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Comparison of Methods and Approaches for Combining Multi-Temporal raster data in Geographic Information Systems Gabe Emerson Computer Science Department emers089 @ umn.edu University of Minnesota Computer Science Department Chris Heuer University of Minnesota heue0034 @ umn.edu ABSTRACT type of activity is evident in the differing backgrounds of Analysis of remote sensing data often involves the comparison or researchers, from human migration studies to geological programs combination of imagery collected at different times. Depending on and climate-change scientists. the temporal resolution, season, end-use, and other factors, this Because of the wide range of applications and approaches to this data can be difficult to deal with and integrate in a meaningful problem, classifying research into distinct subsets is not a simple fashion. This paper investigates several methods of combining and task. A number of possible classifiers and categorization schemes using multi-temporal raster data. Projects using a variety of these exist, including methodology, end-use, data acquisition and methods are studied and described. Each method is evaluated for source, data type, etc. We believe that while each scheme may its overall performance and usefulness based on a number of have its merits, methodology is the most logical and effective factors. In addition, potential ways to improve or combine classifier for this field. existing methodologies are proposed. 1.1 Novelty 1. INTRODUCTION At present there do not exist any literature surveys for this topic, When collecting raster data through satellite imaging or other despite its recent rise in importance and number of applications. remote sensing, a trade off occurs in the ground surface detail Surveys and comparisons do exist for object and vector-oriented available in different seasons. For example; the most data on spatio-temporal data , but such studies are outside the scope of vegetative cover can be obtained via infrared and near-infrared this investigation. Our work presents a diversity of raster-based sensing during leaf-on periods. Conversely, the highest level of approaches, including applications currently underway and imaging detail can be obtained during leaf-off periods, when proposed, and highlights the most important commonalities in vegetative cover does not obscure surface features such as roads, methodology and data structures. streams, and structures. In addition, seasonal or temporal We evaluate each source for efficiency, cross-application attributes of vegetative cover or other features may need to be portability, and usefulness. We also investigate possible compared, such as algal blooms, forest canopy changes, and improvements not currently identified in existing research, and seasonal socioeconomic activities. suggest possible combinations of methods which could lead to While comparison of temporally-separated images traditionally additional improvements. requires side-by-side examination, it is possible that composite multi-temporal imagery may provide the maximum amount of 1.2 Validation information. For example, a feature visible at one time (such as The categorization and classification of multi-temporal research algae blooms) may need to be viewed in context with features can be done in several ways. We show that while several such visible in other times (pollution events, storm runoff, etc). Our classifications are possible, the methodology-based classification aim is to investigate current research into processing, combining, appears most effective. An alternative classification scheme based and visualizing multi-temporal raster imagery for the purposes of on end-use is examined and shown to be less informative than the increased data content and context. methodology classification. The idea of combining raster aerial imagery in order to extract maximum useful data has recently attracted attention from several 2. CLASSIFICATION OF MULTI- directions. The diversity of approaches and applications for this TEMPORAL RASTER COMBINATION MEHODS Permission to make digital or hard copies of all or part of this work for Methodologies of the research projects illustrated here varied personal or classroom use is granted without fee provided that copies greatly, but can be generally classified into four types. Those are not made or distributed for profit or commercial advantage and that classes are defined as follows. copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, A. "Snapshot". This method presents raster data from multiple requires prior specific permission and/or a fee. times as a series of static images. This method was commonly used in early multi-temporal data sets, as it is the simplest and requires the least processing on the part of the GIS system. Data from different times can be stored as separate raster layers. Georeferencing is carried out by matching constant features coverage can be filled with estimations based on nearest temporal between raster sets, via ground truthing, or between rasters and neighbors. vector base maps. Interpretation, overlays, and visualization are D. "Abstracting". This end use involves abstracting or simplifying largely the responsibility of the user, although a few tools exist for multi-temporal data into a generalization