Comput. & Graphics, Vol. 20, No. 3, pp. 395-403, 1996
Copyright 0 1996 Elsevier Science Ltd
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Computer Graphics in Brazil
SPRING: INTEGRATING REMOTE SENSING AND GIS BY
OBJECT-ORIENTED DATA MODELLING
GILBERT0 CAMARA,+ RICARDO CARTAXO MODESTO SOUZA,
UBIRAJARA MOURA FREITAS and JUAN GARRIDO
Image Processing Division (DPI), National Institute for Space Research (INPE), P.O. Box 515,
12201-010 SPo Jo& dos Campos, Brazil
e-mail: gilberto@dpi.inpe.br
Abstract-This work discusses the design and implementation of SPRING, a geographical information
system designed to support environmental projects over large spatial data base. SPRING is based on an
object-oriented data model, which caters for the diversity of data sources and formats, and combines the
ideas of “fields” and “objects”. The system includes functions for image processing, eographical nalysis
and digital terrain modelling, integrated with a spatial data base environment. The paper describes the
project objectives, the object-oriented data model, the LEGAL spatial language and system functionality.
Copyright 0 1996 Elsevier Science Ltd
1. INTRODUCTION
Brazil, being a large and diverse country, faces a
great number of issues related to the handling of its
natural resources. Ranging from deforestation of
tropical forests in the North to the impacts of rapid
urbanization in the Southeast, and including drought
assessment and management of water resources in
the Northeast, country planners face challenges at a
continental scale.
The need for GIS technology in Brazil is best
enlightened by considering the problem of monitor-
ing deforestation on the Amazon forest. Arguably,
no single environmental issue has captured so much
international attention in recent years. The process of
land use and cover change detection in the region,
carried out by Brazil’s National Institute for Space
Research (INPE), relies on Remote Sensing and GIS
technology as the only possible way to cover more
than S,OOO,OOO km’ (the area of Legal Brazilian
Amazonia).
These environmental issues have motivated sig-
nificant investments in the use and development of
Remote Sensing and GIS technology at INPE. The
SGI/SITIM system, developed by INPE for IBM PCS
from 1982 to 1991, is currently in use in the majority
of the country’s Remote Sensing laboratories. In
1991, we started the development of SPRING, a
software designed to meet Brazil’s challenges on
natural and human resources monitoring, with the
following design objectives:
l Operate as a seamless geographical data base, with
a large volume of data, without being limited by
+ Author for correspondence.
tiling schemes, cale and projection. Object identity
should be maintained on the whole data base.
Support both raster and vector data geometries
and integration of remote sensing data into a GIS,
with functions for image processing, digital terrain
modelling, spatial analysis and data base query
and manipulation.
Achieve full scalability, that is, capable of working
with full functionality from desktop PCs running
Windows or OS/2 to high-performance UNIX
workstations.
Provide an easy-to-use, yet powerful environment,
with a combination of menu-driven applications
and a spatial algebra language.
This paper describes the general features of the
SPRING software and is organized as follows.
Section 2 includes a brief discussion of previous
work, showing the main areas of innovation of the
project. The object-oriented data model used in
SPRING is presented in Section 3 and the LEGAL
query and manipulation language is part of Section 4.
Section 5 contains a review of the functionality
available in version 2.0 of the system and Section 6
describes the project status.
2. RELATION TO PREVIOUS WORK
There is a general consensus on GIS literature
about the two broad classes of models of geographic
information: field-based and object-based [l]. While
the former deals with spatial distributions over a
geographical region, the latter deals with discrete,
identifiable entities on the geographical space. [2].
These two approaches have led to two separate areas
of research: definition of spatial operations on
discrete objects 131, and definition of operations on

396 G. C&nara, R. C. M. Souza,
fields (the “map algebra” of Tomlin [4]). Further-
more, most current GIS implementations provide
different sub-systems for map algebra, spatial queries
and image processing.
To improve upon this situation, there is a need for
a comprehensive approach which unifies the field-
based and object-based models, and provides the
basis for answering questions such as: How can a set
of discrete objects be converted into a field? Are there
operations which generate a set of objects from a
field? What are the acceptable formats for fields and
objects? How to design a general query and
manipulation language?
Another important issue is the integration of
remote sensing data into a GIS. It is necessary to
include such data into a general GIS data model, and
to provide suitable algorithms for information
extraction on images.
In order to answer these questions, our approach
was to derive, initially, an object-oriented data model
for GIS data which combines the ideas of “fields”
and “objects” [5]. Based on the model, we investi-
gated the operations over geo-fields and over geo-
objects, and the transformations between geo-fields
and geo-objects [6]. The definition of operations was
used to derive a query and manipulation language, a
user interface and the different functions which
comprise SPRING. Innovative algorithms for spatial
indexing [7], image segmentation [8], region-based
classification by neural networks ]9] and TIN
generation [IO] enable improved performance on
environmental assessment problems and the simpli-
fication of the integration of remote sensing data into
the GIS environment.
3. AN ORJlXT-ORlRNTED DATA MODEL FOR GIS DATA
In order to derive an object-oriented model, it is
necessary to distinguish between the conceptual level
U. M. Freitas and J. Garrido
(where the abstract entities are defined) and the
implementation level (where the graphical represen-
tations are constructed).
3.1. The conceptual evel
We take the perspective that an environmental GIS
should consider both fields and objects as abstract
data types, and we have tried to find useful general-
izations and specializations from these basic notions.
The conceptual level of the model is shown in Fig. 1.
In our definitions, we consider a geographical
region R to be a part of the surface of the Earth,
represented in a suitable cartographical projection”
Given a geographical region R, we define a geo-
graphical field (or geo-field) as an objectf= [R, 1, yl,
where I is a mapping between locations in R and
values in V.
A geo-field represents a geographical variable
which takes values over all locations of a region of
the Earth. We shall denote the class of geo-fields by
GEO-FIELDt, which can be specialized into the
classes THEMATIC (when V is finite denumerable
set-the themes of the map) and NUMERIC (when Y
is the set of real values). To integrate remote sensing
data into the GIS environment, we define the
REMOTE SENSING class as a specialization of
the NUMERIC class, when V is a set of discrete
values obtained by quantization of the response
obtained by an active or passive sensor.
Given a set of geographical regions RI, R,,, and
a set of attribute domains A,, . . . , A,,,, we define a
geographical object (or geo-object) as an object
go= [al, . . a,, rl, , r,], where ai E Ai is the
value of its attribute in the attribute domain Ai and ri
+ Throughout the text, model and derived classes are
indicated in italic capitals.
Fig. 1. The conceptual level of SPRING’s data model