SolidEarth: A
new Digital Earth system for the modeling and visualization of
the whole Earth space
Zhu Liangfeng, Sun Jianzhong, Li
Chang-ling, Zhang Bing, 2014. SolidEarth: A new Digital Earth system for the
modeling and visualization of the whole Earth space. Frontiers of Earth Science, 8(4): 524-539.
Downloads:
http://dx.doi.org/10.1007/s11707-014-0438-7
http://www.visualearth.org/myweb/paper/2014_FESCI_01.pdf
Abstract: Although many
of the first-generation Digital Earth systems have proven to be quite useful
for the modeling
and visualization of geospatial objects relevant to the Earth’s surface and
near-surface, they were not
designed for the purpose of modeling and application in geological or
atmospheric space. There is a pressing need for a new Digital Earth system that
can process geospatial information with
full dimensionality. In this
paper, we developed a new Digital Earth system, termed
SolidEarth, as an alternative virtual globe for the modeling and visualization
of the whole Earth space including its surface, interior and exterior space.
SolidEarth consists of
four functional components: modeling in geographical space, modeling in
geological space, modeling in atmospheric space, and, integrated visualization
and analysis. SolidEarth has a comprehensive treatment to the third spatial
dimension and a series of sophisticated 3D spatial analysis functions. Therefore, it is
well-suited to the volumetric representation and visual analysis of the inner/outer spheres in the Earth
space. SolidEarth can be widely used in a number of fields
such as geoscience research and education, construction of Digital Earth
applications and other professional practice of Earth science.
Keywords:
Digital Earth; Earth space; full dimensionality; visualization
In January 1998, the idea of a Digital Earth was first formally proposed by former US vice-president
Al Gore at the California Science Center, and a vision of Digital Earth as a
computer-based, multi-resolution, and three-dimensional (3D) representation of
the entire Earth was also articulated at that time (Gore 1999). Since then,
much impressive progress has been made in basic theories, implementing
techniques and building applications of Digital Earth all over the world. To
support the development of the Digital Earth, a series of sophisticated and
powerful virtual globes, such as Google Earth, NASA’s WorldWind, Microsoft’s
Bing Maps, ESRI’s ArcGIS Explorer, Wuhan University’s GeoGlobe, the Chinese
Academy of Sciences Digital Earth Prototype System, Unidata’s Integrated Data
Viewer, Digitnext’s VirtualGeo and other free geo-browsers (Goodchild et al. 2012), have been emerged,
subsequently evoked world-wide interest and entered the public consciousness
(Butler 2006; Craglia et al. 2008; Bailey and Chen 2011; Guo 2012). As the representatives
of the first-generation
Digital Earth system, these virtual
globes not only offer users the capability to image, analyze, synthesize, model and
interpret geospatial
objects and spatial phenomena on different spatial aggregation, but also
possess the ability to enhance science by providing reliable platforms for exploring, discovering, analyzing,
exchanging and sharing geospatial information in scientific research and
pedagogy (Butler 2006; De Paor and Whitmeyer 2011; Martínez-Graña et al. 2013; Wang et al. 2013). Nowadays,
Digital Earth systems are important and everyday tools used by scientists,
educators, government officials and the general public to conduct research,
exchange ideas and share knowledge with a global
perspective in a natural and intuitive way (Yang et al. 2010; Guo 2012; Yu and Gong 2012; Zhu et al. 2014).
The first-generation Digital Earth systems, such as the Google Earth
virtual globe, focus on the access, display, analysis and service of geospatial
information relevant to the Earth’s surface and near-surface (Butler 2006; Craglia et al. 2008; Craglia et al. 2012). They
can help users to process data with better resolution and to extract
information existing in the
geographical space. Therefore, they are particularly useful for geography that
regards geographical entities as research objects. While the first-generation Digital Earth systems have the potential to extend to
nearly all fields of the Earth sciences, the use of these existing
virtual globes
in some specific Earth science subjects (such as oceanography, atmospheric
science, geology and geophysics) and multidisciplinary synthetically research
is encountered
with some impediments. Several experiments have highlighted a
number of shortcomings and some serious limitations when using these existing
virtual globes (Bernardin et al. 2011; De Paor and Whitmeyer 2011; Goodchild 2012). Two critical problems that scientists may encounter when trying to
use the first-generation
Digital Earth systems are listed as follows.
One problem with
existing Digital Earth software systems is their inability to represent the
whole Earth space in 3D comprehensively and clearly. The current virtual globes
are based on a space division of the Earth’s surface, which is tiled seamlessly by
a series of grids with different scales and can be subdivided into arbitrarily
fine grids (Gore 1999; Butler 2006; Goodchild 2008; Bernardin et al. 2011). Users can import geospatial data, like maps, images and 3D ground object
models, and drape them over the corresponding underlying grids. Essentially, this subdivision scheme is a 2D/2.5D division due to the space division only concerns to the surface of the
Earth,
while has nothing to do
with the third spatial dimension extending
above and below the Earth’s surface. Although this global representation
is ideally suited for the modeling, visualization and analysis of geospatial objects existing in
geographical space, it is limited by its defect in spatial dimensions as it
cannot represent the aboveground and underground space of the Earth. Thus, it
is not appropriate in cases where real-3D modeling and analysis of geospatial
objects/phenomena/processes within atmospheric and geological space are required.
