Great Lakes ice classification using satellite C-band SAR multi-polarization data

The objective of this study is to advance development of algorithms to classify and map ice cover on the Laurentian Great Lakes using satellite C-band synthetic aperture radar (SAR) multi-polarization data. During the 1997 winter season, shipborne polarimetric backscatter measurements of Great Lakes ice types, using the Jet Propulsion Laboratory C-band scatterometer, were acquired together with surface-based ice physical characterization measurements and environmental parameters, concurrently with European Remote Sensing Satellite 2 (ERS-2) and RADARSAT-1 SAR data. This fully polarimetric dataset, composed of over 20 variations of different ice types measured at incidence angles from 0° to 60° for all polarizations, was processed and fully calibrated to obtain radar backscatter, establishing a library of signatures for different ice types. Computer analyses of calibrated ERS-2 and RADARSAT ScanSAR images of Great Lakes ice cover using the library in a supervised classification technique indicate that different ice types in the ice cover can be identified and mapped, but that wind speed and direction can cause misclassification of open water as ice based on single frequency, single polarization data. Using RADARSAT-2 quad-pol and ENVISAT ASAR dual-pol data obtained for Lake Superior during the 2009 and 2011 winter seasons, algorithms were developed for small incidence angle (< 35°) and large incidence angle (> 35°) SAR images and applied to map ice and open water. Ice types were subsequently classified using the library of backscatter signatures. Ice-type maps provide important input for environmental management, ice-breaking operations, ice forecasting and modeling, and climate change studies.     

Satellite SAR Remote Sensing of Great Lakes Ice Cover,Part 1. Ice Backscatter Signatures at C Band

For remote sensing of Great Lakes ice cover, a field experiment campaign was conducted in the 1997 winter season across the Straits of Mackinac and Lake Superior. The campaign was coordinated in two expeditions on two different United States Coast Guard icebreaker vessels, the Biscayne Bay in February and the Mackinaw in March. Aboard these icebreakers, the Jet Propulsion Laboratory C-band polarimetric scatterometer was used to measure backscatter signatures of various ice types and open water at incidence angles from 0° to 60°. The radar measurements include incidence angles and polarizations of spaceborne Synthetic Aperture Radars (SAR) on ERS, RADARSAT, and Envisat satellites. The radar data together with in situ measurements form a signature library that can be used to interpret SAR data for ice classification and mapping. Results are presented for backscatter signatures of Great Lakes ice types from thin lake ice to thick brash ice with different snow-cover and surface conditions. The signature library indicates that several ice types can be identified with multi-polarization SAR data; however, single-polarization data can result in misclassification of ice and open water at different ranges of incidence angle and wind conditions. For incidence angles larger than 30°, thick brash ice, the most difficult for icebreaking operations and the most hazardous for ship navigation, can be uniquely identified by co-polarized backscatter for all wind conditions below the gale force.     

Tree-Structured Modeling of the Relationship Between Great Lakes Ice Cover and Atmospheric Circulation Patterns

Seasonal maximum ice concentration (percentage of lake surface covered by ice) for the entire Laurentian Great Lakes and for each Great Lake separately is modeled using atmospheric teleconnection indices. Two methods, Linear Regression (LR) and Classification and Regression Trees (CART), are used to develop empirical models of the interannual variations of maximum ice cover. Thirty-four winter seasons between 1963 and 1998 and nine teleconnection indices were used in the analysis. The ice cover characteristics were different for each Great Lake. The ice cover data lent itself better to CART analysis, because CART does not require a priori assumptions about data distributions characteristics to perform well. The stepwise LR models needed more variables, and in general, did not explain as much of the variance as the CART models. Two variables, the Multivariate ENSO index and Tropical/Northern Hemisphere index, explained much of the interannual variations in ice cover in the CART models. Composite atmospheric circulation patterns for threshold values of these two indices were found to be associated with above-and below-normal ice cover in the Great Lakes. Thus, CART also provided insight into physical mechanisms (atmospheric circulation characteristics) underlying the statistical relationships identified in the models.     

