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The growing availability of free or inexpensive satellite imagery has inspired many researchers to investigate the use of earth observation data for monitoring economic activity around the world. One of the most popular earth observation data sets is the so-called nighttime lights from the Defense Meteorological Satellite Program. Researchers have found positive correlations between nighttime lights and several economic variables. These correlations are based on data measured in levels, with a cross-section of observations within a single time period across countries or other geographic units. The findings suggest that nighttime lights could be used as a proxy for some economic variables, especially in areas or times where data are weak or unavailable. Yet, logic suggests that nighttime lights cannot serve as a good proxy for monitoring the within-in country growth rates all of these variables. Examples examined this paper include constant price gross domestic product, non-agricultural gross domestic product, manufacturing value added, and capital stocks, as well as electricity consumption, total population, and urban population. The study finds that the Defense Meteorological Satellite Program data are quite noisy and therefore the resulting growth elasticities of Defense Meteorological Satellite Program nighttime lights with respect to most of these socioeconomic variables are low, unstable over time, and generate little explanatory power. The one exception for which Defense Meteorological Satellite Program nighttime lights could serve as a proxy is electricity consumption, measured in 10-year intervals. It is hoped that improved data from the recently launched Suomi National Polar-Orbiting Partnership satellite will help expand or improve these outcomes. Testing this should be an important next step.
Capital --- DMSP-OLS --- E-business --- Earth observation --- Economic growth --- Economic monitoring --- Economic theory & research --- Electric power consumption --- Gross domestic product --- Inequality --- Linear regression --- Macroeconomics and economic growth --- Night-time light data --- NPP-VIIRS --- Population --- Poverty reduction --- Private sector development --- Pro-poor growth --- Satellite imagery
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The growing availability of free or inexpensive satellite imagery has inspired many researchers to investigate the use of earth observation data for monitoring economic activity around the world. One of the most popular earth observation data sets is the so-called nighttime lights from the Defense Meteorological Satellite Program. Researchers have found positive correlations between nighttime lights and several economic variables. These correlations are based on data measured in levels, with a cross-section of observations within a single time period across countries or other geographic units. The findings suggest that nighttime lights could be used as a proxy for some economic variables, especially in areas or times where data are weak or unavailable. Yet, logic suggests that nighttime lights cannot serve as a good proxy for monitoring the within-in country growth rates all of these variables. Examples examined this paper include constant price gross domestic product, non-agricultural gross domestic product, manufacturing value added, and capital stocks, as well as electricity consumption, total population, and urban population. The study finds that the Defense Meteorological Satellite Program data are quite noisy and therefore the resulting growth elasticities of Defense Meteorological Satellite Program nighttime lights with respect to most of these socioeconomic variables are low, unstable over time, and generate little explanatory power. The one exception for which Defense Meteorological Satellite Program nighttime lights could serve as a proxy is electricity consumption, measured in 10-year intervals. It is hoped that improved data from the recently launched Suomi National Polar-Orbiting Partnership satellite will help expand or improve these outcomes. Testing this should be an important next step.
Capital --- DMSP-OLS --- E-business --- Earth observation --- Economic growth --- Economic monitoring --- Economic theory & research --- Electric power consumption --- Gross domestic product --- Inequality --- Linear regression --- Macroeconomics and economic growth --- Night-time light data --- NPP-VIIRS --- Population --- Poverty reduction --- Private sector development --- Pro-poor growth --- Satellite imagery
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The management of natural resources can be approached using different data sources and techniques, from images registered by sensors of onboard satellites to UAV platforms, using remote sensing techniques and geographic information systems, among others. The variability of problems and projects to be analyzed, studied, and solved is very wide. This book presents a collection of different experiences, ranging from the location of areas of interest to the simulation of future scenarios of a territory at local and regional scales, considering spatial resolutions ranging from centimeters to hundreds of meters. The common objective of all the works compiled in this book is to support decision-making in environmental management.
