The impact of climate change on vegetation ecosystems is a prominent focus in global climate change research.The climate change affects vegetation growth and ecosystem stability in the upper reaches of the Yellow Rive...The impact of climate change on vegetation ecosystems is a prominent focus in global climate change research.The climate change affects vegetation growth and ecosystem stability in the upper reaches of the Yellow River(UYR).However,the spatiotemporal patterns and driving mechanisms of vegetation growth status(VGS)in the region remain poorly understood.Based on the hydrological model PLS,an innovative WEP-CHC model was developed by integrating regional environmental and vegetation growth characteristics.Furthermore,combined with the PLS-SEM model and other methods,this study systematically investigated the spatiotemporal patterns and driving mechanisms of VGS in the UYR.The results indicated that:①VGS exhibited significant spatiotemporal variation trends within the study area.In the study period of 1970–2020,the GPP onset time was significantly advanced(p<0.05)while the GPP peak value was significantly increased.Spatial analysis revealed significant spatial complexity in the GPP onset time and peak values across the region.②Soil freeze-thaw conditions significantly influenced VGS(p<0.05).The complete thawing time of permafrost was closely coincided with the GPP onset time,with a correlation coefficient exceeding 0.84.After controlling soil freeze-thaw effects using partial correlation analysis,it was found that better initial soil hydrothermal conditions would lead to better VGS;③The model constructed with annual hydrothermal conditions(AHC),soil freeze-thaw period(SFTP),vegetation growth season(VGS),initial soil hydrothermal conditions(ISHC),and annual solar radiation conditions(ASRC),demonstrated good explanatory power for vegetation growth.The R^(2)values of PLS-SEM were above 0.76 in all five subregions.However,their effects on VGS varied significantly across subregions.Overall,AHC and SFTP were the dominant factors in all subregions.Furthermore,the impacts of ISHC and VGC were statistically insignificant,whereas the effects of ASRC exhibited high complexity.This study not only provides new insights into the current state of hydrological-ecological coupling in the UYR but also offers a new tool for ecological conservation and sustainable water management in other cold regions and similar watersheds worldwide.展开更多
The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric ...The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric scattering and directly reflect the vegetation parameter information.In this study,the abandoned mining area in the Helan Mountains,China was taken as the study area.Based on hyperspectral remote sensing images of Zhuhai No.1 hyperspectral satellite,we used the pixel dichotomy model,which was constructed using the normalized difference vegetation index(NDVI),to estimate the vegetation coverage of the study area,and evaluated the vegetation growth status by five vegetation indices(NDVI,ratio vegetation index(RVI),photochemical vegetation index(PVI),red-green ratio index(RGI),and anthocyanin reflectance index 1(ARI1)).According to the results,the reclaimed vegetation growth status in the study area can be divided into four levels(unhealthy,low healthy,healthy,and very healthy).The overall vegetation growth status in the study area was generally at low healthy level,indicating that the vegetation growth status in the study area was not good due to short-time period restoration and harsh damaged environment such as high and steep rock slopes.Furthermore,the unhealthy areas were mainly located in Dawukougou where abandoned mines were concentrated,indicating that the original mining activities have had a large effect on vegetation ecology.After ecological restoration of abandoned mines,the vegetation coverage in the study area has increased to a certain extent,but the amplitude was not large.The situation of vegetation coverage in the northern part of the study area was worse than that in the southern part,due to abandoned mines mainly concentrating in the northern part of the Helan Mountains.The combination of hyperspectral remote sensing data and vegetation indices can comprehensively extract the characteristics of vegetation,accurately analyze the plant growth status,and provide technical support for vegetation health evaluation.展开更多
Objective To investigate the physical growth status of pediatric patients with transfusion-dependent thalassemia(TDT)and analyze the effects of treatmentrelated and socioeconomic factors on physical growth.Methods Bas...Objective To investigate the physical growth status of pediatric patients with transfusion-dependent thalassemia(TDT)and analyze the effects of treatmentrelated and socioeconomic factors on physical growth.Methods Based on the specialized thalassemia database from gene therapy clinical research at the Institute of Hematology&Hospital of Blood Diseases,Chinese Academy of Medical Sciences&Peking Union Medical College,we collected data on height and weight development,familyy economic status,and medical records of 338 pediatric patients with TDT from October 2023 to May 2024.展开更多
