Abstract Loess-paleosol sequences have been used in Asia to study climate and environmental changes during the Quaternary. The scarcity of age control datasets and proxy indices analysis data for Asian loess has limited our understanding of loess depositional processes and the reconstruction of paleoclimatic changes from loess-paleosol records. In this study, we present a dataset that includes 1785 quartz optically stimulated luminescence ages and 1038 K-feldspar post-infrared infrared stimulated luminescence ages from 128 loess-paleosol sequences located in different regions of Asia. We generate 38 high-resolution age-depth models of loess records based on the provided datasets. We provide data on 12,365 grain size records, 14,964 magnetic susceptibility records, 2204 CaCO 3 content records, and 3326 color reflection records. This dataset contains the most detailed and accurate chronologies and proxy index data for loess records in Asia yet published. It provides fundamental data for understanding the spatial-temporal variations in loess depositional processes and climatic changes across the continent during the mid-late Quaternary.
The velocity of saltating sand is a key parameter in aeolian movement,but remains poorly understanding because of limitations in the available measurement technology.High-speed photography illuminated by intensive and continuous laser provides an efficient method to track the trajectories of saltating particles.From a digital recording of the trajectory,particle velocity and other movement parameters can be derived.However,accurate and quick reconstruction of particle trajectories from video images is not yet possible,as this requires the use of prohibitively laborious manual methods.In addition,light reflection from the bed decreases the contrast between near-surface particles and the bed.This makes saltation information near the surface difficult to obtain,even though this data is vitally important for studying particle entrainment processes and the mechanisms by which particles leave the surface.In this paper,we used motion-detection algorithms based on the subtraction of consecutive images and developed a new series of high-contrast images from the original images.We then describe two novel methods for tracking the trajectories of saltating particles and determining the velocity field in a cloud of blowing sand,respectively.We tracked particles by combining a manual operation with a computerized operation,and then the movement parameters of saltating sand particles in air and near the bed surface,such as the lift-off velocity and angle,impact velocity and angle,characteristic length and height,and instantaneous velocity and accelerated velocity vector at each point in the trajectory were quickly and accurately calculated by polynomial fitting method.Compared with traditional manual track method,this novel technique can easier to obtain a large number of continuous points within one or more trajectories.We evaluated the velocity field by a novel two-frame PTV algorithm based on the concept of maximizing the match efficiency.This approach overcomes some disadvantages of the conventional PTV methods,such as similar movement patterns within a small region,a uniform particle distribution,and high particle density.For the flow of saltating particles,there is high heterogeneity in the particle velocities due to the existence of two distinct grain populations:ascending and descending grains.Therefore,this improved PTV method not only can satisfy the requirement for a large sample size but also is more suitable for measuring the velocity of sand particles in an air/particle two-phase flow.The results shed new light on the complicated mechanisms involved in sand saltation and should prove useful in formulating and evaluating more accurate theoretical models of saltation.
To study the characteristics of content and distribution of light Rare Earth Elements (LREEs) in rat testes.Based on animal weight, 60 healthy male SD rats were randomly divided into two control groups and four experimental groups. After four week treat, the testes were collected except High-II . The High- II group was freely fed for another four week without the Citrate REEs. The LREE concentrations in the testes were determined by the inductively coupled plasma mass spectrometer (ICP-MS).(1) The concentrations of LREEs in testes increased with the increase of the doses. The concentrations of LREEs of High-II were much lower than those of High-I significantly (P < 0.05). (2) The distribution patterns of La, Ce, Pr in the testes approximated to the Odd Harkin's rule, but the Nd was an exception (negative deviation). 3. The correlation coefficients of LREEs concentrations between hair and testis were about 0.4, while those between blood and testis were much lower and have no significance.(1) Dose response relationship could exist in the accumulation of LREEs in the rat testes. (2) Testis could have its own distribution pattern of LREEs. (3) The accumulation of LREEs in testis could be reversible. (4) As it was, a biomarker of the LREEs in hair could be much better than that of blood.
According to ALOS,SPOT5 high-resolution remote sensing image analysis and field investigation,we have completed the remote sensing information visual extraction and vegetation mapping in Ulan Buh Desert,while combined with GPS field records,we have finished accuracy evaluation,the result was satisfactory.The accuracy of visual interpretation was about 93.3%,the area Haloxylon Ammodendron was 165 410.62 hm2,accounting for Ulan Buh Desert 17.27% of total land area.Meanwhile,methods of remote sensing image interpretation,which were suitable for Haloxylon forest in the Ulan Buh Desert,were also discussed.
Abstract. Based on MOD09GA/MYD09GA surface reflectance data, a new MODIS snow-cover-extent (SCE) product from 2000 to 2020 over China has been produced by the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains two preliminary clear-sky SCE datasets â Terra-MODIS and Aqua-MODIS SCE datasets and a final daily cloud-gap-filled (CGF) SCE dataset. The first two datasets are generated mainly through optimizing snow-cover discriminating rules over land-cover types, and the latter dataset is produced after a series of gap-filling processes such as aggregating the two preliminary datasets, reducing cloud gaps with adjacent information in space and time, and eliminating all gaps with auxiliary data. The validation against 362 China Meteorological Administration (CMA) stations shows that during snow seasons the overall accuracy (OA) values of the three datasets are larger than 93â%, all of the omission error (OE) values are constrained within 9â%, and all of the commission error (CE) values are constrained within 10â%. Bias values of 0.98, 1.02, and 1.03 demonstrate on a whole that there is no significant overestimation nor a significant underestimation. Based on the same ground reference data, we found that the new product accuracies are obviously higher than standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For example, compared with the CE of 23.78â% that the MYD10A1 product shows, the CE of the new Aqua-MODIS SCE dataset is 6.78â%; the OA of the new CGF SCE dataset is up to 93.15â% versus 89.54â% of MOD10A1F product and 84.36â% of MYD10A1F product. Besides, as expected, snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates that the OA has increased by 3â10 percentage points, the OE has dropped by 1â8 percentage points, and the CE has dropped by 4â21 percentage points. Therefore, our product has virtually provided more reliable snow knowledge over China; thereby, it can better serve for hydrological, climatic, environmental, and other related studies there.
The Normalized Difference Snow Index (NDSI) is an effective index for snow-cover mapping at large scales, but in forested regions the identification accuracy for snow using the NDSI is low because of forest cover effects. In this study, typical evergreen coniferous forest zones on Qilian Mountain in the Upper Heihe River Basin (UHRB) were chosen as example regions. By analyzing the spectral signature of snow-covered and snow-free evergreen coniferous forests with Landsat Operational Land Imager (OLI) data, a novel spectral band ratio using near-infrared (NIR) and shortwave infrared (SWIR) bands, defined as (ρnir − ρswir)/(ρnir + ρswir), is proposed. Our research shows that this band ratio, named the normalized difference forest snow index (NDFSI), can be used to effectively distinguish snow-covered evergreen coniferous forests from snow-free evergreen coniferous forests in UHRB.
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