Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data

China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making eorts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two dierent paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed

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

Nighttime Lights as a Proxy for Economic Performance of Regions

Studying and managing regional economic development in the current globalization era demands prompt, reliable, and comparable estimates for a region’s economic performance. Night-time lights (NTL) emitted from residential areas, entertainment places, industrial facilities, etc., and captured by satellites have become an increasingly recognized proxy for on-ground human activities. Compared to traditional indicators supplied by statistical offices, NTLs may have several advantages. First, NTL data are available all over the world, providing researchers and official bodies with the opportunity to obtain estimates even for regions with extremely poor reporting practices. Second, in contrast to non-standardized traditional reporting procedures, the unified NTL data remove the problem of inter-regional comparability. Finally, NTL data are currently globally available on a daily basis, which makes it possible to obtain these estimates promptly. In this book, we provide the reader with the contributions demonstrating the potential and efficiency of using NTL data as a proxy for the performance of regions

Urban Intensities : the Urbanization of the Iberian Mediterranean Coast in the Light of Nighttime Satellite Images of the Earth

The contribution shares the approach of critical urban studies that have conceptualized urbanization more as a process than as a sum of spatial forms. Thus, the contribution studies the urbanization process not only from the point of view of the physical occupation of land but also considers changes in the intensity of the uses of space. To fulfill this aim, the new sources of nocturnal satellite images are particularly useful. These allow us to observe the intensity of urban uses both in terms of their distribution over space and their recurrence over time. The research focuses on the Iberian Mediterranean coast and permits the verification of the intensity of the urban uses of the space for the whole of this area and their seasonal variations throughout the year. The source of the study are the nighttime satellite images of the Earth for the 2012-2017 period from the NASA SNPP satellite equipped with the VIIRS-DNB instrument. By establishing a threshold of urban light the research shows that those districts with the greatest extensions of urban light do not necessarily correspond with the most densely populated areas. Similarly the absence of urban light does not necessarily indicate the absence of urban uses. Finally, the variations of intensity of light prove to be a good indicator of seasonal variations of activity in tourist areas

Estimación de la captura ilegal de Dosidicus gigas por la flota que opera fuera de la ZEE del Perú (2013-2016)