Stem respiration is an important part of the activity of a tree and is an important source of CO2 evolution from a forest ecosystem. Presently, no standard methods are available for the accurate estimation of total st...Stem respiration is an important part of the activity of a tree and is an important source of CO2 evolution from a forest ecosystem. Presently, no standard methods are available for the accurate estimation of total stem CO2 efflux from a forest. In the current study, a 33-year-old (by the year 2001) larch (Larix gmelini Rupr.) plantation was measured throughout 2001-2002 to analyze its monthly and seasonal patterns of stem respiration. Stem respiration rate was also measured at different heights, at different daily intervals and any variation in the larch plantation was recorded. The relationship between stem temperature, growth status and respiration rate was analyzed. Higher respiration rates were recorded in upper reaches of the larch tree throughout the season and these were affected partially by temperature difference. Midday depression was found in the diurnal changes in stem respiration. In the morning, but not in the afternoon, stem respiration was positively correlated with stem temperature. The reason for this variation may be attributed to water deficit, which was stronger in the afternoon. In the larch plantation, a maximum 7-fold variation in stem respiration was found. The growth status (such as mean growth rate of stem and canopy projection area) instead of stem temperature difference was positively correlated with this large variation. An S-model (sigmoid curve) or Power model shows the greatest regression of the field data. In the courses of seasonal and annual changes of stem respiration, peak values were observed in July of both years, but substantial interannual differences in magnitude were observed. An exponential model can clearly show this regression of the temperature-respiration relationship. In our results, Q(10) values ranged from 2.22 in 2001 to 3.53 in 2002. Therefore, estimation of total stem CO2 efflux only by a constant Q(10) value may give biased results. More parameters of growth status and water status should be considered for more accurate estimation.展开更多
Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield.Currently,high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for anal...Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield.Currently,high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for analyzing the growth status of plants,such as water and nutrient content.In this study,a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping.Four varieties of poplar saplings were cultivated,and 5 different irrigation treatments were applied.Color images of the plant samples were captured for analysis.Two tasks,including leaf posture calculation and drought stress identification,were conducted.First,instance segmentation was used to extract the regions of the leaf,petiole,and midvein.A dataset augmentation method was created for reducing manual annotation costs.The horizontal angles of the fitted lines of the petiole and midvein were calculated for leaf posture digitization.Second,multitask learning models were proposed for simultaneously determining the stress level and poplar variety.The mean absolute errors of the angle calculations were 10.7° and 8.2° for the petiole and midvein,respectively.Drought stress increased the horizontal angle of leaves.Moreover,using raw images as the input,the multitask MobileNet achieved the highest accuracy(99%for variety identification and 76%for stress level classification),outperforming widely used single-task deep learning models(stress level classification accuracies of<70%on the prediction dataset).The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.展开更多
A crop monitoring system was developed to nondestructively monitor the crop growth status in the field.With a two channel multispectral camera with one lens,controlling platform,wireless remote control module and cont...A crop monitoring system was developed to nondestructively monitor the crop growth status in the field.With a two channel multispectral camera with one lens,controlling platform,wireless remote control module and control software,the system was able to synchronously acquire visible image(red(R),green(G),blue(B):400-700 nm)and near-infrared(NIR)image(760-1000 nm).The tomato seedlings multi-spectral images collection experiment in the greenhouse was conducted by using the developed system from the seeding stage to fruiting stage.More than 240 couples of tomato seedlings pictures were acquired with the Soil and Plant Analyzer Development(SPAD)value measured at the same time.The obtained images were available to process,and some vegetation indexes,such as normalized difference vegetation index(NDVI),ratio vegetation index(RVI)and normalized difference green index(NDGI),were calculated.Considering the SPAD value and the correlation coefficient between SPAD and other parameters in different fertilization treatments,the multiple linear regressions(MLR)model for estimating tomato seedlings chlorophyll content was built based on the average gray value in red,green,blue and NIR,vegetable indexes,NDVI,RVI and NDGI in the 33.3%(N1),66.6%(N2),and 100%(N3)nutrient levels during seeding stage and blossom and fruit stage.The R2 of the model is 0.88.The results revealed that the developed crop monitoring system provided a feasible tool to detect the growth status of tomato.More filed experiments and multi-spectral image analysis will be investigated to evaluate the crop growth status in the near future.展开更多
基金funded by the National Key R&D Program(2021YFC3200203,2023YFC3206303)the Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)National Natural Science Foundation of China(52394233,52122902).
