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Author Yao, J.Q.; Zhai, H.R.; Tang, X.M.; Gao, X.M.; Yang, X.D.
Title (up) 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 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
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Author He, L.; Páez, A.; Jiao, J.; An, P.; Lu, C.; Mao, W.; Long, D.
Title (up) Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data Type Journal Article
Year 2020 Publication ISPRS International Journal of Geo-Information Abbreviated Journal Ijgi
Volume 9 Issue 6 Pages 342
Keywords Remote Sensing; Public Safety
Abstract In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns of a population. However, current studies are limited by the availability of high precision demographic characteristics, such as social activities and the origins of residents. In this research, we use spatially referenced mobile phone data to measure the size and activity patterns of various types of ambient population, and further investigate the link between urban larceny-theft and population with multiple demographic and activity characteristics. A series of crime attractors, generators, and detractors are also considered in the analysis to account for the spatial variation of crime opportunities. The major findings based on a negative binomial model are three-fold. (1) The size of the non-local population and people’s social regularity calculated from mobile phone big data significantly correlate with the spatial variation of larceny-theft. (2) Crime attractors, generators, and detractors, measured by five types of Points of Interest (POIs), significantly depict the criminality of places and impact opportunities for crime. (3) Higher levels of nighttime light are associated with increased levels of larceny-theft. The results have practical implications for linking the ambient population to crime, and the insights are informative for several theories of crime and crime prevention efforts.
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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 2220-9964 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2997
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Author Duan, X.; Hu, Q.; Zhao, P.; Wang, S.; Ai, M.
Title (up) An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery Type Journal Article
Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 12 Issue 6 Pages 1029
Keywords Remote Sensing
Abstract Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities.
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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 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2870
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Author Lu, L.; Weng, Q.; Xie, Y.; Guo, H.; Li, Q.
Title (up) An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery Type Journal Article
Year 2019 Publication Energy Abbreviated Journal Energy
Volume 189 Issue Pages 116351
Keywords Remote Sensing; Energy; electric power consumption; Night lights
Abstract Industrialization and urbanization have led to a remarkable increase of electric power consumption (EPC) during the past decades. To assess the changing patterns of EPC at the global scale, this study utilized nighttime lights in conjunction with population and built-up datasets to map EPC at 1 km resolution. Firstly, the inter-calibrated nighttime light data were enhanced using the V4.0 Gridded Population Density data and the Global Human Settlement Layer. Secondly, linear models were calibrated to relate EPC to the enhanced nighttime light data; these models were then employed to estimate per-pixel EPC in 2000 and 2013. Finally, the spatiotemporal patterns of EPC between the periods were analyzed at the country, continental, and global scales. The evaluation of the EPC estimation shows a reasonable accuracy at the provincial scale with R2 of 0.8429. Over 30% of the human settlements in Asia, Europe, and North America showed apparent EPC growth. At the national scale, moderate and high EPC growth was observed in 45% of the built-up areas in East Asia. The spatial clustering patterns revealed that EPC decreased in Russia and the Western Europe. This study provides fresh insight into the spatial pattern and variations of global electric power consumption.
Address Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, PR China; qweng(at)indstate.edu
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0360-5442 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2701
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Author Votsi, N.-E.P.; Kallimanis, A.S.; Pantis, I.D.
Title (up) An environmental index of noise and light pollution at EU by spatial correlation of quiet and unlit areas Type Journal Article
Year 2017 Publication Environmental Pollution (Barking, Essex : 1987) Abbreviated Journal Environ Pollut
Volume 221 Issue Pages 459-469
Keywords Remote Sensing
Abstract Quietness exists in places without human induced noise sources and could offer multiple benefits to citizens. Unlit areas are sites free of human intense interference at night time. The aim of this research is to develop an integrated environmental index of noise and light pollution. In order to achieve this goal the spatial pattern of quietness and darkness of Europe was identified, as well as their overlap. The environmental index revealed that the spatial patterns of Quiet and Unlit Areas differ to a great extent highlighting the importance of preserving quietness as well as darkness in EU. The spatial overlap of these two environmental characteristics covers 32.06% of EU surface area, which could be considered a feasible threshold for protection. This diurnal and nocturnal metric of environmental quality accompanied with all direct and indirect benefits to human well-being could indicate a target for environmental protection in the EU policy and practices.
Address Department of Ecology, School of Biology, Aristotle University, 54124, U.P. Box 119, Thessaloniki, Greece
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0269-7491 ISBN Medium
Area Expedition Conference
Notes PMID:27989389 Approved no
Call Number LoNNe @ kyba @ Serial 1662
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