Assessing progress towards sustainable development goals for Chinese urban land use: A new cloud model approach.

Rapid urbanization poses great challenges to China's urban land use sustainability (ULUS). Land is the essential space to achieve the Sustainable Development Goals (SDGs) of the United Nations, so SDGs provide a new guide to evaluate land use sustainability. However, there is still a lack of SDGs-oriented assessment of urban land use at national level. Moreover, there is still a need to address the problems about the randomness and fuzziness within evaluation, which tends to cause more uncertainties. Here we developed a SDGs-oriented evaluation framework based on the cloud model and derived the spatial and temporal patterns of urban land use sustainability for China at the prefecture-level from 2004 to 2019. Then, we used the McKinsey matrix to classify the types of urban land use sustainability, and examined their main drivers using the Geodetector method. The results showed that the development level of ULUS in China was high in the east and low in the west. High-value hotspots were mainly distributed in primary and secondary urban agglomerations in China. From 2004 to 2019, the development level of ULUS in China gradually increased, but the growth rate slowed down. In 2009 the value of central China exceeded that of the northeast. In contrast, the coordination level of ULUS had declined in more than 50% of Chinese cities during the study period. The high values were in southern China, northeast China, and Chengdu-Chongqing urban agglomeration, while the low values were in central and southern Liaoning and the urban agglomeration in the Central Plains. The development level was mainly controlled by anthropogenic activities and urban development, while natural conditions constrained the improvement of the coordination level. Combining the development and coordination, we found that cities with higher development level often had a wide range of coordination level, and suggestions were put forward for different regions to achieve sustainable land use. Our research provides scientific guidance for China's territory planning and sustainable urban development.

Predicting the patterns of change in spring onset and false springs in China during the twenty-first century.

Spring onset has generally shifted earlier in China over the past several decades in response to the warming climate. However, future changes in spring onset and false springs, which will have profound effects on ecosystems, are still not well understood. Here, we used the extended form of the Spring Indices model (SI-x) to project changes in the first leaf and first bloom dates, and predicted false springs for the historical (1950-2005) and future (2006-2100) periods based on the downscaled daily maximum/minimum temperatures under two emission scenarios from 21 General Circulation Models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5). On average, first leaf and first bloom in China were projected to occur 21 and 23 days earlier, respectively, by the end of the twenty-first century in the Representative Concentration Pathway (RCP) 8.5 scenario. Areas with greater earlier shifts in spring onset were in the warm temperate zone, as well as the north and middle subtropical zones of China. Early false spring risk increased rapidly in the warm temperate and north subtropical zones, while that declined in the cold temperate zone. Relative to early false spring risk, late false spring risk showed a common increase with smaller magnitude in the RCP 8.5 scenario but might cause greater damage to ecosystems because plants tend to become more vulnerable to the later occurrence of a freeze event. We conclude that future climate warming will continue to cause earlier occurrence of spring onset in general, but might counterintuitively increase plant damage risk in natural and agricultural systems of the warm temperate and subtropical China.

Accurate and rapid detection of soil and fertilizer properties based on visible/near-infrared spectroscopy.

Accurate information of soil macronutrient contents and fertilizer macronutrient contents is the precondition of precision fertilization; however, how to detect soil and fertilizer information rapidly, reliably, and inexpensively remains a great challenge. Visible and near-infrared (VIS/NIR) diffuse reflectance spectroscopy proves to be an effective tool for extensive investigation of soil and fertilizer properties. This study first collected many soil and chemical fertilizer samples and performed both spectral scanning and chemical analysis. During the correlation between the collected VIS/NIR spectra and the measured data, different spectral pretreatment, sample selection, and wavelength optimization methods were applied for improving the accuracy and robustness of the prediction models. After appropriate spectral processing and selection of representative samples, both principal component regression and genetic algorithm (GA) can adequately reduce the number of variables and pick out the characteristic variables, which not only enhanced prediction speed but also greatly improved prediction accuracy. In particular, using GA-based models, organic matter content (OMC), total N and pH value in soil and N, P, and K contents in fertilizer can all be accurately predicted.

Combined effects of night warming and light pollution on predator-prey interactions.

