Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known ...Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known as water guzzlers by consuming anywhere between 70%and 90%of all human water use globally.Given these facts and the increase in global population to nearly 10 billion by the year 2050,the need for routine,rapid,and automated cropland mapping year-after-year and/or season-after-season is of great importance.The overarching goal of this study was to generate standard and routine cropland products,year-after-year,over very large areas through the use of two novel methods:(a)quantitative spectral matching techniques(QSMTs)applied at continental level and(b)rule-based Automated Cropland Classification Algorithm(ACCA)with the ability to hind-cast,now-cast,and future-cast.Australia was chosen for the study given its extensive croplands,rich history of agriculture,and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing.This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer(MODIS)250-m normalized difference vegetation index 16-day composite time-series data for 16 years:2000 through 2015.The products consisted of:(1)cropland extent/areas versus cropland fallow areas,(2)irrigated versus rainfed croplands,and(3)cropping intensities:single,double,and continuous cropping.An accurate reference cropland product(RCP)for the year 2014(RCP2014)produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015.A comparison between the ACCA-derived cropland products(ACPs)for the year 2014(ACP2014)versus RCP2014 provided an overall agreement of 89.4%(kappa=0.814)with six classes:(a)producer’s accuracies varying between 72%and 90%and(b)user’s accuracies varying between 79%and 90%.ACPs for the individual years 2000–2013 and 2015(ACP2000–ACP2013,ACP2015)showed very strong similarities with several other studies.The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015,thus highlighting the value of the study in food security analysis.展开更多
In a cloud environment,consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost.On the other hand,Cloud Service Providers(CSPs)see...In a cloud environment,consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost.On the other hand,Cloud Service Providers(CSPs)seek to maximize their profits by attracting and serving more consumers based on their resource capabilities.The literature has discussed the problem by considering either consumers’needs or CSPs’capabilities.A problem resides in the lack of explicit models that combine preferences of consumers with the capabilities of CSPs to provide a unified process for resource allocation and task scheduling in a more efficient way.The paper proposes a model that adopts a Multi-Criteria Decision Making(MCDM)method,called Analytic Hierarchy Process(AHP),to acquire the information of consumers’preferences and service providers’capabilities to prioritize both tasks and resources.The model also provides a matching technique to assign each task to the best resource of a CSP while preserves the fairness of scheduling more tasks for resources with higher capabilities.Our experimental results prove the feasibility of the proposed model for prioritizing hundreds of tasks/services and CSPs based on a defined set of criteria,and matching each set of tasks/services to the best CSPS.展开更多
The concept of dual image reversible data hiding(DIRDH) is the technique that can produce two camouflage images after embedding secret data into one original image.Moreover,not only can the secret data be extracted ...The concept of dual image reversible data hiding(DIRDH) is the technique that can produce two camouflage images after embedding secret data into one original image.Moreover,not only can the secret data be extracted from two camouflage images but also the original image can be recovered.To achieve high image quality,Lu et al.'s method applied least-significant-bit(LSB) matching revisited to DIRDH.In order to further improve the image quality,the proposed method modifies LSB matching revisited rules and applies them to DIRDH.According to the experimental results,the image quality of the proposed method is better than that of Lu et al.'s method.展开更多
The development of compaction bands in saturated soils, which is coupling-rate, inertial and pore-pressure-dependent, under axisymmetric loading was discussed, using a simple model and a matching technique at the movi...The development of compaction bands in saturated soils, which is coupling-rate, inertial and pore-pressure-dependent, under axisymmetric loading was discussed, using a simple model and a matching technique at the moving boundary of a band. It is shown that the development of compaction bands is dominated by the coupling-rate and pore-pressure effects of material. The soil strength makes the band shrinking, whilst pore pressure diffusion makes the band expand. Numerical simulations were carried out in this paper.展开更多
This paper describes the development of shear bands in saturated soil under simple shear using a matching technique at the moving boundary of a shear band, and it is shown that the development of shear bands is affect...This paper describes the development of shear bands in saturated soil under simple shear using a matching technique at the moving boundary of a shear band, and it is shown that the development of shear bands is affected by the coupling strain rate and pore pressure of material.Some numerical solutions have been presented.展开更多
