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Author Yao, J.Q.; Zhai, H.R.; Tang, X.M.; Gao, X.M.; Yang, X.D. url  doi
openurl 
  Title Amazon Fire Monitoring and Analysis Based on Multi-source Remote Sensing Data Type Journal Article
  Year 2020 Publication IOP Conference Series: Earth and Environmental Science Abbreviated Journal IOP Conf. Ser.: Earth Environ. Sci.  
  Volume 474 Issue Pages (down) 042025  
  Keywords Remote Sensing  
  Abstract In August 2019, a large-scale fire broke out in the Amazon rainforest, bringing serious harm to the ecosystem and human beings. In order to accurately monitor the dynamic change of forest fire in Amazon rainforest and analyse the impact of fire spreading and extinction on the environment, firstly, based on NPP VIIRS data covering the Amazon fire area, the sliding window threshold method is adopted to extract the fire point, and the cause of fire change is monitored and analysed according to the time series. Secondly, based on the time series of CALIPSO data, the vertical distribution changes of atmospheric pollutants in the amazon fire area are analysed, and the comprehensive analysis is carried out by combining NPP VIIRS data. The experimental results show that only NPP VIIRS data is used to predict the fire, and the combination of CALIPSO data can better monitor the forest fire and predict the fire development trend. The combination of optical image and laser radar has greater advantages in dynamic fire monitoring and fire impact analysis. The method described in this paper can provide basic data reference for real-time and accurate prediction of forest fires and provide new ideas for dynamic fire monitoring.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1755-1315 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2927  
Permanent link to this record
 

 
Author Wang, X.; Cheng, H. url  doi
openurl 
  Title Study on the Temporal and Spatial Pattern Differences of Chinese Light Curl Based on DMSP/OLS Type Journal Article
  Year 2019 Publication IOP Conference Series: Earth and Environmental Science Abbreviated Journal IOP Conf. Ser.: Earth Environ. Sci.  
  Volume 310 Issue Pages (down) 032072  
  Keywords Remote Sensing  
  Abstract Nighttime light data can detect surface gleams that can intuitively reflect human socioeconomic activity.This paper uses the DMSP/OLS nighttime lighting data from 2001 to 2007 to analyze the coupling relationship between regional economic development and nighttime light intensity in China using regression model.The results show that the brightest areas of nighttime light are mainly concentrated in the Beijing-Tianjin-Hebei region, the Yangtze River Delta region, and the Pearl River Delta region. With the change of theyear, the brightness of the three regions is brighter year by year, indicating that the economy is more and more developed.The linear regression model of total brightness and GDP of regional light: Y=792.218+0.024X, linear slope is 0.024, indicating a positive correlation trend.The provinces and cities with the highest total brightness of the provinces and cities are Guangdong Province, Shandong Province, and Jiangsu Province, and the lowest provinces and cities are Qinghai Province and Tibet Autonomous Region.The total brightness of regional lights in China's provinces and cities is well coupled with GDP. The total brightness of regional lights in all provinces and cities is weakened from east to west. The brightness of the 11 provinces in the eastern region is the strongest, including Beijing, Tianjin, Hebei, Liaoning, Shanghai, and Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan Province.The second most powerful lighting is the eight provinces in the central region including Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan.The weakest lighting is in the western regions of Sichuan, Chongqing, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Guangxi, Inner Mongolia and other provinces (cities).In the east of the Hu Huanyong line, the nighttime lighting is higher than the west of the Hu Huanyong line.The eastern part of China's seven geographical divisions (Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Shandong, Fujian, and Taiwan) has the brightest night lights.The northwestern region (Shaanxi, Gansu, Qinghai, Ningxia Hui Autonomous Region, Xinjiang Uygur Autonomous Region, and Inner Mongolia Autonomous Region) has a weak night light.The brightness information of nighttime remote sensing data selected in this study can reflect the level of regional economic development.  
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  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1755-1315 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2670  
Permanent link to this record
 

 
Author Nurbandi, W.; Yusuf, F.R.; Prasetya, R.; Afrizal, M.D. url  doi
openurl 
  Title Using Visible Infrared Imaging Radiometer Suite (VIIRS) Imagery to identify and analyze light pollution Type Journal Article
  Year 2016 Publication IOP Conference Series: Earth and Environmental Science Abbreviated Journal IOP Conf. Ser.: Earth Environ. Sci.  
  Volume 47 Issue Pages (down) 012040  
  Keywords Remote Sensing; Indonesia; VIIRS; VIIRS-DNB; ground validation; Yogyakarta  
  Abstract Light pollution is any adverse effect of artificial lighting including sky glow, glare, light trespass, light clutter, decreased visibility at night, and energy waste. Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. Remote sensing can be used for identification of light pollution. The purpose of this study is to identify and analyze the light pollution by using remote sensing imagery. This study uses VIIRS DNB Free Cloud Composites imagery to identify light pollution in Yogyakarta province and surrounding areas. VIIRS imagery which obtained is processed to get information of light pollution by classifying the information into several classes presented in a map. Selected few sample points as test sites to determine the actual condition. Field work conducted at theree location, they are Yogyakarta City, Depok Beach, and Gajah Mungkur reservoir. Night sky condition analysis conducted field tests as well as night time shooting the night sky conditions. Analysis of the night sky conditions are calculated qualitatively using Bortle Dark-Sky Scale with a value range of 1-9. Field test results show that Yogyakarta City has a value of 8, Depok has a value of 3, and Gajah Mungkur Reservoir has a value of 4. The conclusion of study is VIIRS imagery can be used for identification light pollution and calculation analysis of light pollution can use Bortle Dark-Sky Scale.  
  Address Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia; wahyunurbandi.ipa130 (at) gmail.com  
  Corporate Author Thesis  
  Publisher IOP Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1755-1307 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number IDA @ john @ Serial 1652  
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Author Ratnasari, N.; Candra, E.D.; Saputra, D.H.; Perdana, A.P. url  doi
openurl 
  Title Urban Spatial Pattern and Interaction based on Analysis of Nighttime Remote Sensing Data and Geo-social Media Information Type Journal Article
  Year 2016 Publication IOP Conference Series: Earth and Environmental Science Abbreviated Journal IOP Conf. Ser.: Earth Environ. Sci.  
  Volume 47 Issue Pages (down) 012038  
  Keywords remote sensing; geo-social media; spatial pattern; spatial interaction; urban; Indonesia  
  Abstract Urban development in Indonesia significantly increasing in line with rapid development of infrastructure, utility, and transportation network. Recently, people live depend on lights at night and social media and these two aspects can depicted urban spatial pattern and interaction. This research used nighttime remote sensing data with the VIIRS (Visible Infrared Imaging Radiometer Suite) day-night band detects lights, gas flares, auroras, and wildfires. Geo-social media information derived from twitter data gave big picture on spatial interaction from the geospatial footprint. Combined both data produced comprehensive urban spatial pattern and interaction in general for Indonesian territory. The result is shown as a preliminary study of integrating nighttime remote sensing data and geospatial footprint from twitter data.  
  Address Undergraduate Program of Cartography and Remote Sensing, Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia; nila.ratnasari(at)mail.ugm.ac.id  
  Corporate Author Thesis  
  Publisher IOP Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1755-1307 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number IDA @ john @ Serial 1653  
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