We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Se...We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.展开更多
2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(...2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(Sentinel-1A dual-polarized water index,SDWI)法,并结合地形因子对平原和山区分别提取水体信息,建立一套洪水淹没区监测流程;然后通过该流程利用灾前、灾中两期合成孔径雷达数据提取2020年7月27日巢湖流域、淮河流域行蓄洪区洪水淹没范围。结果显示:SDWI比直接用后向散射系数提取水体具有优势;7月27日巢湖流域洪水淹没区面积为524.8 km^(2),其中受洪灾较重的是白石天河子流域,西河子流域次之;淮河流域安徽境内行蓄洪区,沿淮的4个地市淹没面积从大到小依次为淮南市、阜阳市、六安市、蚌埠市。研究表明,基于Sentinel-1A数据,采用SDWI和地形因子建立的洪水淹没区监测流程对平原和山区都具有较好的准确性、适用性,且具有较高的时效性,便于及时开展洪水灾害监测。展开更多
Gout is caused by the deposition of uric acid as monosodium urate（MSU）. Chronic hyperuricemia is the necessary condition for MSU deposition, which arises from over-production and/or under-excretion of uric acid. Ren...Gout is caused by the deposition of uric acid as monosodium urate（MSU）. Chronic hyperuricemia is the necessary condition for MSU deposition, which arises from over-production and/or under-excretion of uric acid. Renal under-excretion of uric acid accounts for greater than 90% of the patients with hyperuricemia, making URAT1 inhibitors, which act through uricosuric effect a promising class of urate-lowering therapy（ULT）. This review aims at the summary and discussion of the latest development of URAT1 inhibitors for the treatment of hyperuricemia and gout and providing an insight into their structure-activity relationship（SAR）, which will be helpful to the design of URAT1 inhibitors for both academic research and pharmaceutical industry. The current development pipeline of URAT1 inhibitors is promising and encouraging.展开更多
Mapping abandoned land is very important for accurate agricultural management.However,in karst mountainous areas,continuous high-resolution optical images are difficult to obtain in rainy weather,and the land is fragm...Mapping abandoned land is very important for accurate agricultural management.However,in karst mountainous areas,continuous high-resolution optical images are difficult to obtain in rainy weather,and the land is fragmented,which poses a great challenge for remote sensing monitoring of agriculture activities.In this study,a new method for identifying abandoned land is proposed:firstly,a few Google Earth images are used to transform arable land into accurate vectorized geo-parcels;secondly,a time-series data set was constructed using Sentinel-1A Alpha parameters for 2020 on each farmland geoparcel;thirdly,the semi-variation function(SVF)was used to analyze the spatial-temporal characteristics,then identify abandoned land.The results show:(1)On the basis of accurate spatial information and boundary of farmland land,the SAR time-series dataset reflects the structure and time-series response.abandoned land with an accuracy of 80.25%.The problem of remote sensing monitoring in rainy regions and complex surface areas is well-resolved.(2)The spatial heterogeneity of abandoned land is more obvious than that of cultivated land within geoparcels.The step size for significant changes in the SVF of abandoned land is shorter than that of cultivated land.(3)The SVF time sequence curve presented a strong peak feature when farmland was abandoned.This reveals that the internal spatial structure of abandoned land is more disordered and complex.It showed that time-series variations of spatial structure within cultivated land have broader applications in remote sensing monitoring of agriculture in complex imaging environments.展开更多
This work presents a modified formula for the fractal box counting dimension.The method is based on the utilisation of the probability distribution formula in the fractal box count.The purpose of this method is to use...This work presents a modified formula for the fractal box counting dimension.The method is based on the utilisation of the probability distribution formula in the fractal box count.The purpose of this method is to use it for the discrimination of oil spill areas from the surrounding features,e.g.sea surface and look-alikes,using RADARSAT-1 SAR Wide beam mode(W1),Standard beam mode(S2)and Standard beam mode(S1)data acquisition under different wind speeds.The results show that the new formula is able to discriminate between oil spills and look-alike areas.The results also illustrate that the new fractal formula identifies well the deficiency of oil spills in pairs of S2 data.Further,there are no significant differences between fractal values of look-alikes,low wind zone,and current shear features in different beam modes for acquisition of RADARSAT-1 SAR data.The W1 mode data,however,show an error standard deviation of 0.002,thus performing a better discrimination of oil spills than the S1 and S2 mode data.展开更多
文摘We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.