Universidad Nacional Agraria La Molina. Facultad de Pesquería. Departamento Académico de Manejo Pesquero y Medio AmbienteEl presente estudio estima la captura ilegal de Dosidicus gigas de la flota industrial que operó fuera de la ZEE del Perú entre 2013 y 2016 usando tres bases de datos: i) Datos proporcionados por el satélite Suomi National Polar Partnership (SNPP) que registran diariamente las operaciones de pesca que usan potentes focos hasta 300 kW para atraer al calamar hacia la superficie. ii) Rutas de navegación de los barcos obtenidas del sistema de identificación automática (AIS). iii) Datos de las embarcaciones autorizadas y registradas en la Organización Regional de Ordenación Pesquera del Pacífico Sur (OROP-PS) y que realizaron la captura del calamar gigante en el área FAO-87. El análisis fue realizado para el periodo entre 2013 y 2016 y consistió en la cuantificación del número de embarcaciones usando imágenes satelitales, la cual fue validada con datos reales del sistema AIS obteniendo resultados robustos, un error cuadrático medio (ECM) de 1.98 barcos y un sesgo en la estimación de -0.43 barcos. Los puntos de luces de las embarcaciones pesqueras fueron comparados con los tracks de las embarcaciones pesqueras del sistema AIS. Para lo cual se estimó un valor de umbral de velocidad menor a 2.3 nudos, la cual determina una operación de pesca nocturna. Luego, se comparó con la lista de embarcaciones autorizadas de la OROPPS para así cuantificar el número de embarcaciones ilegales y utilizando un nuevo valor de CPUE mensual calcular la captura ilegal. Las estimaciones indicaron que la captura ilegal representó el 39.4 % (2013), 30.7 % (2014), 20.0 % (2015) y el 18.4% (2016) y para todo el periodo de estudio (2013-2016) el 27.2% de la captura total registrada oficialmente en la OROP-PS. Esto resultados evidencia la existencia de embarcaciones que operan ilegalmente fuera de la ZEE de Perú y Chile y que pueden ser detectados usando las imágenes nocturnas y los datos AIS. Este nuevo procedimiento plantea la posibilidad de conocer la dimensión real de la flota, así como reconstruir las capturas y estimar la magnitud de la pesca INDNR en pesquerías que usan lucesThe present study estimates the illegal catch of Dosidicus gigas by industrial fishing fleet that operated outside the Peruvian EEZ using three databases. First, daily imagery provided by The Suomi National Polar-orbiting Partnership (NPP) satellite and used to detect fishing boats using powerful lamps of up 300 kW to attract the squid to the surface. Second, navigation track information of fishing boats obtained from Automatic Identification System (AIS). Third, registry of the vessels authorized to operate in the South Pacific Regional Fisheries Management Organisation (SPRFMO) area and the catch data for capture of Jumbo flying squid in the South East Pacific (FAO-87). The analysis was performed for the period between 2013 and 2016 and consisted in the quantification of the number of vessels detected with satellite images and validation with AIS information showing robust results including a root-mean-square error (RMSE) of 1.98 ships and a BIAS in the estimate of -0.43 ships. The number of light points of fishing vessels was compared with the tracks of fishing vessels estimated from the AIS analyses. For this, a speed threshold value of fewer than 2.3 knots was estimated, which is consistent with a night fishing operation with lights. Then, it was compared with the list of authorized vessels in the SPRFMO registry to quantify the number of illegal vessels and using a new monthly CPUE value, the illegal catch was calculated. The estimations indicated that the illegal catch represented 39.4% (2013), 30.7% (2014), 20.0% (2015) and 18.4% (2016) and for the entire study period (2013-2016) 27.2% of the total catch officially registered in the SPRFMO. These results demonstrate that unauthorized and unidentified vessels operate outside the EEZ of Peru and Chile and can be detected using nighttime satellite images and AIS information. This new procedure makes it possible to estimate the real size of the fleet as well as reconstruct total catches and estimate the real magnitude of IUU fishing in fisheries where lights are used to attract catchTesi

Utility of High Resolution Human Settlement Data for Assessment of Electricity Usage Patterns

Electricity is vital for modern human civilization, and its demands are expected to significantly rise due to urban growth, transportation modernization, and increasing industrialization and energy accessibility. Meeting the present and future demands while minimizing the environmental degradation from electricity generation pathways presents a significant sustainability challenge. Urban areas consume around 75% of global energy supply yet urban energy statistics are scarce all over the world, creating a severe hindrance for the much-needed energy sustainability studies. This work explores the scope of geospatial data-driven analysis and modeling to address this challenge. Identification and measurements of human habitats, a key measure, is severely misconceived. A multi-scale analysis of high, medium, and coarse resolution datasets in Egypt and Taiwan illustrates the increasing discrepancies from global to local scales. Analysis of urban morphology revealed that high-resolution datasets could perform much better at all scales in diverse geographies while the power of other datasets rapidly diminishes from the urban core to peripheries. A functional inventory of urban settlements was developed for three cities in the developing world using very high-resolution images and texture analysis. Analysis of correspondence between nighttime lights emission, a proxy of electricity consumption, and the settlement inventory was the conducted. The results highlight the statistically significant relationship between functional settlement types and corresponding light emission, and underline the potential of remote sensing data-driven methods in urban energy usage assessment. Lastly, the lack of urban electricity data was addressed by a geospatial modeling approach in the United States. The estimated urban electricity consumption was externally validated and subsequently used to quantify the effects of urbanization on electricity consumption. The results indicate a 23% lowering of electricity consumption corresponding to a 100% increase in urban population. The results highlight the potential of urbanization in lowering per-capita energy usage. The opportunity and limits to such energy efficiency were identified with regards to urban population density. The findings from this work validate the applicability of geospatial data in urban energy studies and provide unique insights into the relationship between urbanization and electricity demands. The insights from this work could be useful for other sustainability studies

