Dambo mapping for environmental monitoring using Landsat TM and SAR imagery: Case study in the Zambian Copperbelt

The purpose of this study was to monitor the impact of mining in the Zambian Copperbelt, specifically using dambos as an environmental indicator for pollution. Data fusion using a Brovey transform was used for combining speckle filtered radar data with optical data to effectively map natural dambos and dambos that have degraded due to human impact. Comparative analysis of raw images and fusion product reveals that, whereas natural dambos show low values on Landsat reflective bands and low backscatter response in SAR imagery, degraded dambos have mixed spectral responses. Degraded dambos are difficult to identify in either optical or SAR images alone, but a fusion product highlights complimentary spectral information, making these environmental indicators uniquely identifiable.     

Detection of temporary flooded areas with potential floodwater mosquito production using imaging radar

Temporarily flooded areas can produce enormous numbers of floodwater mosquitoes, causing tremendous nuisance to people living in the vicinity. The aim of this study is to develop a remote-sensing method for detecting temporary flooded areas that can produce floodwater mosquitoes. For this objective, synthetic aperture radar (SAR) imagery from the European Remote Sensing satellite (ERS-2) and the Environmental Satellite (Envisat) are chosen. The images cover both flooded and dry periods around Lake Färnebofjärden, located in the lowlands of the River Dalälven, central Sweden, during the vegetation season between 2000 and 2006. Unsupervised classification and principal component analysis (PCA) are tested as methods for detecting floodwater mosquito production sites. In the unsupervised classification experiment, four types of images are tested. The classification of a synthetic colour image gives the best result with an overall accuracy of 85.7% and a kappa value of 0.7, as well as a 46% detection rate of field-mapped flooded areas. PCA is performed on a data set of 16 time series radar images. The resulting principal component (PC) bands provide information about flooding probability as well as vegetation structures. Regular flooding increases the probability for an area to provide breeding sites for floodwater mosquitoes. Thus, this approach will be very useful in estimating the risk of floodwater mosquito establishment.     

Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery

A multi-year study was carried out to evaluate ERS synthetic aperture radar (SAR) imagery for monitoring surface hydrologic conditions in wetlands of southern Florida. Surface conditions (water level, aboveground biomass, soil moisture) were measured in 13 study sites (representing three major wetland types) over a 25-month period. ERS SAR imagery was collected over these sites on 22 different occasions and correlated with the surface observations. The results show wide variation in ERS backscatter in individual sites when they were flooded and non-flooded. The range (minimum vs. maximum) in SAR backscatter for the sites when they were flooded was between 2.3 and 8.9 dB, and between 5.0 and 9.0 dB when they were not flooded. Variations in backscatter in the non-flooded sites were consistent with theoretical scattering models for the most part. Backscatter was positively correlated to field measurements of soil moisture. The MIchigan MIcrowave Canopy Scattering (MIMICS) model predicts that backscatter should decrease sharply when a site becomes inundated, but the data show that this drop is only 1-2 dB. This decrease was observed in both non-wooded and wooded sites. The drop in backscatter as water depth increases predicted by MIMICS was observed in the non-wooded wetland sites, and a similar decrease was observed in wooded wetlands as well. Finally, the sensitivity of backscatter and attenuation to variations in aboveground biomass predicted by MIMICS was not observed in the data.The results show that the inter- and intra-annual variations in ERS SAR image intensity in the study region are the result of changes in soil moisture and degree of inundation in the sites. The correlation between changes in SAR backscatter and water depth indicates the potential for using spaceborne SAR systems, such as the ERS for monitoring variations in flooding in south Florida wetlands.     

Repeat-pass multi-temporal interferometric SAR coherence variations with Amazon floodplain and lake habitats

