Indonesia has experienced extensive land-cover change and frequent vegetation and land fires in the past few decades. We combined a new land-cover dataset with satellite data on the timing and location of fires to make the first detailed assessment of the association of fire with specific land-cover transitions in Riau, Sumatra. During 1990 to 2017, secondary peat swamp forest declined in area from 40,000 to 10,000 km2 and plantations (including oil palm) increased from around 10,000 to 40,000 km2. The dominant land use transitions were secondary peat swamp forest converting directly to plantation, or first to shrub and then to plantation. During 2001–2017, we find that the frequency of fire is greatest in regions that change land-cover, with the greatest frequency in regions that transition from secondary peat swamp forest to shrub or plantation (0.15 km−2 yr−1). Areas that did not change land cover exhibit lower fire frequency, with shrub (0.06 km−2 yr−1) exhibiting a frequency of fire >60 times the frequency of fire in primary forest. Our analysis demonstrates that in Riau, fire is closely connected to land-cover change, and that the majority of fire is associated with the transition of secondary forest to shrub and plantation. Reducing the frequency of fire in Riau will require enhanced protection of secondary forests and restoration of shrub to natural forest.
The high demand for garlic is not comparable with the results of domestic garlic production. Indonesian garlic needs fulfilled by imports up to 95% of national needs. The Ministry of Agriculture has a program of the cultivation of garlic in Sembalun, East Lombok, West Nusa Tenggara in order to realize garlic self-sufficiency. This study aims to identify the garlic land in Sembalun using the Sentinel 1A satellite image. The image consists of dual-polarization VV and VH values. Images were acquired in July and November 2019 for the area of Sembalun, East Lombok, West Nusa Tenggara Indonesia. Preprocessing data steps involve applying orbits, calibrations, speckle filters, terrain corrections, and linear to dB. Support vector machine algorithm is used to classify Sentinel 1A images. Hyper parameter tuning was done to get the best parameters which are regularization parameter (C) 10, gamma 1, and the RBF kernel. The classification model has accuracy of 76%, precision of 71% and recall of 89%.
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