Development of the Great Lakes Ice-circulation Model (GLIM): Application to Lake Erie in 2003–2004

To simulate ice and water circulation in Lake Erie over a yearly cycle, a Great Lakes Ice-circulation Model (GLIM) was developed by applying a Coupled Ice-Ocean Model (CIOM) with a 2-km resolution grid. The hourly surface wind stress and thermodynamic forcings for input into the GLIM are derived from meteorological measurements interpolated onto the 2-km model grids. The seasonal cycles for ice concentration, thickness, velocity, and other variables are well reproduced in the 2003/04 ice season. Satellite measurements of ice cover were used to validate GLIM with a mean bias deviation (MBD) of 7.4%. The seasonal cycle for lake surface temperature is well reproduced in comparison to the satellite measurements with a MBD of 1.5%. Additional sensitivity experiments further confirm the important impacts of ice cover on lake water temperature and water level variations. Furthermore, a period including an extreme cooling (due to a cold air outbreak) and an extreme warming event in February 2004 was examined to test GLIM's response to rapidly-changing synoptic forcing.     

Retrieval of ice/water observations from synthetic aperture radar imagery for use in lake ice data assimilation

High-resolution lake ice/water observations retrieved from satellite imagery through efficient, automated methods can provide critical information to lake ice forecasting systems. Synthetic aperture radar (SAR) data is well-suited to this purpose due to its high spatial resolution (approximately 50 m). With recent increases in the volume of SAR data available, the development of automated retrieval methods for these data is a priority for operational centres. However, automated retrieval of ice/water data from SAR imagery is difficult, due to ambiguity in ice and open water signatures, both in terms of image tone and in terms of parameterized texture features extracted from these images. Convolutional neural networks (CNNs) can learn features from imagery in an automated manner, and have been found effective in previous studies on sea ice concentration estimation from SAR. In this study the use of CNNs to retrieve ice/water observations from dual-polarized SAR imagery of two of the Laurentian Great Lakes, Lake Erie and Lake Ontario, is investigated. For data assimilation, it is crucial that the retrieved observations are of high quality. To this end, quality control measures based on the uncertainty of the CNN output to eliminate incorrect retrievals are discussed and demonstrated. The quality control measures are found to be effective in both dual-polarized and single-polarized retrievals. The ability of the CNN to downscale the coarse resolution training labels is demonstrated qualitatively.     

Verification and Application of a Bio-optical Algorithm for Lake Michigan Using SeaWiFS: a 7-year Inter-annual Analysis

In this paper we utilize 7 years of SeaWiFS satellite data to obtain seasonal and interannual time histories of the major water color-producing agents (CPAs), phytoplankton chlorophyll (chl), dissolved organic carbon (doc), and suspended minerals (sm) for Lake Michigan. We first present validation of the Great Lakes specific algorithm followed by correlations of the CPAs with coincident environmental observations. Special attention is paid to the satellite observations of the extensive episodic event of sediment resuspension and calcium carbonate precipitation out of the water. We then compare the obtained time history of the CPA's spatial and temporal distributions throughout the lake to environmental observations such as air and water temperature, wind speed and direction, significant wave height, atmospheric precipitation, river runoff, and cloud and lake ice cover. Variability of the onset, duration, and spatial extent of both episodic events and seasonal phenomena are documented from the SeaWiFS time series data, and high correlations with relevant environmental driving factors are established. The relationships between the CPAs retrieved from satellite data and environmental observations are then used to speculate on the future of Lake Michigan under a set of climate change scenarios.     

A band-ratio algorithm for retrieving open-lake chlorophyll values from satellite observations of the Great Lakes

The U.S. Environmental Protection Agency's Great Lakes National Program Office (GLNPO) has collected water quality data from the five Great Lakes annually since 1993. We used the GLNPO observations made since 2002 along with coincident measurements made by the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate-resolution Imaging Spectroradiometer (MODIS) to develop a new band-ratio algorithm for estimating chlorophyll concentrations in the Great Lakes from satellite observations. The new algorithm is based on a third-order polynomial model using the same maximum band ratios employed in the standard NASA algorithms (OC4 for SeaWiFS and OC3M for MODIS). The sensor-specific coefficients for the new algorithm were obtained by fitting the relationship to several hundred matched field and satellite observations. Although there are some seasonal variations in some lakes, the relationship between the observed chlorophyll values and those modeled using the new coefficients is fairly stable from lake to lake and across years. The accuracy of the satellite chlorophyll estimates derived from the new algorithm was improved substantially relative both to the standard NASA retrievals and to previously published algorithms tuned to individual lakes. Monte-Carlo fits to randomly selected subsets of the observations allowed us to estimate the uncertainty associated with the retrievals purely as a function of the satellite data. Our results provide, for the first time, a single simple band ratio method for retrieving chlorophyll concentrations in the offshore “open” waters of the Great Lakes from satellite observations.     