文摘The impact of climate change on vegetation ecosystems is a prominent focus in global climate change research.The climate change affects vegetation growth and ecosystem stability in the upper reaches of the Yellow River(UYR).However,the spatiotemporal patterns and driving mechanisms of vegetation growth status(VGS)in the region remain poorly understood.Based on the hydrological model PLS,an innovative WEP-CHC model was developed by integrating regional environmental and vegetation growth characteristics.Furthermore,combined with the PLS-SEM model and other methods,this study systematically investigated the spatiotemporal patterns and driving mechanisms of VGS in the UYR.The results indicated that:①VGS exhibited significant spatiotemporal variation trends within the study area.In the study period of 1970–2020,the GPP onset time was significantly advanced(p<0.05)while the GPP peak value was significantly increased.Spatial analysis revealed significant spatial complexity in the GPP onset time and peak values across the region.②Soil freeze-thaw conditions significantly influenced VGS(p<0.05).The complete thawing time of permafrost was closely coincided with the GPP onset time,with a correlation coefficient exceeding 0.84.After controlling soil freeze-thaw effects using partial correlation analysis,it was found that better initial soil hydrothermal conditions would lead to better VGS;③The model constructed with annual hydrothermal conditions(AHC),soil freeze-thaw period(SFTP),vegetation growth season(VGS),initial soil hydrothermal conditions(ISHC),and annual solar radiation conditions(ASRC),demonstrated good explanatory power for vegetation growth.The R^(2)values of PLS-SEM were above 0.76 in all five subregions.However,their effects on VGS varied significantly across subregions.Overall,AHC and SFTP were the dominant factors in all subregions.Furthermore,the impacts of ISHC and VGC were statistically insignificant,whereas the effects of ASRC exhibited high complexity.This study not only provides new insights into the current state of hydrological-ecological coupling in the UYR but also offers a new tool for ecological conservation and sustainable water management in other cold regions and similar watersheds worldwide.
基金This research was supported by the Ningxia Hui Autonomous Region Key Research and Development Plan(2022BEG03052).
文摘The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric scattering and directly reflect the vegetation parameter information.In this study,the abandoned mining area in the Helan Mountains,China was taken as the study area.Based on hyperspectral remote sensing images of Zhuhai No.1 hyperspectral satellite,we used the pixel dichotomy model,which was constructed using the normalized difference vegetation index(NDVI),to estimate the vegetation coverage of the study area,and evaluated the vegetation growth status by five vegetation indices(NDVI,ratio vegetation index(RVI),photochemical vegetation index(PVI),red-green ratio index(RGI),and anthocyanin reflectance index 1(ARI1)).According to the results,the reclaimed vegetation growth status in the study area can be divided into four levels(unhealthy,low healthy,healthy,and very healthy).The overall vegetation growth status in the study area was generally at low healthy level,indicating that the vegetation growth status in the study area was not good due to short-time period restoration and harsh damaged environment such as high and steep rock slopes.Furthermore,the unhealthy areas were mainly located in Dawukougou where abandoned mines were concentrated,indicating that the original mining activities have had a large effect on vegetation ecology.After ecological restoration of abandoned mines,the vegetation coverage in the study area has increased to a certain extent,but the amplitude was not large.The situation of vegetation coverage in the northern part of the study area was worse than that in the southern part,due to abandoned mines mainly concentrating in the northern part of the Helan Mountains.The combination of hyperspectral remote sensing data and vegetation indices can comprehensively extract the characteristics of vegetation,accurately analyze the plant growth status,and provide technical support for vegetation health evaluation.
文摘Objective To investigate the physical growth status of pediatric patients with transfusion-dependent thalassemia(TDT)and analyze the effects of treatmentrelated and socioeconomic factors on physical growth.Methods Based on the specialized thalassemia database from gene therapy clinical research at the Institute of Hematology&Hospital of Blood Diseases,Chinese Academy of Medical Sciences&Peking Union Medical College,we collected data on height and weight development,familyy economic status,and medical records of 338 pediatric patients with TDT from October 2023 to May 2024.
