The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate ...The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.展开更多
为解决T_(2)谱成像精度低,影响检测结果的准确性的问题,对基于贝叶斯的核磁共振T_(2)谱成像方法进行了研究。首先给出了NMR(Nuclear Magnetic Resonance)信号的基本特征,并基于贝叶斯原理,推导了NMR信号的似然函数,构建了T_(2)谱成像框...为解决T_(2)谱成像精度低,影响检测结果的准确性的问题,对基于贝叶斯的核磁共振T_(2)谱成像方法进行了研究。首先给出了NMR(Nuclear Magnetic Resonance)信号的基本特征,并基于贝叶斯原理,推导了NMR信号的似然函数,构建了T_(2)谱成像框架。其次,采用改进的马尔科夫链蒙特卡洛策略,得到T_(2)谱及其不确定度。最后,通过随机构造服从多峰的混合高斯概率密度函数的T_(2)谱模型,验证了基于贝叶斯的核磁共振T_(2)谱成像方法的有效性。该方法可应用于通信原理综合实验内容,也可用于创新性训练实验。展开更多
The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, th...The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.展开更多
Background Leaf area index(LAI)is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration.For this reason,the LAI is often used as a key input parameter in e...Background Leaf area index(LAI)is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration.For this reason,the LAI is often used as a key input parameter in ecosystem services’modeling,which is emerging as a critical tool for steering upcoming urban reforestation strategies.However,LAI field measures are extremely time-consuming and require remarkable economic and human resources.In this context,spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8,may represent a feasible and economic solution for estimating the LAI at the city scale.Nonetheless,as far as we know,only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems.To fill such a gap,we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI,using field measurements collected with the LI-COR LAI 2200c as a reference.We hypothesized that Sentinel-2 data,owing to their finer spatial and spectral resolution,perform better in estimating vegetation’s structural parameters compared to Landsat 8.Results We found that Landsat 8-derived models have,on average,a slightly better performance,with the best model(the one based on NDVI)showing an R^(2) of 0.55 and NRMSE of 14.74%,compared to R^(2) of 0.52 and NRMSE of 15.15%showed by the best Sentinel-2 model,which is based on the NBR.All models were affected by spectrum saturation for high LAI values(e.g.,above 5).Conclusion In Mediterranean ecosystems,Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season.Therefore,the uncertainty introduced using satellite-derived LAI in ecosystem services’assessments should be systematically accounted for.展开更多
Soil salinization and water scarcity are main restrictive factors for irrigated agriculture development in arid regions.Knowing dynamics of soil water and salt content is an important antecedent in remediating saliniz...Soil salinization and water scarcity are main restrictive factors for irrigated agriculture development in arid regions.Knowing dynamics of soil water and salt content is an important antecedent in remediating salinized soils and optimizing irrigation management.Previous studies mostly used remote sensing technologies to individually monitor water or salt content dynamics in agricultural areas.Their ability to asses different levels of crop water and salt management has been less explored.Therefore,how to extract effective diagnostic features from remote sensing images derived spectral information is crucial for accurately estimating soil water and salt content.In this study,Linear spectral unmixing method(LSU)was used to obtain the contribution of soil water and salt to each band spectrum(abundance),and endmember spectra from Sentinel-2 images.Calculating spectral indices and selecting optimal spectal combination were individually based on soil water and salt endmember spectra.The estimation models were constructed using six machine learning algorithms:BP Neural Network(BPNN),Support Vector Regression(SVR),Partial Least Squares Regression(PLSR),Random Forest Regression(RFR),Gradient Boost Regression Tree(GBRT),and eXtreme Gradient Boosting tree(XGBoost).The results showed that the spectral indices calculated from endmember spectra were able to effectively characterize the response of crop spectral properties to soil water and salt,which circumvent spectral ambiguity induced by water-salt mixing.NDRE spectral index was a reliable indicator for estimating water and salt content,with determination coefficients(R2)being 0.55 and 0.57,respectively.Compared to other models,LSU-XGBoost model achieved the best performance.This model properly reflected the process of soil water-salt dynamics in farmland during crop growth period.This study provided new methods and ideas for soil water-salt estimation in dry irrigated agricultural areas,and provided decision support for gover-nance of salinized land and optimal management of irrigation.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42101414)Natural Science Found for Outstanding Young Scholars in Jilin Province(No.20230508106RC)。
文摘The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.
