scholarly journals Mapping and Statistical Analysis of NO2 Concentration for Local Government Air Quality Regulation

2019 ◽  
Vol 11 (14) ◽  
pp. 3809 ◽  
Author(s):  
Jieun Ryu ◽  
Chan Park ◽  
Seong Woo Jeon
Keyword(s):  
Air Pollutants ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Image Data ◽  
Observation Data ◽  
Concentration Data ◽  
Administrative Unit ◽  
Satellite Image Data ◽  
Increasing Demand ◽  
Administrative Units

With the growing interest in healthy living worldwide, there has been an increasing demand for more accurate measurements of the concentrations of air pollutants such as NO2. In particular, analyzing the characteristics and sources of air pollutants by region could improve the effectiveness of environmental policies applied in accordance with the environmental characteristics of individual regions. In this study, a detailed nationwide NO2 concentration map was generated using the cokriging interpolation technique, which integrates ground observations and satellite image data. The root-mean-square standardized (RMSS) error for this technique was close to 1, which indicates high accuracy. Using spatially interpolated NO2 concentration data, an administrative unit map was generated. When comparing the data for four NO2 data sources (observation data, satellite image data, detailed national data interpolated using cokriging, and NO2 concentrations averaged by an administrative unit based on the interpolated NO2 concentration data), the average concentrations were highest for remote sensing data. Land use regression (LUR) models of urban and non-urban regions were then developed to analyze the characteristics of the NO2 concentration by region using NO2 concentrations for the administrative units.

scholarly journals Analysis of Coral Reef Area Based on Landsat 8 Satellite Image Data Processing using the Lyzenga Algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 18-24
Author(s):  
Laila Martina Azka
Keyword(s):  
Coral Reef ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Image Data ◽  
Correction Method ◽  
Landsat 8 ◽  
Index Method ◽  
Accuracy Level ◽  
Satellite Image Data ◽  
Map Accuracy

Indonesia has the potential of invaluable biological resources from an economic and ecological perspective, one of which is coral reef resources. To determine the condition of the coral reef ecosystem in Indonesian waters as an effort to monitor and monitor using remote sensing data. The purpose of this research is the creation of an information system through a website that can help users access information effectively and efficiently.Utilization of Landsat 8 satellite image data to observe the bottom of the waters directly on coral reef mapping using the Lyzenga Algorithm method, also known as the depthinvariant index method or the water column correction method. This method produces a baseline index that is not affected by depth and works well in clear shallow waters such as in coral reef habitat areas (Maritorena, 1996). The results showed that the image processing data had a map accuracy level for Menjangan Besar and Menjangan Kecil islands of 77.78%, while the map accuracy level for Karimunjawa and Kemujang Islands was 72.23%. And from the processed data, it is known that coral reef cover in general has increased from 2014 to 2018, for Karimunjawa and Kemujan Islands an increase of 17.4% for Menjangan Besar and Menjangan Kecil Islands only a slight increase of 0.27% and for Bunaken Island an increase of 9.1%. And there is one island that is processed has decreased, namely Bunaken Island by 4.5%.

scholarly journals Verification of multispectral data processing for the Sentinel-2A bands, field ASD FieldSpec® 3FR and UAV with the DJI STS-VIS

2019 ◽  
pp. 29-39 ◽  
Author(s):  
Stanislav Dugin ◽  
Oksana Sybirtseva ◽  
Stanislav Golubov ◽  
Yelizaveta Dorofey
Keyword(s):  
Pearson Correlation ◽  
Plant Cover ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Image Data ◽  
Multispectral Data ◽  
Maximum Value ◽  
Substantial Correlation ◽  
Satellite Image Data ◽  
Sentinel 2A

The study of plant cover have been performed by the hyperspectral remote sensing method using ASD FieldSpec® 3FR and DJI STS-VIS measurements. The orthophotoplans are compiled for the test plots of interest at the spatial resolution of 2.5 cm. The substantial correlation for the results of terrestrial verification for the satellite image data in the range of Sentinel-2A bands are confirmed. 15 vegetation indices for the Sentinel-2А wavelength bands were drawn at the Pearson correlation coefficient r > 0.97, with a maximum value of the correlation error of 0.07.

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