about the feature or change-detection and linear image subtraction. features being studied. The data can be used to make statements B. "Temporal Averaging". In this approach, data from multiple about overall change trends, patterns can be derived, or statistical times is averaged to portray a baseline mean or median for the data can be produced that does not require detailed knowledge of entire time period being studied. Raster sets can be averaged the original data elements either on a pixel-by-pixel value level, or on a larger scale by As we show in section five, this classification scheme is too averaging the spatial coverage of like-valued regions. limiting in describing individual research projects. C. "Compositing". This method involves the combination of multi-temporal data sets for maximum imagery detail. Images 3. PROJECTS BY CLASSIFICATIONS from different times or seasons are combined and represented in a way that allows features visible at one time to be portrayed along 3.1 Snapshot with features which may obscure them in another time. This can Remote Sensing and GIS methods for dynamics studies in the be done by vectorizing one or more of the feature sets, by Caspian Sea Coastal Zone by E.A. Baldina and I.A Labutina.  animating multiple images, or by creating an new raster image to This research focuses on analysis of changing environmental portray combined data. conditions in Europe's Caspian Sea, specifically the Volga Delta D. "Visualization". In the most simple form, this can be an wetlands. This area has been designated as internationally animation of a spatial region where each frame is from a different important waterfowl habitat. The authors utilized aerial and sequential time. More complex visualizations can take existing satellite imagery from 1951, 1977-1981, 1989-1992, and 1996- data and predict future values, or can highlight and modify images 1999 to study seasonal and yearly variation in sea levels and to show temporal cause-and-effect relationships and dynamics not vegetation cover. obvious from a simple animation. The operations performed on multi-temporal data were mainly to co-reference imagery with varying resolution, both spatial and 2.1 Alternate Classification temporal. The more recent ERS SAR satellite data provided a We identified one other major classification scheme as most likely much finer temporal resolution (more frequent overflights), but to meet the needs of our readers. Out of the range of possible older aerial photography provided additional data on historical classifications, that of final end-use seems to be the second-most coastlines and land cover. Raster sets from different years and effective for multi-temporal data sets. This classification would capture sources had significant variation in angular distortion, divide up research based on its eventual use and application, spatial resolution, and coverage extent, which made ignoring methods or approaches used to reach the final product. georeferencing difficult. The changing sea levels in the area also Categories for this type of classification could include the complicated georeferencing, as there was not always a common following data uses: topography for the region. In addition, some data sets offered insufficient spatial coverage to allow identification of control A. "Tracking". At the basic level, multi-temporal data is mainly points for georeferencing. about tracking change over time. Whether it be feature attributes, feature location, or other changes, the basic metric being The authors overcame these problems by creating a common produced is a record of change. While such a record is often vector base map expressing the most constant and most easily useful by itself, it can also be used to produce more detailed identifiable geographic features of the study region. Raster images information such as the following examples. were then referenced from control points identifiable on both the rasters and the vector base. For the small-extent aerials, it was B. "Predicting". In this case, the goal is to show probable future found that combining historic surface topography maps with the values based on the history of previous values. A change over more recent vector base allowed control points to be identified in time in raster cell values or spatial coverage can be traced through small areas. Afterwards the small-extent aerials were stitched time to show patterns and dynamics, and can then be extrapolated together into a mosaic image. into the future based on known or inferred cause-and-effect relationships. For example, urban sprawl can be measured as the Once the common frame of reference and multi-temporal images rate of development spread from an urban source, and can be used had been integrated, it was found that historical maps predating to predict the loss of agricultural land surrounding a city. aerial imagery could also be added to the system. Maps from the late 19th and early 20th century were scanned into raster images C. "Completing". Similar to the "predicting" goal, this use and referenced into the common coordinate system. involves filling in the gaps for areas obscured or invisible at one time by inference from other time. As a trivial example, nighttime While the display of multi-temporal data as separate "snapshots" ground cover can be inferred from