Wind Fields over the Great Lakes Measured by the SeaWinds Scatterometer on the QuikSCAT Satellite

This paper demonstrates the utility of satellite scatterometer measurements for wind retrieval over the Great Lakes on a daily basis. We use data acquired by the SeaWinds Scatterometer on the QuikSCAT (QSCAT) satellite launched in June 1999 to derive wind speeds and directions over the lakes at a resolution of 12.5 km, which is two times finer than the QSCAT standard ocean wind product at a resolution of 25 km. To evaluate QSCAT performance for high-resolution measurements of lake wind vectors, we compare QSCAT results with Great Lakes Coastal Forecasting System (GLCFS) nowcast wind fields and with standard QSCAT measurements of ocean wind vectors. Although the satellite results over the Great Lakes are obtained with an ocean model function, QSCAT and GLCFS wind fields compare well together for low to moderate wind conditions (4–32 knots). For wind speed, the analysis shows a correlation coefficient of 0.71, a bias of 2.6 knots in mean wind speed difference (nowcast wind is lower) with a root-mean-square (rms) deviation of 3.8 knots. For wind direction, the correlation coefficient is 0.94 with a very small value of 1.3° in mean wind direction bias and an rms deviation of 38° for all wind conditions. When excluding the low wind range of 4–12 knots, the rms deviation in wind direction reduces to 22°. Considering QSCAT requirements designed for ocean wind measurements and actual evaluations of QSCAT performance over ocean, results for high-resolution lake wind vectors indicate that QSCAT performs well over the Great Lakes. Moreover, we show that wind fields derived from satellite scatterometer data before, during, and after a large storm in October 1999, with winds stronger than 50 knots, can monitor the storm development over large scales. The satellite results for storm monitoring are consistent with GLCFS nowcast winds and lake buoy measurements. A geophysical model function can be developed specifically for the Great Lakes using long-term data from satellite scatterometers, to derive more accurate wind fields for operational applications as well as scientific studies.     

Great Lakes Ice Thickness Prediction

Weekly ice thickness data, collected from 24 bay, harbor, and river sites on the Great Lakes, were correlated with freezing degree-day accumulations to develop regression equations between ice thickness and freezing degree-days. The data base at ice measurement sites was 3 to 8 winters in length. Ths standard error of estimate varied for individual regression equations and averaged between 7 and 8 cm for five forms of regression equations. Because the regression equations are empirical, the range of input data used to predict ice thickness should be limited to the range of values used in the derivation.     

Researcher disciplines and the assessment techniques used to evaluate Laurentian Great Lakes coastal ecosystems

The Laurentian Great Lakes of North America have been a focus of environmental and ecosystem research since the Great Lakes Water Quality Agreement in 1972. This study provides a review of scientific literature directed at the assessment of Laurentian Great Lakes coastal ecosystems. Our aim was to understand the methods employed to quantify disturbance and ecosystem quality within Laurentian Great Lakes coastal ecosystems within the last 20 years. We focused specifically on evidence of multidisciplinary articles, in authorship or types of assessment parameters used. We sought to uncover: 1) where Laurentian Great Lakes coastal ecosystems are investigated, 2) how patterns in the disciplines of researchers have shifted over time, 3) how measured parameters differed among disciplines, and 4) which parameters were used most often. Results indicate research was conducted almost evenly across the five Laurentian Great Lakes and that publication of coastal ecosystems studies increased dramatically ten years after the first State of the Great Lakes Ecosystem Conference in 1994. Research authored by environmental scientists and by multiple disciplines (multidisciplinary) have become more prevalent since 2003. This study supports the likelihood that communication and knowledge-sharing is happening between disciplines on some level. Multidisciplinary or environmental science articles were the most inclusive of parameters from different disciplines, but every discipline seemed to include chemical parameters less often than biota, physical, and spatial parameters. There is a need for an increased understanding of minor nutrient, toxin, and heavy metal impacts and use of spatial metrics in Laurentian Great Lakes coastal ecosystems.     

