The Dst index,designed as a proxy of ring current intensity,is known to be also affected by other magnetospheric current systems,e.g.magnetopause current.The pressure-corrected Dst index is obtained by removing the ef...The Dst index,designed as a proxy of ring current intensity,is known to be also affected by other magnetospheric current systems,e.g.magnetopause current.The pressure-corrected Dst index is obtained by removing the effects of the solar wind dynamic pressure and the quiet time ring current.However,all previous studies treated the correction coefficient as an averaged parameter for storms of different intensity.In this paper,based on the Burton's equations and employing two independent methods,we will show a positive correlation between pressure-correction coefficient b and the intensity of the storms.We divided our storm database(872 storms in total) into three categories according to the intensity of storms.In order to improve the accuracy of calculating,we also used the higher-resolution SYM-H index data instead of Dst index to compute the corrected Dst index during different storms.Furthermore,we are able to provide corrected magnetic storm index with high-time resolution(-1 min).展开更多
Dynamic monitoring of plant cover and soil erosion often uses remote sensing data, especially for estimating the plant cover rate (vegetation coverage) by vegetation index. However, the latter is influenced by atmosph...Dynamic monitoring of plant cover and soil erosion often uses remote sensing data, especially for estimating the plant cover rate (vegetation coverage) by vegetation index. However, the latter is influenced by atmospheric effects and methods for correcting them are still imperfect and disputed. This research supposed and practiced an indirect, fast, and operational method to conduct atmospheric correction of images for getting comparable vegetation index values in different times. It tries to find a variable free from atmospheric effects, e.g., the mean vegetation coverage value of the whole study area, as a basis to reduce atmospheric correction parameters by establishing mathematical models and conducting simulation calculations. Using these parameters, the images can be atmospherically corrected. And then, the vegetation index and corresponding vegetation coverage values for all pixels, the vegetation coverage maps and coverage grade maps for different years were calculated, i.e., the plant cover monitoring was realized. Using the vegetation coverage grade maps and the ground slope grade map from a DEM to generate soil erosion grade maps for different years, the soil erosion monitoring was also realized. The results show that in the study area the vegetation coverage was the lowest in 1976, much better in 1989, but a bit worse again in 2001. Towards the soil erosion, it had been mitigated continuously from 1976 to 1989 and then to 2001. It is interesting that a little decrease of vegetation coverage from 1989 to 2001 did not lead to increase of soil erosion. The reason is that the decrease of vegetation coverage was chiefly caused by urbanization and thus mainly occurred in very gentle terrains, where soil erosion was naturally slight. The results clearly indicate the details of plant cover and soil erosion change in 25 years and also offer a scientific foundation for plant and soil conservation.展开更多
Leaf Area Index(LAI)is a key parameter in vegetation analysis and management,especially for mountain areas.The accurate retrieval of LAI based on remote sensing data is very necessary.In a study at the Dayekou forest ...Leaf Area Index(LAI)is a key parameter in vegetation analysis and management,especially for mountain areas.The accurate retrieval of LAI based on remote sensing data is very necessary.In a study at the Dayekou forest center in Heihe watershed of Gansu Province,we determined the LAI based on topographic corrections of a SPOT-5.The large variation in the mountain terrain required preprocessing of the SPOT-5 image,except when orthorectification, radiation calibration and atmospheric correction were used.These required acquisition of surface reflectance and several vegetation indexes and linkage to field measured LAI values.Statistical regression models were used to link LAI and vegetation indexes.The quadratic polynomial model between LAI and SAVI (L=0.35)was determined as the optimal model considering the R and R2 value.A second group of LAI data were reserved to validate the retrieval result.The model was applied to create a distribution map of LAI in the area.Comparison with an uncorrected SPOT-5 image showed that topographic correction is necessary for determination of LAI in mountain areas.展开更多
基金supported by the Key Project of the National Natural Science Foundation of China (Grant No 40831061)
文摘The Dst index,designed as a proxy of ring current intensity,is known to be also affected by other magnetospheric current systems,e.g.magnetopause current.The pressure-corrected Dst index is obtained by removing the effects of the solar wind dynamic pressure and the quiet time ring current.However,all previous studies treated the correction coefficient as an averaged parameter for storms of different intensity.In this paper,based on the Burton's equations and employing two independent methods,we will show a positive correlation between pressure-correction coefficient b and the intensity of the storms.We divided our storm database(872 storms in total) into three categories according to the intensity of storms.In order to improve the accuracy of calculating,we also used the higher-resolution SYM-H index data instead of Dst index to compute the corrected Dst index during different storms.Furthermore,we are able to provide corrected magnetic storm index with high-time resolution(-1 min).
文摘Dynamic monitoring of plant cover and soil erosion often uses remote sensing data, especially for estimating the plant cover rate (vegetation coverage) by vegetation index. However, the latter is influenced by atmospheric effects and methods for correcting them are still imperfect and disputed. This research supposed and practiced an indirect, fast, and operational method to conduct atmospheric correction of images for getting comparable vegetation index values in different times. It tries to find a variable free from atmospheric effects, e.g., the mean vegetation coverage value of the whole study area, as a basis to reduce atmospheric correction parameters by establishing mathematical models and conducting simulation calculations. Using these parameters, the images can be atmospherically corrected. And then, the vegetation index and corresponding vegetation coverage values for all pixels, the vegetation coverage maps and coverage grade maps for different years were calculated, i.e., the plant cover monitoring was realized. Using the vegetation coverage grade maps and the ground slope grade map from a DEM to generate soil erosion grade maps for different years, the soil erosion monitoring was also realized. The results show that in the study area the vegetation coverage was the lowest in 1976, much better in 1989, but a bit worse again in 2001. Towards the soil erosion, it had been mitigated continuously from 1976 to 1989 and then to 2001. It is interesting that a little decrease of vegetation coverage from 1989 to 2001 did not lead to increase of soil erosion. The reason is that the decrease of vegetation coverage was chiefly caused by urbanization and thus mainly occurred in very gentle terrains, where soil erosion was naturally slight. The results clearly indicate the details of plant cover and soil erosion change in 25 years and also offer a scientific foundation for plant and soil conservation.
基金supported by NaturalScience Foundation of China(Grant No.41171330&40871173)the State Key BasicResearch Project(Grant No.2007CB714404)
文摘Leaf Area Index(LAI)is a key parameter in vegetation analysis and management,especially for mountain areas.The accurate retrieval of LAI based on remote sensing data is very necessary.In a study at the Dayekou forest center in Heihe watershed of Gansu Province,we determined the LAI based on topographic corrections of a SPOT-5.The large variation in the mountain terrain required preprocessing of the SPOT-5 image,except when orthorectification, radiation calibration and atmospheric correction were used.These required acquisition of surface reflectance and several vegetation indexes and linkage to field measured LAI values.Statistical regression models were used to link LAI and vegetation indexes.The quadratic polynomial model between LAI and SAVI (L=0.35)was determined as the optimal model considering the R and R2 value.A second group of LAI data were reserved to validate the retrieval result.The model was applied to create a distribution map of LAI in the area.Comparison with an uncorrected SPOT-5 image showed that topographic correction is necessary for determination of LAI in mountain areas.