daytime ground cover (this is one of the simplest, this research illustrates how it is not always does not hold for all day/night attribute patterns, such as land use easy to create such snapshots and coordinate them into a common or socioeconomic activity). In a more realistic example, monsoon- and correct georeferencing system. season ground cover which is obscured by clouds can be derived from dry-season imagery of the same area. Gaps in temporal 3.2 Visualization different times, and can include "continue existence" (between GIS and Remote Sensing techniques for the assembly of a time A and time B), create, eliminate, destroy, and reincarnate. database characterizing the spatio-temporal character of the Additional operations deal with historically existing object state South Australian continental shelf environments by Brett A. transitions. Bryan, Martine Kinloch, and David B. Gerner.  Once this model has been defined, the author describes a set of The authors of this paper developed a GIS database for part of operators to support temporal zooming. This concept essentially Australia's near-coast ocean, in order to address what they saw as allows abstraction or articulation (increased detail) of an object's a lack of data for this biologically important region. Some of the history. For example, the change in regional identity of "USSR" main features studied were monthly and yearly water temperature, to "Former USSR" is an abstraction of many events that made up biological activity, and seasonal current patterns. Temporal the dissolution of the Soviet Union, and many resulting new (or resolution for each data set varied based on the sensor platforms reincarnated) regional entity identities. The first operator which used and the availability of sampling data from various satellites supports this type of zooming is "uncover", which returns prior and sea-surface observations. A spatial referencing system was states linked to a current state, and refines the level of detail to established based on location and the relatively constant sea expose past states of an object. "Extend" returns the following bottom topography. state of a current state, and can be used with uncover to move from a course-grained temporal view to a more detailed one. The The authors are very meticulous about outlining their data other two temporal operators are "Expand", which shows related collection, storage, and analysis methods. Details of precision sequences of an object's history, and "Collapse", which returns a control and error factors are carefully and systematically covered. less detailed sequence of an objects' history. This is important because several of the features being studied are observable only via second-hand effects or related factors. For While this research is not directly related to a specific field of example, biological activity is not directly measurable via raster inquiry, it is a unique method of visualizing multi-temporal data imagery, but the related quantity of chlorophyll a in the water which is applicable to a wide range of uses. The ability to change column has an affect on the color of the water surface. Similarly, from temporal views at different levels of detail can lead to better undersea temperatures were calculated based on observed surface understanding of the overall temporal dynamics of a data set, infrared data and current data, to predict water column mixing. while retaining all of the original data in a form that can be easily accessed and visualized along with the abstracted overview. The visualization method comes into play in the processing of chlorophyll a data. The researchers created animated images of yearly chlorophyll data in order to show the spatio-temporal 3.3 Temporal Averaging GIS and Remote Sensing techniques for the assembly of a dynamics of the feature set. An animation of sea-surface database characterizing the spatio-temporal character of the temperature between 1997 and 2002 was also created. Data South Australian continental shelf environments by Brett A. averaging performed on these sets is discussed further under the Bryan, Martine Kinloch, and David B. Gerner.  "Temporal Averaging" subsection below. As mentioned in the previous subsection, Bryan et al collected a large amount of ocean data via repeated remote sensing over Temporal Zooming by Kathleen Hornsby.  several years. The authors performed monthly averaging on sea- surface color data to produce yearly composites of cloud-free This paper deals specifically and directly with the issues of imagery. This allowed the authors to study the spread and change portraying and visualizing temporal data, and does not apply the in chlorophyll a, used as an indicator of biological activity. methodology to any specific use other than example cases. Hornsby's main idea is that temporal data can be displayed at Sea-surface temperature was also averaged before being processed varying levels of detail or resolution in the same way that spatial into a visualization (discussed above). This study draws it's data can be zoomed in and out to show different extents and temperature source data from the NASA AVHRR scanning resolutions. The concept of changing temporal granularity via radiometer. The visible and near-infrared bands are useful for zooming was first discussed in this work. cloud detection and