A second problem is
that most current Digital Earth systems generally fall
short of advanced functions in 3D visualization and spatial analysis
for geospatial objects. The
first-generation
Digital Earth systems were designed for the
purpose of modeling, visualizing and analyzing geographic objects that can
be draped over the solid Earth terrain model (De Paor and Whitmeyer 2011). Using
elegant engineering (such as multi-scale representation, self-adaptive
visualization, progressive transmission, and clever server-side data caching
techniques), current
virtual globes can effectively transmit and vividly visualize some specific
geospatial data, like 2D vector maps, 2D raster images, 2.5D digital elevation
models and 3D vector models, over the Internet (Butler 2006; Craglia et al. 2008). However, they are not able to offer existing
tools or built-in functions to
transmit, render and visualize 3D volumetric
data automatically and
seamlessly, especially when the data sets are in large volume. High-quality
volume visualization is important and particularly useful to Earth scientists
of all disciplines due to the volumetric data is widely used in
the modeling and analysis of the physical, chemical and other properties within
atmospheric and geological space. More importantly, the first-generation Digital Earth systems cannot provide professional
volumetric/structural analytic tools to support true 3D scientific analysis
directly on 3D volumetric models (Shen et al.
2013). Thus, it is
either hard or impossible to visualize and analyze the spatial and temporal
relationships/correlations
between geographical, geological and atmospheric objects.
With the
implementation of a variety of global earth
observation programs, especially Earth deep exploration
programs since the 1970s, scientists began to conveniently
gather large quantities of geospatial data to imagine the
three-dimensional structure and composition of the Earth (Dong et al. 2011). Nowadays, the
scope of human cognition and activity has been
extended
to the entire Earth space including the Earth’s subsurface and atmosphere, as
well as the Earth’s surface. There are increasing demands for an integrated interpreting,
modeling, visualizing and analyzing of the interior and exterior
space of the entire Earth. However, the
current Digital Earth systems have limitations when they come to
mapping and modeling geological and atmospheric features. The increasing
pressure of achieving a comprehensive and complete understanding of the whole
Earth space has created a requirement for extending the first-generation Digital Earth system into the next generation that can process 3D geospatial entities and geo-phenomena with a coherent
representation, management, modeling, visualization, analysis and application.
It is an essential task to develop a new Digital
Earth system with full spatial dimensionality and efficient geospatial
analysis functions.
To keep up with such
scientific demands, we designed and developed a new Digital Earth
application, termed SolidEarth, to
overcome the above-mentioned limitations. SolidEarth integrates
visualization and analysis methods of high-resolution data, like
images, DEMs, 3D vector models and 3D volumetric models, over large spatial
extents with global GIS techniques. SolidEarth is a new Digital Earth system that allows visualizing and analyzing the exterior/interior space
of the Earth at different levels. It provides more than just models existing in the Earth’s surface
with conventional
functions that enable fast access, visualization and analysis of the global
images, terrains and other multi-dimensional
vector/raster data, it offers users the exclusive
capability to model, visualize, locate, navigate and analyze the exterior and
interior space of the Earth. It could dramatically improve the efficiency of
the first-generation Digital Earth system, and can be widely used in a number of
fields such as geo-scientific research and education, construction of Digital
Earth application system and other professional practice of Earth science.
The development of SolidEarth is driven by the huge
interests and needs drawn from relevant disciplines like geology, geophysics,
meteorology and
oceanography(Bailey and Chen 2011; De Paor and Whitmeyer 2011; Dong et al. 2011; Yu et al. 2012). Compared to the previous Digital Earth systems, the
most important feature of SolidEarth is it provides more abundant, sophisticated,
powerful and professional functions as specialized as required by geoscience
researchers. More specifically, SolidEarth must support the following:
(1) Reconstruction and visualization of
3D structure models that give the boundaries between the different defined
earth spheres and 3D property models for the spatial distribution of the
physical, chemical and other properties within the exterior and interior space
of the Earth.
(2) Spatial analysis for geometrical
structures and property parameters of geological objects hidden behind the
Earth’s surface. It should provide users with a series of true 3D analytic
tools, such as searching, querying, freely roaming and arbitrary incision
directly on 3D solid models.
(3) Multi-resolution representation,
fast network transmission and self-adaptive visualization of large-scaled 3D
geospatial information, which mainly consists of 3D volumetric data, on the
Internet.
To keep up with above demands, we designed
and developed SolidEarth
cooperating with SinoProbe Group (Dong et al. 2011), aiming to establish a new Digital Earth system for the integrative
representation, modeling, visualization and analysis of the whole Earth space.
In this system, SolidEarth should not only provide
computer models to reflect the actual conditions of earth spheres, but also
provide a specialized scientific platform to enable studies, communication and
display of earth sciences and relevant applications. More importantly, through
modeling, visualization and analysis of geospatial data from a variety of global earth
observation programs, SolidEarth should work to promote the socialization and
popularization of scientific advancements in explorations of the Earth space,
especially the Earth’s deep interior.
These goals are implemented within the four
components that are discussed in the following sections: first, a novel
geospatial data model is developed to describe and represent multi-source geospatial
information with full dimensionality. Second, the general modeling procedures
for the reconstruction of 3D structure and property models in the Earth space
are presented respectively. Third, a web-based data transmission and
visualization framework is proposed. And finally, a prototype system is developed to implement these functions.
In 3D space, the Earth has a distinct feature of sphere structure
(Fowler 2005). With the Earth’s surface as a boundary, the Earth space is
divided into two parts: one is the outer sphere and the other is inner. Both of
those two parts have significant differences in structures and properties, as
well as processes occurring in their space. The outer sphere, also termed the
atmospheric space, extends from the Earth’s surface to the Karman line, with
approximate thickness of 100 kilometers. The inner space, also termed the
geological space, refers to the solid portion beneath the Earth’s surface.
Especially, in order to highlight the significance of the surface and
near-surface space, geoscientists often extract the Earth’s surface and near-surface
from the border between the outer and the inner spheres, and investigate it as the geographical space. Therefore,
the Earth’s space not-strictly comprises of three subspaces from the top to the
bottom: the atmospheric space, the geographical space, and the geological
space. Each subspace
has different extent, characteristics, objects, as well as geospatial data and
dimensionality features.
