Temporal and Spatial Evolution of Lakes in the Bashang Plateau for Nearly Recent 30 Years

Lakes are important ecological water sources in the Bashang Plateau. Its expansion or shrinkage directly affects the ecological security of the plateau and its surrounding areas. In this study, Landsat images from 1984 to 2015 were selected to monitor the area of lakes in the Bashang Plateau and to analyze the spatiotemporal evolution and driving forces of lakes in the Bashang Plateau. The results showed that there were 47 lakes in the Bashang Plateau in 2015, with a total area of 37.63 km2, mainly distributed in the central and western regions of the region. From 1984 to 2015, the lakes in Bashang Plateau showed a shrinking trend. At the same time, there are obvious stage differences in lake changes. During 1984-1996, the number of lakes increased by 99 and the total area increased by 124.43 km2. From 1996 to 2015, the number of lakes decreased by 142, and the total area decreased by 183.96 km2. Before 1996, climate change was the dominant factor. However, the shrinkage of lakes after 1996 is the result of climate change and human activities. Among them, the large-scale planting of water consuming crops such as vegetables is the main human activity mode leading to lake shrinkage. This study will help to understand the expansion and contraction factors of the Bashang Plateau lakes in Hebei province and provide a reference for the future protection and management of the lakes.

Mapping soil erodibility in southeast China at 250 m resolution:Using environmental variables and random forest regression with limited samples

Soil erodibility(K factor)mapping has been accomplished mainly by soil map-linked or geo-statistical interpolation.However,the resulting maps usually have coarse spatial resolution at a regional scale.The objectives of this study were a)to map the K factors using a set of environmental variables and random forest(RF)model,and b)to identify the important environmental variables in the predictive mapping on a regional scale.We collected 101 surface soil samples across southeast China in the summer of 2019.For each sample,we measured the particle size distribution and organic matter content,and calculated the K factors using the nomograph equation.The hyperparameters of RF were optimized through 5-fold cross validation(m_(ay)=2,n_(tree)=500,p=63),and a digital map with 250 m resolution was generated for the K factor.The lower and upper limits of a 90% prediction interval were also pro-duced for uncertainty analysis.It was found that the important environmental variables for the K factor prediction were relief,climate,land surface temperature and vegetation indexes.Since the existing K factor map has an average polygonal area of 6.8 km^(2),our approach dramatically improves the spatial resolution of the K factor to 0.0625 km^(2).The new method captures more distinct differences in spatial details,and the spatial distribution of the K factor derived from RF prediction followed a similar pattern with kriging interpolation.This suggests the presented approach in this study is effective for mapping the K factor with limited sampling data.

Enhanced dSTORM imaging using fluorophores interacting with cucurbituril

Advanced fluorescence microscopy including single-molecule localization-based super-resolution imaging techniques requires bright and photostable dyes orproteins asfluorophores.The photophysical properties of fluorophores have been proven to be crucial for super-resolution microscopy's localization precision and imaging resolution.Fluorophores TAMRA and Atto Rho6 G,which can interact with macrocyclic host cucurbit[7]uril(CB7) to form host-guest compounds,were found to improve the fluorescence intensity and lifetimes of these dyes.We enhanced the localization precision of direct stochastic optical reconstruction microscopy(dSTORM) by introducing CB7 into the imaging buffer,and showed that the number of photons as well as localizations of both TAMRA and Atto Rho6 G increase over 2 times.

Simulating soil erodibility in southeastern China using a sequential Gaussian algorithm

Soil erodibility(K)is a key factor of soil erosion,and its appropriate quantification and interpolation are vitally important for soil and water conservation.The traditional point-represent-polygon approaches and common kriging method for the estimation of K,however,do not sufficiently represent the original data.The objectives of this study were to simulate the spatial distribution of K using a sequential Gaussian algorithm and analyze the uncertainty in evaluating the risk of soil erodibility in southeastern China.We determined 101 sampling points in the area and collected disturbed soil samples from the 0-20 cm layer at each point.Soil properties were determined,and K was calculated using five common models:the EPIC(Erosion/Productivity Impact Calculator),approximate nomograph,Torri,Shirazi,and Wang models.Among the chosen models,the EPIC model performed the best at estimating K(KEPIC),which ranged from 0.019 to 0.060 t ha h(ha MJ mm)^(-1),with a mean of 0.043 t ha h(ha MJ mm)^(-1).The KEPIC was moderately spatially variable and had a limited spatial structure,increasing from south to north in our study area,and all spatial simulations using the cooperative kriging(CK)interpolation and the sequential Gaussian simulation(SGS)with 10,25,50,100,200,and 500 realizations had acceptable accuracies.The CK interpolation narrowed the range,and the SGS maintained the original characteristics of the calculated data.The proportions of the risk area were 38.0% and 10.1%,when the risk probability for K was 60% and 80%,respectively,and high risk areas were mostly located in the north.The results provide scientific guidance for managing the risk of soil erodibility in southeastern China.