the thermal infrared bands are used to measure surface temperature. To increase accuracy for sea-surface Hornsby develops a new data model and a series of operations to temperature, the authors processed raw data into 15-day averages, support this concept. The model allows descriptions of change for considering only valid non-cloud data for each cell's average identifiable spatial (or feature) objects at different times. value. The temperature data was derived from the infrared bands Operations defining object creation, destruction, "reincarnation", through an application of a linear function and correlation with splits, joins, and other possible behaviors of objects can be ground-truthing data from ships and ocean-observing buoys. expressed and stored in Hornsby's model. Examples of temporal events or operations include the creation of a nation-state, a split into multiple countries, and the possible destruction of a country Mapping the land cover of the forested area of Canada with as an independent entity (such as through absorption into another Landsat data by Mike Wulder.  country). Previous spatio-temporal data models had no way of storing such relationships and time-based status of objects. This paper presents a proposed large-scale land cover Hornsby's model stores such data as "Object Identity states". An classification program for assessing the forest resources of object can exist, be non-existing without history (never existed), Northern Canada. The author lays out the general spatial or can be non-existing with history (existed in the past). classification methods to be used by the program, including Operations describe transitions in the object state between standard K-means spatial clustering on a relatively small set of Comparison of Methods for Estimation of Kyoto Protocol interesting categories. While the methodology for temporal Products of Forests From Multitemporal Landsat by David G. classification is not fully explained, Wulder states that a mosaic of Goodenough, A.S. Bhogal, Hao Chen, and Andrew Dyk.  images combining leaf-on and leaf-off should be produced in This research aimed to calculate estimated forest harvesting for order to aid seasonal land-cover classification. He also proposes determining adherence to international environmental treaties. that ranging imagery over seasons and years will help avoid The authors used remote-sensing data from multiple years in erroneous or missing data caused by clouds or unseasonal snow imitation of the methods assumed to be used by nations cover. By averaging the set of images over time, the short-term monitoring each other. Initially, this research was done with effects of weather can be reduced, and a greater number of cells single-season leaf-on remote sensing images from three years of can be assigned near-true values. Landsat flights. In the most recent version, the authors combine multi-seasonal leaf-on and leaf-off images in order to separate mature deciduous trees from under story and 2nd-growth Spatio-Temporal Aggregates over Raster Image Data by Jiue vegetation (which would appear after logging). Zhang, Michael Gertz, and Demet Aksoy  The study co-referenced imagery into a common spatial area (a Zhang et al. propose that a continuous stream of raster data (such region of Alberta, Canada), using GIS techniques. Image sets as a live video or image stream transmitted by a remote sensor) were classified by systematic sampling and ground truthing from can be averaged into discrete time or duration-based values. government harvest maps, then by using spatial clustering on both While their main focus is on the summation and aggregation of the leaf-on and "leaf-all" (both leaf-on and leaf-off) data. It was such a data stream (see further details in next section), they also found that the combined leaf-all data sets were much more discuss applications which take averages based on the stream accurate when compared to traditional forest inventory methods, aggregation. For example, their method supports such queries as offering a ground-vegetation classification accuracy of 91-95%, an average ground-cover or surface temperature value for a region compared to 81-90% for leaf-on only data sets. during a certain interval of time. As the ability to average is a relatively minor feature based upon the creation of aggregate sets, we further discuss this paper's features in the Compositing Photogrammetric and GIS techniques for the development of section. vegetation databases of mountainous areas: Great Smokey Mountains National Park. by Roy Welch, Marguerite Madden, 3.4 Compositing and Thomas Jordan.  Integration of multi-seasonal remotely-sensed images for improved landuse classification of a hilly watershed using While the primary focus of this paper is the combination of geographical information systems by J. Adinarayana and N. Rama techniques used to orthorectify imagery in the case of severe Krishna.  