The atmospheric space is the research field of
atmospheric science. Geography mainly focuses on spatial entities/phenomena
existing in the geographical space, as well as the human, social and economic
information. Geology and geophysics are concerned with geological objects/phenomena within the geological space. The
geographical space is intersected with the atmospheric and geological space,
and the endogenous and exogenous relief-forming processes are simultaneously
acting on the Earth’s surface. Therefore, those three subspaces are not quite
distinct from each other. With the steadily expanding research spectrums of a
variety of sub-disciplines in Earth sciences, the requirement of an integrated and
comprehensive research on the whole Earth space tends to be more and more urgent. Therefore, it is necessary to find appropriate
geospatial data models to coherently represent all kinds of geospatial objects.
A spatial data model is a mathematical construct for abstracting,
classifying, describing and expressing real spatial objects/phenomena as data (Jones 1989; Wu 2004; Wu and Xu 2004; Zhang et al. 2009). Current spatial data models used to store geospatial
data in GIS can be
divided into three major categories: 2D data models, 2.5D data models and 3D
data models.
2D data models are also classified into three
different types: 2D vector data model, 2D raster data model, and 2D hybrid data
model. 2.5D data models, supplemented with a z-value reflecting the elevation for each pair of 2D coordinate (x, y),
mainly used for constructing digital elevation models (DEMs). 3D data models
can be classified into four major subclasses as volumetric models, vector
models, mixed models and integrated models, and there are several
representational models for each subclass (Wu 2004; Wu and Xu 2004; Turner 2006). 3D volumetric data
models, which are based on the spatial partition, represent a spatial object as
a combination of primitive volumes (De Floriani and Falcidieno 1988; Wu 2004). The conventional 3D
volumetric data models include constructive solid geometry (CSG), 3D-raster,
octree, tetrahedral network (TEN), tri-prism (TP), generalized tri-prism (GTP),
Geocellular, etc. 3D vector data models, which describe solid volumes in terms
of their enclosing surfaces, emphasize on the surface representation for the spatial
objects (De Floriani and Falcidieno 1988; Wu 2004). The conventional 3D
vector data models include boundary representation (BRep), wire framework and
non-uniform rational B-splines (NURBS). 3D mixed data models, which use two or
more vector/volumetric data models to describe one spatial object at the same
time, takes advantages of vector data model for fast visualization and of
volumetric data model for efficient spatial analysis, and well adapts to
different modeling requirements derived from various background conditions and
spatial resolutions (Wu 2004). The conventional 3D mixed data models include
BRep-CSG, GTP-TEN and BRep-GTP-TEN. 3D integrated data models firstly apply
various single data models to describe different types of spatial objects respectively,
and then integrated them into a unified 3D space to fully represent multiple
types of spatial objects. The conventional 3D integrated data models include
CSG+TIN+GTP, BRep+TIN+GTP and object-oriented data model.
For practicality, each of those spatial data
models has both advantages and disadvantages in several aspects like geometric
representation of geospatial objects or phenomena, space partition, topological
description and consistency maintenance (Wu 2004). At the present time, due to
the difference in their adaptabilities, none of the existing data models can
faultlessly represent all or most of the geospatial objects. In addition, since
there are tremendous differences in data acquisition methods, morphological
features, modeling approaches and applied targets between different geospatial
objects and geo-phenomena, each of those existing data models can only
successfully deal with certain geospatial objects in a particular range of
research fields or spatial dimensionality. Therefore, to current Digital Earth
applications, it is necessary to develop integrated geospatial data models and
associated data structures, of which apply various single data models to
describe and model different geospatial objects respectively. Using the concept
of the integral modeling, all of the established models can be integrated into
a 3D virtual globe environment based on a unifying geospatial coordinate
system, and finally it leads to the full representation of the entire Earth
space and geospatial objects.
By now, it is quite mature to integrate 2D and
2.5D data models into the first-generation Digital Earth systems. But up to now there are still no
perfect methods or easy-to-handle software systems supporting 3D geospatial
data models completely. Several shortcomings are magnified when using the
existing Digital Earth systems to represent 3D geospatial objects. One of the
most outstanding problems is that the current Digital Earth systems only
support 3D geospatial objects expressed by 3D vector data models like wire
framework and BRep. That is, they cannot directly support 3D geospatial objects
which are expressed by 3D volumetric data models. Using 3D vector data models,
it is convenient to construct, update and visualize such models as
natural/man-made ground objects, geologic bodies/structures, and geometric
structures of atmosphere; and the amount of data to be transferred and
visualized is much smaller than using volumetric data models. However, it is either hard or impossible to
do 3D geospatial analysis since 3D vector data models lack the description of
the real-3D topological relationships between different geospatial objects. In
addition, 3D vector data models are impotent to subdivide the geological and
atmospheric space with arbitrary spatial extent into a series of small
subspaces perfectly and seamlessly. 3D volumetric data models are ideally
suited for describing and subdividing the continuous Earth space with the
feature of gradual changing, and they are suitable for various spatial
operations and geospatial analysis. However, a number of complex improvements
and optimizations for visualization algorithms need to be conducted to make up
such blemishes as large data size, slow computing speed and inefficient network
transmission of 3D volumetric models. The flaw of the current geospatial data
models has greatly restricted the further development and applications of the
first-generation
Digital Earth. For geoscientists and software developers of Digital Earth
systems, it is an important goal to design and develop a new Digital Earth
system supporting both 3D vector and 3D volumetric data models.