文摘Stem respiration is an important part of the activity of a tree and is an important source of CO2 evolution from a forest ecosystem. Presently, no standard methods are available for the accurate estimation of total stem CO2 efflux from a forest. In the current study, a 33-year-old (by the year 2001) larch (Larix gmelini Rupr.) plantation was measured throughout 2001-2002 to analyze its monthly and seasonal patterns of stem respiration. Stem respiration rate was also measured at different heights, at different daily intervals and any variation in the larch plantation was recorded. The relationship between stem temperature, growth status and respiration rate was analyzed. Higher respiration rates were recorded in upper reaches of the larch tree throughout the season and these were affected partially by temperature difference. Midday depression was found in the diurnal changes in stem respiration. In the morning, but not in the afternoon, stem respiration was positively correlated with stem temperature. The reason for this variation may be attributed to water deficit, which was stronger in the afternoon. In the larch plantation, a maximum 7-fold variation in stem respiration was found. The growth status (such as mean growth rate of stem and canopy projection area) instead of stem temperature difference was positively correlated with this large variation. An S-model (sigmoid curve) or Power model shows the greatest regression of the field data. In the courses of seasonal and annual changes of stem respiration, peak values were observed in July of both years, but substantial interannual differences in magnitude were observed. An exponential model can clearly show this regression of the temperature-respiration relationship. In our results, Q(10) values ranged from 2.22 in 2001 to 3.53 in 2002. Therefore, estimation of total stem CO2 efflux only by a constant Q(10) value may give biased results. More parameters of growth status and water status should be considered for more accurate estimation.
基金supported by the National Key Research and Development Program of China(2023YFE0123600)the National Natural Science Foundation of China(NSFC32171790,32171818,and 62305166)+1 种基金the Jiangsu Province Agricultural Science,Technology Independent Innovation Fund Project(CX(23)3126)333 Project of Jiangsu Province.
文摘Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield.Currently,high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for analyzing the growth status of plants,such as water and nutrient content.In this study,a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping.Four varieties of poplar saplings were cultivated,and 5 different irrigation treatments were applied.Color images of the plant samples were captured for analysis.Two tasks,including leaf posture calculation and drought stress identification,were conducted.First,instance segmentation was used to extract the regions of the leaf,petiole,and midvein.A dataset augmentation method was created for reducing manual annotation costs.The horizontal angles of the fitted lines of the petiole and midvein were calculated for leaf posture digitization.Second,multitask learning models were proposed for simultaneously determining the stress level and poplar variety.The mean absolute errors of the angle calculations were 10.7° and 8.2° for the petiole and midvein,respectively.Drought stress increased the horizontal angle of leaves.Moreover,using raw images as the input,the multitask MobileNet achieved the highest accuracy(99%for variety identification and 76%for stress level classification),outperforming widely used single-task deep learning models(stress level classification accuracies of<70%on the prediction dataset).The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.
基金948 Project(No.2011-G32)High Technology Research and Development Research Fund(No.2013AA102303).
文摘A crop monitoring system was developed to nondestructively monitor the crop growth status in the field.With a two channel multispectral camera with one lens,controlling platform,wireless remote control module and control software,the system was able to synchronously acquire visible image(red(R),green(G),blue(B):400-700 nm)and near-infrared(NIR)image(760-1000 nm).The tomato seedlings multi-spectral images collection experiment in the greenhouse was conducted by using the developed system from the seeding stage to fruiting stage.More than 240 couples of tomato seedlings pictures were acquired with the Soil and Plant Analyzer Development(SPAD)value measured at the same time.The obtained images were available to process,and some vegetation indexes,such as normalized difference vegetation index(NDVI),ratio vegetation index(RVI)and normalized difference green index(NDGI),were calculated.Considering the SPAD value and the correlation coefficient between SPAD and other parameters in different fertilization treatments,the multiple linear regressions(MLR)model for estimating tomato seedlings chlorophyll content was built based on the average gray value in red,green,blue and NIR,vegetable indexes,NDVI,RVI and NDGI in the 33.3%(N1),66.6%(N2),and 100%(N3)nutrient levels during seeding stage and blossom and fruit stage.The R2 of the model is 0.88.The results revealed that the developed crop monitoring system provided a feasible tool to detect the growth status of tomato.More filed experiments and multi-spectral image analysis will be investigated to evaluate the crop growth status in the near future.