Research & information: general --- secondary succession monitoring --- Natura 2000 threats --- tree detection --- archival photographs --- spectro-textural classification --- granulometric analysis --- GLCM --- alpine grassland --- fractional vegetation cover --- ground survey --- precision evaluation --- multi-scale LAI product validation --- PROSAIL model --- EBK --- crop growth period --- adaptive K-means algorithm --- heavy industry heat sources --- NPP-VIIRS --- active fire data --- night-time light data --- spatial autocorrelation --- spatial pattern --- spatial relationship --- natural wetlands changes --- associated influencing factors --- mainland China --- farmland abandonment mapping --- textural segmentation --- aerial imagery --- land use --- Poznań --- agent based modeling --- disaster management --- resource allocation --- high severity level --- first come first serve --- geographical information system --- bearing capacity --- analytic hierarchy process --- geographical survey of national conditions --- hotspot analysis --- topsis algorithm --- automatic identification system data --- 21st Century Maritime Silk Road region --- oil flow analysis --- maritime oil chokepoint --- Middle East Respiratory Syndrome --- seismic parameters --- GIS --- seismicity --- spatial analysis --- b-value --- earthquake catalog --- future scenarios --- prelude --- dynamic of land use --- Spatial Decision Support System, CORINE Land Cover --- remote sensing --- geographic information system
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Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches.
Research & information: general --- Geography --- sequential estimation --- InSAR time series --- groundwater --- land subsidence and rebound --- earthquake --- rapid mapping --- damage assessment --- deep learning --- convolutional neural networks --- ordinal regression --- aerial image --- landslide --- machine learning models --- remote sensing --- ensemble models --- validation --- ice storm --- forest ecosystems --- disaster impact --- post-disaster recovery --- ice jam --- snowmelt --- flood mapping --- monitoring and prediction --- VIIRS --- ABI --- NUAE --- flash flood --- BRT --- CART --- naive Bayes tree --- geohydrological model --- landslide susceptibility --- Bangladesh --- digital elevation model --- random forest --- modified frequency ratio --- logistic regression --- automatic landslide detection --- OBIA --- PBA --- random forests --- supervised classification --- landslides --- uncertainty --- K-Nearest Neighbor --- Multi-Layer Perceptron --- Random Forest --- Support Vector Machine --- agriculture --- drought --- NDVI --- MODIS --- landslide deformation --- InSAR --- reservoir water level --- Sentinel-1 --- Three Gorges Reservoir area (China) --- peri-urbanization --- urban growth boundary demarcation --- climate change --- climate migrants --- natural hazards --- flooding --- land use and land cover --- night-time light data --- Dhaka
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Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches.
sequential estimation --- InSAR time series --- groundwater --- land subsidence and rebound --- earthquake --- rapid mapping --- damage assessment --- deep learning --- convolutional neural networks --- ordinal regression --- aerial image --- landslide --- machine learning models --- remote sensing --- ensemble models --- validation --- ice storm --- forest ecosystems --- disaster impact --- post-disaster recovery --- ice jam --- snowmelt --- flood mapping --- monitoring and prediction --- VIIRS --- ABI --- NUAE --- flash flood --- BRT --- CART --- naive Bayes tree --- geohydrological model --- landslide susceptibility --- Bangladesh --- digital elevation model --- random forest --- modified frequency ratio --- logistic regression --- automatic landslide detection --- OBIA --- PBA --- random forests --- supervised classification --- landslides --- uncertainty --- K-Nearest Neighbor --- Multi-Layer Perceptron --- Random Forest --- Support Vector Machine --- agriculture --- drought --- NDVI --- MODIS --- landslide deformation --- InSAR --- reservoir water level --- Sentinel-1 --- Three Gorges Reservoir area (China) --- peri-urbanization --- urban growth boundary demarcation --- climate change --- climate migrants --- natural hazards --- flooding --- land use and land cover --- night-time light data --- Dhaka
Choose an application
The management of natural resources can be approached using different data sources and techniques, from images registered by sensors of onboard satellites to UAV platforms, using remote sensing techniques and geographic information systems, among others. The variability of problems and projects to be analyzed, studied, and solved is very wide. This book presents a collection of different experiences, ranging from the location of areas of interest to the simulation of future scenarios of a territory at local and regional scales, considering spatial resolutions ranging from centimeters to hundreds of meters. The common objective of all the works compiled in this book is to support decision-making in environmental management.
