Satellite-derived nighttime light (NTL) data have been extensively used as an efficient proxy measure for monitoring urbanization dynamics and socioeconomic activity. This is because remotely sensed NTL signals can be quantitatively connected to demographic and socioeconomic variables at regional and global scales. The recently composited cloud-free NTL imagery derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite provides spatially detailed observations of human settlements. We quantitatively estimated socioeconomic development inequalities across 30 provinces and municipalities in mainland China using VIIRS NTL data associated with both regional gross domestic product (GDP) and population census data. We quantitatively investigated relations between NTL, GDP, and population using a linear regression model. Our results suggest that NTL radiances have significant positive correlations with GDP and population at different levels. Several inequality coefficients, commonly used in economics, were derived from VIIRS NTL data and statistical data at multiple spatial scales. Compared with the statistical data, NTL-derived inequality coefficients enabled us to elicit more detailed information on differences in regional development at multiple levels. Our study of provinces and municipalities revealed that county-level inequality was more significant than city-level inequality. The results of population-weighted NTL inequality indicate an obvious regional disparity with NTL distribution being more unequal in China’s undeveloped western regions compared with more developed eastern regions. Our findings suggest that given the timely and spatially explicit advantages of VIIRS, NTL data are capable of providing comprehensive information regarding inequality at multiple levels, which is not possible through the use of traditional statistical sources.

China has been undergoing a remarkably rapid urbanization process in the last several decades. Urbanization is a complicated phenomenon involving imbalanced transformation processes, such as population migrations, economic advancements and human activity dynamics. It is important to evaluate the imbalances between transformation processes to support policy making in the realms of environmental management and urban planning. The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) nighttime lights time series imagery provides a consistent and timely measure to estimate socioeconomic dynamics and changes in human activity. In this study, we jointly compared the annual ranks of three variables: the population, the gross domestic product (GDP) and the sum of weighted DMSP/OLS nighttime lights to estimate spatial and temporal imbalances in the urbanization processes of 226 cities in China between 1994 and 2011. We used ternary plots and a Euclidean distance-based method to quantitatively estimate the spatial and temporal imbalances between cities and to classify diverse urban development patterns in China. Our results suggest that, from 1994 to 2011, the imbalances of urbanization processes observed in the eastern, western and middle cities decreased, respectively, by 35.26%, 29.04% and 25.84%; however, imbalances in the northeast increased by 33.29%. The average decrement in imbalances across all urbanization processes in the 226 cities was 17.58%. Cities in the eastern region displayed relatively strong attractions to population, more rapid economic development processes and lower imbalances between socioeconomic and anthropogenic dynamics than cities in other regions. Several types of urban development patterns can be identified by comparing the morphological characteristics of temporal ternary plots of the 226 cities in China. More than one third (35.40%) of the 226 cities presented balanced states during the period studied; however, the remainder showed alternative urban development patterns.

Stable nighttime light (NTL) data, derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS), are typically considered a proxy measure of the dynamics of human settlements and have been extensively used to quantitative estimates of demographic variables, economic activity, and land-use change in previous studies at both regional and global scales. The utility of DMSP data for characterizing spatio-temporal trends in urban development at a local scale, however, has received less attention. In this study, we utilize a time series of DMSP data to examine the spatio-temporal characteristics of urban development in 285 Chinese cities from 1992 to 2009, at both the local and national levels. We compare linear models and piecewise linear models to identify the turning points of nighttime lights and calculate the trends in nighttime light growth at the pixel level. An unsupervised classification is applied to identify the patterns in the nighttime light time series quantitatively. Our results indicate that nighttime light brightness in most areas of China exhibit a positive, multi-stage process over the last two decades; however, the average trends in nighttime light growth differ significantly. Through the piecewise linear model, we identify the saturation of nighttime light brightness in the urban center and significant increases in suburban areas. The maps of turning points indicate the greater the distance to the city center or sub-center, the later the turning point occurs. Six patterns derived from the classification illustrate the various characteristics of the nighttime light time series from the local to the national level. The results portray spatially explicit patterns and conspicuous temporal trends of urbanization dynamics for individual Chinese cities from 1992 to 2009.

Spatially-detailed forest height data are useful to monitor local, regional and global carbon cycle. LiDAR remote sensing can measure three-dimensional forest features but generating spatially-contiguous forest height maps at a large scale (e.g., continental and global) is problematic because existing LiDAR instruments are still data-limited and expensive. This paper proposes a new approach based on an artificial neural network (ANN) for modeling of forest canopy heights over the China continent. Our model ingests spaceborne LiDAR metrics and multiple geospatial predictors including climatic variables (temperature and precipitation), forest type, tree cover percent and land surface reflectance. The spaceborne LiDAR instrument used in the study is the Geoscience Laser Altimeter System (GLAS), which can provide within-footprint forest canopy heights. The ANN was trained with pairs between spatially discrete LiDAR metrics and full gridded geo-predictors. This generates valid conjugations to predict heights over the China continent. The ANN modeled heights were evaluated with three different reference data. First, field measured tree heights from three experiment sites were used to validate the ANN model predictions. The observed tree heights at the site-scale agreed well with the modeled forest heights (R = 0.827, and RMSE = 4.15 m). Second, spatially discrete GLAS observations and a continuous map from the interpolation of GLAS-derived tree heights were separately used to evaluate the ANN model. We obtained R of 0.725 and RMSE of 7.86 m and R of 0.759 and RMSE of 8.85 m, respectively. Further, inter-comparisons were also performed with two existing forest height maps. Our model granted a moderate agreement with the existing satellite-based forest height maps (R = 0.738, and RMSE = 7.65 m (R² = 0.52, and RMSE = 8.99 m). Our results showed that the ANN model developed in this paper is capable of estimating forest heights over the China continent with a satisfactory accuracy. Forth coming research on our model will focus on extending the model to the estimation of woody biomass.

This study develops a modeling framework for utilizing the large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and Land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-Radiometer (MODIS) imagery, meteorological data, and forest measurements for monitoring stocks of total biomass (including aboveground biomass and root biomass). The forest tree height models were separately used according to the artificial neural network (ANN) and the allometric scaling and resource limitation (ASRL) tree height models which can both combine the climate data and satellite data to predict forest tree heights. Based on the allometric approach, the forest aboveground biomass model was developed from the field measured aboveground biomass data and the tree heights derived from two tree height models. Then, the root biomass should scale with the aboveground biomass. To investigate whether this approach is efficient for estimating forest total biomass, we used Northeast China as the object of study. Our results generally proved that the method proposed in this study could be meaningful for forest total biomass estimation (R² = 0.699, RMSE = 55.86).