Rapid land cover classification using a 36-year time series of multi-source remote sensing data

Long-time series land cover classification information is the basis for scientific research on urban sprawl, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the Random Forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of sample points without land class change determined by counting the sample points of each band of landsat time series from 1986 to 2022 was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of TM and ETM+ sensor data from 2013 to 2022; (iii) the addition of a mining land cover type increase the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and -forest area. Among the land classifications by multi-source remote sensing, the combined variables spectral band + index + topography + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and using sensors under complex terrain conditions. The use of GEE cloud computing platform enabled rapid analysis of remotely sensed data to produce land cover maps with high-accuracy and a long time series

The Beishan underground research laboratory for geological disposal of high-level radioactive waste in China: Planning, site selection, site characterization and in situ tests

With the rapid development of nuclear power in China, the disposal of high-level radioactive waste (HLW) has become an important issue for nuclear safety and environmental protection. Deep geological disposal is internationally accepted as a feasible and safe way to dispose of HLW, and underground research laboratories (URLs) play an important and multi-faceted role in the development of HLW repositories. This paper introduces the overall planning and the latest progress for China's URL. On the basis of the proposed strategy to build an area-specific URL in combination with a comprehensive evaluation of the site selection results obtained during the last 33 years, the Xinchang site in the Beishan area, located in Gansu Province of northwestern China, has been selected as the final site for China's first URL built in granite. In the process of characterizing the Xinchang URL site, a series of investigations, including borehole drilling, geological mapping, geophysical surveying, hydraulic testing and in situ stress measurements, has been conducted. The investigation results indicate that the geological, hydrogeological, engineering geological and geochemical conditions of the Xinchang site are very suitable for URL construction. Meanwhile, to validate and develop construction technologies for the Beishan URL, the Beishan exploration tunnel (BET), which is a 50-m-deep facility in the Jiujing sub-area, has been constructed and several in situ tests, such as drill-and-blast tests, characterization of the excavation damaged zone (EDZ), and long-term deformation monitoring of surrounding rocks, have been performed in the BET. The methodologies and technologies established in the BET will serve for URL construction. According to the achievements of the characterization of the URL site, a preliminary design of the URL with a maximum depth of 560 m is proposed and necessary in situ tests in the URL are planned. Keywords: Beishan, Xinchang site, Granite, Underground research laboratory (URL), High-level radioactive waste (HLW), Geological disposa

Vulnerability Analysis of Main Aftershock Sequence of Aqueduct Based on Incremental Dynamic Analysis Method

At present, traditional seismic design methods often ignore the structural damage caused by aftershocks in the evaluation of structural stability. In this paper, seven main aftershock sequences were constructed by using the attenuation method. The incremental dynamic analysis method (IDA) was used to analyze the nonlinear dynamic time history of the aqueduct structure. The main aftershock vulnerability curve of the aqueduct structure was obtained by taking the seismic intensity IM and the maximum ratio of the plastic strain energy to the total strain energy Dp as the structural performance parameter. The analysis results show that the residual displacement of the aqueduct increases by 33%, 66%, 44%, 37%, 0.01%, 60%, and 59%, respectively, under the seven main aftershock sequences. The incremental damage percentages of the aftershock at the end of the period were 9.85%, 15.00%, 26.53%, 2.10%, 0.9%, 35.97%, and 9.85%, respectively. The main aftershock made the damage at the bottom of the arch and the aqueduct more extensive. When the earthquake intensity is 0.3 g, the exceedance probabilities of moderate damage and severe damage are 62.68% and 14.39%, respectively, under the action of the main aftershock sequence. The exceedance probabilities under the action of the main aftershock sequence are 38.52% and 12.08% higher than that of the single main earthquake, respectively

Extraction and Separation of Eight Ginsenosides from Flower Buds of <i>Panax Ginseng</i> Using Aqueous Ionic Liquid-Based Ultrasonic-Assisted Extraction Coupled with an Aqueous Biphasic System