As with other existing Digital Earth systems,
we use an integrated data model to describe and represent multi-source
geospatial information with full dimensionality. But unlike previous Digital
Earth systems, this integrated data model not only involves the integration of
2D/2.5D data
models and 3D vector data models, but also extends to the integration of 3D
volumetric data models.
Figure 1 gives an overview of how different geospatial data are represented and
integrated in SolidEarth. The geospatial data are classified into ten different
types: (1) remote-sensing image, (2) digital elevation model, (3) map, (4)
geologic map, (5) 3D structure model for atmosphere, (6) 3D property model for
atmospheric space environment elements, (7) 3D geological structure model, (8)
3D geological property model, (9) 3D ground object model, and (10) observed
& probing data.
[Figure 1 near here]
2D raster data model is suitable to express
remote-sensing image data. Map and geologic map can be expressed by 2D vector
data model. To express digital elevation model, 2.5D DEM data model is the most
appropriate choice. To express 3D structure model for atmosphere, 3D geological
structure model, 3D ground object model, and observed & probing data, we
use a 3D vector data model which is based on boundary representation (BRep).
3D property model for atmospheric and
geological space can be represented by a set of values on a latitude, longitude
and depth 3D grid covering the exterior and interior of the Earth (Postpischl et al. 2011). Atmospheric or geological
property values are attached to corresponding mesh units (voxels) which spread
over the whole Earth space. Therefore, to express 3D property model within
atmospheric and geological space, we use Geocellular voxels (Denver and Phillips 1990; Wu 2004; Turner 2006) as the base of 3D volume solids. As
a mutant of 3D-raster structure, Geocellular has a normal latitude-longitude
grid partition in the lateral direction (Figure 2A), while the spatial partition
along the vertical direction is not invariable but changed according to the
actual data fields or the controlling interface of geospatial objects (Figure 2B). Geocellular supports predictive
modeling in 3D with its remarkable characteristic of simplicity, commonality,
stability, suitability for multi-scale subdivision and self-adaptive
visualization of the Earth space, high-efficiency, and practicability. Using
this partly deformable Geocellular structure, we can successfully create
arbitrary fine 3D grids to simulate actual spatial distributions of property
fields by adaptively subdividing the Earth space.
[Figure 2 near here]
It should be pointed out that all geospatial
models, no matter 2D or 2.5D, must be converted and integrated into a universal
3D space defined by the Digital Earth virtual globe environment. Using 2.5D
digital elevation models, we can directly construct terrain models in 3D since
DEMs have elevation information for the third spatial dimension. 2D data, such
as remote-sensing images, maps and geologic maps, can be considered as ground
overlays which can be draped over the terrain model of the Earth, or hanged
over the Earth’s surface at proper altitudes (De Paor and Whitmeyer 2011).
As noted above, there are six types of 3D geospatial data need to be displayed and analyzed
in Digital Earth platforms (shown in Section 3.2): observed & probing data,
3D ground object model, 3D structure model for atmosphere, 3D geological
structure model, 3D property model for atmospheric space environment elements,
and 3D geological property model. Among them, the first two types are modeling
results which are constructed from partial, scattered sample data obtained from
field measurements; and the other four types are relevant to the construction
of the Earth model, then we can lump them together under the research field of
modeling the Earth in 3D.
Representing observed & probing data or
building 3D ground object models are relatively simple tasks. Using several
often-used 3D modeling software tools like Sketchup, AutoCAD, 3D Studio Max and
Maya, models can be defined independently of Digital Earth platforms in their
own coordinate space, and constructed and saved as a general interchange file
format (such as COLLADA file types). After attaching geographic coordinate
information, models can be imported into Digital Earth
systems, and can be
translated, rotated, and scaled to fit into the Earth coordinate system (De
Paor and Whitmeyer 2011).
In Digital Earth system, how to reconstruct 3D
Earth models at different levels, including local, regional and global scales,
is a critical problem faced by geoscientists of all disciplines. The Earth has
a complex geometric structure and its properties are changeable with geospatial
locations. The Earth models are mathematical models that can be used to
describe geometric structure and spatial distributions of property element
fields within both inner and outer spheres of the Earth. In principle the Earth
models have to be 3D and able to represent structures and properties in the
whole Earth space. Therefore, we can broadly separate the Earth models into two
categories: one is the structure model that gives the boundaries between
different defined geospatial units (Turner 2006; Zhu et al. 2012); and the other is property model that reflects the
spatial distributions of geospatial property element fields, including
atmospheric space environment elements (such as atmospheric density,
temperature, stress, composition, etc) and geological properties (such as
seismic velocity, elastic modulus, gravity, etc) (Wang et al. 2005; Royse et al. 2009) . Those
two models should be constructed in two different ways.
Based on the most popular existing global
structural models, such as the IASP91 Earth model (Kennett et al. 1995), the Preliminary Reference Earth Model (PREM)
(Dziewonski and Anderson 1981), the global crustal model CRUST 5.1 (Mooney et al. 1998) and its updated version
CRUST 2.0, and the atmospheric structural model, control interfaces for each
sublayer of earth spheres are drafted under the constraints of actual observed
and probing data sets, and then a BRep-based 3D structure model for the Earth
is generated using properly constructed methods (Wu 2004; Turner 2006; Zhu et al. 2012).
In recent years, geoscientists have developed a
series of sophisticated numerical models to quantitatively simulate the average
distributions of geospatial property elements from macroscopic view (Wang et al.
2005), such as the Preliminary Reference Earth Model (PREM) that describes the
variation of elastic properties and density in the interior of the Earth
(Dziewonski and Anderson 1981), the International Reference Ionosphere model
(IRI-2001) that provides densities, composition and temperatures of the
ionosphere (Bilitza 2001), and the MSIS-2000 neutral atmosphere model that
describes the major variations of the temperature and densities in the neutral
atmosphere (Picone et al. 2002).