Interactions between multiple anthropogenic environmental changes can drive non-additive effects in ecological systems, and the non-additive effects can in turn be amplified or dampened by spatial covariation among environmental changes. We investigated the combined effects of night-time warming and light pollution on pea aphids and two predatory ladybeetle species. As expected, neither night-time warming nor light pollution changed the suppression of aphids by the ladybeetle species that forages effectively in darkness. However, for the more-visual predator, warming and light had non-additive effects in which together they caused much lower aphid abundances. These results are particularly relevant for agriculture near urban areas that experience both light pollution and warming from urban heat islands. Because warming and light pollution can have non-additive effects, predicting their possible combined consequences over broad spatial scales requires knowing how they co-occur. We found that night-time temperature change since 1949 covaried positively with light pollution, which has the potential to increase their non-additive effects on pea aphid control by 70% in US alfalfa. Our results highlight the importance of non-additive effects of multiple environmental factors on species and food webs, especially when these factors co-occur.

Determining the relative importance of climatic drivers on spring phenology in grassland ecosystems of semi-arid areas.

Understanding climate controls on spring phenology in grassland ecosystems is critically important in predicting the impacts of future climate change on grassland productivity and carbon storage. The third-generation Global Inventory Monitoring and Modeling System (GIMMS3g) normalized difference vegetation index (NDVI) data were applied to derive the start of the growing season (SOS) from 1982-2010 in grassland ecosystems of Ordos, a typical semi-arid area in China. Then, the conditional Granger causality method was utilized to quantify the directed functional connectivity between key climatic drivers and the SOS. The results show that the asymmetric Gaussian (AG) function is better in reducing noise of NDVI time series than the double logistic (DL) function within our study area. The southeastern Ordos has earlier occurrence and lower variability of the SOS, whereas the northwestern Ordos has later occurrence and higher variability of the SOS. The research also reveals that spring precipitation has stronger causal connectivity with the SOS than other climatic factors over different grassland ecosystem types. There is no statistically significant trend across the study area, while the similar pattern is observed for spring precipitation. Our study highlights the link of spring phenology with different grassland types, and the use of coupling remote sensing and econometric tools. With the dramatic increase in global change research, Granger causality method augurs well for further development and application of time-series modeling of complex social-ecological systems at the intersection of remote sensing and landscape changes.

Comparison of the driving forces of spring phenology among savanna landscapes by including combined spatial and temporal heterogeneity.

Understanding spatial and temporal dynamics of land surface phenology (LSP) and its driving forces are critical for providing information relevant to short- and long-term decision making, particularly as it relates to climate response planning. With the third generation Global Inventory Monitoring and Modeling System (GIMMS3g) Normalized Difference Vegetation Index (NDVI) data and environmental data from multiple sources, we investigated the spatio-temporal changes in the start of the growing season (SOS) in southern African savannas from 1982 through 2010 and determined its linkage to environmental factors using spatial panel data models. Overall, the SOS occurs earlier in the north compared to the south. This relates in part to the differences in ecosystems, with northern areas representing high rainfall and dense tree cover (mainly tree savannas), whereas the south has lower rainfall and sparse tree cover (mainly bush and grass savannas). From 1982 to 2010, an advanced trend was observed predominantly in the tree savanna areas of the north, whereas a delayed trend was chiefly found in the floodplain of the north and bush/grass savannas of the south. Different environmental drivers were detected within tree- and grass-dominated savannas, with a critical division being represented by the 800 mm isohyet. Our results supported the importance of water as a driver in this water-limited system, specifically preseason soil moisture, in determining the SOS in these water-limited, grass-dominated savannas. In addition, the research pointed to other, often overlooked, effects of preseason maximum and minimum temperatures on the SOS across the entire region. Higher preseason maximum temperatures led to an advance of the SOS, whereas the opposite effects of preseason minimum temperature were observed. With the rapid increase in global change research, this work will prove helpful for managing savanna landscapes and key to predicting how projected climate changes will affect regional vegetation phenology and productivity.

Multi-channel nonlinearity compensation of PDM-QPSK signals in dispersion-managed transmission using dispersion-folded digital backward propagation.

We demonstrate nonlinearity compensation of 37.5-GHz-spaced 128-Gb/s PDM-QPSK signals using dispersion-folded digital-backward-propagation and a spectrally-sliced receiver that simultaneously receives three WDM signals, showing mitigation of intra-channel and inter-channel nonlinear effects in a 2560-km dispersion-managed TWRS-fiber link. Intra-channel and adjacent inter-channel nonlinear compensation gains when WDM channels are fully populated in the C-band are estimated based on the GN-model.

Deciphering the storage mechanism of biochar anchored with different morphology Mn3O4 as advanced anode material for lithium-ion batteries.