Ionic liquids (ILs) are recognized as a possible replacement of traditional organic solvents, and ILs have been widely applied to extract various compounds. The present work aims to extract ginsenosides from Panax ginseng flower buds using aqueous ionic liquid based ultrasonic assisted extraction (IL-UAE). The extraction yields of 1-alkyl-3-methylimidazolium ionic liquids with different anions and alkyl chains were evaluated. The extraction parameters of eight ginsenosides were optimized by utilizing response surface methodology (RSM). The model demonstrated that a high yield of total ginsenosides could be obtained using IL-UAE, and the optimum extraction parameters were 0.23 M [C4mim][BF4], ultrasonic time of 23 min, temperature of extraction set to 30 &#176;C, and liquid-solid ratio of 31:1. After that, an aqueous biphasic system (ABS) was used to separate ginsenosides further. The nature and concentration of salt, as well as the value of pH in ionic liquid were evaluated, and the optimal conditions (6.0 mL IL extract, 3 g NaH2PO4, and pH 5.0) were obtained. The preconcentration factor was 2.58, and extraction efficiency reached 64.53%. The results indicate that as a simple and efficient method, an IL-UAE-ABS can be considered as a promising method for extracting and separating the natural active compounds from medicinal herbs

Spatio-Temporal Synergy between Urban Built-Up Areas and Poverty Transformation in Tibet

Understanding the causes of poverty and identifying the transformation characteristics of poverty is the basis for achieving poverty eradication. In order to clarify the availability of construction land for poverty assessment, this paper explores the spatio-temporal synergy between urban built-up areas and poverty transformation in Tibet. The following conclusions are drawn: (1) the built-up areas in Tibetan counties have been growing from 2013 to 2019; (2) the proportion of counties with very low and low levels of relative poverty have decreased significantly, and the overall spatial characteristics of poverty are &ldquo;high in the center and low in the surroundings&rdquo;; (3) the overall coupling-coordination level between the built-up areas and the relative poverty level is gradually improving from the initial antagonism, and the relative-poverty index shows a significant negative correlation with coupling coordination (correlation coefficient of &minus;0.63); and (4) the built-up area has a strong explanatory power for the spatial distribution of regional relative-poverty transfer compared to temperature, precipitation, elevation, and slope. The results of the study prove that the built-up area cannot be directly used as an indicator factor when constructing the multidimensional relative-poverty model and, instead, should use urban built-up areas by region to participate in poverty-estimation models based on regional economic development

Visual and Fluorescent Detection of Tyrosinase Activity by Using a Dual-Emission Ratiometric Fluorescence Probe

In this work, we designed a dual-emission ratiometric fluorescence probe by hybridizing two differently colored quantum dots (QDs), which possess a built-in correction that eliminates the environmental effects and increases sensor accuracy. Red emissive QDs were embedded in the silica nanoparticle as reference while the green emissive QDs were covalently linked to the silica nanoparticle surface to form ratiometric fluorescence probes (RF-QDs). Dopamine (DA) was then conjugated to the surface of RF-QDs via covalent bonding. The ratiometric fluorescence probe functionalized with dopamine (DA) was highly reactive toward tyrosinase (TYR), which can catalyze the oxidization of DA to dopamine quinine and therefore quenched the fluorescence of the green QDs on the surface of ratiometric fluorescence probe. With the addition of different amounts of TYR, the ratiometric fluorescence intensity of the probe continually varied, leading to color changes from yellow-green to red. So the ratiometric fluorescence probe could be utilized for sensitive and selective detection of TYR activity. There was a good linear relationship between the ratiometric fluorescence intensity and TYR concentration in the range of 0.05–5.0 μg mL^–1, with the detection limit of 0.02 μg mL^–1. Significantly, the ratiometric fluorescence probe has been used to fabricate paper-based test strips for visual detection of TYR activity, which validates the potential on-site application

A Random Forest Algorithm for Landsat Image Chromatic Aberration Restoration Based on GEE Cloud Platform&mdash;A Case Study of Yucat&aacute;n Peninsula, Mexico