elevation changes without the aid of ground truthing, the authors also set forth some interesting compositing methods for multi- Adinarayana and Krishna deal with the issue of partially-obscured seasonal raster data. The study focuses on land cover and temporal raster data. Their work aims at developing landuse vegetation classification for a mountainous and largely classifications for a region of India affected by seasonal monsoon inaccessible national park, in which over story (tall trees) and rains. These rains and the associated cloud cover cause surface under story (brush and shrubs) both play a role in determining the features to be obscured throughout most of the "Kharif" potential fire hazard of forest stands. The researchers used aerial (Monsoon) season when some important cultivation takes place. photos from leaf-on and leaf-off periods to develop a detailed In contrast, relatively clear data can be obtained during the "Rabi" database of vegetation cover, which allows dynamic calculation of (post-Monsoon) season, but this season alone does not provide feature attributes based on multi-seasonal data and interactive data complete agricultural data. input. For example, once an area's over story and under story vegetation classes have been recorded, a user can enter the current The authors developed a set of classification rules for assigning rainfall conditions (wet, drought, etc), and the database can return values to land cover in the region, based on a supervised an estimation of fire danger and fuel conditions for the chosen classification raster set from the rainy Kharif season, the raster set area. for the cloud-free Rabi season, and other raster feature sets for soil properties. An expert system was designed to use these Classification was made based on leaf-on aerial photography from classification rules, in order to derive a landuse map for the Fall of 1997 and 1998, and leaf-off photography from early regions obscured by cloud and shadow during the monsoons. Spring of 1998. A combination of digital and manual hard-copy Training data for the expert system was collected via ground image analysis allowed these sets to be coreferecned and truthing for eight classes of land cover, including forest, pastures, converted into vector coverage zones. The resulting products open land, several types of agriculture, and cloudy or shadowed included the combined understory/overstory classifications cells. A two-dimensional cross table of knowledge-based rules mentioned before, as well as seasonal snapshots of separate under was developed, such as "(If Kharif + Rabi = vegetation, Drainage and over story conditions which could be incorporated into other Density = low, Soil = Chromusterets, and Slope =< 5 degrees, analyses. Such techniques could easily be applied to pure raster Then Class = Cropped pasture)". Various other logical cell data sets, although complex operations may take additional combinations of Kharif and Rabi seasonal data were used to time and resources than when working with vector conversions. produce both an improved landuse map for the obscured areas, and an improved overall multi-season GIS dataset. A Web-based browsing and spatial analysis system for regional wide range of data for a multitude of purposes, or is it narrowly natural resource analysis and mapping by Ranga Raju Vatsavai, application and topic-specific? Thomas E. Burk, B. Tyler Wilson, and Shashi Shekhar.  Baldina and Labutina's  Caspian Sea dynamics study is very This paper typifies the standard change detection analysis method efficient, in that the only processing done is georeferencing raster of temporal raster processing. Subsequent raster sets in an archive sets from different times into a common spatial reference frame. can have cell-by-cell subtraction performed on them to show the This snapshot-type processing requires few computational amount and patterns of change over time. The authors of this resources. The automation potential depends largely on the quality paper point out that such operations are very resource-intensive of data collected. Skewed or distorted images are difficult for when performed on a GIS server, and are bandwidth-intensive algorithmic methods to match, and are more suited to human when performed on clients which must download each raster from analysis. If archived raster sets from sources with varying the server. They propose that a pre-processing or "pre-realization" resolution and distortion are being processed, then the potential of common spatial and temporal analysis functions such as for automation is low with today's technology. The result is change-detection can reduce the overall per-request server-side immediately useful in that it provides side-by-side comparisons of and client network resource usage. Since preprocessing of all feature change over time, but the potential exists for further possible change-detection combinations would be prohibitive for processing to enrich the final product. Accuracy is high when large data sets, the authors propose a set of criteria for feature points are collocated on a vector base map, especially determining if pre-realization is beneficial. when large scale but small-extent maps are combined to provide a greater number of reference points. This simple method is applicable to a wide range of projects and is relatively Spatio-Temporal Aggregates over Raster Image Data by Jiue application-unspecific. On the whole, this method is simple and Zhang, Michael Gertz, and Demet Aksoy  fast, but is somewhat outdated in modern use and provides limited initial results. As mentioned in the section on spatio-temporal averaging, Zhang and co's work is based on the ability to combine a continuous Adinarayana and Krishna's  classification