Uncertainty is the most important factor affecting the quality of the remote sensing image classification.Aiming at the characteristics ofboth the random and the fuzzy uncertainties in the process of the remote sensin...Uncertainty is the most important factor affecting the quality of the remote sensing image classification.Aiming at the characteristics ofboth the random and the fuzzy uncertainties in the process of the remote sensing image classification,a method based on the mixed entropy model is proposed to measure these two uncertainties comprehensively,and a multi-scale evaluation index is established.Based on the analysis of the basic principles of the mixed entropy model,a method of using the statistical data of the feature space and the fuzzy classifier to establish the information entropy,the fuzzy entropy and the mixed entropy is proposed.At the same time,on the scale of the pixel and the category,the index of the mixed entropy of the pixel and the mixed entropy of the category are established to evaluate the uncertainty of the classification.展开更多
How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness ...How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer （MODIS） surface reflectance product （MOD09A1） data to evaluate the importance of spectral and temporal features for crop classification. In doing so, a feature selection strategy based on separability index （SI） was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index （NDTI）, land surface water index （LSWl） and enhanced vegetation index （EVI） are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy （producers＇ accuracy of 94.03% and users＇ accuracy of 93.77%） with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.展开更多
The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Be...The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei＇an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest （RF） algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can im- prove the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.展开更多
Rice planting patterns have changed dramatically over the past several decades in northeast China （NEC） due to the combined influence of global change and agricultural policy. Except for its great implications for e...Rice planting patterns have changed dramatically over the past several decades in northeast China （NEC） due to the combined influence of global change and agricultural policy. Except for its great implications for environmental protection and climate change adaption, the spatio-temporal changes of rice cultivation in NEC are not clear. In this study, we conducted spatio-temporal analyses of NEC＇s major rice production region, Heilongjiang Province, by using satellite-derived rice cultivation maps. We found that the total cultivated area of rice in Heilongjiang Province increased largely from 1993 to 2011 and it expanded spatially to the northern and eastern part of the Sanjiang Plain. The results also showed that rice cultivation areas experienced a larger increase in the region managed by the Reclamation Management Bureau （RMB） than that managed by the local provincial government. Rice cultivation changes were closely related with those geographic factors over the investigated periods, represented by the geomorphic （slope）, climatic （accumulated temperature）, and hydrological （watershed） variables. These findings provide clear evidence that crop cultivation in NEC has been modified to better cope with the global change.展开更多
Hydrological monitoring and seasonal forecasting is an active research field because of its potential applications in hydrological risk assessment,preparedness and mitigation.In recent decades,developments in ground a...Hydrological monitoring and seasonal forecasting is an active research field because of its potential applications in hydrological risk assessment,preparedness and mitigation.In recent decades,developments in ground and satellite measurements have made the hydrometeorological information readily available,and advances in information technology have facilitated the data analysis in a real-time manner.New progress in climate research and modeling has enabled the prediction of seasonal climate with reasonable accuracy and increased resolution.These emerging techniques and advances have enabled more timely acquisition of accurate hydrological fluxes and status,and earlier warning of extreme hydrological events such as droughts and floods.This paper gives current state-of-the-art understanding of the uncertainties in hydrological monitoring and forecasting,reviews the efforts and progress in operational hydrological monitoring system assisted by observations from various sources and experimental seasonal hydrological forecasting,and briefly introduces the current monitoring and forecasting practices in China.The grand challenges and perspectives for the near future are also discussed,including acquiring and extracting reliable information for monitoring and forecasting,predicting realistic hydrological fluxes and states in the river basin being significantly altered by human activity,and filling the gap between numerical models and the end user.We highlight the importance of understanding the needs of the operational water management and the priority to transfer research knowledge to decision-makers.展开更多
