County-level CO2 emissions and sequestration in China during 1997–2017

With the implementation of China’s top-down CO2 emissions reduction strategy, the regional differences should be considered. As the most basic governmental unit in China, counties could better capture the regional heterogeneity than provinces and prefecture-level city, and county-level CO2 emissions could be used for the development of strategic policies tailored to local conditions. However, most of the previous accounts of CO2 emissions in China have only focused on the national, provincial, or city levels, owing to limited methods and smaller-scale data. In this study, a particle swarm optimization-back propagation (PSO-BP) algorithm was employed to unify the scale of DMSP/OLS and NPP/VIIRS satellite imagery and estimate the CO2 emissions in 2,735 Chinese counties during 1997–2017. Moreover, as vegetation has a significant ability to sequester and reduce CO2 emissions, we calculated the county-level carbon sequestration value of terrestrial vegetation. The results presented here can contribute to existing data gaps and enable the development of strategies to reduce CO2 emissions in China

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

Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

Continuous urbanization and industrialization lead to plenty of rural residents migrating to cities for a living, which seriously accelerated the population hollowing issues. This generated series of social issues, including residential estate idle and numerous vigorous laborers migrating from undeveloped rural areas to wealthy cities and towns. Quantitatively determining the population hollowing characteristic is the priority task of realizing rural revitalization. However, the traditional field investigation methods have obvious deficiencies in describing socio-economic phenomena, especially population hollowing, due to weak efficiency and low accuracy. Here, this paper conceives a novel scheme for representing population hollowing levels and exploring the spatiotemporal dynamic of population hollowing. The nighttime light images were introduced to identify the potential hollowing areas by using the nightlight decreasing trend analysis. In addition, the entropy weight approach was adopted to construct an index for evaluating the population hollowing level based on statistical datasets at the political boundary scale. Moreover, we comprehensively incorporated physical and anthropic factors to simulate the population hollowing level via random forest (RF) at a grid-scale, and the validation was conducted to evaluate the simulation results. Some findings were achieved. The population hollowing phenomenon decreasing gradually was mainly distributed in rural areas, especially in the north of the study area. The RF model demonstrated the best accuracy with relatively higher R2 (Mean = 0.615) compared with the multiple linear regression (MLR) and the geographically weighted regression (GWR). The population hollowing degree of the grid-scale was consistent with the results of the township scale. The population hollowing degree represented an obvious trend that decreased in the north but increased in the south during 2016–2020 and exhibited a significant reduction trend across the entire study area during 2019–2020. The present study supplies a novel perspective for detecting population hollowing and provides scientific support and a first-hand dataset for rural revitalization

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