of hilly watersheds stream of raster data into a single composite or sum. For a given has a medium efficiency, as it requires extensive rule-based time, all previous data contributing to the current situation can be processing by an expert system. When initial setup and training combined into a single summary set. This is useful for analyzing time for the expert system is taken into account, overall efficiency potentially unbounded sets (where data is still coming in), for is rather low. Accuracy was reported to be high based on the identifying maximums of coverage or attribute values, and for authors' own estimations and ground-truthing data. The results of reducing huge amounts of raw data (such as remote sensing video the multi-seasonal combined vegetation dataset were immediately or imagery) into a more compact storage format. Problems can useful, providing new data about a time period not directly arise in existing approaches to this problem, since missing pixels, measurable due to atmospheric conditions. Cross-platform breaks in the stream, or not-yet-scanned imagery may skew the applicability seems very promising, as an expert system could be value of the desired region. The paper solves this by computing a trained to take any set of temporal rasters and combine them into a number of answers for segments of the query region in which new raster with a known or trained rule set. Automation is already some image data only contributes partially. This segmentation a major factor, although the system's training takes a good deal of assures that each object (image) only provides data for a particular human input. Overall this is a very good method for processing segment. The segmentation is part of a two-part algorithm, the large amounts of data and producing accurate renderings of second component of which computes the aggregate value for hidden features, but takes more time to set up and perform than each section using a BA-tree approach. This allows for simplified other methods. summation-based raster cell output such as counts, sums, and Bryan, Kinloch and Gerner's  animated visualization sets are averages (see previous section). quite useful to the end user and provide a highly-understandable abstraction of feature change over time which could be applied to 4. COMPARISON almost any investigation of change over time. The accuracy of We compare the effectiveness of each method proposed in the their method depends upon the accuracy of input data, the data reviewed papers based on a number of metrics. First, the used in this particular research seemed to be mainly second-hand efficiency of the method at reaching an end-use product should be effects of invisible biological and climatological factors. high, that is, the method should not take undue time in processing Automation potential and efficiency are both high, as the creation and outputting it's final product. Second, the ability to automate of simple and moderately complex animations from multiple the method is investigated. If the method in question is wholly or raster frames is a very simple operation. partially manual, can it be translated into an algorithmic Vatsavai et al's  proposals for improving resource use in multi- operation? Thirdly, how applicable is the result of the method to temporal GIS systems are very good for improving efficiency. The what people need? Is the result immediately useful in its field of method is applicable to any application which utilizes change- inquiry, or does it require further post-processing before a detection as an analysis operation, but the final product may not conclusion can be drawn from the data? Fourth, how accurate are be immediately useful without further processing. Automation is the results? What degree of accuracy does the method or proposed supported through rules for data set preprocessing selection. approach offer when compared to traditional ground truthing or Innate accuracy is high, as the data is not altered beyond simple other previous work? Finally, how well does the method apply to raster subtraction. other fields of investigation? Is it something that can be used on a Wulder's  proposal to remove short-term imagery anomalies via Project Efficiency Automation Usefulness Accuracy Cross- long-term averaging shows promise in the area of cross-platform field applicability. However, he does not detail any algorithmic methods or discuss the potential efficiency of such an approach, dependent so the effectiveness in these areas is questionable. Accuracy and value to the user depends on the degree of trust placed in the data,  Medium Yes immediate High Yes a large number of cloudy or snowy datasets could degrade the overall average value away from the ideal.  High Yes immediate data- Yes dependent Welch, Madden, and Jordan's  national park vegetation database is fairly inefficient in it's data collection and integration  High Yes after High Yes methods, and does not lend itself well to automation. These additional failings are due largely to the inaccessibility of the study region processing and the difficulty of conducting georeferencing on a uniformly tree-covered image. It is possible that a different data set would  Unknown Unknown data- data- Yes not experience the same issues. Their user-friendly situational- dependent dependent dependant database provides very useful data output, and could be applied to a wide range of uses with minor modification.  