We have analysed interferometric coherence variations in Japanese Earth Resources Satellite (JERS-1) L-band synthetic aperture radar (SAR) data at three central Amazon sites: Lake Balbina, Cabaliana and Solim[otilde]es-Purús. Because radar pulse interactions with inundated vegetation typically follow a double-bounce travel path that returns energy to the antenna, coherence will vary with vegetation type as well as with physical and temporal baselines. Lake Balbina consists mostly of upland forests and inundated trunks of dead, leafless trees whereas Cabaliana and Solim[otilde]es-Purús are dominated by flooded forests. Balbina has higher coherence values than either Cabaliana or Solim[otilde]es-Purús probably because the dead, leafless trees support strong double-bounce returns. The mean coherences of flooded woodland are 0.28 in Balbina and 0.11 in both Cabaliana and Solim[otilde]es-Purús. With increasing temporal baselines, flooded and nonflooded wetland habitats show a steadily decreasing trend in coherence values whereas terra-firme and especially open-water habitats have little variation and remain lower in value. Flooded and nonflooded wetland coherence varies with the season whereas terra-firme and open water do not have similarly evident seasonal variations. For example, flooded habitats in all three study regions show annual peaks in coherence values that are typically 0.02 greater than coherence values from temporal baselines 180 days later, yet open water shows no variation with time. Our findings suggest that, despite overall low coherence values, repeat-pass interferometric coherence of flooded habitats is capable of showing the annual periodicity of the Amazon flood wave.     

Use of SAR satellites for mapping zonation of vegetation communities in the Amazon floodplain

Radarsat and JERS-1 imagery were used for mapping zonation of vegetation communities in the Amazon floodplain. Imagery analysis indicates that at periods of minimum water level the backscattering values of both C and L bands are the lowest and as the water level rises, so do the backscattering values. JERS-1 imagery exhibits a larger dynamic range of backscattering in response to the ground cover for the two extremes of water level (10?dB) compared to Radarsat imagery. The backscattering differences from different ground cover allowed the use of a region-based classification that produced seasonal maps with accuracies higher than 95% for vegetated areas of the floodplain. These seasonal maps were used to estimate the spatial distribution and time of inundation and the vegetation cover of the floodplain. It was possible to determine that semi-aquatic vegetation, tree-like aquatic plants, and shrub-like trees colonize regions flooded for at least 300?days?year^?1. Secondary colonizers, such as tall well-developed floodplain forest, cover regions flooded for approximately 150?days?year^?1, and floodplain climax forest colonize regions inundated for approximately 60?days?year^?1.     

The contribution of ALOS/PALSAR-1 multi-temporal data to map permanently and temporarily flooded coastal wetlands

ABSTRACT

Although regional wetland mapping studies have mostly relied on optical sensors, synthetic aperture radar (SAR) sensors are being increasingly applied. The aim of this study is to analyse the ability of the Phased Array type L-band Synthetic Aperture Radar on board of the Advanced Land Observing Satellite (ALOS/PALSAR-1) data to identify, delineate and monitor wetlands, and to evaluate the importance of scene selection in a highly unpredictable wetland. Three SAR scene sets (Year A, Year B and Inter-annual) were built for this purpose, considering the intra-annual and inter-annual hydrologic variability and the phenologic variability of the studied coastal wetland. Seven land cover types were defined, including three permanently flooded wetland classes, three temporarily flooded wetland classes and one non-wetland class. An object-based unsupervised classification approach was applied on each multi-temporal set. The obtained clusters were characterized by a temporal signature and assigned to the seven land cover types using a decision tree with user-defined thresholds. The accuracy assessment of each product was performed using a set of 258 data sites, including field collected data and data retrieved from Landsat 8 Operational Land Imager (OLI) imagery acquired during the dates of the field campaign. The Year B set showed the best accuracy (83.4% overall, 75% Kappa coefficient (κ)) and the lowest omission and commission mean errors (16.6% and 16.1% respectively). The classes that were best differentiated are permanently flooded wetlands (PFW) and non-wetlands (NW) in all sets.     

基于ENVISat ASAR影像的湿地植被冠层下淹水范围提取以扎龙保护区为例

以扎龙自然保护区湿地为例,结合ENVISat ASAR多极化(HH/HV)雷达影像与传统的光学影像Landsat TM (band1~5,7),分析雷达影像后向散射系数与Landsat TM影像不同波段反射率在淹水植被、非淹水植被、明水面和裸土不同地表覆被类型的差异。选择训练样本,采用分类回归树(Classification and Regression Tree,CART)模型,分别对两种影像进行分类,可视化表达湿地植被淹水范围空间分布情况。基于实测的植被冠层下淹水范围与非淹水范围样本点对两种数据源的分类结果进行精度验证。结果表明:HH/HV极化影像中,植被覆盖下水体的后向散射系数与其他地表覆被类型有明显区别,分类结果总精度为79.49%,Kappa系数为0.70,湿地植被淹水范围提取精度较高。而TM影像分类结果中,由于部分地区植被覆盖水体,淹水植被分类误差较高。将雷达影像引入沼泽湿地研究,提高了植被淹水范围提取效果,为有效分析湿地生态水文过程提供基础,对湿地水资源合理利用及生物多样性保护具有重要意义。     

