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Accompanied by China's rapid economic growth, significant urban greening has occurred in Chinese cities, in particular in the urban core areas. In contrast, rapid urbanization and economic growth also led to a high probability of vegetation degradation in urban fringe regions. However, these significant spatial differences in urban greenness associated with economic growth in Chinese cities are not well understood. This study explored the spatiotemporal characteristics of the nighttime light (NTL) and annual maximum enhanced vegetation index (EVImax) in urban areas from 2001 to 2020. A strong decoupling status between economic growth and urban greenness on the national scale was found. Overall, 49.15% of urban areas showed a decoupling status. Spatially, this percentage of urban areas with a decoupling status would significantly decrease when the long-term average NTL surpasses 51. Moreover, this significant threshold of decoupling status was found in 189 cities out of 344 (54.65%) in China. This threshold in each city showed significant spatial heterogeneity but can mostly be attributed to the gradient in the long-term average precipitation (Pmean) of each city during the period of 2001–2020. Specifically, a spatial increase in Pmean of 100 mm responded to a decrease in the threshold of 0.4 DN (p < 0.01). In contrast, there was no significant correlation between the threshold and the economic growth status of each city. Our results provide valuable insights for coordinating the development of urban greening and economic growth. [ABSTRACT FROM AUTHOR]

Little attention has been paid to the impact of future urban expansion patterns on carbon emissions based on the existing urban pattern of a region. This study used the Central Guizhou Urban Agglomeration as the study area, and the relationships between regional urbanization and CO2 emissions in the study area were analyzed based on historical data. Urban growth patterns were then simulated in four scenarios that focused on the next 15 years, and they were based on the cellular automaton model. In each different scenario, the CO2 emissions were predicted, and some implications regarding the impact of those emissions were provided. The results showed that as urban land-use intensity increases, CO2 emissions first increase then decrease; however, the rate of decline for CO2 emissions is much slower than the rate at which it rises. Moreover, in the next 15 years, urban expansion will lead to a significant increase in CO2 emissions. The CO2 emissions were found to be lowest in the spatial agglomeration scenario and highest in the spatial dispersion scenario. The spatial agglomeration scenario was conducive to understanding how CO2 emissions eventually peak; however, different cities in the study area should adopt different urban expansion patterns. These research results can provide a reference guide for the government with regard to urban planning. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated that the pattern of land surface dynamic feedbacks (LSDF) based on remote sensing images after a rainfall event can be used to derive environmental covariates to assist in predicting soil texture variation over low-relief areas. However, the impact of the rainfall magnitude on the performance of these covariates has not been thoroughly investigated. The objective of this study was to investigate this impact during ten observation periods following rainfall events of different magnitudes (0–40 mm). An individual predictive soil mapping method (iPSM) was used to predict soil texture over space based on the environmental covariates derived from land surface dynamic feedbacks. The prediction error showed strong negative correlation with rainfall magnitude ( Pearson ’ s r between root-mean squared error of prediction and rainfall magnitude = −0.943 for percentage of sand and −0.883 for percentage of clay). When the rainfall reaches a certain magnitude, the prediction error becomes stable. The recommended rain magnitude (threshold) using LSDF method in this study area is larger than 20 mm for both sand and clay percentage. The predictive maps based on different observed periods with similar rainfall magnitudes show only slight differences. Rainfall magnitude can thus be said to have a significant impact on the prediction accuracy of soil texture mapping. Greater rainfall magnitude will improve the prediction accuracy when using the LSDF. And high wind speed, high evaporation and low relative humidity during the observed periods also improved the prediction accuracy, all by stimulating differential soil drying. [ABSTRACT FROM AUTHOR]