Satellite ocean color remote sensing plays a crucial role in monitoring marine environment at both regional and global scales.However,due to the reduced accuracy of atmospheric correction models under large solar zeni...Satellite ocean color remote sensing plays a crucial role in monitoring marine environment at both regional and global scales.However,due to the reduced accuracy of atmospheric correction models under large solar zenith angles(≥70°),publicly available satellite ocean color pro-ducts lack valid datasets for high-latitude oceans(≥50°S or≥50°N)during winter.Based on a neural network atmospheric correction model designed for high solar zenith angle observation environments(which used a Rayleigh scattering lookup table generated by PCOART-SA to compute Rayleigh scattering radiance and a neural network model to invert remote sensing reflectance from Rayleigh-corrected radiance),this study has established a monthly ocean color product dataset for high-latitude oceans,named NN-LAT50,covering the per-iod from 2003 to 2020.We validated the accuracy of the ocean color products in NN-LAT50 dataset using multiple in situ datasets,and the results indicated that NN-LAT50 had more reliable and accurate retrie-vals compared to the NASA released ocean color products in high latitude oceans.Furthermore,during autumn and winter,coverage of the NN-LAT50 dataset far exceeds that of products released by NASA.For instance,during the winter in the Southern Hemisphere,the cover-age rates are 3.02%for MODIS/Aqua,21.59%for VIIRS,and 1.74%for OLCI,while the NN-LAT50 dataset maintains a coverage rate of 38.64%.This study is the frst to establish a long-term(2003-2020)ocean color product dataset covering high-latitude oceans during winter,which can significantly enhance the observation of ecological changes in polar and subpolar oceans.展开更多
文摘Uncertainty is the most important factor affecting the quality of the remote sensing image classification.Aiming at the characteristics ofboth the random and the fuzzy uncertainties in the process of the remote sensing image classification,a method based on the mixed entropy model is proposed to measure these two uncertainties comprehensively,and a multi-scale evaluation index is established.Based on the analysis of the basic principles of the mixed entropy model,a method of using the statistical data of the feature space and the fuzzy classifier to establish the information entropy,the fuzzy entropy and the mixed entropy is proposed.At the same time,on the scale of the pixel and the category,the index of the mixed entropy of the pixel and the mixed entropy of the category are established to evaluate the uncertainty of the classification.
基金financially supported by the Non-Profit Research Grant of the National Administration of Surveying,Mapping and Geoinformation of China (201512028)the National Natural Science Foundation of China (41271112)
文摘How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer （MODIS） surface reflectance product （MOD09A1） data to evaluate the importance of spectral and temporal features for crop classification. In doing so, a feature selection strategy based on separability index （SI） was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index （NDTI）, land surface water index （LSWl） and enhanced vegetation index （EVI） are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy （producers＇ accuracy of 94.03% and users＇ accuracy of 93.77%） with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.
基金financially supported by the Opening Foundation of the Key Laboratory of Agricultural Information Technology,Ministry of Agriculture,China (2014009)the Non-Profit Research Grant of the National Administration of Surveying,Mapping and Geoinformation of China (201512028)+1 种基金the National Natural Science Fo undation of China (41271112)the Youth Foundation of Heilongjiang Academy of Agricultural Science,China (QN024)
文摘The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei＇an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest （RF） algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can im- prove the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.
基金financially supported by the Opening Foundation of the Key Laboratory of Agricultural Information Technology,Ministry of Agriculture,China (2016009)the National Natural Science Foundation of China (41501111 and 41271112)
文摘Rice planting patterns have changed dramatically over the past several decades in northeast China （NEC） due to the combined influence of global change and agricultural policy. Except for its great implications for environmental protection and climate change adaption, the spatio-temporal changes of rice cultivation in NEC are not clear. In this study, we conducted spatio-temporal analyses of NEC＇s major rice production region, Heilongjiang Province, by using satellite-derived rice cultivation maps. We found that the total cultivated area of rice in Heilongjiang Province increased largely from 1993 to 2011 and it expanded spatially to the northern and eastern part of the Sanjiang Plain. The results also showed that rice cultivation areas experienced a larger increase in the region managed by the Reclamation Management Bureau （RMB） than that managed by the local provincial government. Rice cultivation changes were closely related with those geographic factors over the investigated periods, represented by the geomorphic （slope）, climatic （accumulated temperature）, and hydrological （watershed） variables. These findings provide clear evidence that crop cultivation in NEC has been modified to better cope with the global change.