Low No data- High Yes Accuracy was shown to be acceptably high despite the lack of dependent ground-truthing availability.  High Yes immediate High Yes Goodenough et al.  show that their method for forest product use determination is more efficient that single-season multi-year  Low Some data- data- Yes data analysis, and is in fact nearly as accurate as traditional dependent dependent ground-based resource-estimation studies. The output produced is immediately useful for determining statistical data on forestry  High Yes data- High Some harvests. Analysis is already done largely by automated systems, dependent and could be applied to other studies which use multi-seasonal data for enhanced ground-cover classification. Table 1. Hornsby's  Temporal Zooming concept is very useful for any Table 1 shows a cross-project comparison of the 5 evaluation large or complex temporal data set in which different levels of metrics. From this table, it is apparent that the most difference detail can enhance understanding. The initial setup and between methods lies in their degree of usefulness (immediate vs assignment of object states and transitions are likely to be slow post-processing), and accuracy (high vs data-dependent). Every and resource-demanding. Accuracy is less of a factor in this method seemed to have potential for wide application, most were methodology, as data is not actually altered, but the accuracy of at least as efficient as existing methods, and more than half the visualization should be high to avoid misinterpretation. As allowed some degree of automation. there is no real-world implementation and only theoretical examples given by the author, the potential accuracy and end-use 5. VALIDATION functionality are not immediately clear. We feel that the Methodology-based classification system is the best choice for this survey. The end-use-based classification Zhang et al's.  Spatio-temporal aggregation approach is scheme which we present as an alternative falls short in several applicable mainly to streaming raster data, but could potentially areas. Firstly, data is often in a specific format and only certain be applied to historical raster collections in order to observe tools are available to work with it. By choosing a system based on patterns, averages, or the results of change over time. The end-use, the techniques utilized to reach that goal may be proposed method of segmenting data before aggregating was incompatible with available data. It is true that in cases where the shown to be highly efficient, contributing no significant delay to research begins from scratch with no data, or with cross-platform the overall aggregation computations used in previous methods.In raw data, choosing a system based on end-use may be convenient. addition, the accuracy of data aggregates returned by this method However, our second point is that choosing a system based on is higher than that of previous methods, as the partitioning end-use can also limit functionality of the system. Generally, a approach reduces the skewing effects of neighbors, outliers, and user would have to choose a single system that could complete missing data. Automation takes the form of a two-step algorithm their primary goal. If the user wanted to do other things with their using tree-based summation. Depending on the desired end- data, or if the data and end products were to be shared between product, the output is either immediately useful (such as an users, a proprietary or specialized system based on end-use may averaged value for an attribute), or may require some additional not be able to handle a wider range of operations. processing. As such, we feel that a classification system based solely on end- use does not meet the requirements needed to evaluate and Project Efficiency Automation Usefulness Accuracy Cross- categorize research projects. Users attempting to tailor a system to field their needs would be limited to only a few choices, and may not be presented with the full range of possible options when using  High data- immediate High Yes such a classification system. In addition, readers and reviewers using such a system to classify research will be forced to pigeon- hole projects into one or more categories, which is inappropriate most room for improvement. Depending on the data set and the for widely-applicable research such as Hornsby's Temporal desired product, it may be useful to create a temporal averaging or Zooming . abstraction of the snapshot data. It may also be beneficial to combine snapshots into composites to show feature sets common Our chosen classification scheme allows a higher degree of to multiple times or conditions. In the specific case of this abstraction when comparing papers, so that multiple projects can research, users could possibly have benefited from an averaged potentially be accurately classified in multiple ways. By using plot of sea-level conditions as compared to the average health and methodology as a classifier, we believe that we can effectively extent of vegetation. show multiple types of research in a wide range of end uses, as well as a range of cross-method approaches. For example, we While Bryan, Kinloch and Gerner's  visualization method does show how