Based on those numerical models, we can create acceptable 3D property models
for various property element fields. As shown in Figure 3, the generation of 3D property
model is based on the following steps:
Step 1: Using existing numerical models for
geospatial property elements, generate the Geocellular-based data fields for
various property elements (denoted as F1);
Step 2: Discretize observed and probing sample
data for property elements to generate scatter-point-based sample data fields
(denoted as F2);
Step 3: Optimize and adjust F1 with the restriction of
the sample data extracted from F2,
and generate the improved property element data fields (denoted as F);
Step 4: Self-adaptively normalize data sets in F to bring them into a range that is
more familiar or normal to human vision;
Step 5: Intelligently zone normalized F into a series of proximate data sets
using clustering procedure, convert the Geocellular-based data structure into
3D isosurface prior to display;
Step 6: Render, display and analyze the
modeling result in 3D.
[Figure 3 near here]
During the modeling and visualization process for 3D geospatial objects,
challenges arise in the rapidly accessing, timely updating and real-time rendering of geospatial models when
geometric shapes of the models are complicated and the volume of data is huge.
In order to enhance the efficiency of visualizing large volume of 3D geospatial
information in the Internet, we propose a systematic framework, within which
multi-scale representation of 3D geospatial information is implemented to
transmit, visualize 3D geospatial models in SolidEarth. This framework includes
a multi-scale models organization method with a level of detail (LOD) rendering strategy, and a web-based data
transmission and self-adaptive visualization workflow suited for all types of
geospatial models.
In geosciences, scale means the level of detail (LOD) describing certain
spatial objects within certain earth space. In Digital Earth system, viewer may
perform trans-scale roaming operation in the virtual scene since the scale of
3D scene is automatically changing with viewpoint. As a rule, the closer the
distance from the viewer to the visualized object becomes the more small
details of the object become distinguishable, the greater the distance becomes
the more small details become indistinguishable (Bernardin et al. 2011). Thus, multi-scale representation of 3D geospatial
objects, mainly embodied with different LODs, is necessary when rapidly,
continuous visualization and analysis is needed.
LOD, which provides the real-time, high-quality
rendering for 3D computer graphics, usually refers to generate and delineate a series
of target models, in which details are changing gradually, from a source model.
Based on the natural principle for objective generation (Li and Openshaw 1993),
those who are smaller, far from viewer or less important in 3D scene are drawn
with less detail in order to achieve a tradeoff between system performance and
visualization fidelity.
LOD can be classified into two different types:
the static LOD, and the dynamic LOD, depending on their generating methods and
application fields. In SolidEarth, we need to apply different LOD strategies to
deal with different types of 3D geospatial objects since these models have
different representations and characteristics.
The dynamic LOD strategy is suited for 3D
volumetric models which appropriate to simplification and subdivision. Start at
the finest resolved 3D volumetric model in which full details are provided, we
can generate a series of coarse resolved models with different scales by using
real-time reduction algorithms for dynamic LOD. When models are transmitted on
the Internet and visualized in the client, we can choose proper model with a
reasonable LOD according to the distance from the center of the model to the
viewpoint, avoiding invariably using the finest resolved model. The dynamic LOD
strategy not only can greatly reduce the data flow transmitted on the Internet
and the voxel quantity in 3D scene, but also can ensure the consistency of the
geometry data and the continuity of the vision through stable, smoothing
transitions between adjoining LODs.
By contrast, the static LOD strategy is
propitious to 3D vector models because the jumping between adjoining LODs is
tolerable as those models are zooming in or zooming out in 3D scene. We
generate more than one copies of a 3D vector model. Each copy corresponds to a
particular resolution, and all copies are getting together and saved into the
database to construct the corresponding pyramid structure. When models need to
be transmitted and displayed, the proper model with a reasonable LOD is chosen
automatically based on current viewing parameters, such as the distance from
the model to the viewpoint, the pixel area of the model projected into image
space, or the intensity of illumination.
As limited by current network bandwidth and transferring speed, the
existing Digital Earth systems such as Google Earth adopt spatially tiled
structure, multi-scale representation and progressive transmission method to
publish massive, high-resolution remote-sensing images and other available
geographic data on the Internet (Butler 2006; Craglia et al. 2008). These approaches drastically enhance capabilities of
the Internet through reducing the size of file transfers, and allow near-real-time
visualization and analysis of multiple large data sets on a decent broadband
connection. Taking advantage of these approaches in a fashion similar to the
existing first-generation Digital Earth systems (Zhang et al. 2009), we propose a web-based data transmission and
self-adaptive visualization workflow suited for all types of geospatial
information, especially including 3D geospatial models.
As shown in Figure 4, all 3D geospatial data are
integrated and stored in the geospatial database on the server side. Since
vector and volumetric models have different representations, we need to apply
different methods to deal with these models. For a given vector geospatial
object, we store a series of 3D vector models with multiple scales or
resolutions in the geospatial database. However, for a given voxel geospatial
object, we only store one 3D volumetric model, termed Mvol, with a single scale and highest resolution in the
geospatial database. Thus, the server side need to adopt the pre-determined
model reduction algorithms (such as progressive meshes method) to simplify Mvol to quasi-instantly generate multi-scale volumetric models
with lower resolution according to the requests of the client side.
[Figure 4 near here]
In this workflow, once the server side received
the data requests send by client side, the server immediately retrieves the
geospatial database through 3D spatial index, such as LOD-R tree (Zhu et al. 2007), to acquire the proper
models with certain ranges and details most appropriate for current viewing
parameters. And subsequently, the acquired models are progressively transmitted
to the client. And finally, the client creates cache files for the acquired
models, the displaying and analysis can be successfully accomplished in the
visualization component of the client.