基金National Natural Science Foundation of China,No.41425002National Basic Research Program of China,No.2012CB955403+2 种基金National Youth Topnotch Talent Support Program in ChinaChina Special Fund for Meteorological Research in the Public Interest(Major projects),No.GYHY201506001-7The Beijing Science and Technology Plan Project,No.Z141100003614052
文摘Hydrological monitoring and seasonal forecasting is an active research field because of its potential applications in hydrological risk assessment,preparedness and mitigation.In recent decades,developments in ground and satellite measurements have made the hydrometeorological information readily available,and advances in information technology have facilitated the data analysis in a real-time manner.New progress in climate research and modeling has enabled the prediction of seasonal climate with reasonable accuracy and increased resolution.These emerging techniques and advances have enabled more timely acquisition of accurate hydrological fluxes and status,and earlier warning of extreme hydrological events such as droughts and floods.This paper gives current state-of-the-art understanding of the uncertainties in hydrological monitoring and forecasting,reviews the efforts and progress in operational hydrological monitoring system assisted by observations from various sources and experimental seasonal hydrological forecasting,and briefly introduces the current monitoring and forecasting practices in China.The grand challenges and perspectives for the near future are also discussed,including acquiring and extracting reliable information for monitoring and forecasting,predicting realistic hydrological fluxes and states in the river basin being significantly altered by human activity,and filling the gap between numerical models and the end user.We highlight the importance of understanding the needs of the operational water management and the priority to transfer research knowledge to decision-makers.
基金funded by the National Key Research and Development Program of China(Grant#2023YFC3108101)the National Natural Science Foundation of China(Grants#42176177,#42206183,and#U22B2012)+3 种基金the Zhejiang Provincial Natural Science Foundation of China(Grant#LDT23D06021D06)the"Pioneer"R&D Program of Zhejiang(Grant#2023C03011)the Zhejiang Provincial Natural Science Foundation of China(Grant#LY24D060005)the Science Foundation of Donghai Laboratory(Grant#DH-2023QH0002).
文摘Satellite ocean color remote sensing plays a crucial role in monitoring marine environment at both regional and global scales.However,due to the reduced accuracy of atmospheric correction models under large solar zenith angles(≥70°),publicly available satellite ocean color pro-ducts lack valid datasets for high-latitude oceans(≥50°S or≥50°N)during winter.Based on a neural network atmospheric correction model designed for high solar zenith angle observation environments(which used a Rayleigh scattering lookup table generated by PCOART-SA to compute Rayleigh scattering radiance and a neural network model to invert remote sensing reflectance from Rayleigh-corrected radiance),this study has established a monthly ocean color product dataset for high-latitude oceans,named NN-LAT50,covering the per-iod from 2003 to 2020.We validated the accuracy of the ocean color products in NN-LAT50 dataset using multiple in situ datasets,and the results indicated that NN-LAT50 had more reliable and accurate retrie-vals compared to the NASA released ocean color products in high latitude oceans.Furthermore,during autumn and winter,coverage of the NN-LAT50 dataset far exceeds that of products released by NASA.For instance,during the winter in the Southern Hemisphere,the cover-age rates are 3.02%for MODIS/Aqua,21.59%for VIIRS,and 1.74%for OLCI,while the NN-LAT50 dataset maintains a coverage rate of 38.64%.This study is the frst to establish a long-term(2003-2020)ocean color product dataset covering high-latitude oceans during winter,which can significantly enhance the observation of ecological changes in polar and subpolar oceans.