secondary succession monitoring --- Natura 2000 threats --- tree detection --- archival photographs --- spectro-textural classification --- granulometric analysis --- GLCM --- alpine grassland --- fractional vegetation cover --- ground survey --- precision evaluation --- multi-scale LAI product validation --- PROSAIL model --- EBK --- crop growth period --- adaptive K-means algorithm --- heavy industry heat sources --- NPP-VIIRS --- active fire data --- night-time light data --- spatial autocorrelation --- spatial pattern --- spatial relationship --- natural wetlands changes --- associated influencing factors --- mainland China --- farmland abandonment mapping --- textural segmentation --- aerial imagery --- land use --- Poznań --- agent based modeling --- disaster management --- resource allocation --- high severity level --- first come first serve --- geographical information system --- bearing capacity --- analytic hierarchy process --- geographical survey of national conditions --- hotspot analysis --- topsis algorithm --- automatic identification system data --- 21st Century Maritime Silk Road region --- oil flow analysis --- maritime oil chokepoint --- Middle East Respiratory Syndrome --- seismic parameters --- GIS --- seismicity --- spatial analysis --- b-value --- earthquake catalog --- future scenarios --- prelude --- dynamic of land use --- Spatial Decision Support System, CORINE Land Cover --- remote sensing --- geographic information system
Choose an application
Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches.
Research & information: general --- Geography --- sequential estimation --- InSAR time series --- groundwater --- land subsidence and rebound --- earthquake --- rapid mapping --- damage assessment --- deep learning --- convolutional neural networks --- ordinal regression --- aerial image --- landslide --- machine learning models --- remote sensing --- ensemble models --- validation --- ice storm --- forest ecosystems --- disaster impact --- post-disaster recovery --- ice jam --- snowmelt --- flood mapping --- monitoring and prediction --- VIIRS --- ABI --- NUAE --- flash flood --- BRT --- CART --- naive Bayes tree --- geohydrological model --- landslide susceptibility --- Bangladesh --- digital elevation model --- random forest --- modified frequency ratio --- logistic regression --- automatic landslide detection --- OBIA --- PBA --- random forests --- supervised classification --- landslides --- uncertainty --- K-Nearest Neighbor --- Multi-Layer Perceptron --- Random Forest --- Support Vector Machine --- agriculture --- drought --- NDVI --- MODIS --- landslide deformation --- InSAR --- reservoir water level --- Sentinel-1 --- Three Gorges Reservoir area (China) --- peri-urbanization --- urban growth boundary demarcation --- climate change --- climate migrants --- natural hazards --- flooding --- land use and land cover --- night-time light data --- Dhaka
Choose an application
The management of natural resources can be approached using different data sources and techniques, from images registered by sensors of onboard satellites to UAV platforms, using remote sensing techniques and geographic information systems, among others. The variability of problems and projects to be analyzed, studied, and solved is very wide. This book presents a collection of different experiences, ranging from the location of areas of interest to the simulation of future scenarios of a territory at local and regional scales, considering spatial resolutions ranging from centimeters to hundreds of meters. The common objective of all the works compiled in this book is to support decision-making in environmental management.
Research & information: general --- secondary succession monitoring --- Natura 2000 threats --- tree detection --- archival photographs --- spectro-textural classification --- granulometric analysis --- GLCM --- alpine grassland --- fractional vegetation cover --- ground survey --- precision evaluation --- multi-scale LAI product validation --- PROSAIL model --- EBK --- crop growth period --- adaptive K-means algorithm --- heavy industry heat sources --- NPP-VIIRS --- active fire data --- night-time light data --- spatial autocorrelation --- spatial pattern --- spatial relationship --- natural wetlands changes --- associated influencing factors --- mainland China --- farmland abandonment mapping --- textural segmentation --- aerial imagery --- land use --- Poznań --- agent based modeling --- disaster management --- resource allocation --- high severity level --- first come first serve --- geographical information system --- bearing capacity --- analytic hierarchy process --- geographical survey of national conditions --- hotspot analysis --- topsis algorithm --- automatic identification system data --- 21st Century Maritime Silk Road region --- oil flow analysis --- maritime oil chokepoint --- Middle East Respiratory Syndrome --- seismic parameters --- GIS --- seismicity --- spatial analysis --- b-value --- earthquake catalog --- future scenarios --- prelude --- dynamic of land use --- Spatial Decision Support System, CORINE Land Cover --- remote sensing --- geographic information system
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