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- W4319997196 abstract "The combination of its equatorial location and number of islands of Indonesia provides numbers of biomass storage capability, bioenergy resources and abundant natural wealth of forest, while known as home to the world's third largest tropical forest. Forest loss can cause many adverse impacts on ecosystem balance and biodiversity by changing the configuration and structure of the forest landscape. Over the last twenty years, the deforestation in Indonesia keeps increasing with uneven distribution between the western and eastern part of the country. Consistent and efficient observation of forest loss using remote sensing data provides important information for understanding the role of forest in the role of forest conversion in the global carbon cycle, biodiversity analysis, and selecting potential strategies on reducing deforestation. In this study, a Global Forest Change Datasets were utilized to analyze, calculate, and map forest loss in Papua, Indonesia from 2001 to 2021. Cloud data processing is carried out using the google earth engine to obtain the total forest loss data in district scale of Papua Province. Three indicators used in this method, the tree cover in the year of 2000 as the forest baseline, yearly forest gain (2000 - 2012), and yearly forest loss (2001 - 2021). The results of the study show that in the last two decades, Papua Province has lost 2,806% or 9.062 km2 of its forest cover area, while the semi-urban and urban areas are 2-20 times more prone to deforestation compared to the dense forest area. Although increasing each year, Papua Province has a very low threat of deforestation compared to other provinces in Indonesia. However, the anthropogenic conversion to the productive plantation site might become a significant hazard in the upcoming years, not only in urban and semi-urban area, but also in remote and dense forest areas." @default.
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- W4319997196 date "2023-01-01" @default.
- W4319997196 modified "2023-09-26" @default.
- W4319997196 title "Forest loss analysis and calculation with geospatial artificial intelligence: A case study of papua province" @default.
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- W4319997196 doi "https://doi.org/10.1016/j.procs.2022.12.145" @default.
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