A model for determining satellite-derived primary productivity estimates for Lake Michigan

A new MODIS based satellite algorithm to estimate primary production (PP) has been generated and evaluated for Lake Michigan. The Great Lakes Primary Productivity Model (GLPPM) is based on previous models that required extensive in situ data but it can utilize remotely sensed observations as input for some model variables and therefore allows greater spatial resolution for primary productivity estimates. The Color Producing Agent Algorithm (CPA-A) is utilized to obtain robust chlorophyll a values and the NASA KD2M approach is used to obtain the diffuse attenuation coefficient (K_d). Only incident PAR and carbon fixation rates are additionally needed to generate the primary productivity estimate. Comparisons of the satellite derived PP estimates from single monthly images to average monthly field measurements made by NOAA/GLERL found good agreement between estimates. Satellite derived PP estimates were used to estimate a preliminary Lake Michigan annual primary production of 8.5 Tg C/year. The new algorithm can be easily adapted to work on all the Great Lakes and therefore can be used to generate time series dating back to late 1997 (launch of SeaWiFs). These time series can contribute to improved assessment of Great Lakes primary productivity changes as a result of biological events, such as Dreissenid mussel invasions, climatic change and anthropogenic forcing.     

Nearshore Community Characteristics Related to Shoreline Properties in the Great Lakes

Successful protection and restoration of Great Lakes nearshore ecosystems will likely rely on management of terrestrial resources along Great Lakes shorelines. However, relationships between biological communities and changing shoreline environmental properties are poorly understood. We sought to begin understanding the potential roles of shoreline geomorphological and land cover properties in structuring nearshore biological communities in the Laurentian Great Lakes. Despite high variability in densities (benthic macroinvertebrates and zooplankton) and catch per unit effort (CPUE, shallow water and nearshore fish) within and among lake areas, several biological community patterns emerged to suggest that nearshore aquatic communities respond to shoreline features via the influences of these features on nearshore substrate composition and stability. Benthic macroinvertebrate densities were not different between shoreline types, although they were generally lower at nearshore sites with less stable substrates. Shallow water fish CPUE and zooplankton densities were generally lower for nearshore areas adjacent to developed mid-bluff shorelines and sites characterized by less stable substrates. Larger fish CPUE appeared to be unresponsive to local shoreline and substrate properties of nearshore zones. The emergence of these patterns despite significant ecological differences among lake areas (e.g., productivity, community composition, etc.) suggests that shoreline development may have comparable influences on nearshore ecosystems throughout the Great Lakes, providing a terrestrialbased indicator of relative nearshore biological and ecological integrity.     

A simple water clarity-turbidity index for the Great Lakes

There are a multitude of satellite-derived water clarity and turbidity indicators to support the decision making of environmental managers and policy makers. However, water quality dynamic ranges addressed by these indicators can differ significantly, subjecting non-expert users to potential pitfalls. Here we propose a satellite water clarity-turbidity index (CTI) as a simplified way to capture major changes in water clarity/turbidity across all water types in the Great Lakes. The CTI is defined to merge key information from three prerequisite variables derived from Visible Infrared Imaging Radiometer Suite (VIIRS) measurements, namely, the Secchi disk depth, the particulate backscattering coefficient, and the nephelometric turbidity, which are suitable for clear, intermediate, and turbid waters, respectively. Application to the Great Lakes shows that with one parameter, the CTI can illustrate major spatial and temporal patterns that are not entirely visible with each of the three original indicators alone. Using the CTI, we identified significant decrease in water turbidity in Lakes Michigan and Huron from 2000 to 2005, during which daily variability of CTI in August initially spiked and then gradually decreased most likely owing to diminishing whiting events. The CTI is a convenient and holistic assessment tool for water quality management.     

