Macroeconomic Management

Ginting, Edimon; Aji, Priasto

doi:10.7135/upo9781843313786.007

Cited by 2 publications

(2 citation statements)

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“…Remote sensing data were widely used for land cover monitoring activities. They were Ginting et al (2012) who conducted research on land cover by utilizing remote sensing data, particularly Landsat data to monitor in Karo Regency; and Yulius et al (2014) who monitored land cover changes on Bungus Teluk Kabung Bay. Furthermore, Tampubolon et al, (2015) analyzed the changes on critical land of Medan; Tuni et al (2013) classified land closure or land used in North Halmahera; and Ceccarelli et al (2013) who examined land cover in Oristano and Campania, Provinces of Rome.…”

Section: Introductionmentioning

confidence: 99%

Comparing Atmospheric Correction Methods for Landsat Oli Data

Dewi

Trisakti²

2017

IJReSES

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Abstract. Landsat data used for monitoring activities to land cover because it has spatial resolution and high temporal. To monitor land cover changes in an area, atmospheric correction is needed to be performed in order to obtain data with precise digital value picturing current condition. This study compared atmospheric correction methods namely Quick Atmospheric Correction (QUAC), Dark Object Subtraction (DOS) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH). The correction results then were compared to Surface Reflectance (SR) imagery data obtained from the United States Geological Survey (USGS) satelite. The three atmospheric correction methods were applied to Landsat OLI data path/row126/62 for 3 particular dates. Then, sample on vegetation, soil and bodies of water (waterbody) were retrieved from the image. Atmospheric correction results were visually observed and compared with SR sample on the absolute value, object spectral patterns, as well as location and time consistency. Visual observation indicates that there was a contrast change on images that had been corrected by using FLAASH method compared to SR, which mean that the atmospheric correction method was quite effective. Analysis on the object spectral pattern, soil, vegetation and waterbody of images corrected by using FLAASH method showed that it was not good enough eventhough the reflectant value differed greatly to SR image. This might be caused by certain variables of aerosol and atmospheric models used in Indonesia. QUAC and DOS made more appropriate spectral pattern of vegetation and water body than spectral library. In terms of average value and deviation difference, spectral patterns of soil corrected by using DOS was more compatible than QUAC.

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Section: Introductionmentioning

confidence: 99%

Comparing Atmospheric Correction Methods for Landsat Oli Data

Dewi

Trisakti²

2017

IJReSES

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“…In Indonesia, for example, in 2011 fuel and electricity subsidies amounted to 3.4% of GDP, which was larger than government spending on infrastructure that year. It is estimated that the richest 10% of households consumed 40% of the total subsidized gasoline, and the top half of households used almost 84% of it (Ginting and Aji 2012).…”

Section: Conditional Cash Transfers In the Philippines: The Pantawid mentioning

confidence: 99%

Disembedding Autonomy

Carroll¹,

Jarvis

Asia After the Developmental State

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Macroeconomic Management

Cited by 2 publications

References 0 publications

Comparing Atmospheric Correction Methods for Landsat Oli Data

Comparing Atmospheric Correction Methods for Landsat Oli Data

Disembedding Autonomy

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