Spatially explicit and reliable data on poverty is critical for both policy makers and researchers. However, such data remain scarce particularly in developing countries. Current research is limited in using environmental data from different sources in isolation to estimate poverty despite the fact that poverty is a complex phenomenon which cannot be quantified either theoretically or practically by one single data type. This study proposes a random forest regression (RFR) model to estimate poverty at 10 km × 10 km spatial resolution by combining features extracted from multiple data sources, including the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) nighttime light (NTL) data, Google satellite imagery, land cover map, road map and division headquarter location data. The household wealth index (WI) drawn from the Demographic and Health Surveys (DHS) program was used to reflect poverty level. We trained the RFR model using data in Bangladesh and applied the model to both Bangladesh and Nepal to evaluate the model’s accuracy. The results show that the R2 between the actual and estimated WI in Bangladesh is 0.70, indicating a good predictive power of our model in WI estimation. The R2 between actual and estimated WI of 0.61 in Nepal also indicates a good generalization ability of the model. Furthermore, a negative correlation is observed between the district average WI and the poverty head count ratio (HCR) in Bangladesh with the Pearson Correlation Coefficient of -0.6. Using Gini importance, we identify that proximity to urban areas is the most important variable to explain poverty which contribute to 37.9% of the explanatory power. Compared to the study that used NTL and Google satellite imagery in isolation to estimate poverty, our method increases the accuracy of estimation. Given that the data we use are globally and publicly available, the methodology reported in this study would also be applicable in other countries or regions to estimate the extent of poverty.

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To realize energy conservation and environmental protection, solar street lights have been widely used in urban areas in China. To reasonably and effectively utilize solar street lights, the original street lights must be located, and the solar street light potential must be assessed. The Jilin1-03B (JL1-3B) satellite provides next-generation nighttime light data with a high spatial resolution and in three spectral bands. Consequently, the street lights can be extracted from the nighttime light data. We used the road network dataset from the open street map with a specific buffer to extract the road area as a constraint region. Next, the grayscale brightness of JL1-3B images was obtained by integrating all the three bands to locate the street light by using a local maximum algorithm. Then, the values of the original three bands were utilized to classify the types of street lights as high-pressure sodium (HPS) lamps or light-emitting diode lamps. Finally, we simulated the replacement of all the HPS lamps with solar street lights and assessed the corresponding solar energy potential by using the digital surface model data and hourly cloud cover data through the SHORTWAVE-C model. The accuracy of location of the street lights was approximately 90%. Replacing an HPS lamp by one solar street light for 20 years can save 1.85 × 10 4 kWh of electrical energy, 7.41 t of standard coal, 5.03 t of C emissions, 18.47 t of CO 2 emissions, 0.55 t of SO 2 emissions, and 0.28 t of NO X emissions.

Building-level population data are of vital importance in disaster management, homeland security, and public health. Remotely sensed data, especially LiDAR data, which allow measures of three-dimensional morphological information, have been shown to be useful for fine-scale population estimations. However, studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution. In this article, we proposed a framework to estimate population at the building level by integrating POI data, nighttime light (NTL) data, and LiDAR data. Building objects were first derived using LiDAR data and aerial photographs. Then, three categories of building-level features, including geometric features, nighttime light intensity features, and POI features, were, respectively, extracted from LiDAR data, Luojia1-01 NTL data, and POI data. Finally, a well-trained random forest model was built to estimate the population of each individual building. Huangpu District in Shanghai, China, was chosen to validate the proposed method. A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R2=0.65 at the building level and R2=0.79 at the community level. The NTL radiance intensity was found to have a positive relationship with population in residential areas, while a negative relationship was found in office and commercial areas. Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data, the accuracy of building-level population estimation can be improved.

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