Small eddies observed in Lake Superior using SAR and sea surface temperature imagery

Making use of the fine resolution of satellite SAR imagery, we observe small eddies during the spring and summer months in several locations in Lake Superior. During these months there is a thermal gradient between warmer nearshore waters and colder offshore waters which enhances cyclonic coastal currents. Using spaceborne SAR imagery from the European Space Agency's ERS-1 and ERS-2 missions from 1992 to 1998, we observe small eddies, identifying and mapping basic eddy characteristics including diameter, location, and rotational sense. In total, 45 eddies were located, of which 41 were cyclonic and 4 anticyclonic. Average diameter was 9.8 km and average distance to shore was 8.1 km. Based on sea surface temperature data from AVHRR, the eddies are located within the region of sharp thermal gradients of order 3–5 °C per 3 km. Spatial and temporal coverage was uneven, however, more eddies were seen in SAR images taken in late summer along the southern and eastern shores as well as areas where the boundary current interacts with topographic features including islands and promontories.     

Fish Habitat Use Within and Across Wetland Classes in Coastal Wetlands of the Five Great Lakes: Development of a Fish-based Index of Biotic Integrity

The relative importance of Great Lake, ecoregion, wetland type, and plant zonation in structuring fish community composition was determined for 61 Great Lakes coastal wetlands sampled in 2002. These wetlands, from all five Great Lakes, spanned nine ecoregions and four wetland types (open lacustrine, protected lacustrine, barrier-beach, and drowned river mouth). Fish were sampled with fyke nets, and physical and chemical parameters were determined for inundated plant zones in each wetland. Land use/cover was calculated for 1- and 20-km buffers from digitized imagery. Fish community composition within and among wetlands was compared using correspondence analyses, detrended correspondence analyses, and non-metric multidimensional scaling. Within-site plant zonation was the single most important variable structuring fish communities regardless of lake, ecoregion, or wetland type. Fish community composition correlated with chemical/physical and land use/cover variables. Fish community composition shifted with nutrients and adjacent agriculture within vegetation zone. Fish community composition was ordinated from Scirpus, Eleocharis, and Zizania, to Nuphar/Nymphaea, and Pontederia/Sagittaria/Peltandra to Spargainium to Typha. Once the underlying driver in fish community composition was determined to be plant zonation, data were stratified by vegetation type and an IBI was developed for coastal wetlands of the entire Great Lakes basin.     

A review of U.S. Army Corps of Engineers airborne coastal mapping in the Great Lakes

The U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) collected its first airborne coastal mapping data on the Great Lakes in 1995. Since then, the JALBTCX has collected nearly 5 billion elevation and depth measurements and created over 2000 geographic information system (GIS) products for the shorelines of the Great Lakes. With improvements in airborne coastal mapping technology and the introduction of regional management for the USACE, surveys expanded into the regional scale, multi-sensor National Coastal Mapping Program (NCMP). The NCMP was initiated in 2004 to produce recurrent, regional, high-resolution light detection and ranging (lidar) elevation data and hyperspectral and aerial imagery to support regional sediment management, regulatory functions, flood damage reduction, asset management, emergency operations, and environmental stewardship. The JALBTCX is a collaboration among the USACE, the U.S. Naval Oceanographic Office (NAVOCEANO), and the National Oceanic and Atmospheric Administration (NOAA). The partners have worked together on airborne coastal mapping and charting since the late 1980s with the goal of advancing airborne lidar bathymetry and associated technologies. The collaboration has fielded three generations of airborne sensors and has transferred this technology to the commercial sector, supporting an expanding market for bathymetric lidar. The purpose of this paper is to provide an overview of the history of USACE survey efforts in the Great Lakes (1995–2012), an in-depth review of the resulting imagery and lidar data products, and new information product developments and applications to support environmental and coastal engineering throughout the Great Lakes region.     

Satellite ocean color algorithms: A review of applications to the Great Lakes

We review the literature dealing with retrievals of chlorophyll concentrations in the Great Lakes from satellite observations. Most studies show that the satellite estimates of chlorophyll concentrations are linearly related to the observed concentrations, though they tend to overestimate concentrations at lower values and underestimate them at higher values. Deviations from a consistent, accurate, linear relationship can be attributed to temporal and spatial variations in the inherent optical properties of the color producing agents in the water as well as to varying concentrations of non-algal substances that interfere with the retrievals. We confirmed these results by using a simple optical model to examine the sensitivity of the retrieved chlorophyll values to the concentrations of interfering substances and to differences in model parameters. Because the spatial and temporal optical properties of the Great Lakes are unpredictable, no retrieval method is likely to produce accurate results all the time. The papers we reviewed show that simple band ratio algorithms can provide chlorophyll estimates that are proportional to in situ concentrations. The published literature suggests that the band ratio methods will be of most value in regions where the concentrations of non-algal interfering substances are minimal. Because of these limitations we recommend that future papers presenting chlorophyll analysis based on satellite data provide confirming field observations that include measurements of chlorophyll, suspended particles and dissolved organic carbon. We also recommend that Great Lakes scientists explore novel methods for retrieving chlorophyll concentrations from satellite observations that have proven useful in other optically complex waters.     

