4
Integration of Remotely Sensed Data into
Geographical Information Systems
Nandish M. Mattikalli\ Edwin T. Engman 2
lCambridge Research Associates, 1430 Spring HilI Road, Suite 200 McLean, Virginia
22102, USA
~ASA-Goddard Space Flight Center, Greenbelt, MD 20771, USA
4.1 Introduction
Remotely sensed data and infonnation derived from them have a wide range of
applications in hydrology and water resources management (Schultz, 1988; Engman and Gurney, 1991). Remote sensing and its associated image processing
technology provide access to spatial and temporal infonnation on watershed, regional, continental and global scales. Further, new sensors and imaging technology are increasing the capability of remote sensing to acquire infonnation at a
variety of spatial and temporal scales. Management and efficient utilization of
such infonnation is going to be one of the major challenges of the coming decade.
With the advent of space programs such as the Earth Observing System (EOS),
this problem is going to become even more complex especially because a variety
of new sensors are employed to cover the full range of the electromagnetic spectrum. Effective utilization of this large spatial data volume is dependent upon
existence of an efficient, geographic handling and processing system that will
transfonn these data into usable infonnation. A major tool for handling spatial
data is the Geographical Infonnation System (GIS).
GIS provides appropriate methods for efficient storage, retrieval, manipulation,
analysis and display of large volumes of spatially referenced data. Accordingly,
GIS consists of four basic components: data input and editing, storage of geographic databases, data analysis and spatial modeling, and data visualization and
presentation (Fig. 4.1). The data may be collected from fieldwork, extraction of
map data, air photo interpretation, and interpretation and classification of remotely
sensed images. Data input may be carried out by manual digitization or computer
assisted semi-automatic methods. Collected data are then organized into a series of
spatially geo-registered layers, with each layer relating to a particular theme (e.g.,
vegetation, soils, geology, topography etc.) or a set of layers relating to temporal
variation of a theme (e.g., changes in land-use or variation of soil moisture etc.).
Data input and editing (i.e., to correct digitizing errors, establishing topological
relationships etc.) are the most time-consuming and labor intensive tasks. Data
analysis and spatial modeling capability are the most important characteristics of a
GIS. Conventional analysis and manipulation operations include retrieval, reclassification procedures (e.g., reclassifying soils map into a penneability map), map
overlay (e.g., merging of various data layers to calculate soil erosion), proximity
G. A. Schultz et al. (eds.), Remote Sensing in Hydrology and Water Management
© Springer-Verlag Berlin Heidelberg 2000
Integration of Remotely Sensed Data into
Geographical Information Systems
Nandish M. Mattikalli\ Edwin T. Engman 2
lCambridge Research Associates, 1430 Spring HilI Road, Suite 200 McLean, Virginia
22102, USA
~ASA-Goddard Space Flight Center, Greenbelt, MD 20771, USA
4.1 Introduction
Remotely sensed data and infonnation derived from them have a wide range of
applications in hydrology and water resources management (Schultz, 1988; Engman and Gurney, 1991). Remote sensing and its associated image processing
technology provide access to spatial and temporal infonnation on watershed, regional, continental and global scales. Further, new sensors and imaging technology are increasing the capability of remote sensing to acquire infonnation at a
variety of spatial and temporal scales. Management and efficient utilization of
such infonnation is going to be one of the major challenges of the coming decade.
With the advent of space programs such as the Earth Observing System (EOS),
this problem is going to become even more complex especially because a variety
of new sensors are employed to cover the full range of the electromagnetic spectrum. Effective utilization of this large spatial data volume is dependent upon
existence of an efficient, geographic handling and processing system that will
transfonn these data into usable infonnation. A major tool for handling spatial
data is the Geographical Infonnation System (GIS).
GIS provides appropriate methods for efficient storage, retrieval, manipulation,
analysis and display of large volumes of spatially referenced data. Accordingly,
GIS consists of four basic components: data input and editing, storage of geographic databases, data analysis and spatial modeling, and data visualization and
presentation (Fig. 4.1). The data may be collected from fieldwork, extraction of
map data, air photo interpretation, and interpretation and classification of remotely
sensed images. Data input may be carried out by manual digitization or computer
assisted semi-automatic methods. Collected data are then organized into a series of
spatially geo-registered layers, with each layer relating to a particular theme (e.g.,
vegetation, soils, geology, topography etc.) or a set of layers relating to temporal
variation of a theme (e.g., changes in land-use or variation of soil moisture etc.).
Data input and editing (i.e., to correct digitizing errors, establishing topological
relationships etc.) are the most time-consuming and labor intensive tasks. Data
analysis and spatial modeling capability are the most important characteristics of a
GIS. Conventional analysis and manipulation operations include retrieval, reclassification procedures (e.g., reclassifying soils map into a penneability map), map
overlay (e.g., merging of various data layers to calculate soil erosion), proximity
G. A. Schultz et al. (eds.), Remote Sensing in Hydrology and Water Management
© Springer-Verlag Berlin Heidelberg 2000