A method of identifying chronic stress by EEG

There are a lot of studies on chronic stress assessment applying psychology instruments or hormones analysis. However, there are only few studies using electroencephalogram (EEG), which is a non-invasive method providing objective inspection on brain functioning. In this paper, we analyzed overall complexity and spectrum power of certain EEG bands (theta, alpha and beta) collected from two groups of human subjects—high stress versus moderate stress at prefrontal sites (Fp1, Fp2 and Fpz). The results showed that the differences of nonlinear features (C0, LZC, D2, L1 and RE) and linear features (power and alpha asymmetry score) between two groups are significant. C0, LZC and D2 significantly increased in stress group at Fp1 and Fp2, while L1 and RE significantly decreased. And those with chronic stress have higher left prefrontal power. Finally, we suggest that it may be effective to discriminate the high-stress people from moderate-stress people by EEG.     

Estimating forest structure in wetlands using multitemporal SAR

Data from 202 forest plots on the Roanoke River floodplain, North Carolina were used to assess the capabilities of multitemporal radar imagery for estimating biophysical characteristics of forested wetlands. The research was designed to determine the potential for using widely available data from the current set of satellite-borne synthetic aperture radar (SAR) sensors to study forests over broad geographic areas and complex environmental gradients. The SAR data set included 11 Radarsat scenes, 2 ERS-1 images, and 1 JERS-1 scene. Empirical analyses were stratified by flood status such that sites were compared only if they exhibited common flooding characteristics. In general, the results indicate that forest properties are more accurately estimated using data from flooded areas, probably because variations in surface conditions are minimized where there is a continuous surface of standing water. Estimations yielded root mean square errors (RMSEs) for validation data around 10 m²/ha for basal area (BA), and less than 3 m for canopy height. The r² values generally exceeded .65 for BA, with the best predictions coming from sample sites for which both nonflooded and flooded SAR scenes were available. The addition of early spring normalized difference vegetation index (NDVI) values from Landsat Thematic Mapper (Landsat TM) improved model predictions for BA in forests where BA levels were <55 m²/ha. Further analyses indicated a very limited sensitivity of the individual SAR scenes to differences in forest composition, although soil properties in nonflooded areas exerted a weak but nevertheless important influence on backscatter.     

Delineating dambo catenary soil-landscape units using aerial gamma-ray and terrain data: a comparison of classification approaches

Soil data are largely absent for most of Africa. For landscapes with recognizable catenary elements, this data gap can be filled by mapping the catenary units and assigning them with known soil properties. An example is the landscape map for a region with dambos in central Uganda, which shows the four catenary units in order from well-drained to seasonal wetland: uplands, margins, floors, and bottoms. However, this map was created using optical data, which are cost prohibitive and are also limited by cloud cover. We evaluated the potential of freely available aerial gamma-ray spectrometry (AGRS) data as an alternative source of classification inputs. Analysis of variance based upon field data for a region with dambos in central Uganda showed gamma activity to differ along the catenary sequence, with landscape position explaining an appreciable proportion of variation of potassium (28%), thorium (27%), and uranium (46%). Using the three gamma channels, together with terrain indices derived from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) as inputs, three classifiers were evaluated – conditional inference trees (CITs), random forests (RF), and multinomial-iterative self-organizing data analysis (ISODATA). While untransformed terrain and gamma predictors were used for the first two methods, we applied the ISODATA classification to landscape unit probability maps generated using multinomial principal components regression. For the CIT classification, all decision rules were based on terrain data, which might explain why the map was slightly less accurate (unweighted kappa = 0.61, linear weighted kappa = 0.73) than the map created using a RF classifier (unweighted kappa = 0.63, linear weighted kappa = 0.74), where both terrain and gamma predictors were used. But the existence of artefacts of margins within uplands in the map based on CIT modelling, and not that created using RF, is because the former missed the smoothing effect of gamma, attributed to zonal differences in activity of all three gamma channels. The multinomial-ISODATA predictions were poor (unweighted kappa = 0.56, linear weighted kappa = 0.67), partly because the regression model could not adequately resolve differences between bottoms and floors. However, we did find the probability maps generated using multinomial regression to be useful end products that capture the continuous nature of landscape unit transitions. It is important to note that in this study we used 90 m grid resolution gamma and terrain data to predict features that transition over distances of less than 10 m, so better results might be possible with finer-resolution gamma and terrain data.     