In the process of the client visualization, we
use the focus-context approach to display massive geospatial information in a
full, constant and coherent operation. The focus-context approach allows users
to view not only those areas of interest to viewers with the most accurate
geometric representation, but also the overall impression of the surrounding
regions relevant to the focus in a lower resolution (Bernardin et al. 2011). The combination of above
approaches leads to the viewpoint-based transform and self-adaptive
visualization of 3D geospatial data, and improves the responsiveness and the interactivity
for the visualization and analysis of the client side.
To demonstrate the effectiveness of our proposed approaches, SolidEarth
(Figure 5), an
experimental system that designed for the purpose of modeling and analysis of
the whole Earth space,
was programmed in Microsoft Visual C++, AVS/Express (Advanced Visual Systems
Inc. 2012) and the OpenGL
graphics library on the PC platform. As a prototype application of the next-generation Digital Earth system,
SolidEarth inherits plenty of basic functions that have been implemented in the
first-generation
Digital Earth system, focusing on comprehensively validating the modeling,
visualization, integration and analysis of geospatial objects existing in the
entire Earth space. SolidEarth consists of four basic functional components
that are discussed in the following sections: modeling in geographical space,
modeling in geological space, modeling in atmospheric space, and, integrated
visualization and analysis.
[Figure 5 near here]
As with existing Digital Earth systems, the fundamental geographical
data from a variety of resources, including remote-sensing images, topography
data, digital maps, ground object models and other available geographical
contexts, can be meshed, integrated and published within SolidEarth. Based on
these data, both displaying of global geographical objects and subsequent
analysis can be accomplished from macro-vision to micro-detail in a 3D virtual
global environment. Users can build digital elevation models using
high-resolution terrain data to measure global topography and how it varies in
3D space. As Figure 6 shows, satellite and aerial images can be fused into terrain models to
restore the 3D shape of the Earth’s surface. The thematic maps, such as
user-defined regions of interest (ROI), social, economic, infrastructure and
environmental data, can be imported into SolidEarth and draped over the
underlying terrain models.
[Figure 6 near here]
The most distinctive function of SolidEarth is to build 3D solid models
in geological space to imagine the structural characteristics of geological
objects, the spatial distributions of geological properties, and the spatial
correlations between different geological units, at both local and planetary
scales. Solid models of geological objects in 3D can provide the detailed
definition of the boundaries and properties of different phenomena and complex
structures, and then help to predict the spatial variation of geological
characterization within the Earth (Hack et
al. 2006; Turner 2006; Zhu et al. 2012). In SolidEarth, geological models are broadly
separated into two categories (Hack et al.
2006; Turner 2006):
one is the structure model that defines the geometric boundaries between
different geological objects; another is the property element model that
defines the spatial distributions of physical, chemical or other properties
within different geological units.
SolidEarth adopts a
boundary-representation-based (BRep-based) 3D
vector data model for defining 3D structure models of various geological
objects, including strata, faults, folds, intrusions, underground rivers, karst
caves, ore bodies, oil/gas reservoirs and other complex structures. In order to
build geologically reasonable structure models, several often-used interpolation
schemes, like the inverse distance weighted (IDW), natural neighbor,
the nearest neighbor distance, radial basis function (RBF), and Kriging
methods, are integrated into SolidEarth to interpolate the shapes of geological
objects between widely spaced sample points. Furthermore, several more complex
approaches to construct structurally complex or poorly sampled geo-objects,
such as surface modeling, section modeling and interactive modeling (Wu 2004; Wu and Xu 2004; Wu et al. 2005; Hack et al. 2006; Turner 2006; Calcagno et al. 2008; Guillen et al. 2008), also can
be applied in SolidEarth to overcome the disadvantages of 3D spatial
interpolation. In addition, large quantities of probing data in a variety of
formats, such as borehole, cross-section and exploration seismic data, can be
integrated into SolidEarth to successfully replicate actual spatial shapes and
correlation relationships of different geological objects (Figure 7).
[Figure 7 near here]
SolidEarth offers an automatic process to
construct 3D property model in geological space. This process involves two
steps: in the first step, the geological space can be subdivided into a series
of 3D volumetric meshes by applying discretization methods. Taking the
fundamental geological framework defined by 3D geological structure models as
spatial datum, SolidEarth automatically generates 3D volume solids that consist
of large number of Geocellular voxels and constrained
by geometric framework of geological objects. In the second step, using
representative sample data and the existing numerical pattern for a given geological
property element, users adopt the construction methods for 3D geospatial
property elements (shown in Section 4) to calculate geological property values
attached to each Geocellular voxel. Thus, the final solid model filled with
Geocellular voxels is built and then fed to the real-time visualization component
of SolidEarth for subsequent visualization and 3D spatial
analysis (Figure 8).
[Figure 8 near here]
With advanced visualization tools provided by
SolidEarth, users can freely explore 3D geological models with vivid appearance
in a variety of ways. Instead of hanging over or
projecting onto the globe’s surface (De Paor and Whitmeyer 2011; Navin and de Hoog 2011; Zhu et
al. 2014), subsurface
models are placed in the correct locations beneath the Earth’s surface. Users
can fly through the surface of the Earth, and roam virtually in geological
space. Several operations for
3D-interaction of the solid model, such as 3D observation, slice up, arbitrary
incision, virtual drilling, virtual roaming, spotting and measurement of
property value in any spatial position, excavation of foundation pit or tunnel,
distance/area/volume calculation of particular geological unit, etc., can be
performed freely since the solid representation of geological space is very suitable
for spatial analysis and spatial query (Figure 9). All above functions open the eyes of users to
the deep of the Earth, and will help to comprehensively recognize and research
to the composition, structure, property and evolution of geological space that
occur beneath the surface of the Earth in an all-round, multi-view manner.