With the growth of cloud computing, the use of the Google Earth Engine (GEE) platform to conduct research on water inversion, natural disaster monitoring, and land use change using long time series of Landsat images has also gradually become mainstream. Landsat images are currently one of the most important image data sources for remote sensing inversion. As a result of changes in time and weather conditions in single-view images, varying image radiances are acquired; hence, using a monthly or annual time scale to mosaic multi-view images results in strip color variation. In this study, the NDWI and MNDWI within 50 km of the coastline of the Yucat&aacute;n Peninsula from 1993 to 2021 are used as the object of study on GEE platform, and mosaic areas with chromatic aberrations are reconstructed using Landsat TOA (top of atmosphere reflectance) and SR (surface reflectance) images as the study data. The DN (digital number) values and probability distributions of the reference image and the image to be restored are classified and counted independently using the random forest algorithm, and the classification results of the reference image are mapped to the area of the image to be restored in a histogram-matching manner. MODIS and Sentinel-2 NDWI products are used for comparison and validation. The results demonstrate that the restored Landsat NDWI and MNDWI images do not exhibit obvious band chromatic aberration, and the image stacking is smoother; the Landsat TOA images provide improved results for the study of water bodies, and the correlation between the restored Landsat SR and TOA images with the Sentinel-2 data is as high as 0.5358 and 0.5269, respectively. In addition, none of the existing Landsat NDWI products in the GEE platform can effectively eliminate the chromatic aberration of image bands

Accurate Capture and Identification of Exosomes: Nanoarchitecture of the MXene Heterostructure/Engineered Lipid Layer

Recently, exosome detection has become an important breakthrough in clinical diagnosis. However, the effective capture and accurate identification of cancer exosomes in a complex biomatrix are still a tough task. Especially, the large size and non-conductivity of exosomes are not conducive to highly sensitive electrochemical or electrochemiluminescence (ECL) detection. Therefore, we have developed a Ti3C2Tx–Bi2S3–x heterostructure/engineered lipid layer-based nanoarchitecture to overcome the limitations. The engineered lipid layer not only specifically captured and efficiently fused CD63 positive exosomes but also showed excellent antifouling property in the biological matrix. Moreover, the MUC1 aptamer-modified Ti3C2Tx–Bi2S3–x heterostructure further identified and covered the gastric cancer exosomes that have been trapped in the engineered lipid layer. In the self-luminous Faraday cage-type sensing system, the Ti3C2Tx–Bi2S3–x heterostructure with sulfur vacancies extended the outer Helmholtz plane and amplified the ECL signal. Therefore, this sensor can be used to detect tumor exosomes in ascites of cancer patients without additional purification. It provides a new pathway to detect exosomes and other large-sized vesicles with high sensitivity

A Random Forest Algorithm for Landsat Image Chromatic Aberration Restoration Based on GEE Cloud Platform—A Case Study of Yucatán Peninsula, Mexico

With the growth of cloud computing, the use of the Google Earth Engine (GEE) platform to conduct research on water inversion, natural disaster monitoring, and land use change using long time series of Landsat images has also gradually become mainstream. Landsat images are currently one of the most important image data sources for remote sensing inversion. As a result of changes in time and weather conditions in single-view images, varying image radiances are acquired; hence, using a monthly or annual time scale to mosaic multi-view images results in strip color variation. In this study, the NDWI and MNDWI within 50 km of the coastline of the Yucatán Peninsula from 1993 to 2021 are used as the object of study on GEE platform, and mosaic areas with chromatic aberrations are reconstructed using Landsat TOA (top of atmosphere reflectance) and SR (surface reflectance) images as the study data. The DN (digital number) values and probability distributions of the reference image and the image to be restored are classified and counted independently using the random forest algorithm, and the classification results of the reference image are mapped to the area of the image to be restored in a histogram-matching manner. MODIS and Sentinel-2 NDWI products are used for comparison and validation. The results demonstrate that the restored Landsat NDWI and MNDWI images do not exhibit obvious band chromatic aberration, and the image stacking is smoother; the Landsat TOA images provide improved results for the study of water bodies, and the correlation between the restored Landsat SR and TOA images with the Sentinel-2 data is as high as 0.5358 and 0.5269, respectively. In addition, none of the existing Landsat NDWI products in the GEE platform can effectively eliminate the chromatic aberration of image bands