• Considering the spatial heterogeneity of rainfall improved the LSDF in a large area. • The adjusted LSDF improved the accuracy of soil mapping. • The LSDF adjustment decreases with increasing rainfall, and the decreasing rate slows. The land surface dynamic feedback (LSDF) information captured by time-series remote sensing data during the soil-drying process after a rainfall event provides effective covariates for digital soil mapping over low-relief areas. However, current methods used to capture LSDF require a uniform rainfall magnitude in the geographic space; a condition that is not often met for large areas. Here, we propose a LSDF construction method considering the spatial heterogeneity of rainfall magnitudes by adjusting the evaporation variables in the LSDF. For this, the relationships between evaporation and rainfall magnitudes were first established. The LSDFs from various locations for rainfall events with different magnitudes were then adjusted based on these relationships. Using a case study, the adjusted LSDFs after two rainfall events were then used to predict soil texture over a low-relief area. The results showed that the cubic polynomial model performed best when constructing the relationship between evaporation adjustment and rainfall magnitude, giving the highest R 2 value and a low Akaike information criterion. Adjustment to the LSDF decreases with increasing rainfall and the rate of change in the adjustment also decreases with increasing rainfall. For both rainfall events, prediction accuracies with the adjusted LSDFs were higher than those based on the original LSDFs. Furthermore, the greater the adjustment, the greater the improvement in the accuracy. We conclude that the proposed construction method for LSDF, accounting for the spatial heterogeneity of rainfall magnitudes, offers improved predictive power for digital soil mapping over large areas. [ABSTRACT FROM AUTHOR]

The soil spectral dynamic feedback captured from high temporal resolution remote sensing data, such as MODIS, during the soil drying process after a rainfall could assist with digital soil mapping. However, this method is ineffective in utilizing the images with a relatively high spatial resolution. There are an insufficient number of images in the soil drying process since those high spatial resolution images tend to have a low temporal resolution. This study is aimed at generating soil spectral dynamic feedback by integrating the feedback captured from the images with a high spatial resolution during the process of multiple drying after different rainfall events. The Landsat 8 data with a temporal resolution of 16 day was exemplified. Each single spectral feedback obtained from Landsat 8 was first adjusted to eliminate the impact of different rainfall magnitudes. Then, the soil spectral dynamic feedback was reorganized and generated based on the adjusted feedback. Finally, the soil spectral dynamic feedback generated based on Landsat 8 was used for mapping topsoil texture and compared with the mapping results based on the MODIS data and the fusion data of MODIS and Landsat 8. As revealed by the results, not only could the generated soil spectral dynamic feedback based on Landsat 8 data improve the details of the spatial distribution of soil texture, but it also enhances the accuracy of mapping. The mapping accuracy based on Landsat 8 data is higher than that based on the MODIS data and fusion data. The improvements of accuracy are more obvious in the areas with more complex surface conditions. This study widens the scope of application for soil spectral dynamic feedback and provides support for large-scale and high-precision digital soil mapping. [ABSTRACT FROM AUTHOR]

The absence data (samples) for landslide susceptibility mapping using data-driven methods are not available directly and often approximated by locations where no landslides have occurred. The existing methods for generating absence data cannot quantify the reliability of candidate absence data and thus such data reduce the quality of prediction. In this paper, a new approach to absence data generation, referred to as similarity based sampling, was proposed for landslide susceptibility mapping using data-driven methods. First, the reliability of candidate absence data is quantified based on the dissimilarity in environmental conditions (covariate conditions) between the absence data and the presence data (which are the landslide occurrences). The absence data whose reliability value is higher than a given threshold were selected to be used. The proposed approach was validated through its application to three data-driven methods (i.e. logistic regression, support vector machine and random forest) for landslide susceptibility mapping. A case study was conducted in the Youfang catchment in southern Gansu Province of China. Ten groups of absence data were generated each corresponding to one of the ten different thresholds of reliability ranging from 0.0 to 0.9. The results show that the prediction accuracy of the data-driven methods rose when the threshold increased from 0.0 to 0.5, but the accuracy decreases as the threshold continues to increase after 0.5, that is, from 0.5 to 0.9. The best performance was obtained when the threshold was 0.5. The proposed method was compared with existing methods for absence data generation (i.e. buffer controlled and target space exteriorization). These results show that the similarity-based approach has a better performance than these existing methods for landslide susceptibility mapping using data-driven methods. • Similarity Based Sampling is proposed to generate absence data. • Landslide susceptibility mapping using statistic method • Prediction efficiency index was used to evaluate the accuracy of model. [ABSTRACT FROM AUTHOR]