文摘为解决T_(2)谱成像精度低,影响检测结果的准确性的问题,对基于贝叶斯的核磁共振T_(2)谱成像方法进行了研究。首先给出了NMR(Nuclear Magnetic Resonance)信号的基本特征,并基于贝叶斯原理,推导了NMR信号的似然函数,构建了T_(2)谱成像框架。其次,采用改进的马尔科夫链蒙特卡洛策略,得到T_(2)谱及其不确定度。最后,通过随机构造服从多峰的混合高斯概率密度函数的T_(2)谱模型,验证了基于贝叶斯的核磁共振T_(2)谱成像方法的有效性。该方法可应用于通信原理综合实验内容,也可用于创新性训练实验。
文摘The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.
基金Servizi Ecosistemici e Infrastrutture Verdi urbane e peri-urbane nell’area Metropolitana Romana:stima del contributo delle foreste naturali di Castelporziano nel miglioramento della qualitàdell’aria della cittàdi RomaAccademia Nazionale delle Scienze detta dei XL,in collaborazione con Segretariato Generale della Presidenza della Repubblica+1 种基金PRO-ICOS_MED Potenziamento della Rete di Osservazione ICOS-Italia nel Mediterraneo-Rafforzamento del capitale umano”funded by the Ministry of ResearchPNRR,Missione 4,Componente 2,Avviso 3264/2021,IR0000032-ITINERIS-Italian Integrated Environmental Research Infrastructures System CUP B53C22002150006。
文摘Background Leaf area index(LAI)is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration.For this reason,the LAI is often used as a key input parameter in ecosystem services’modeling,which is emerging as a critical tool for steering upcoming urban reforestation strategies.However,LAI field measures are extremely time-consuming and require remarkable economic and human resources.In this context,spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8,may represent a feasible and economic solution for estimating the LAI at the city scale.Nonetheless,as far as we know,only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems.To fill such a gap,we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI,using field measurements collected with the LI-COR LAI 2200c as a reference.We hypothesized that Sentinel-2 data,owing to their finer spatial and spectral resolution,perform better in estimating vegetation’s structural parameters compared to Landsat 8.Results We found that Landsat 8-derived models have,on average,a slightly better performance,with the best model(the one based on NDVI)showing an R^(2) of 0.55 and NRMSE of 14.74%,compared to R^(2) of 0.52 and NRMSE of 15.15%showed by the best Sentinel-2 model,which is based on the NBR.All models were affected by spectrum saturation for high LAI values(e.g.,above 5).Conclusion In Mediterranean ecosystems,Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season.Therefore,the uncertainty introduced using satellite-derived LAI in ecosystem services’assessments should be systematically accounted for.
基金the National Natural Science Foundation of China for the project(No.52279047).
文摘Soil salinization and water scarcity are main restrictive factors for irrigated agriculture development in arid regions.Knowing dynamics of soil water and salt content is an important antecedent in remediating salinized soils and optimizing irrigation management.Previous studies mostly used remote sensing technologies to individually monitor water or salt content dynamics in agricultural areas.Their ability to asses different levels of crop water and salt management has been less explored.Therefore,how to extract effective diagnostic features from remote sensing images derived spectral information is crucial for accurately estimating soil water and salt content.In this study,Linear spectral unmixing method(LSU)was used to obtain the contribution of soil water and salt to each band spectrum(abundance),and endmember spectra from Sentinel-2 images.Calculating spectral indices and selecting optimal spectal combination were individually based on soil water and salt endmember spectra.The estimation models were constructed using six machine learning algorithms:BP Neural Network(BPNN),Support Vector Regression(SVR),Partial Least Squares Regression(PLSR),Random Forest Regression(RFR),Gradient Boost Regression Tree(GBRT),and eXtreme Gradient Boosting tree(XGBoost).The results showed that the spectral indices calculated from endmember spectra were able to effectively characterize the response of crop spectral properties to soil water and salt,which circumvent spectral ambiguity induced by water-salt mixing.NDRE spectral index was a reliable indicator for estimating water and salt content,with determination coefficients(R2)being 0.55 and 0.57,respectively.Compared to other models,LSU-XGBoost model achieved the best performance.This model properly reflected the process of soil water-salt dynamics in farmland during crop growth period.This study provided new methods and ideas for soil water-salt estimation in dry irrigated agricultural areas,and provided decision support for gover-nance of salinized land and optimal management of irrigation.