Biochar is regarded as a promising lithium-ion batteries anode material, owing to its high cost-effectiveness. However, the poor specific capacity and cycling stability have limited its practical applications. A straightforward and cost-efficient solvothermal method is presented for synthesizing Mn3O4/biochar composites in this study. By adjusting solvothermal temperatures, Mn3O4 with different morphology is prepared and anchored on the biochar surface (MKAC-T) to improve the electrochemical performance. Due to the morphological effect of nanospherical Mn3O4 on the biochar surface, the MKAC-180 anode material demonstrates outstanding reversible capacity (992.5 mAh/g at 0.2 A/g), significant initial coulombic efficiency (61.1 %), stable cycling life (605.3 mAh/g at 1.0 A/g after 1000 cycles), and excellent rate performance (385.8 mAh/g at 1.6 A/g). Moreover, electro-kinetic analysis and ex-situ physicochemical characterizations are employed to illustrate the charge storage mechanisms of MKAC-180 anode. This study provides valuable insights into the "structure-activity relationship" between Mn3O4 microstructure and electrochemical performance for the Mn3O4/biochar composites, illuminating the industrial utilization of biomass carbon anode materials.

Circuit theory-based ecological security pattern could promote ecological protection in the Heihe River Basin of China.

Building ecological security patterns is essential to maintain regional ecological security and achieve sustainable development in the inland river basins with ecologically vulnerable environment. Numerous methods have been developed to build the ecological security pattern. However, to our knowledge, rare studies have quantified to what extent the derived pattern can improve ecological protection in the future. Taking Heihe River Basin (HRB), the second largest inland river basin in China, as the study area, we applied the circuit theory to build the ecological security pattern of HRB, and simulated how our built pattern contributed to ecological protection using the CLUMondo model. The results showed that the ecological security pattern of HRB contained 17 ecological sources, 35 key ecological corridors, and some ecological strategic points. The ecological sources were distributed in areas with better ecological conditions such as the Qilian Mountain Nature Reserve and Heihe National Wetland Park. The ecological corridors showed a pattern of "two horizontal and three vertical belts." Pinch points were mostly close to ecological sources or distributed on the corridors that played a key role in landscape connectivity, while barriers were mainly distributed on the corridors with large ecological resistance in the middle and lower reaches. The optimal ecological security pattern presented a "one screen, one belt, four districts and multiple centers" shape in HRB and could more effectively promote ecological protection compared to current development and protection scenarios. Our study provides a reliable decision-making guide for ecological protection and restoration of HRB, and can be extended to build ecological security patterns for broad-scale arid areas.

Nonlinearity compensation using dispersion-folded digital backward propagation.

A computationally efficient dispersion-folded (D-folded) digital backward propagation (DBP) method for nonlinearity compensation of dispersion-managed fiber links is proposed. At the optimum power level of long-haul fiber transmission, the optical waveform evolution along the fiber is dominated by the chromatic dispersion. The optical waveform and, consequently, the nonlinear behavior of the optical signal repeat at locations of identical accumulated dispersion. Hence the DBP steps can be folded according to the accumulated dispersion. Experimental results show that for 6,084 km single channel transmission, the D-folded DBP method reduces the computation by a factor of 43 with negligible penalty in performance. Simulation of inter-channel nonlinearity compensation for 13,000 km wavelength-division multiplexing (WDM) transmission shows that the D-folded DBP method can reduce the computation by a factor of 37.

Fuzzy evaluation of the ecological security of land resources in mainland China based on the Pressure-State-Response framework.

The extensive exploitation and use of land resources has caused a variety of land degradation problems including soil erosion, desertification and salinization in China, which gradually raises our concerns of ecological security. However, there still lacks an understanding of ecological security of land resources at the national scale. Moreover, few studies conduct the validation and uncertainty analysis of models for ecological security evaluation, which tends to undermine the reliability of evaluation results. Here we followed the Pressure-State-Response (PSR) framework to systematically construct the evaluation index system for ecological security, and developed fuzzy evaluation models to convert the original index data into individual index scores. After that, we used the multiplicative model to aggregate the individual index scores into a comprehensive evaluation score for the ecological security level of land resources across the Chinese mainland. To enhance the reliability of evaluation results, we validated our results by comparing with the proxies of ecological effects including landscape pattern index, land use change rate and net primary productivity, and made uncertainty analysis using the Monte Carlo method. Finally, we applied an obstacle model to quantify the negative contribution of pressure, state and response which would deter the security from achieving the optimal condition. The results showed that our model could effectively reflect the ecological security level of land resources. The pressure was higher in the east and lower in the west of China, and that of urban areas was much higher than the rural areas, reflecting the disturbance of socio-economic activities. The state condition was strongly related to natural conditions. The response level, determined mainly by socio-economic conditions, was higher in the southeast and northwest of China but lower in the northeast and southwest of China. The ecological security level was structured by natural and socio-economic conditions and demonstrated a high level of security in the southeast while a low level in the northwest. Developed urban areas often had low security due to strong socio-economic pressure. Areas with unfavorable natural and environmental conditions had poor state level, which tended to cause lower response capability, and consequently led to a low security level. Our research improves the understanding of national ecological security and its obstacle factors, which supports the management and sustainable use of land resources at the national scale.