Liquid flooded flow-focusing microfluidic device for in situ generation of monodisperse microbubbles

Current microbubble-based ultrasound contrast agents are administered intravenously resulting in large losses of contrast agent, systemic distribution, and strict requirements for microbubble longevity and diameter size. Instead we propose in situ production of microbubbles directly within the vasculature to avoid these limitations. Flow-focusing microfluidic devices (FFMDs) are a promising technology for enabling in situ production as they can produce microbubbles with precisely controlled diameters in real-time. While the microfluidic chips are small, the addition of inlets and interconnects to supply the gas and liquid phase greatly increases the footprint of these devices preventing the miniaturization of FFMDs to sizes compatible with medium and small vessels. To overcome this challenge, we introduce a new method for supplying the liquid (shell) phase to a FFMD that eliminates bulky interconnects. A pressurized liquid-filled chamber is coupled to the liquid inlets of an FFMD, which we term a flooded FFMD. The microbubble diameter and production rate of flooded FFMDs were measured optically over a range of gas pressures and liquid flow rates. The smallest FFMD manufactured measured 14.5 × 2.8 × 2.3 mm. A minimum microbubble diameter of 8.1 ± 0.3 μm was achieved at a production rate of 450,000 microbubbles/s (MB/s). This represents a significant improvement with respect to any previously reported result. The flooded design also simplifies parallelization and production rates of up to 670,000 MB/s were achieved using a parallelized version of the flooded FFMD. In addition, an intravascular ultrasound (IVUS) catheter was coupled to the flooded FFMD to produce an integrated ultrasound contrast imaging device. B-mode and IVUS images of microbubbles produced from a flooded FFMD in a gelatin phantom vessel were acquired to demonstrate the potential of in situ microbubble production and real-time imaging. Microbubble production rates of 222,000 MB/s from a flooded FFMD within the vessel lumen provided a 23 dB increase in B-mode contrast. Overall, the flooded design is a critical contribution towards the long-term goal of utilizing in situ produced microbubbles for contrast enhanced ultrasound imaging of, and drug delivery to, the vasculature.     

Development of a sub-pixel analysis method applied to dynamic monitoring of floods

Traditional ‘in situ’ measurement techniques often fail to record the spatial distribution of floodplains. In that case, remote sensing provides inexpensive and reliable methodologies to map flooded areas and compute flood damage. The identification and monitoring of floods, due to their highly dynamic nature, require the use of high-time-resolution satellite images with the drawback that such images usually have low to medium spatial resolution. In this context, the traditional classification techniques would not be suitable for delineating floods because they use ‘hard methods’ of classification, where the coarse pixel is assigned to a unique land cover class, generating inaccurate maps of the flooded area. In contrast, the ‘soft methods’ assign several land cover classes within the coarse pixels. In this article, the theoretical basis regarding an innovative methodology of sub-pixel analysis (SA) to identify flooded areas is developed. The improvement in flood delineation is achieved with the use of primary topographic attributes, which stem from a digital elevation model (DEM). The methodology was applied to the monitoring of flood events in the lower Senegal River Valley, using satellite images with moderate spatial resolution. The proposed methodology was demonstrated to be effective for mapping the flood extent: the correct mapping of flooded areas was about 80% in all considered regions, whilst the better performance of supervised classification was 53%.     

