According to the mechanism of rice growth,if nitrogen deficiency occurs,not only rice leaf but also sheath shows special symptoms:sheaths become short,stems appear light green,older sheath become lemon-yellowish.Nitro...According to the mechanism of rice growth,if nitrogen deficiency occurs,not only rice leaf but also sheath shows special symptoms:sheaths become short,stems appear light green,older sheath become lemon-yellowish.Nitrogen nutrition status of rice could be identified based on the differences of color and shape of leaf and sheath under different levels of nitrogen nutrition.Machine vision technology can be used to non-destructively and rapidly identify rice nutrition status,but image acquisition via digital camera is susceptible to external conditions,and the images are of poor quality.In this research,static scanning technology was used to collect images of rice leaf and sheath.From those images,14 color and shape characteristic parameters of leaf and sheath were extracted by R,G,B mean value function and region props function in MATLAB.Based on the relationship between nitrogen content and the characteristics extracted from the images,the leaf R,leaf length,leaf area,leaf tip R,sheath G,and sheath length were chosen to identify nitrogen status of rice by using Support Vector Machine(SVM).The results showed that the overall identification accuracies of different nitrogen nutrition were 94%,98%,96%and 100%for the four growth stages,respectively.Different years of data were used for validation,identification accuracies were 88%,98%,90%and 100%,respectively.The results showed that additional sheath characteristics can effectively increase the identification accuracy of nitrogen nutrition status and the methodology developed in the study is capable of identifying nitrogen deficiency accurately in the rice.展开更多
Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese cities.With their enormous input of fertilizers and pesticides,greenhouses have...Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese cities.With their enormous input of fertilizers and pesticides,greenhouses have considerably changed the local soil quality and environmental risk factors.The ability to obtain timely and accurate information regarding the spatial distribution of greenhouses could make an important contribution to local agricultural management and soil protection.This paper attempts to present a practical framework for extracting suburban greenhouses,integrating remote sensing data from Landsat-8 and object-oriented classification.Inheritance classification was implemented,and various properties,including texture and neighborhood features in addition to spectral information,were investigated through the popular random forest technique for feature selection prior to SVM classification to improve the mapping accuracy.The results demonstrated that object-based classification incorporating non-spectral features yielded a significant improvement compared with the classification results obtained using only the spectral information in traditional per-pixel classification.Both the producer’s and user’s accuracy were higher than 85%for greenhouse identification.Although it remained a challenge to completely distinguish greenhouses from sparse plants,the final greenhouse map indicated that the proposed object-based classification scheme,providing multiple feature selections and multi-scale analysis,yielded worthwhile information when applied to a continuous series of the freely available Landsat-8 imagery data.展开更多
Atmospheric vapor pressure deficit(VPD)increases with climate warming and may limit plant growth.However,gross primary production(GPP)responses to VPD remain a mystery,offering a significant source of uncertainty in t...Atmospheric vapor pressure deficit(VPD)increases with climate warming and may limit plant growth.However,gross primary production(GPP)responses to VPD remain a mystery,offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics.In this study,in-situ measurements,satellite-derived data,and Earth System Models(ESMs)simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD:first increasing and then declining.When VPD exceeds these thresholds,atmospheric drought stress reduces soil moisture and stomatal conductance,thereby decreasing the productivity of terrestrial ecosystems.Current ESMs underscore CO_(2) fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds.These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand.Incorporating VPD,soil moisture,and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.展开更多
基金the National Natural Science Foundation of China(Grant No.31172023)Zhejiang Province Postdoctoral Foundation(BSH1502132).
文摘According to the mechanism of rice growth,if nitrogen deficiency occurs,not only rice leaf but also sheath shows special symptoms:sheaths become short,stems appear light green,older sheath become lemon-yellowish.Nitrogen nutrition status of rice could be identified based on the differences of color and shape of leaf and sheath under different levels of nitrogen nutrition.Machine vision technology can be used to non-destructively and rapidly identify rice nutrition status,but image acquisition via digital camera is susceptible to external conditions,and the images are of poor quality.In this research,static scanning technology was used to collect images of rice leaf and sheath.From those images,14 color and shape characteristic parameters of leaf and sheath were extracted by R,G,B mean value function and region props function in MATLAB.Based on the relationship between nitrogen content and the characteristics extracted from the images,the leaf R,leaf length,leaf area,leaf tip R,sheath G,and sheath length were chosen to identify nitrogen status of rice by using Support Vector Machine(SVM).The results showed that the overall identification accuracies of different nitrogen nutrition were 94%,98%,96%and 100%for the four growth stages,respectively.Different years of data were used for validation,identification accuracies were 88%,98%,90%and 100%,respectively.The results showed that additional sheath characteristics can effectively increase the identification accuracy of nitrogen nutrition status and the methodology developed in the study is capable of identifying nitrogen deficiency accurately in the rice.
基金The authors are grateful for the support of the National Ecological Survey and Evaluation(2000-2010)under the auspices of the Remote Sensing Program of the Chinese Ministry of Environmental Protection(No.STSN-05-11).
文摘Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese cities.With their enormous input of fertilizers and pesticides,greenhouses have considerably changed the local soil quality and environmental risk factors.The ability to obtain timely and accurate information regarding the spatial distribution of greenhouses could make an important contribution to local agricultural management and soil protection.This paper attempts to present a practical framework for extracting suburban greenhouses,integrating remote sensing data from Landsat-8 and object-oriented classification.Inheritance classification was implemented,and various properties,including texture and neighborhood features in addition to spectral information,were investigated through the popular random forest technique for feature selection prior to SVM classification to improve the mapping accuracy.The results demonstrated that object-based classification incorporating non-spectral features yielded a significant improvement compared with the classification results obtained using only the spectral information in traditional per-pixel classification.Both the producer’s and user’s accuracy were higher than 85%for greenhouse identification.Although it remained a challenge to completely distinguish greenhouses from sparse plants,the final greenhouse map indicated that the proposed object-based classification scheme,providing multiple feature selections and multi-scale analysis,yielded worthwhile information when applied to a continuous series of the freely available Landsat-8 imagery data.
基金supported by the Chinese National Science Foundational Project(32160292,32171759,and 31930070)the National Key Research and Development Program of China(2017YFA0604403 and 2016YFA0600804)+2 种基金JIANGXI DOUBLE THOUSAND PLANS(jxsq2020101080)the Natural Science Foundation of Jiangxi province(20224BAB205008)supported by University of New Hampshire。
文摘Atmospheric vapor pressure deficit(VPD)increases with climate warming and may limit plant growth.However,gross primary production(GPP)responses to VPD remain a mystery,offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics.In this study,in-situ measurements,satellite-derived data,and Earth System Models(ESMs)simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD:first increasing and then declining.When VPD exceeds these thresholds,atmospheric drought stress reduces soil moisture and stomatal conductance,thereby decreasing the productivity of terrestrial ecosystems.Current ESMs underscore CO_(2) fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds.These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand.Incorporating VPD,soil moisture,and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.