Statistical tests for non-independent partitions of large autocorrelated datasets.

Large sets of autocorrelated data are common in fields such as remote sensing and genomics. For example, remote sensing can produce maps of information for millions of pixels, and the information from nearby pixels will likely be spatially autocorrelated. Although there are well-established statistical methods for testing hypotheses using autocorrelated data, these methods become computationally impractical for large datasets. • The method developed here makes it feasible to perform F-tests, likelihood ratio tests, and t-tests for large autocorrelated datasets. The method involves subsetting the dataset into partitions, analyzing each partition separately, and then combining the separate tests to give an overall test. • The separate statistical tests on partitions are non-independent, because the points in different partitions are not independent. Therefore, combining separate analyses of partitions requires accounting for the non-independence of the test statistics among partitions. • The methods can be applied to a wide range of data, including not only purely spatial data but also spatiotemporal data. For spatiotemporal data, it is possible to estimate coefficients from time-series models at different spatial locations and then analyze the spatial distribution of the estimates. The spatial analysis can be simplified by estimating spatial autocorrelation directly from the spatial autocorrelation among time series.

Folded digital backward propagation for dispersion-managed fiber-optic transmission.

In periodically dispersion managed long-haul transmission systems, waveform distortion is dominated by chromatic dispersion. As a result of the periodic waveform evolution, the nonlinear behavior also repeats itself in every dispersion period. It is shown that, under the weakly nonlinear assumption, nonlinear effects accumulated in a large number (K) of spans can be approximated by nonlinear effects accumulated in a single span with the same dispersion map and K times the nonlinearity. Thus, significant savings in computational load can be achieved in digital compensation of fiber nonlinearity using folded digital backward propagation (DBP). Simulation results show that the required computation for DBP of dispersion managed transoceanic transmission systems can be reduced by up to 2 orders of magnitude with negligible penalty using folded DBP.

Spatial-temporal pattern of land use conflict in China and its multilevel driving mechanisms.

Land use conflict describes the incoordination of land use structure when meeting the diverse human demands under the deterioration of natural environment, which is a sensitive indicator of human-environmental interaction. The increased demand for different land use types due to rapid population growth and urbanization in China places tremendous pressure on limited land resources, which raises great concerns about land use conflict. To solve them, nation-scale assessment is essential, but such kind of research is still lacking due to the high data requirements. Here we drew on the conceptual framework of ecological risk assessment and the theories in landscape ecology, and developed a methodology to derive the spatio-temporal patterns of land use conflict in China from 2001 to 2017. We then used multilevel regression model to identify the driving factors of land use conflict at different levels. The results showed that the areas with strong land use conflict had a higher frequency of land use change, indicating that our model based on the framework of ecological risk assessment could effectively measure land use conflict. Land use conflict showed significant differences between two sides of the Hu Huanyong line, an important division line of population density and socio-economic background. The Main types of land use conflict in China included the strong competition between the use of cultivated land and grassland, the rapid expansion of construction land and the high risk of desertification. Among the driving forces, population density had a positive impact on land use conflict at the upper level, and altitude had a negative impact at the bottom level. Our research provides essential information to solve land use conflict through scientific land use planning and management and further to achieve the sustainable use of land resources.

A spatiotemporal natural-human database to evaluate road development impacts in an Amazon trinational frontier.

Road construction and paving bring socio-economic benefits to receiving regions but can also be drivers of deforestation and land cover change. Road infrastructure often increases migration and illegal economic activities, which in turn affect local hydrology, wildlife, vegetation structure and dynamics, and biodiversity. To evaluate the full breadth of impacts from a coupled natural-human systems perspective, information is needed over a sufficient timespan to include pre- and post-road paving conditions. In addition, the spatial scale should be appropriate to link local human activities and biophysical system components, while also allowing for upscaling to the regional scale. A database was developed for the tri-national frontier in the Southwestern Amazon, where the Inter-Oceanic Highway was constructed through an area of high biological value and cultural diversity. Extensive socio-economic surveys and botanical field work are combined with remote sensing and reanalysis data to provide a rich and unique database, suitable for coupled natural-human systems research.