Too many cooks in the kitchen? A comprehensive comparison of NGO spending and development in Nepal

There are over 51,000 non-governmental organizations (NGOs) registered in Nepal, yet the country continually ranks low in various measures of human development. As is the case in many developing countries, NGOs operate with limited oversight and impact evaluations that document their progress are nearly non-existent. This research proposes a method for testing the efficacy and effectiveness of NGO presence in Nepal using the remote sensing technologies and foreign aid tracking provided by the country’s government. By comparing data from validated machine learning methods that measure economic activity with NGO funding, this paper helps identify more accurately the effectiveness of NGO involvement in Nepal and provide a backcheck on its accuracy. The findings indicate that there is no significant relationship between provincial GDP, which acts as a proxy for economic development, and NGO funding. This implies that even though the provinces receive hundreds of millions of dollars each year, NGO and government aid are not the magic bullets for poverty alleviation and economic development

Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China

Nighttime light imagery offers a unique view of the Earth’s surface. In the past, the nighttime light data collected by the DMSP-OLS sensors have been used as an efficient means to correlate regional and global socio-economic activities. With the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite in 2011, the day-night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard represents a major advancement in nighttime imaging capabilities, because it surpasses its predecessor DMSP-OLS in radiometric accuracy, spatial resolution and geometric quality. In this paper, four variables (total night light, light area, average night light and log average night light) are extracted from nighttime radiance data observed by the VIIRS-DNB composite in 2013 and nighttime digital number (DN) data from the DMSP-OLS stable dataset in 2012, respectively, and correlated with 12 socio-economic parameters at the provincial level in mainland China during the corresponding period. Background noise of DNB composite data is removed using either a masking method or an optimal threshold method. In general, the correlation of these socio-economic data with the total night light and light area of VIIRS-DNB composite data is better than with the DMSP-OLS stable data. The correlations between total night light of denoised DNB composite data and built-up area, gross regional product (GRP) and power consumption are higher than 0.9 and so are the correlations between the light area of denoised DNB composite data and city and town population, built-up area, GRP, power consumption and waste water discharge. However, the correlations of socio-economic data with the average night light and log average night light of VIIRS-DNB composite data are not as good as with the DMSP-OLS stable data. To quantitatively analyze the reasons for the correlation difference, a cubic regression method is developed to correct the saturation effect of the DMSP stable data, and we artificially convert the pixel value of the DNB composite into six bits to match the DMSP stable data format. The correlation results between the processed data and socio-economic data show that the effects of saturation and quantization are two of the reasons for the correlation difference. Additionally, on this basis, we estimate the total night light ratio between saturation-corrected DMSP stable data and finite quantization DNB composite data, and it is found that the ratio is ~11.28 ± 4.02 for China. Therefore, it appears that a different acquisition time is the other reason for the correlation difference

Land Squandering and Social Crisis in the Spanish City

The last two decades have been marked by intense and accelerated economic, political, and cultural processes that have affected urban spaces. These changes have occurred in different parts of cities (traditional centers, edges, peripheries) and at different levels of the urban system (large and medium-sized cities and in their respective areas of influence). Possibly the clearest expression of the spatial effects on cities can be perceived in their morphological transformations, their territorial dimensions, or in their social problems. Until 2008, urban–territorial processes were a reflection of the logic and inconsistencies of an expansive economic context and of a structural context that favored the development of cities through concurrent processes and actors. As a result, the built land and amount of urbanized and built surfaces increased, together with processes of the expansion and modernization of cities. Since 2008, the expansive economic cycle has ended, and there have been diverse negative consequences. Notably, the construction sector has come to an abrupt halt. Access to credit has also been reduced, and unemployment has increased. The economic recession has caused sociodemographic and socioeconomic issues exemplified by housing vulnerability, with dispossession, evictions, a shortage of social housing, and energy poverty