[Figure 9 near here]
Similar to geospatial objects in geological space, there are two types
of models need to be constructed in atmospheric space: the structure model that
defines the stratified boundaries between different atmospheric layers, and the
property model that reflects the spatial distributions of atmospheric space
environment element fields (Wang et al.
2005).
As opposed to geological space, which often
have complicated, volatile and discontinuous interfaces between different
geological units, the geometric shape of atmosphere is relatively simple as
there are continuous interfaces between different sublayers. Thus, all
sublayers in atmospheric space can be regarded as continuous stratified
objects. Based on the existing stratification models and continually updated
probing data of atmosphere, SolidEarth adopts conventional solid modeling
methods (Turner 2006; Zhu et al. 2012) for geo-objects
to construct structure models within atmospheric space.
SolidEarth provides users with a series of
numerical patterns (such as IRI-2001, MSIS-2000, MET and HWM93) (Wang et al.
2005), global and regional climate/weather models, data processing schemes and
spatial interpolation methods to build 3D property model in atmospheric space.
Depending on different distribution characteristics and application
requirements, different 3D volumetric models, which correspond to different
property element fields like density, temperature, stress or composition of
atmosphere, can be automatically generated and fed to the visualization
component of SolidEarth to reveal the spatial variations of atmospheric space
environment element fields (Figure 10).
[Figure 10 near here]
All geospatial models with a unified geographic coordinate system can be
seamlessly integrated into a real-time, user-friendly visualization component
of SolidEarth to carry out the visualization and geospatial
analysis process. A general-purposed but powerful user interface for
interacting with geospatial data is provided to manage, display and analyze
heterogeneous datasets from a wide range of sources and disciplines at one
virtual scene.
In SolidEarth, we can integrate and visualize
multiple types of geospatial objects/features simultaneously, from the
structures and properties at local level up to the changes and mechanisms
between different geospatial objects at global scale, in the correct location of
the Earth space. The model can be updated quickly and easily when new probing
data or numerical patterns became available. Through the use of advanced
visualization techniques like layering stack, transparency setting and
focus-context visualization, geospatial objects in geographical, geological and atmospheric space
can all be viewed and compared at the same time (Figure 11). Thus, SolidEarth provides the
opportunity for geoscientists to detect and visually analyze spatial
relationships and correlations between geographical, geological and atmospheric
objects. This could dramatically improve the efficiency of exploring
relationships hidden behind the complex and large volume of geospatial data,
lead to an increased comprehensive understanding of the whole Earth, and maybe
produce new knowledge and promote new scientific discoveries.
[Figure 11 near here]
Compared with the first-generation Digital Earth system, the significant features and
substantial advantages of SolidEarth are obvious:
(1) Coherent representation,
integrated access and efficient management of multi-dimensional geospatial
information. In SolidEarth, both two- and three- dimensional geospatial
data sets from a wide range of sources and disciplines are expressed coherently
in the Earth space; users can effectively access these massive, heterogeneous,
and multi-resolution data sets that obtained from multiple sources and many
different disciplines.
(2) Rapidly modeling, seamlessly
integration and visually analysis for earth spheres. SolidEarth allows users not only to model
geographical objects existing in the Earth’s surface and near-surface, but also
to effectively create 3D structure and property models of earth spheres.
Moreover, all those models can be integrated into a real-time, user-friendly visualization component of SolidEarth to carry out the
visualization and geospatial analysis process. With
the advantages of 3D volume visualization, self-adaptive visualization,
transparent display, texture mapping, and other new techniques that
synthetically applied into SolidEarth, it is convenient to implement
interactive operations and near real-time visualization for large-scaled
geospatial models in 3D, and to gain insight into the Earth’s interior and
exterior.
SolidEarth is based on relatively recent advancements in
remote sensing, geographic information science, geospatial technologies, as
well as developments in the modeling and visualization of multi-dimensional geospatial information.
It offers users the capability to model, manage, display and analyze 3D
geospatial data characterized as large-extent, multi-scaled,
multi-source,
massive and heterogeneous. The main potential user communities for SolidEarth
are geoscientists and educators. As a powerful platform to make geospatial data
more useful and user friendly, SolidEarth can support nearly all
scientific domains and research projects that are broadly centered on
gathering, modeling, analyzing and interpreting geospatial information with
full dimensionality in an integrated view. SolidEarth is expected to make a
significant contribution to the description, understanding, prediction and
demonstration of 3D structure
and property of the Earth on both local and planetary scales in a
virtual global environment. Based on SolidEarth, it is
convenient (a) to create refined, high-resolution, three- or four- dimensional
structure and property models of earth spheres using large quantitative of
global observation data; (b) to develop professional analytical models for a
number of ongoing and new geosciences research projects such as global change
simulation, geodynamics simulation, Earth system simulation, construction of
Digital Earth applications; (c) to intuitively reveal how changes in geological
and atmospheric space affect the Earth’s surface; (d) and to visually integrate
the refined model of earth spheres, geographic information, remote sensing
images, deep exploration data and traditional 2D GIS functions with great
flexibility to construct server and application system of multi-dimensional,
dynamic geospatial information.
We have introduced SolidEarth as an
alternative Digital Earth system for the modeling and visualization of the
geospatial information. SolidEarth marks a significant advancement
in the field of Digital Earth science and technology, as it tries to combine
advantages of the first-generation Digital Earth system with 3D modeling and analysis functions
of earth spheres, and overcomes limitations of the conventional 2D space
partition scheme that distorts spatial relationships between geological,
atmospheric and geographical objects. The most significant feature of
SolidEarth is that it has a comprehensive treatment of the third spatial
dimension and a series of sophisticated, advanced 3D spatial analysis
functions. Thus, it is well-suited to volumetric representations of the entire
Earth space and the visual analysis of inner and outer spheres of the Earth,
and eventually changes the way we interact with geospatial information.