Origin and Evolution of the Great Lakes

This paper presents a synthesis of traditional and recently published work regarding the origin and evolution of the Great Lakes. It differs from previously published reviews by focusing on three topics critical to the development of the Great Lakes: the glaciation of the Great Lakes watershed during the late Cenozoic, the evolution of the Great Lakes since the last glacial maximum, and the record of lake levels and coastal erosion in modern times.The Great Lakes are a product of glacial scour and were partially or totally covered by glacier ice at least six times since 0.78 Ma. During retreat of the last ice sheet large proglacial lakes developed in the Great Lakes watershed. Their levels and areas varied considerably as the oscillating ice margin opened and closed outlets at differing elevations and locations; they were also significantly affected by channel downcutting, crustal rebound, and catastrophic inflows from other large glacial lakes.Today, lake level changes of about a 1/3 m annually, and up to 2 m over 10 to 20 year time periods, are mainly climatically-driven. Various engineering works provide small control on lake levels for some but not all the Great Lakes. Although not as pronounced as former changes, these subtle variations in lake level have had a significant effect on shoreline erosion, which is often a major concern of coastal residents.     

An algorithm to retrieve chlorophyll,dissolved organic carbon,and suspended minerals from Great Lakes satellite data

An algorithm that utilizes individual lake hydro-optical (HO) models has been developed for the Great Lakes that uses SeaWiFS, MODIS, or MERIS satellite data to estimate concentrations of chlorophyll, dissolved organic carbon, and suspended minerals. The Color Producing Agent Algorithm (CPA-A) uses a specific HO model for each lake. The HO models provide absorption functions for the Color Producing Agents (CPAs) (chlorophyll (chl), colored dissolved organic matter (as dissolved organic carbon, doc), and suspended minerals (sm)) as well as backscatter for the chlorophyll, and suspended mineral parameters. These models were generated using simultaneous optical data collected with in situ measurements of CPAs collected during research cruises in the Great Lakes using regression analysis as well as using specific absorption and backscatter coefficients at specific chl, doc, and sm concentrations. A single average HO model for the Great Lakes was found to generate insufficiently accurate concentrations for Lakes Michigan, Erie, Superior and Huron. These new individual lake retrievals were evaluated with respect to EPA in situ field observations, as well as compared to the widely used OC3 MODIS retrieval. The new algorithm retrievals provided slightly more accurate chl values for Lakes Michigan, Superior, Huron, and Ontario than those obtained using the OC3 approach as well as providing additional concentration information on doc and sm. The CPA-A chl retrieval for Lake Erie is quite robust, producing reliable chl values in the reported EPA concentration ranges. Atmospheric correction approaches were also evaluated in this study.     

输水渠道冰盖增厚物理模型试验相似律研究

随着南水北调中线工程冰期输水相关问题研究的深入,理论研究成果亟需物理模型试验和原型观测数据的支持和验证,由于原型观测难度较大,且影响因素不可控,物理模型试验方法成为解决冰水力学问题的有效途径。现在国内外学者开展冰水力学模型试验的成果基础上,结合冰水力学理论研究成果,对以冻结模型冰为试验材料的输水渠道冰盖增厚物理模型试验相似律展开研究,并通过物理模型试验进行了验证。研究表明:冰凌下潜临界流速的比尺为λ0.5,水力加厚冰盖沉积厚度的比尺为λ,均遵循重力相似准则;对于力学加厚冰盖,由于冰凌黏结力不遵循重力相似准则,结冰期需设法控制黏结力;融冰期可按照重力相似准则设计,但需控制环境温度在结冰点以上,以减小冰凌间的黏结力;因此输水渠道冰盖增厚物理模型试验宜采用冻结模型冰为试验对象,试验应按照重力相似准则进行设计。