VOXEL-BASED ANALYSIS AND VISUALIZATION OF RAINFALL DATA Shalini Venkataraman Staff Researcher, Scientific Visualization Center for Computation and Technology Louisiana State University [email protected] Kwabena O. Asante, PhD Environmental Scientist SAIC USGS EROS 47914 252nd Street Sioux Falls, SD 57198 [email protected] ABSTRACT The remote sensing of hydrologic variables such as rainfall, soil moisture and streamflow is becoming increasingly more important as we strive to improve our understanding of the earth’s climate. Because these variables change more rapidly than other land cover characteristics, a high frequency of image acquisition is required. This in turn results in a large volume of data that must be compared to detect changes and trends in moisture flux. Traditional remote sensing tools are not well suited to this analysis because of the large number of scenes involved. In this paper, we explore the use of 3D geovisualization techniques to analyze a large time series of satellite imagery. We take advantage of the 3 dimensional voxel data structure to transform the image collection in a space-time cube. We employ 3D voxel-based visualization methods also known as volume visualization to perform feature analysis, change detection and segmentation of this large spatio-temporal dataset. We demonstrate that the use of the space- time cube offers even those hydrometeorologists that prefer to look at 2D maps and graphs with improved access to the time series data through software tools for dynamic graphing and slicing. In a practical application, we use the space-time cube to present the evolution of the annual rainfall cycle over Africa as a single visual image. INTRODUCTION Hydrometeorologists involved in the study of hydrologic variables such as rainfall, soil moisture and streamflow have traditionally relied on point measurements to characterize these parameters. Two-dimensional graphs have remained the main tools for characterizing the temporal variations of these parameters, while surface fitting techniques such as kriging, spline and inverse distance weight interpolation have been relied on to produce spatially uniform representations of the parameters. Under the operational geostationary weather satellite systems GOES and Meteosat, hydrometeorological events are monitored on a near real time basis. The primary objective of these operational meteorological systems is the provision of timely information to support early warning and emergency operations. Accuracy and internal consistency are often sacrificed in favor of timeliness of data delivery. As a result, little attention has been paid to temporal patterns in the datasets. The Tropical Rainfall Measurement Mission (TRMM) marked the first research mission dedicated to the remote sensing of moisture fluxes. The Gravity Recovery and Climate Experiment (GRACE), the Global Precipitation Measurement (GPM) mission, the soil moisture mission (HYDROS) and the proposed Water Elevation Recovery (WaTER) mission are all expected to produce internally consistent time series datasets from which the characterization of temporal patterns will assume greater importance. Existing analytical tools used within the hydrologic sciences are not well suited to handling such time series analysis. The use of Geographic Information System (GIS) and remote sensing software for analyzing spatially distributed datasets is just beginning to become routine within the field, and most hydrologists still rely on spreadsheets for time series analysis. GIS software has evolved out of the work of the geographic science community and is therefore well equipped for spatial analysis tasks but not temporal analysis. Time series analysis of land cover change occurring over periods of several years remains the main thrust in this field. Moisture fluxes, Pecora 16 “Global Priorities in Land Remote Sensing” October 23 – 27, 2005 * Sioux Falls, South Dakota on the other hand evolve at time scales on the order of several minutes to days, resulting in a huge volume of data with rapidly changing signatures which pose a major challenge to existing GIS and remote sensing software. By contrast, the field of scientific visualization has always dealt with large datasets mostly occurring in two fields: imaging and computational science. 3D imaging devices continue to increase the resolution of their sampled volumes with the current generation approaching a giga-voxel. Similarly, computational science such as fluid dynamics continues to increase the mesh resolution for large scale simulations thereby increasing the size of data to be visualized. This has led to the development of efficient algorithms to read, analyze and visualize very large datasets in real-time. Specifically, a class of techniques known as volume visualization has been proven very effective to understand 3D data as is without resorting to intermediate polygonal representation. Within the earth sciences, 3D visualization tools have long been employed in the geological science to visualize subsurface phenomena such as reservoir formations and aquifers. These