Although our attempts have been made to make
SolidEarth as easy as possible for end users, the concrete functions and
operations of SolidEarth specifically designed for dealing with full
dimensionality of geospatial information is still undergoing modification.
Based on our work with SinoProbe program (Dong
et al. 2011) and our current
research needs, we believe at least five priority aspects need further research
and development in the future:
(1) Modeling and
analysis of 3D vector fields in the Earth space. Vector fields which have directions as well as
sizes, such as gravity, electromagnetic and flow field, are widely distributed
in the Earth space. Visualization of 3D vector fields not only can display the
direction information of those fields, but also may lead to new insights of
spatial structure. We are planning to employ such techniques as data probe,
advection, vector plot and texture-based method to perform 3D reconstruction
and visual analysis of vector fields on SolidEarth.
(2) XML-based access,
management and exchange of 3D volumetric models. Currently, XML-based markup languages like KML
and CityGML have became the standard descriptive languages that widely embraced
by geoscientists as a means to represent geographical objects. However, those
languages were not designed for the purpose of representing 3D volumetric models. Thus,
they are not suited for the representation and exchange of atmospheric/geological objects over the
Internet. In order to create, display, exchange and share geospatial
objects with full dimensionality, we should develop the XML-based distribution
and exchange techniques for 3D Earth models, and the standardization and
interoperation methods for 3D volumetric model under web browser environment.
(3) 3D visually
analytic tools for geological applications. SolidEarth should be regarded as not only a
visualization system for geospatial information, but also a geologic instrument
that encourage virtual geologic investigation (Bernardin et al. 2011). In the future, more easy-to-use 3D analytic tools
relevant to the needs of professional users, such as virtual geologic compass
and 3D model editor, need to be integrated into SolidEarth to auxiliary support
geological and geophysical analysis.
(4) Assessment and
representation of uncertainty in 3D geospatial data and models. Up to now it is difficult to make
effective assessments of the precision in geospatial models according to a
unified and flexible mode. In the future, a series of sophisticated models
relevant to the assessment and representation of uncertainty in 3D geospatial
data/models, such as the general theoretical model of accuracy assessment for
geospatial data/models, the practical operating model for a given geospatial
objects, and the 3D spatial distribution model for uncertainty in geospatial
data/models (Zhu and Zhuang 2010), need to be developed and integrated into
SolidEarth to meet the special concern on the issues of uncertainty in the
scientific community (Goodchild et al.
2012).
(5) Dynamic
visualization and analysis techniques for temporal geospatial information. Because the Earth is
three-dimensional in space and can be viewed as four-dimensional when time is
considered (Hack et al. 2006; Li et al. 2011), future improvements to SolidEarth include the need to
robustly handle spatio-temporal data that reflect the dynamic process of earth
spheres. We should combine the temporal GIS technique with SolidEarth to
simulate structures and properties of the entire Earth using geospatial data in
four dimensions (latitude, longitude, altitude and time).
This research was supported by the National
Science and Technology Program of China (Grant No. SinoProbe-08), the National
Natural Science Foundation of China (Grant No. 40902093), the National Social
Science Foundation of China (Grant No. 07CZZ019) and the Open Foundation of
Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration (Grant No. SHUES2011A06).
We would like to thank the Editor and two anonymous reviewers for their helpful
and constructive suggestions for improving the paper.
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Figure 1. Classification of geospatial information and geospatial data
model.
Figure 2. Geocellular voxel structure applied
to 3D property models. (A) Geocellular employs a normal
latitude-longitude grid as the basis for the spatial partition in the lateral
direction. (B) The spatial partition
along the vertical direction is deformable according to the actual data fields
or the controlling interface of geospatial objects.
Figure 3. Modeling flow of geospatial
property elements in 3D.
Figure 4. Network transmission and
self-adaptive visualization of 3D geospatial information.
Figure 5. User interface of client side in
SolidEarth.
Figure 6. Integration and visualization of
geographic information relevant to the Earth’s surface and near-surface in
SolidEarth. This figure illustrates the integration and display of
remote-sensing images, DEMs and maps at the same view. Note that at the
upper-left part of the screen only terrain model visible, whilst at other parts
remote-sensing images and maps are draped over the underlying rugged terrain.
Figure 7. Integration and visualization of
geological structure model and geographic objects. This figure illustrates the integration,
display and analysis of remote-sensing images, DEMs, ground objects, and 3D
solid models of geological structures generating from boreholes, at one view.
Figure 8. Example of modeling and
visualization for 3D geospatial property elements in SolidEarth. This figure
illustrates an overview of the 3D spatial distribution of compressional wave
velocity (Vp) field within the
interior space of the Earth.
Figure 9. Virtual roaming and spatial query
of the property information within the interior space of the Earth. In
SolidEarth, users can perform such operations as virtual roaming by swooping
over geological space, spotting and measurement of property value in any
spatial position by clicking 3D solid models.
Figure
10. Example of
modeling and visualization in atmospheric space. In this figure, the
geometrical boundaries for individual sublayers and the temperature field model
in atmosphere are integrated and visualized simultaneously. Note that the
opacity for each sublayer in atmosphere is increased from the interstellar
space to the Earth’s space.
Figure
11. Integration,
visualization and analysis of geospatial objects in geographical, geological
and atmospheric space. In this figure, the stratified boundaries between
different atmospheric sublayers, the temperature field model within atmospheric
space, remote-sensing images of the Earth’s surface, and 3D structural model of
the interior Earth are integrated, displayed and analyzed at the same view.