Using a combination of nighttime light and MODIS data to estimate spatiotemporal patterns of CO2 emissions at multiple scales

Using POI and multisource satellite datasets for mainland China's population spatialization and spatiotemporal changes based on regional heterogeneity

Geospatial big data and remote sensing data are widely used in population spatialization studies. However, the relationship between them and population distribution has regional heterogeneity in different geographic contexts. It is necessary to improve data processing methods and spatialization models in areas with large geographical differences. We used land cover data to extract human activity, nighttime light and point-of-interest (POI) data to represent human activity intensity, and considered differences in geographical context to divide mainland China into northern, southern and western regions. We constructed random forest models to generate gridded population distribution datasets with a resolution of 500 m, and quantitatively evaluated the importance of auxiliary data in different geographical contexts. The street-level accuracy assessment showed that our population dataset is more accurate than WorldPop, with a higher R2 and smaller deviation. The improved datasets provided broad potential for exploring the spatial-temporal changes in grid-level population distribution in China from 2010 to 2020. The results indicated that the population density and settlement area have increased, and the overall pattern of population distribution has remained highly stable, but there are significant differences in population change patterns among cities with different urbanization processes. The importance of the ancillary data to the models varied significantly, with POI contributing the most to the southern region and the least to the western region. Moreover, POI had relatively less influence on model improvement in undeveloped areas. Our study could provide a reference for predicting social and economic spatialized data in different geographical context areas using POI and multisource satellite data.

Rasterizing CO2 emissions and characterizing their trends via an enhanced population-light index at multiple scales in China during 2013-2019

Climate change caused by CO2 emissions (CE) has received widespread global concerns. Obtaining precision CE data is necessary for achieving carbon peak and carbon neutrality. Significant deficiencies of existing CE datasets such as coarse spatial resolution and low precision can hardly meet the actual requirements. An enhanced population-light index (RPNTL) was developed in this study, which integrates the Nighttime Light Digital Number (DN) Value from the National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and population density to improve CE estimation accuracy. The CE from the Carbon Emission Accounts & Datasets (CEADS) was divided into three sectors, namely urban, industrial, and rural, to differentiate the heterogeneity of CE in each sector. The ordinary least square (OLS), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models were employed to establish the quantitative relationship between RPNTL and CE for each sector. The optimal model was determined through model comparison and precision evaluation and was utilized to rasterize CE for urban, industrial, and rural areas. Additionally, hot spot analysis, trend analysis, and standard deviation ellipses were introduced to demonstrate the spatiotemporal dynamic characteristics of CE at multiple scales. The performance of the GTWR outperformed other methods in estimating CE. The enhanced RPNTL demonstrated a higher coefficient of determination (R2 = 0.95) than the NTL (R2 = 0.92) in predicting CE, particularly in rural regions where the R2 value increased from 0.76 to 0.81. From 2013 to 2019, high CE was observed in eastern and northern China, while a decreasing trend was detected in northeastern China and Chengdu-Chongqing. Conversely, the Yangtze River Delta, Pearl River Delta, Fenwei Plain, and Henan Province showed an increasing trend. The center of gravity for industrial and rural CE is shifting towards western regions, whereas that for urban CE is moving northward. This study provides valuable insights for decision-making on CE control.

Spatiotemporal characteristics of carbon emissions in Shaanxi, China, during 2012-2019: a machine learning method with multiple variables

Global warming attributed to the emission of greenhouse gases has caused unprecedented extreme weather events, such as excessive heatwave and rainfall, posing enormous threats to human life and sustainable development. China, as the toppest CO2 emitter in the world, has promised to achieve carbon emission peak by 2030. However, it is difficult to estimate county-level carbon emissions in China because of the lack of statistical data. Previous studies have established relationship between carbon emission and nighttime light; however, using only nighttime light for carbon emission modeling ignores the impact of natural or other socioeconomic factors on emissions. In this paper, we adopted the back propagation neural network to estimate carbon emissions at county level in Shaanxi, China, using nighttime light, Normalized Difference Vegetation Index, precipitation, land surface temperature, elevation, and population density. Trend analysis, spatial autocorrelation, and standard deviation ellipse were employed to analyze the spatiotemporal distributions of carbon emission during 2012-2019. Three metrics (R2, root mean square error, and mean absolute error) were adopted to validate the accuracy of the proposed model, with the values of 0.95, 1.30, and 0.58 million tons, respectively, demonstrating a comparable estimation performance. The results present that carbon emissions in Shaanxi Province rise from 256.73 in 2012 to 305.87 million tons in 2019, formatting two hotspots in Xi'an and Yulin city. The proposed model can estimate carbon emissions of Shaanxi Province at a finer scale with an acceptable accuracy, which can be efficiently applied in other spatial or temporal domains after being localized, providing technical supports for carbon reduction.

Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data

An accurate carbon emissions map is of great significance for urban planning to reduce carbon emissions, mitigate the heat island effect, and avoid the impact of high temperatures on human health. However, little research has focused on carbon emissions maps at the land patch level, which makes poor integration with small and medium-sized urban planning based on land patches. In this study, a vectorization method for spatial allocation of carbon emissions at the land patch level was proposed. The vector maps and spatial autocorrelation of carbon emissions in Zhangdian City, China were explored using multi-source data. In addition, the differences between different streets were analyzed, and the carbon emissions ratio of the land patch was compared. The results show that the vector carbon emissions map can help identify the key carbon reduction land patches and the impact factors of carbon emissions. The vector maps of Zhangdian City show that in 2021, the total carbon emissions and carbon absorptions were 4.76 × 10⁹kg and 4.28 × 10⁶kg respectively. Among them, industrial land accounted for 70.16% of carbon emissions, mainly concentrated in three industrial towns. Forest land carbon absorption accounted for 98.56%, mainly concentrated in the peripheral streets away from urban areas. The Moran's I of land patch level carbon emissions was 0.138, showing a significant positive spatial correlation. The proportion of land patches is an important factor in determining carbon emissions, and the adjustment of industrial structure is the most critical factor in reducing carbon emissions. The results achieved can better help governments develop different carbon reduction strategies, mitigate the heat island effect, and support low-carbon and health-oriented urban planning.