文摘2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(Sentinel-1A dual-polarized water index,SDWI)法,并结合地形因子对平原和山区分别提取水体信息,建立一套洪水淹没区监测流程;然后通过该流程利用灾前、灾中两期合成孔径雷达数据提取2020年7月27日巢湖流域、淮河流域行蓄洪区洪水淹没范围。结果显示:SDWI比直接用后向散射系数提取水体具有优势;7月27日巢湖流域洪水淹没区面积为524.8 km^(2),其中受洪灾较重的是白石天河子流域,西河子流域次之;淮河流域安徽境内行蓄洪区,沿淮的4个地市淹没面积从大到小依次为淮南市、阜阳市、六安市、蚌埠市。研究表明,基于Sentinel-1A数据,采用SDWI和地形因子建立的洪水淹没区监测流程对平原和山区都具有较好的准确性、适用性,且具有较高的时效性,便于及时开展洪水灾害监测。
基金Supported by Key Projects of Tianjin Science and Technology Support Plan(16YFZCSY00910)Natural Science Foundation of Shandong Province(ZR2015BM028)
文摘Gout is caused by the deposition of uric acid as monosodium urate（MSU）. Chronic hyperuricemia is the necessary condition for MSU deposition, which arises from over-production and/or under-excretion of uric acid. Renal under-excretion of uric acid accounts for greater than 90% of the patients with hyperuricemia, making URAT1 inhibitors, which act through uricosuric effect a promising class of urate-lowering therapy（ULT）. This review aims at the summary and discussion of the latest development of URAT1 inhibitors for the treatment of hyperuricemia and gout and providing an insight into their structure-activity relationship（SAR）, which will be helpful to the design of URAT1 inhibitors for both academic research and pharmaceutical industry. The current development pipeline of URAT1 inhibitors is promising and encouraging.
基金supported by the Guizhou Provincial Science and Technology Foundation(Qiankehe ZK[2022]-302)the National Natural Science Foundation of China,(Grant NO.41661088,41631179 and 42071316)+2 种基金the National Key Research and Development Program of China(Grant NO.2017YFB0503600)the Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China,Ministry of Natural Resources(No.2022NRM0004)Excellent Youth Project of Hunan Provincial Education Department(22B0725)。
文摘Mapping abandoned land is very important for accurate agricultural management.However,in karst mountainous areas,continuous high-resolution optical images are difficult to obtain in rainy weather,and the land is fragmented,which poses a great challenge for remote sensing monitoring of agriculture activities.In this study,a new method for identifying abandoned land is proposed:firstly,a few Google Earth images are used to transform arable land into accurate vectorized geo-parcels;secondly,a time-series data set was constructed using Sentinel-1A Alpha parameters for 2020 on each farmland geoparcel;thirdly,the semi-variation function(SVF)was used to analyze the spatial-temporal characteristics,then identify abandoned land.The results show:(1)On the basis of accurate spatial information and boundary of farmland land,the SAR time-series dataset reflects the structure and time-series response.abandoned land with an accuracy of 80.25%.The problem of remote sensing monitoring in rainy regions and complex surface areas is well-resolved.(2)The spatial heterogeneity of abandoned land is more obvious than that of cultivated land within geoparcels.The step size for significant changes in the SVF of abandoned land is shorter than that of cultivated land.(3)The SVF time sequence curve presented a strong peak feature when farmland was abandoned.This reveals that the internal spatial structure of abandoned land is more disordered and complex.It showed that time-series variations of spatial structure within cultivated land have broader applications in remote sensing monitoring of agriculture in complex imaging environments.
文摘This work presents a modified formula for the fractal box counting dimension.The method is based on the utilisation of the probability distribution formula in the fractal box count.The purpose of this method is to use it for the discrimination of oil spill areas from the surrounding features,e.g.sea surface and look-alikes,using RADARSAT-1 SAR Wide beam mode(W1),Standard beam mode(S2)and Standard beam mode(S1)data acquisition under different wind speeds.The results show that the new formula is able to discriminate between oil spills and look-alike areas.The results also illustrate that the new fractal formula identifies well the deficiency of oil spills in pairs of S2 data.Further,there are no significant differences between fractal values of look-alikes,low wind zone,and current shear features in different beam modes for acquisition of RADARSAT-1 SAR data.The W1 mode data,however,show an error standard deviation of 0.002,thus performing a better discrimination of oil spills than the S1 and S2 mode data.
