NASA's Black Marble Product Suite: Validation Strategy

NASA's Black Marble nighttime lights product suite (VNP46) is available at 500m resolution since January 2012 with data fro the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-terrain, vegetation, snow, lunar and stray light corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Current and planned validation activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at difference geographic locations and time periods representing the full range of retrieval conditions

Evolution of the energy consumed by street lighting in Spain estimated with DMSP-OLS data

© Pergamon-Elsevier Science Ltd. This research was supported by an FPU grant (Formación de Profesorado Universitario) from the Spanish Ministry of Science and Innovation (MCINN) to Alejandro Sánchez de Miguel. This work has been partially funded by the Spanish MICINN (AYA2009-10368, AYA2012-30717, AYA2012-31277), by the Spanish program of International Campus of Excellence Moncloa (CEI) and by the Madrid Regional Government through the AstroMadrid Project (CAM S2009/ESP-1496, http://www.laeff.cabinta-csic.es/ projects/astromadrid/main/index.php). The support of the Spanish Network for Light Pollution Studies (Ministerio de Economía y Competitividad, Acción Complementaria AYA2011-15808-E) is acknowledged. Thanks goes also to Francisco Ocaña and Jessica Starkey for the critical review of this text.We present the results of the analysis of satellite imagery to study light pollution in Spain. Both calibrated and non-calibrated DMSP-OLS images were used. We describe the method to scale the non-calibrated DMSP-OLS images which allows us to use differential photometry techniques in order to study the evolution of the light pollution. Population data and DMSP-OLS satellite calibrated images for the year 2006 were compared to test the reliability of official statistics in public lighting consumption. We found a relationship between the population and the energy consumption which is valid for several regions. Finally the true evolution of the electricity consumption for street lighting in Spain from 1992 to 2010 was derived; it has been doubled in the last 18 years in most of the provinces. (C) 2013 Elsevier Ltd. All rights reserved,Depto. de Física de la Tierra y AstrofísicaFac. de Ciencias FísicasTRUEMinisterio de Ciencia e Innovacion (MICINN)Comunidad de MadridCampus de Excelencia Internacional (CEI) Moncloa, Españapu

Estimando indicadores socioeconômicos de pequenas bacias hidrográficas através de imagens noturnas de satélite no apoio à gestão dos recursos hídricos

Small watersheds lack socioeconomic data. These data are essential in land use decision-making and in water resources management, especially when determining its economic value. In order to contribute to filling this notable gap, this study presents an approach to estimate this type of information for small watersheds (from 5 to 100 km²), applying nighttime light (NTL) satellite images and available socioeconomic records from larger locale. Three socioeconomic indicators were chosen to test the method: Gross Domestic Product, population and jobs. The relationship between these three socioeconomic indicators and the radiance quantified from the NTL images was acquired through simple regression analysis applied at the 497 municipalities of the State of Rio Grande do Sul (RS), southern Brazil. The polynomial fit equations presented the best Coefficient of Determination, being further submitted to validation by using data from 50 municipalities of the neighboring State of Santa Catarina. The validation showed a very good estimation performance. The validated equations were used to estimate these socioeconomic indicators for small watersheds located in the municipality of Caxias do Sul, RS, in three different years: 2011, 2014 and 2018. Findings indicate that this novel application of NTL for estimating socioeconomic data can be a helpful tool towards land use and water resources management of small watersheds.Há falta de dados socioeconômicos para pequenas bacias hidrográficas. Esses dados são fundamentais para a tomada de decisões na gestão dos recursos hídricos, principalmente na determinação do seu valor econômico. Para contribuir em preencher essa lacuna, este estudo apresenta um método para estimar esse tipo de informação para pequenas bacias hidrográficas (de 5 a 100 km²), aplicando imagens noturnas de satélite e dados socioeconômicos disponíveis de regiões maiores. Três indicadores socioeconômicos foram selecionados para testar o método: Produto Interno Bruto (PIB), população e emprego. A relação entre esses três indicadores e a radiância quantificada nas imagens noturnas foi obtida por meio de análise de regressão simples aplicada nos 497 municípios do Estado do Rio Grande do Sul (RS). As equações do ajuste polinomial apresentaram o melhor Coeficiente de Determinação, sendo posteriormente submetidas à validação com dados de 50 municípios localizados no Estado de Santa Catarina. A validação mostrou um desempenho de estimação muito bom. As equações validadas foram usadas para estimar esses indicadores socioeconômicos para pequenas bacias hidrográficas localizadas no município de Caxias do Sul, RS, em três anos distintos: 2011, 2014 e 2018. Os resultados indicam que esta nova aplicação de imagens noturnas de satélite para estimar dados socioeconômicos pode ser uma ferramenta útil para a gestão do uso do solo e dos recursos hídricos de pequenas bacias hidrográficas

Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the US Atlantic and Gulf Coasts Using Nighttime Remote Sensing

