforestation, giving direction for future potential areas of REDD+ implementation in the region.

Key concepts: Deforestation (computer science), Geography, Livelihood, Population, Land use, Land cover, Forest cover, State forest

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