The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia,using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a sh...The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia,using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period.Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season(June–October),followed by a fallow during the rabi season(November–February).These cropland areas are not suitable for growing rabi-season rice due to their high water needs,but are suitable for a short-season(≤3 months),low water-consuming grain legumes such as chickpea(Cicer arietinum L.),black gram,green gram,and lentils.Intensification(double-cropping)in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands.Several grain legumes,primarily chickpea,are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region.The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers:(a)rice crop is grown during the primary(kharif)crop growing season or during the north-west monsoon season(June–October);(b)same croplands are left fallow during the second(rabi)season or during the south-east monsoon season(November–February);and(c)ability to support low water-consuming,short-growing season(≤3 months)grain legumes(chickpea,black gram,green gram,and lentils)during rabi season.Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season,because the moisture/water demand of these crops is too high.The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index(NDVI)time series for one year(June 2010–May 2011)of Moderate Resolution Imaging Spectroradiometer(MODIS)data,using spectral matching techniques(SMTs),and extensive field knowledge.Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics.The producers’and users’accuracies of the cropland fallow classes were between 75%and 82%.The overall accuracy and the kappa coefficient estimated for rice classes were 82%and 0.79,respectively.The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia,with 88.3%in India,0.5%in Pakistan,1.1%in Sri Lanka,8.7%in Bangladesh,1.4%in Nepal,and 0.02%in Bhutan.Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.展开更多
A spiral cloud belt matching(SCBeM)technique is proposed for automatically locating the tropical cyclone(TC)center position on the basis of multi-band geo-satellite images.The technique comprises four steps:fusion of ...A spiral cloud belt matching(SCBeM)technique is proposed for automatically locating the tropical cyclone(TC)center position on the basis of multi-band geo-satellite images.The technique comprises four steps:fusion of multi-band geo-satellite images,extraction of TC cloud systems,construction of a spiral cloud belt template(CBT),and template matching to locate the TC center.In testing of the proposed SCBeM technique on 97 TCs over the western North Pacific during 2012-2015,the median error(ME)was 50 km.An independent test of another 29 TCs in 2016 resulted in a ME of 54 km.The SCBeM performs better for TCs with intensity above“typhoon”level than it does for weaker systems,and is not suitable for use on high-latitude or landfall TCs if their cloud band formations have been destroyed by westerlies or by terrain.The proposed SCBeM technique provides an additional solution for automatically and objectively locating the TC center and has the potential to be applied conveniently in an operational setting.Intercomparisons between the Automated Rotational Center Hurricane Eye Retrieval(ARCHER)and SCBeM methods using events from 2014 to 2016 reveal that ARCHER has better location accuracy.However,when IR imagery alone is used,the ME of SCBeM is 54 km,and in the case of low latitudes and low vertical wind shear the ME is 45-47 km,which approaches that of ARCHER(49 km).Thus,the SCBeM method is simple,has good time resolution,performs well and is a better choice for those TC operational agencies in the case that the microwave images,ASCAT,or other observations are unavailable.展开更多
基金This work was supported by NASA MEaSUREs(grant number NNH13AV82I)U.S.Geological Survey provided sup-plemental funding from other direct and indirect means through its Land Change Science(LCS)Land Remote Sensing(LRS)programs as well as its Climate and Land Use Change Mission Area.
文摘Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known as water guzzlers by consuming anywhere between 70%and 90%of all human water use globally.Given these facts and the increase in global population to nearly 10 billion by the year 2050,the need for routine,rapid,and automated cropland mapping year-after-year and/or season-after-season is of great importance.The overarching goal of this study was to generate standard and routine cropland products,year-after-year,over very large areas through the use of two novel methods:(a)quantitative spectral matching techniques(QSMTs)applied at continental level and(b)rule-based Automated Cropland Classification Algorithm(ACCA)with the ability to hind-cast,now-cast,and future-cast.Australia was chosen for the study given its extensive croplands,rich history of agriculture,and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing.This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer(MODIS)250-m normalized difference vegetation index 16-day composite time-series data for 16 years:2000 through 2015.The products consisted of:(1)cropland extent/areas versus cropland fallow areas,(2)irrigated versus rainfed croplands,and(3)cropping intensities:single,double,and continuous cropping.An accurate reference cropland product(RCP)for the year 2014(RCP2014)produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015.A comparison between the ACCA-derived cropland products(ACPs)for the year 2014(ACP2014)versus RCP2014 provided an overall agreement of 89.4%(kappa=0.814)with six classes:(a)producer’s accuracies varying between 72%and 90%and(b)user’s accuracies varying between 79%and 90%.ACPs for the individual years 2000–2013 and 2015(ACP2000–ACP2013,ACP2015)showed very strong similarities with several other studies.The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015,thus highlighting the value of the study in food security analysis.
文摘In a cloud environment,consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost.On the other hand,Cloud Service Providers(CSPs)seek to maximize their profits by attracting and serving more consumers based on their resource capabilities.The literature has discussed the problem by considering either consumers’needs or CSPs’capabilities.A problem resides in the lack of explicit models that combine preferences of consumers with the capabilities of CSPs to provide a unified process for resource allocation and task scheduling in a more efficient way.The paper proposes a model that adopts a Multi-Criteria Decision Making(MCDM)method,called Analytic Hierarchy Process(AHP),to acquire the information of consumers’preferences and service providers’capabilities to prioritize both tasks and resources.The model also provides a matching technique to assign each task to the best resource of a CSP while preserves the fairness of scheduling more tasks for resources with higher capabilities.Our experimental results prove the feasibility of the proposed model for prioritizing hundreds of tasks/services and CSPs based on a defined set of criteria,and matching each set of tasks/services to the best CSPS.