原油总碳含量的粒子群优化集成神经网络预测模型

原油评价新技术的研究和应用成为目前世界石油炼制企业致力发展的方向,也是今后发展的必然趋势.本文采用核磁共振(nuclear magnetic resonance, NMR)光谱技术和粒子群优化集成神经网络(particle swarm optimiza tion-ensemble neural network, PSO-ERNN)建立了一种快速评价原油总碳物性指标预测模型.该模型以随机向量函数连接网络(random vector functional link network, RVFL)作为基本模型,采用正则化负相关学习策略集成基本模型,并采用粒子群优化算法优化各基本模型的最优隐含层节点数(L)以及集成规模的最佳集成个数(M),最后利用在线学习方法对模型进行更新.实例验证表明,所提出的模型显著提高了预报精度,避免了随机选择L和M对模型精度的影响,对提高原油评价精度与效率和及时满足加工炼制要求具有应用价值.     

Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal

During the rainy season the abundance of mosquitoes over the Ferlo region (Senegal) is linked to dynamic, vegetation cover and turbidity of temporary and relatively small ponds. The latter create a variable environment where mosquitoes can thrive and thus contribute to diffusion and transmission of diseases such as the Rift Valley Fever (RVF, with Aedes vexans arabiensis and Culex poicilipes mosquitoes) in the Ferlo. The small size and complex distribution of ponds require the use of high-spatial resolution satellite images for adequate detection. Here the use of SPOT-5 images (10 m-resolution) allows for detailed assessment of spatio-temporal evolution of ponds, through two new indices: i.e., the Normalized Difference Pond Index (NDPI), and the Normalized Difference Turbidity Index (NDTI). Small ponds less than 0.5 ha dominate whatever the time period. For example they represent nearly 65% of the total ponds during the peak of the rainy season, up to 90% at the end of the same season. Moreover, another product is proposed: the Zone Potentially Occupied by Mosquitoes (ZPOM). During the apex of the summer monsoon, it is found that RVF mosquitoes occupy 25% of the Ferlo region, while only 0.9% of the same area is covered by ponds. Overlapping areas occupied by grazing cattle and mosquitoes, enhance RVF virus transmission. The remotely sensed operational indices and products presented here are meant to better understand the mechanisms at stake and to contribute to the development of early warning systems in a changing climate and environment.     

Human-centered flood mapping and intelligent routing through augmenting flood gauge data with crowdsourced street photos

The number and intensity of flood events have been on the rise in many regions of the world. In some parts of the U.S., for example, almost all residential properties, transportation networks, and major infrastructure (e.g., hospitals, airports, power stations) are at risk of failure caused by floods. The vulnerability to flooding, particularly in coastal areas and among marginalized populations is expected to increase as the climate continues to change, thus necessitating more effective flood management practices that consider various data modalities and innovative approaches to monitor and communicate flood risk. Research points to the importance of reliable information about the movement of floodwater as a critical decision-making parameter in flood evacuation and emergency response. Existing flood mapping systems, however, rely on sparsely installed flood gauges that lack sufficient spatial granularity for precise characterization of flood risk in populated urban areas. In this paper, we introduce a floodwater depth estimation methodology that augments flood gauge data with user-contributed photos of flooded streets to reliably estimate the depth of floodwater and provide ad-hoc, risk-informed route optimization. The performance of the developed technique is evaluated in Houston, Texas, that experienced urban floods during the 2017 Hurricane Harvey. A subset of 20 user-contributed flood photos in combination with gauge readings taken at the same time is used to create a flood inundation map of the experiment area. Results show that augmenting flood gauge data with crowdsourced photos of flooded streets leads to shorter travel time and distance while avoiding flood-inundated areas.     

A multi-scale approach to mapping effective Leaf Area Index in Boreal Picea mariana stands using high spatial resolution CASI imagery

This study evaluates the performance of a simple geometric-optics reflectance model, used in combination with multi-spectral clustering, to map spatial patterns of effective Leaf Area Index ( L e ) within Boreal Picea mariana stands. Two metre resolution Compact Airborne Spectrographic Imager (CASI) images, acquired during the winter to minimize variability in understory reflectance, are used to map L e over BOREAS northern and southern Old Black Spruce tower flux sites. A combined multi-spectral clustering and ray-tracing approach is used to map open areas in each site at 2 m scale. A modified version of the Forest Light Interaction Model (FLIM) is then applied over canopy areas using 30 m scale red and near-infrared reflectance values derived from CASI images. Comparison of the combined FLIM and clustering approach (FLIMCLUS) with surface L e measurements in areas with overstory cover indicate a R 2 of 0.67 for the SSA-OBS site and 0.16 for the NSA-OBS site. The poor NSAOBS performance may be due to the low observed range of L e along the transect selected since additional measurements in a sparse canopy area closely match FLIM estimates. The relative standard error at both sites is under 10% and is close to the 5% precision error in surface L e estimates. Comparison of FLIMCLUS with FLIM and the simple ratio indicate substantial differences over open areas where the latter methods map-zero L e values. Further validation over other study sites, including surface data mapping edges between canopy and open areas, is proposed. The FLIM-CLUS L e maps may be useful for testing scale dependent assumptions within remote sensing algorithms and ecosystem flux models applied to the study sites and similar Picea mariana stands.     