Bryan, Kinloch, and Gerner  use both Visualization an adequate job of portraying a long temporal span, it may be and Temporal averaging to meet the needs of their research. We useful to combine this with Hornsby's  zooming visualization also discuss abstracted uses of each approach in ways that could or even Zhang's aggregate functions. Large-scale changes or increase the potential range of uses for each method. We feel that aggregates of periodic changes in sea conditions deemed this is the most useful categorization for researchers trying to significant to the research could be used as low-detail sets, with combine raster spatio-temporal data. By offering a wider and more zooming available to produce more detailed histories on open classification, we hope that readers will have a better idea of command. the choices available to meet their needs. 7. REFERENCES 6. CONCLUSION  Baldina and Labutina, (2002) “Remote sensing and GIS 6.1 Common Elements methods use for dynamics studies in the Caspian Sea coastal While commonalities between approaches in each category should zone” in Geoscience and Remote Sensing Symposium, 2002. be obvious, a few projects deserve having their similarities IGARSS '02. IEEE International. Volume 5. Pages 2838- highlighted. All three papers classified as "Temporal Averaging" 2840 attempt to reduce the effect of imaging outliers on the eventual  Adinarayana and Krishna. (1996) “Integration of multi- raster cell assignments. Both Wulder and Bryan et al. deal with seasonal remotely-sensed images for improved landuse the issues of "raw" weather imagery, in which clouds, cloud classification of a hilly watershed using geographical shadow, rain, and other "null-value" or "obscured value" cells information systems” In International Journal of Remote appear. In the Compositing class, Goodenough et al , Sensing, Volume 17, Issue 9, Pages 1679 – 1688 Adinarayana and Krishna , and Welch et al  are all working  Bryan, Kinloch, and Gerner (2003) “GIS and Remote towards improving data context from dissimilar sets acquired in Sensing techniques for the assembly of a database different seasons. The creation of a "all-season" classifier for land characterizing the spatio-temporal character of the South cover based on leaf-on and leaf-off requires not only linear Australian continental shelf environments” In Proceedings of combinations of pixels, but often complex rule-based analysis of Coastal GIS 2003 7 July 2003, University of the effects of different types and families of vegetation visible at Wollongong,Wollongong, Australia. different times.  Ranga Raju Vatsavai, Thomas E. Burk, B. Tyler Wilson, Despite the fact that we have placed it into a separate class, Shashi Shekhar. (2000) “A Web-based browsing and spatial Wulder's  paper shows some marked similarities to the works analysis system for regional natural resource analysis and by Goodenough et a  and Welch et al.  Each of these mapping”, In Proceedings of the 8th ACM international methods are designed to handle combinations of leaf-on/leaf-off symposium on Advances in geographic information systems, raster data, but the averaging approach used by Wulder  is Pages 95-101. closer to that of Bryan et al. .  Wulder, M. (2002) "Mapping the land cover of the forested area of Canada with Landsat data", In Geoscience and 6.2 Potential Areas for Improvement Remote Sensing Symposium, Volume 3, Pages 1303 - 1306. We feel that the methods described in several of these papers  Welch, R. M. Madden, and T Jordan. (2002). could be enhanced by combination with other methods "Photogrammetric and GIS techniques for the development investigated. For example, the simplified change-detection of vegetation databases of mountainous areas: Great Smokey algorithm discussed by Vatsavai et al  might be improved Mountains National Park". In ISPRS Journal of through visualization. Hornsby's Temporal Zooming could in Photogrammetry and Remote Sensing. Volume 57, Pages 53- fact be used server-side to preprocess common views for a 68. temporal sequence, thus further improving the efficiency of query responses. Change detection products could be replaced by  Goodenough, D. G., A.S. Bhogal, H. Chen, and A. Dyk. animations which would allow users to directly view change over (2001). "Comparison of Methods for Estimation of Kyoto time, and may provide details otherwise overlooked in a single Protocol Products of Forests From Multitemporal Landsat". raster change map. In Geoscience and Remote Sensing Symposium, 2001. IEEE International.Volume 2, Pages 764 - 767 Baldina and Labutina's  snapshot-based approach could also benefit from combination with one or both of the visualization  Hornsby, K. (2001). "Temporal Zooming". In Transactions methods investigated here. As the snapshot technique is one of the in GIS Volume 5, Issue 3, Pages 255-272 simplest ways to deal with multi-temporal data, it has perhaps the  Jeong, S-H, A. Fernandex, N. W. Paton, and T. Griffiths  Zhang, J. , M. Getz, and D. Aksoy (2004). "Spatio-Temporal (2005). "An Experimental Performance Evaluation of Spatio- Aggregates over Raster Image Data" In Proceedings of the Temporal Join Strategies" In Transactions in GIS Volume 9 12th annual ACM international workshop on Geographic Issue 2. Pages 129-156 information systems. Pages 39-46.
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