Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS m...Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.展开更多
Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton plan...Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale.However,previous research studies have predominantly concentrated on specific years using remote sensing data.Challenges still exist in the extraction of cotton areas for long time series with high accuracy.To address this issue,a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale.Bortala and Shuanghe in Xinjiang,China,were selected as the study cases to demonstrate the approach.Specifically,the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands.Then,the cotton samples were selected using the Normalized Difference Vegetation Index(NDVI)series of Moderate Resolution Imaging Spectroradiometer(MODIS)based on its phenological characteristics.Next,cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model.Finally,the performance was evaluated,and the spatiotemporal distribution characteristics of cotton planting areas were analyzed.The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution.The performance evaluation indicated the applicability and suitability of the method,there is a good correlation between the extracted cotton areas and statistical data,and the cotton area of the study area showed an increasing trend.The cotton spatial distribution pattern developed from dispersion to agglomeration.The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.展开更多
Efficient and continuous monitoring of surface water is essential for water resource management.Much effort has been devoted to the task of water mapping based on remote sensing images.However,few studies have fully c...Efficient and continuous monitoring of surface water is essential for water resource management.Much effort has been devoted to the task of water mapping based on remote sensing images.However,few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner.Moreover,water area statistics are sensitive to the satellite image observation quality.This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine(GEE)with a supervised random forest classifier.A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products.The samples for permanent and seasonal water were mapped and collected separately,so that the supplement of seasonal samples can increase the spectral diversity of the sample space.To reduce the uncertainty of the derived water occurrences,temporal correction was applied to repair the classification maps with invalid observations.Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results.Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods,as well as the GSWD and GLAD datasets.展开更多
基金supported by the Platform Development Foundation of the China Institute for Radiation Protection(No.YP21030101)the National Natural Science Foundation of China(General Program)(Nos.12175114,U2167209)+1 种基金the National Key R&D Program of China(No.2021YFF0603600)the Tsinghua University Initiative Scientific Research Program(No.20211080081).
文摘Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.
基金supported by the National Natural Science Foundation of China[grant number 42101342]Third Comprehensive Scientific Expedition to Xinjiang[grant number 2021XJKK1403].
文摘Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale.However,previous research studies have predominantly concentrated on specific years using remote sensing data.Challenges still exist in the extraction of cotton areas for long time series with high accuracy.To address this issue,a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale.Bortala and Shuanghe in Xinjiang,China,were selected as the study cases to demonstrate the approach.Specifically,the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands.Then,the cotton samples were selected using the Normalized Difference Vegetation Index(NDVI)series of Moderate Resolution Imaging Spectroradiometer(MODIS)based on its phenological characteristics.Next,cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model.Finally,the performance was evaluated,and the spatiotemporal distribution characteristics of cotton planting areas were analyzed.The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution.The performance evaluation indicated the applicability and suitability of the method,there is a good correlation between the extracted cotton areas and statistical data,and the cotton area of the study area showed an increasing trend.The cotton spatial distribution pattern developed from dispersion to agglomeration.The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.
基金supported by the National Natural Science Foundation of China[grants numbers 42171375 and 41801263].
文摘Efficient and continuous monitoring of surface water is essential for water resource management.Much effort has been devoted to the task of water mapping based on remote sensing images.However,few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner.Moreover,water area statistics are sensitive to the satellite image observation quality.This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine(GEE)with a supervised random forest classifier.A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products.The samples for permanent and seasonal water were mapped and collected separately,so that the supplement of seasonal samples can increase the spectral diversity of the sample space.To reduce the uncertainty of the derived water occurrences,temporal correction was applied to repair the classification maps with invalid observations.Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results.Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods,as well as the GSWD and GLAD datasets.