Passive microwave observations of inundation area and the area/stage relation in the Amazon River floodplain

Inundation patterns in Amazon River floodplains are revealed by analysis of the 37 GHz polarization difference observed by the Scanning Multichannel Microwave Radiometer on the Nimbus-7 satellite. Flooded area is estimated at monthly intervals for January 1979 through August 1987 using mixing models that account for the major landscape units with distinctive microwave emission characteristics. Results are presented separately for 12 longitudinal reaches along the Amazon River main stem in Brazil as well as for three major tributaries (the Jurua, Purus and Madeira rivers). The total area along the Amazon River main stem that was flooded (including both floodplain and open water) varied between 19000 and 91 000km2. The correlation between flooded area and river stage is used to develop a predictive relationship and reconstruct regional inundation patterns in the floodplain of the Amazon River main stem over the past 94 years of stage records (1903-1996). The mean flooded area along the Amazon River during this 94-year period was 46800km2, of which the openwater surfaces of river channels and floodplain lakes comprised about 20 700km2.     

Identification and mapping of the Amazon habitats using a mixing model

A methodology is described for identifying and mapping floodplain habitats in a reach of the Amazon mainstream. A linear mixing approach was used to determine the fraction of three pure endmembers. This method was tested for two radiometrically rectified Landsat Thematic Mapper (TM) scenes and the proportions of endmembers were used to identify the following classes: (1) clear/mixed water; (2) turbid water; (3) flooded non-forest; (4) flooded forest; (5) human settlements and (6) aquatic vegetation. The results were compared to visually interpreted Landsat TM images.     

Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery

This article presents for the first time the combination of dual-polarimetric C-band Sentinel-1 synthetic aperture radar (SAR) data and quad-polarimetric L-band ALOS-2/PALSAR-2 imagery for mapping of flooded areas with a special focus on flooded vegetation. L-band SAR data is well suited for mapping of flooded vegetation, while C-band enables an accurate extraction open water areas. Polarimetric decomposition-based unsupervised Wishart classification is combined with object-based post-classification refinement and the integration of spatial contextual information and global auxiliary data. In eight different scenarios, focusing on single datasets or fusion of classification results of several ones, respectively, different polarimetric decomposition and classification principles, including the entropy/anisotropy/alpha and the Freeman–Durden–Wishart classification, were investigated. The helix scattering component of the Yamaguchi decomposition, derived from ALOS-2 imagery, showed high suitability to refine the Sentinel-1-based detection of flooded vegetation. A test site at the Evros River (Greek/Turkish border region) was chosen, which was affected by a flooding event that occurred in spring 2015. The validation was based on high spatial resolution optical WorldView-2 imagery acquired with short temporal delay to the SAR data.     

一种最小创建者集合的构建算法

最小创建者集合问题(the Minimum Founder Set problem,MFS)是求解重组体的嵌合体结构或创建者集合的有效模型,提出一种求解该问题的构造性启发式算法H-MFS.在嵌合体中的最小片断长度须不小于L的模型约束下,各重组体的前L列之间及最后L列之间均不能出现断点.基于这个思想,该算法将创建者序列的基因位点分成三个部分分别构建.采用两组真实的生物数据对算法进行测试分析:1.Kreitman的果蝇乙醇脱氢酶数据;2.国际人类基因组单体型图计划发布的CEU种群样本.实验结果显示,该算法能快速有效地求解MFS问题,并且当重组体的SNP位点个数取值较大时,H-MFS仍具有较高的执行效率,有很好的实用价值.