Hurricanes, as one of the most devastating natural hazards, have posed a great threat to people in coastal areas. A better understanding of the spatiotemporal dynamics of human settlement in hurricane-prone areas largely benefits sustainable development. This study uses the nighttime light (NTL) data from the Defense Meteorological Satellite Program\u27s Operational Linescan System (DMSP/OLS) to examine human settlement development in areas with different levels of hurricane proneness from 1992 to 2013. The DMSP/OLS NTL data from six satellites were intercalibrated and desaturated with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) optical imagery to derive the Vegetation Adjusted NTL Urban Index (VANUI), a popular index that quantifies human settlement intensity. The derived VANUI time series was examined with the Mann–Kendall test and Theil–Sen test to identify significant spatiotemporal trends. To link the VANUI product to hurricane impacts, four hurricane-prone zones were extracted to represent different levels of hurricane proneness. Aside from geographic division, a wind-speed-weighted track density function was developed and applied to historical storm tracks which originated in the North Atlantic Basin to better categorize the four levels of hurricane proneness. Spatiotemporal patterns of human settlement in the four zones were finally analyzed. The results clearly exhibit a north–south and inland–coastal discrepancy of human settlement dynamics. This study also reveals that both the zonal extent and zonal increase rate of human settlement positively correlate with hurricane proneness levels. The intensified human settlement in high hurricane-exposure zones deserves further attention for coastal resilience

Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the Us Atlantic and Gulf Coasts Using Nighttime Remote Sensing

Hurricanes, as one of the most devastating natural hazards, have posed a great threat to people in coastal areas. A better understanding of the spatiotemporal dynamics of human settlement in hurricane-prone areas largely benefits sustainable development. This study uses the nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) to examine human settlement development in areas with different levels of hurricane proneness from 1992 to 2013. The DMSP/OLS NTL data from six satellites were intercalibrated and desaturated with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) optical imagery to derive the Vegetation Adjusted NTL Urban Index (VANUI), a popular index that quantifies human settlement intensity. The derived VANUI time series was examined with the Mann– Kendall test and Theil–Sen test to identify significant spatiotemporal trends. To link the VANUI product to hurricane impacts, four hurricane-prone zones were extracted to represent different levels of hurricane proneness. Aside from geographic division, a wind-speed-weighted track density function was developed and applied to historical storm tracks which originated in the North Atlantic Basin to better categorize the four levels of hurricane proneness. Spatiotemporal patterns of human settlement in the four zones were finally analyzed. The results clearly exhibit a north–south and inland–coastal discrepancy of human settlement dynamics. This study also reveals that both the zonal extent and zonal increase rate of human settlement positively correlate with hurricane proneness levels. The intensified human settlement in high hurricane-exposure zones deserves further attention for coastal resilience

Saturation Correction for Nighttime Lights Data Based on the Relative NDVI

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Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014

A new global information baseline describing the spatial evolution of the human settlements in the past 40 years is presented. It is the most spatially global detailed data available today dedicated to human settlements, and it shows the greatest temporal depth. The core processing methodology relies on a new supervised classification paradigm based on symbolic machine learning. The information is extracted from Landsat image records organized in four collections corresponding to the epochs 1975, 1990, 2000, and 2014. The experiment reported here is the first known attempt to exploit global Multispectral Scanner data for historical land cover assessment. As primary goal, the Landsat-made Global Human Settlement Layer (GHSL) reports about the presence of built-up areas in the different epochs at the spatial resolution allowed by the Landsat sensor. Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing from Earth Observation data. An experimental multiple-class land-cover product is also produced from the epoch 2014 collection using low-resolution space-derived products as training set. The classification schema of the settlement distinguishes built-up areas based on vegetation contents and volume of buildings, the latter estimated from integration of SRTM and ASTER-GDEM data. On the overall, the experiment demonstrated a step forward in production of land cover information from global fine-scale satellite data using automatic and reproducible methodology.JRC.G.2-Global security and crisis managemen

Using Multi-Source Data to Assess the Dynamics of Socioeconomic Development in Africa

Frequent and rapid spatially explicit assessment of socioeconomic development is critical for achieving the Sustainable Development Goals (SDGs) at both national and global levels. In the past decades, scientists have proposed many methods for monitoring human activities on the Earth’s surface on various spatiotemporal scales using Defense Meteorological Satellite Program Operational Line System (DMSP-OLS) nighttime lights (NTL) data. However, the DMSP-OLS NTL data and the associated processing methods have limited their reliability and applicability for systematic measuring and mapping of socioeconomic development. This research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) NTL and the Isolation Forest (iForest) machine learning algorithm for more intelligent data processing to capture human activities. I use machine learning and NTL data to map gross domestic product (GDP) at 1 km2. I then use these data products to derive inequality indexes like GINI coefficients and 20:20 ratios at nationally aggregate levels. I have also conducted a case study based on agricultural production information to estimate subnational GDP in Uganda. This flexible approach processes the data in an unsupervised manner on various spatial scales. Assessments show that this method produces accurate sub-national GDP data for mapping and monitoring human development uniformly in Uganda and across the globe