基金supported by MOST under Grants No.105-2410-H-468-010 and No.105-2221-E-468-019
文摘The concept of dual image reversible data hiding(DIRDH) is the technique that can produce two camouflage images after embedding secret data into one original image.Moreover,not only can the secret data be extracted from two camouflage images but also the original image can be recovered.To achieve high image quality,Lu et al.'s method applied least-significant-bit(LSB) matching revisited to DIRDH.In order to further improve the image quality,the proposed method modifies LSB matching revisited rules and applies them to DIRDH.According to the experimental results,the image quality of the proposed method is better than that of Lu et al.'s method.
文摘The development of compaction bands in saturated soils, which is coupling-rate, inertial and pore-pressure-dependent, under axisymmetric loading was discussed, using a simple model and a matching technique at the moving boundary of a band. It is shown that the development of compaction bands is dominated by the coupling-rate and pore-pressure effects of material. The soil strength makes the band shrinking, whilst pore pressure diffusion makes the band expand. Numerical simulations were carried out in this paper.
文摘This paper describes the development of shear bands in saturated soil under simple shear using a matching technique at the moving boundary of a shear band, and it is shown that the development of shear bands is affected by the coupling strain rate and pore pressure of material.Some numerical solutions have been presented.
基金supported by two CGIAR Research Programs:Dryland Cereals,Grain legumes and WLE.The research was also supported by the global food security support analysis data at 30 m project(GFSAD30http://geography.wr.usgs.gov/science/croplands/https://croplands.org/)funded by the NASA MEaSUREs[grant number:NNH13AV82I](Making Earth System Data Records for Use in Research Environments)funding obtained through NASA ROSES solicitation as well as by the Land Change Science(LCS),Land Remote Sensing(LRS),and Climate Land Use Change Mission Area Programs of the U.S.Geological Survey(USGS).
文摘The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia,using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period.Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season(June–October),followed by a fallow during the rabi season(November–February).These cropland areas are not suitable for growing rabi-season rice due to their high water needs,but are suitable for a short-season(≤3 months),low water-consuming grain legumes such as chickpea(Cicer arietinum L.),black gram,green gram,and lentils.Intensification(double-cropping)in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands.Several grain legumes,primarily chickpea,are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region.The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers:(a)rice crop is grown during the primary(kharif)crop growing season or during the north-west monsoon season(June–October);(b)same croplands are left fallow during the second(rabi)season or during the south-east monsoon season(November–February);and(c)ability to support low water-consuming,short-growing season(≤3 months)grain legumes(chickpea,black gram,green gram,and lentils)during rabi season.Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season,because the moisture/water demand of these crops is too high.The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index(NDVI)time series for one year(June 2010–May 2011)of Moderate Resolution Imaging Spectroradiometer(MODIS)data,using spectral matching techniques(SMTs),and extensive field knowledge.Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics.The producers’and users’accuracies of the cropland fallow classes were between 75%and 82%.The overall accuracy and the kappa coefficient estimated for rice classes were 82%and 0.79,respectively.The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia,with 88.3%in India,0.5%in Pakistan,1.1%in Sri Lanka,8.7%in Bangladesh,1.4%in Nepal,and 0.02%in Bhutan.Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.
基金The CMA and JTWC best track archives were obtained from Typhoon Online website and NDBC website respectively.The real-time archives of ARCHER and ADT were downloaded from SSEC.WISC website.This study was supported by the Key Projects of the National Key R&D Program(No.2018YFC1506300)the National Basic Research Program of China(No.2015CB452806)+2 种基金the Key Program for International S&T Cooperation Projects of China(No.2017YFE0107700)the Natural Science Foundation of Shanghai(No.15ZR1449900)the National Natural Science Foundation of China(Nos.41675116,41575046,41775065,and 41405060).
文摘A spiral cloud belt matching(SCBeM)technique is proposed for automatically locating the tropical cyclone(TC)center position on the basis of multi-band geo-satellite images.The technique comprises four steps:fusion of multi-band geo-satellite images,extraction of TC cloud systems,construction of a spiral cloud belt template(CBT),and template matching to locate the TC center.In testing of the proposed SCBeM technique on 97 TCs over the western North Pacific during 2012-2015,the median error(ME)was 50 km.An independent test of another 29 TCs in 2016 resulted in a ME of 54 km.The SCBeM performs better for TCs with intensity above“typhoon”level than it does for weaker systems,and is not suitable for use on high-latitude or landfall TCs if their cloud band formations have been destroyed by westerlies or by terrain.The proposed SCBeM technique provides an additional solution for automatically and objectively locating the TC center and has the potential to be applied conveniently in an operational setting.Intercomparisons between the Automated Rotational Center Hurricane Eye Retrieval(ARCHER)and SCBeM methods using events from 2014 to 2016 reveal that ARCHER has better location accuracy.However,when IR imagery alone is used,the ME of SCBeM is 54 km,and in the case of low latitudes and low vertical wind shear the ME is 45-47 km,which approaches that of ARCHER(49 km).Thus,the SCBeM method is simple,has good time resolution,performs well and is a better choice for those TC operational agencies in the case that the microwave images,ASCAT,or other observations are unavailable.