Understanding the drivers of woody plant diversity in urban parks in a snow climate city of China

Urban parks are an important part of urban ecosystems and play an important role in biodiversity conservation.However,it is still unclear how park characteristics aff ect plant diversity which could hinder the conservation of urban biodiversity due to the ineff ective design of urban parks.The diversity of woody plants in 33 parks of Changchun,China,was measured with plot measurements and linked with urban park characteristics(e.g.,size and age of the park)to uncover the relationship between them.The results show that urban woody plant species were abundant,with 98 species belonging to 51 genera and 26 families in the snow climate city of Changchun.The variation in woody plant diversity was largely explained by internal patch characteristics(e.g.,size,age,shape),and external factors surrounding the park(e.g.,land use type and socioeconomic level)accounted for only 16.0%in our study.For internal patch characteristics,older urban parks with larger areas demonstrated a richer level of plant diversity and increased nonlinearly with increasing park area.The threshold size signifi cantly aff ecting plant diversity variation was approximately 30 ha.Plant diversity had positive linear relationships with the ages of urban parks,supporting the legacy eff ect.In addition,woody plant diversity nonlinearly decreased with increasing park shape index,which suggested that the plant diversity could also be increased by optimizing the park shape.Regarding the external factors surrounding parks,the spatial distribution of woody plant diversity varied greatly from the urban center to the suburbs.House prices around the park had positive linear relationships with woody plant diversity in parks,supporting the luxury eff ect.However,building and road proportions,and green space proportion had no relationship with plant diversity in parks.This study can provide a robust reference for enriching plant diversity in urban parks,thus improving the development of urban sustainable cities.

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.

Effects of urban street trees on human thermal comfort and physiological indices: a case study in Changchun city, China

Planting trees along urban streets is one of the most important strategies to improve the urban thermal environment.However,the net impacts of urban street trees on human thermal comfort and physiological parameters are still less clear.On three similar east-west orientated streets with different degrees of tree cover-low(13%),medium(35%),and high(75%),urban microclimatic parameters and human physiological indices for six male students were simultaneously measured on three cloudless days in summer 2018.The results show that the differences in tree cover were predominant in influencing urban thermal environment and comfort.The street with the highest tree cover had significantly lower physiological equivalent temperature(PET) and more comfortable than the other two streets.The frequency of strong heat stress(PET> 35℃) was 64%,11 %,and 0%,respectively,for streets with low,medium,and high tree cover.For the six male university students,human physiological indices varied greatly across the three streets with different tree cover.Systolic blood pressure,diastolic blood pressure,and pulse rate increased with decreasing tree cover.The results also suggest that urban thermal environment and comfort had considerable impact on human physiological parameters.Our study provides reasons for urban planners to plant trees along streets to improve the thermal environment and promote urban sustainability.

Spatiotemporal pattern of urban forest leaf area index in response to rapid urbanization and urban greening

Rapid urbanization and urban greening have caused great changes to urban forests in China. Understanding spatiotemporal patterns of urban forest leaf area index(LAI) under rapid urbanization and urban greening is important for urban forest planning and management. We evaluated the potential for estimating urban forest LAI spatiotemporally by using Landsat TM imagery. We collected three scenes of Landsat TM(thematic mapper)images acquired in 1997, 2004 and 2010 and conducted a field survey to collect urban forest LAI. Finally, spatiotemporal maps of the urban forest LAI were created using a NDVI-based urban forest LAI predictive model.Our results show that normalized differential vegetation index(NDVI) could be used as a predictor for urban forest LAI similar to natural forests. Both rapid urbanization and urban greening contribute to the changing process of urban forest LAI. The urban forest has changed considerably from 1997 to 2010. Urban vegetated pixels decreased gradually from 1997 to 2010 due to intensive urbanization.Leaf area for the study area was 216.4, 145.2 and173.7 km~2 in the years 1997, 2004 and 2010, respectively.Urban forest LAI decreased sharply from 1997 to 2004 and increased slightly from 2004 to 2010 because of numerous greening policies. The urban forest LAI class distributions were skewed toward low values in 1997 and 2004. Moreover, the LAI presented a decreasing trend from suburban to downtown areas. We demonstrate the usefulness of TM remote-sensing in understanding spatiotemporal changing patterns of urban forest LAI under rapid urbanization and urban greening.

Light polarization and color from guided‐mode resonance filter

The characteristics of reflected light of a 1-D guided-mode resonance filter(GMRF)are studied in this paper.A triple-layer GMRF is designed by using the finite difference time domain method under non-polarized light illumination.Numerical results show that the reflectance spectra of TE and TM polarizations can be changed by altering the fill factor f of the GMRF.Moreover,by calculating the color of the reflected light with the chromaticity theory,we find that the color of reflected light becomes pure when f is 0.9.The results show that the color and polarization degree of the reflected light of a GMRF are tunable by altering the fill factor.

The Design of New Sensorless BLDCM Control System for Electric Vehicle

In order to meet the requirement of reliably running which is by Electric Vehicle for motor controller, the paper is focused on a sensorless brushless DC motor controller design and a commutation point method. By utilizing the saturation effect of stator iron core, six short voltage pulses are employed to estimate the initial rotor position. After that a series of voltage pulses are used to accelerate the motor. When the motor reaches a certain speed at which the back-electromotive force (EMF) method can be applied, the running state of the motor is smoothly switched at the moment determined by the relationship between the terminal voltage waveform and the commutation phases. “Lagging 90?-α commutation” is bring forward to overcome the shortages existing in the traditional method. The experimental results verify the feasibility and validity of the proposed method.

A sampling method based on forecasting and combinatorial optimization for high performance A/B testing

1 Introduction A/B testing[1]is a popular statistical method for many applications,especially,widely used by Internet companies for user experience research.The sampling algorithm is an important part of A/B testing and has a critical impact on the performance of A/B testing.There are two challenges to achieving high performance in A/B testing.(1)The features of individuals being sampled change over time and the sampling results used for evaluation are valid only for a period of time.(2)The sampling methods in A/B testing require determining whether the two groups of samples have an identical distribution while Simple Random Sampling(SRS)has a different purpose,which only concerns whether the sample distribution is identical to the population.