The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flo...The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.展开更多
Paddy rice mapping is crucial for cultivation management,yield estimation,and food security.Guangdong,straddling tropics and subtropics,is a major rice-producing region in China.Mapping paddy rice in Guangdong is esse...Paddy rice mapping is crucial for cultivation management,yield estimation,and food security.Guangdong,straddling tropics and subtropics,is a major rice-producing region in China.Mapping paddy rice in Guangdong is essential.However,there are 2 main difficulties in tropical and subtropical paddy rice mapping,including the lack of high-quality optical images and differences in paddy rice planting times.This study proposed a paddy rice mapping framework using phenology matching,integrating Sentinel-1 and Sentinel-2 data to incorporate prior knowledge into the classifiers.The transplanting periods of paddy rice were identified with Sentinel-1 data,and the subsequent 3 months were defined as the growth periods.Features during growth periods obtained by Sentinel-1 and Sentinel-2 were inputted into machine learning classifiers.The classifiers using matched features substantially improved mapping accuracy compared with those using unmatched features,both for early and late rice mapping.The proposed method also improved the accuracy by 6.44%to 16.10%compared with 3 other comparison methods.The model,utilizing matched features,was applied to early and late rice mapping in Guangdong in 2020.Regression results between mapping area and statistical data validate paddy rice mapping credibility.Our analysis revealed that thermal conditions,especially cold severity during growing stages,are the primary determinant of paddy rice phenology.Spatial patterns of paddy rice in Guangdong result from a blend of human and physical factors,with slope and minimum temperature emerging as the most important limitations.These findings enhance our understanding of rice ecosystems’dynamics,offering insights for formulating relevant agricultural policies.展开更多
Background: Population-based cancer survival is a key metric in evaluating the overall effectiveness of health services and cancer control activities. Advancement in information technology enables accurate vital statu...Background: Population-based cancer survival is a key metric in evaluating the overall effectiveness of health services and cancer control activities. Advancement in information technology enables accurate vital status tracking through multi-source data linkage. However, its reliability for survival estimates in China is unclear.Methods: We analyzed data from Dalian Cancer Registry to evaluate the reliability of multi-source data linkage for population-based cancer survival estimates in China. Newly diagnosed cancer patients in 2015 were included and followed until June 2021. We conducted single-source data linkage by linking patients to Dalian Vital Statistics System, and multi-source data linkage by further linking to Dalian Household Registration System and the hospital medical records. Patient vital status was subsequently determined through active follow-up via telephone calls, referred to as comprehensive follow-up, which served as the gold standard. Using the cohort method, we calculated 5-year observed survival and age-standardized relative survival for 20 cancer types and all cancers combined.Results: Compared to comprehensive follow-up, single-source data linkage overestimated 5-year observed survival by 3.2% for all cancers combined, ranging from 0.1% to 8.6% across 20 cancer types. Multi-source data linkage provided a relatively complete patient vital status, with an observed survival estimate of only 0.3% higher for all cancers, ranging from 0% to1.5% across 20 cancer types.Conclusion: Multi-source data linkage contributes to reliable population-based cancer survival estimates in China. Linkage of multiple databases might be of great value in improving the efficiency of follow-up and the quality of survival data for cancer patients in developing countries.展开更多
基金support provided by the National Natural Sciences Foundation of China(No.41771419)Student Research Training Program of Southwest Jiaotong University(No.191510,No.182117)。
文摘The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.
基金supported in part by the National Key R&D Program of China under grant 2022YFB3903402in part by the National Natural Science Foundation of China under grant 42222106in part by the National Natural Science Foundation of China under grant 61976234.
文摘Paddy rice mapping is crucial for cultivation management,yield estimation,and food security.Guangdong,straddling tropics and subtropics,is a major rice-producing region in China.Mapping paddy rice in Guangdong is essential.However,there are 2 main difficulties in tropical and subtropical paddy rice mapping,including the lack of high-quality optical images and differences in paddy rice planting times.This study proposed a paddy rice mapping framework using phenology matching,integrating Sentinel-1 and Sentinel-2 data to incorporate prior knowledge into the classifiers.The transplanting periods of paddy rice were identified with Sentinel-1 data,and the subsequent 3 months were defined as the growth periods.Features during growth periods obtained by Sentinel-1 and Sentinel-2 were inputted into machine learning classifiers.The classifiers using matched features substantially improved mapping accuracy compared with those using unmatched features,both for early and late rice mapping.The proposed method also improved the accuracy by 6.44%to 16.10%compared with 3 other comparison methods.The model,utilizing matched features,was applied to early and late rice mapping in Guangdong in 2020.Regression results between mapping area and statistical data validate paddy rice mapping credibility.Our analysis revealed that thermal conditions,especially cold severity during growing stages,are the primary determinant of paddy rice phenology.Spatial patterns of paddy rice in Guangdong result from a blend of human and physical factors,with slope and minimum temperature emerging as the most important limitations.These findings enhance our understanding of rice ecosystems’dynamics,offering insights for formulating relevant agricultural policies.
基金supported by the National Key R&D Program of China (2022YFC3600805 and 2021YFC2501900)
文摘Background: Population-based cancer survival is a key metric in evaluating the overall effectiveness of health services and cancer control activities. Advancement in information technology enables accurate vital status tracking through multi-source data linkage. However, its reliability for survival estimates in China is unclear.Methods: We analyzed data from Dalian Cancer Registry to evaluate the reliability of multi-source data linkage for population-based cancer survival estimates in China. Newly diagnosed cancer patients in 2015 were included and followed until June 2021. We conducted single-source data linkage by linking patients to Dalian Vital Statistics System, and multi-source data linkage by further linking to Dalian Household Registration System and the hospital medical records. Patient vital status was subsequently determined through active follow-up via telephone calls, referred to as comprehensive follow-up, which served as the gold standard. Using the cohort method, we calculated 5-year observed survival and age-standardized relative survival for 20 cancer types and all cancers combined.Results: Compared to comprehensive follow-up, single-source data linkage overestimated 5-year observed survival by 3.2% for all cancers combined, ranging from 0.1% to 8.6% across 20 cancer types. Multi-source data linkage provided a relatively complete patient vital status, with an observed survival estimate of only 0.3% higher for all cancers, ranging from 0% to1.5% across 20 cancer types.Conclusion: Multi-source data linkage contributes to reliable population-based cancer survival estimates in China. Linkage of multiple databases might be of great value in improving the efficiency of follow-up and the quality of survival data for cancer patients in developing countries.