technologies have recently been adopted by the meteorological community for studying the vertical profile of atmospheric parameters such as temperature and atmospheric moisture. However, there has been very little work in the application of volume visualization techniques for spatio-temporal analysis of time-varying maps. In this paper, we present a novel application of volume visualization algorithms to analyze time-varying satellite precipitation maps. We “stack” the time varying 2D images to produce a 3D volume known as space-time cube. This data cube is then rendered to a 2D image using a volume rendering technique known as raycasting. This technique allows us to display spatial dynamics as well as changes occurring over time, simultaneously, and to perform data analysis functions on the entire data cube. We present a background study of current methods in spatio- temporal analysis and representation. Subsequently, we describe the volume rendering algorithm used to load and display data in the space-time cube. This is followed a presentation of various tools available for manipulating and analyzing the space-time cube. We conclude with a sample application visualizing the annual rainfall cycle over Africa. BACKGROUND WORK Time series of hydrometeorological events can be viewed from two perspectives. Events can be viewed as they evolve through a fixed frame of reference, referred to as the Eularian view of motion. It is the most commonly used to monitor the progress of an event as it moves through a fixed Cartesian space or to study the passage of events over a fixed location. Examples of events viewed from the Eularian perspective include the distribution of rainfall events at a rain gauge or a trace of the track of a cyclone on a geographic map. Conversely, the Lagrangian view of motion studies the evolution of the event through a frame of reference that moves with the event. The use of a dynamic frame of reference shifts the focus away from tracking the location of the event to tracking the relative position and condition of particles within the moving event. In hydrometeorology, the Lagrangian view of motion is most commonly used to study the condition of rainfall and wind fields around a cyclone or dispersion of chemical plumes in a fluid. By combining a time series of spatially distributed grids in a space-time cube, we enable the user to track the evolution of events in time both in terms of the physical location of the event and the relative condition of particles within the moving event. The space-time cube can consequently be viewed as combining the Eularian and Lagrangian perspective of an event into a single picture. Within hydrometeorology, the most common approach for combining time and space is the Hovmuller diagram. The diagram is a plot showing the variations in intensity of a parameter with changing latitude (or longitude) on one axis and time on the other. It is most commonly used to study climate phenomena that move north and south during the course of a year. Combining time and space in this manner provides the some insight into events that move orthogonally along the Cartesian axes. However, Hovmuller diagrams are of limited utility for events that move at oblique angles to the lines of latitude and longitude, and they do not provide much information of the shape of the event along its path of motion. Modern Geographic Information Systems (GIS) provide rich facilities to interact and visualize data on maps in two or even three dimensions. In GIS, the temporal data is commonly visualized by means of animations, which represent changes in the data. For example, the scientific visualization studio at the NASA Goddard Space Flight Center uses image animations to display TRMM rainfall maps that are generated several times a day. By using animations, the evolution of time series data produced at a high temporal frequency can be conveyed efficiently. However, detailed data analysis is hardly possible when using animations, and users are required to rely on their memory of the previous scenes of the animation to construct a mental image of the evolution of the event. What is required is a medium that enables all slices of the animation to be frozen in a three dimensional space. Pecora 16 “Global Priorities in Land Remote Sensing” October 23 – 27, 2005 * Sioux Falls, South Dakota According to Hedley (1999), Hagerstrand first introduced the space-time cube during the late 1960’s. In its basic appearance, the cube has on its base a representation of the geography (along the x- and y-axis), while the cube’s height represents time (z-axis). However, when the concept was introduced the options to create the graphics were limited to manual methods and the user could only experience one view created by the draftperson. Today, software and graphics have been developed where it is possible to view, manipulate and query this data in real-time. Kraak (2003) explains this approach from a geovisualization perspective specifically in analysis of multi-variate and multi-resolution discrete event data such as path of buses in time using GPS.