Impact of XPM and FWM on the digital implementation of impairment compensation for WDM transmission using backward propagation.

The impact of cross-phase modulation (XPM) and four-wave mixing (FWM) on electronic impairment compensation via backward propagation is analyzed. XPM and XPM+FWM compensation are compared by solving, respectively, the backward coupled Nonlinear Schrödinger Equation (NLSE) system and the total-field NLSE. The DSP implementations as well as the computational requirements are evaluated for each post-compensation system. A 12 x 100 Gb/s 16-QAM transmission system has been used to evaluate the efficiency of both approaches. The results show that XPM post-compensation removes most of the relevant source of nonlinear distortion. While DSP implementation of the total-field NLSE can ultimately lead to more precise compensation, DSP implementation sing the coupled NLSE system can maintain high accuracy with better computation efficiency and low system latency.

Using a coupled dynamic factor - random forest analysis (DFRFA) to reveal drivers of spatiotemporal heterogeneity in the semi-arid regions of southern Africa.

Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude, and spatial distribution of the key environmental and socioeconomic factors driving vegetation change in a southern African savanna. This research was conducted across the Kwando, Okavango and Zambezi catchments of southern Africa (Angola, Namibia, Botswana and Zambia) and explored vegetation cover change across the region from 2001-2010. A novel coupled analysis was applied to model the dynamic biophysical factors then to determine the discrete / social drivers of spatial heterogeneity on vegetation. Previous research applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique, to ten years of monthly remotely sensed vegetation data (MODIS-derived normalized difference vegetation index, NDVI), and a suite of time-series (monthly) environmental covariates: precipitation, mean, minimum and maximum air temperature, soil moisture, relative humidity, fire and potential evapotranspiration. This initial research was performed at a regional scale to develop meso-scale models explaining mean regional NDVI patterns. The regional DFA predictions were compared to the fine-scale MODIS time series using Kendall's Tau and Sen's Slope to identify pixels where the DFA model we had developed, under or over predicted NDVI. Once identified, a Random Forest (RF) analysis using a series of static social and physical variables was applied to explain these remaining areas of under- and over- prediction to fully explore the drivers of heterogeneity in this savanna system. The RF analysis revealed the importance of protected areas, elevation, soil type, locations of higher population, roads, and settlements, in explaining fine scale differences in vegetation biomass. While the previously applied DFA generated a model of environmental variables driving NDVI, the RF work developed here highlighted human influences dominating that signal. The combined DFRFA model approach explains almost 90% of the variance in NDVI across this landscape from 2001-2010. Our methodology presents a unique coupling of dynamic and static factor analyses, yielding novel insights into savanna heterogeneity, and providing a tool of great potential for researchers and managers alike.

Beyond precipitation: physiographic gradients dictate the relative importance of environmental drivers on Savanna vegetation.

BACKGROUND: Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude and spatial distribution of the key environmental factors driving vegetation change in southern African savanna, and how they vary across physiographic gradients. METHODOLOGY/PRINCIPAL FINDINGS: We applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique to ten years of monthly remote sensing data (MODIS-derived normalized difference vegetation index, NDVI) and a suite of environmental covariates: precipitation, mean and maximum temperature, soil moisture, relative humidity, fire and potential evapotranspiration. Monthly NDVI was described by cyclic seasonal variation with distinct spatiotemporal patterns in different physiographic regions. Results support existing work emphasizing the importance of precipitation, soil moisture and fire on NDVI, but also reveal overlooked effects of temperature and evapotranspiration, particularly in regions with higher mean annual precipitation. Critically, spatial distributions of the weights of environmental covariates point to a transition in the importance of precipitation and soil moisture (strongest in grass-dominated regions with precipitation<750 mm) to fire, potential evapotranspiration, and temperature (strongest in tree-dominated regions with precipitation>950 mm). CONCLUSIONS/SIGNIFICANCE: We quantified the combined spatiotemporal effects of an available suite of environmental drivers on NDVI across a large and diverse savanna region. The analysis supports known drivers of savanna vegetation but also uncovers important roles of temperature and evapotranspiration. Results highlight the utility of applying the DFA approach to remote sensing products for regional analyses of landscape change in the context of global environmental change. With the dramatic increase in global change research, this methodology augurs well for further development and application of spatially explicit time series modeling to studies at the intersection of ecology and remote sensing.