Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

damen, m. c. j.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856662364

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: VI, Seiten 959-1074

Year of publication:: 2016

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856662364

Signature of the source:: ZS 312(26,7,3)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: Invited papers

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: The integration of remote sensing and geographic information systems. David G. Goodenough

Document type:: Multivolume work

Structure type:: Chapter

1015 Symposium on Remote Sensing for Resources Development and Environmental Management/Enschede/August 1986 © 1987Balkema, Rotterdam. ISBN 90 6191 674 7 The integration of remote sensing and geographic information systems David G.Goodenough Canada Centre for Remote Sensing, Department of Energy, Mines & Resources, Ottawa ABSTRACT: The integration of remote sensing and geographic information systems is essential for effective resource management. The volume of remote sensing imagery for managing a provincial resource, such as forestry, is such that one must use digital image analysis systems. By combining remote sensing image analysis and geographic information systems, resource managers can have timely and accurate knowledge of a renewable resource. There are, however, several scientific and technical problems that reduce the success of this integration. This paper describes several integration problems and the LANDSAT Digital Image Analysis System (LDIAS) used at the Canada Centre for Remote Sensing (CCRS). Experiments have been conducted integrating a forestry geographic information system for the province of British Columbia with LDIAS. Some of the difficulties encountered require the use of non-algorithmic solutions which use symbolic reasoning. A brief description of expert systems for this integration is given. Several key issues for the future are raised for consideration. 1 INTRODUCTION 1.1 Basic Concepts A geographic information system (GIS) is a data base system for manipulating digital spatial and thematic data. Increasingly, such systems are being used in developed countries to aid in resource management and computer-assisted mapping. A GIS has four components (Marble et al 1984): a data input subsystem, a data storage and retrieval subsystem, a data manipulation and analysis subsystem, and a data reporting subsystem. The data inputs are usually spatial and thematic data derived from a combination of existing maps, aerial photographs, and manual interpretations of remotely sensed imagery. With the data manipulation and analysis subsystem, the user can define spatial procedures to generate derived information, such as the best location economically to harvest a resource, such as timber. The data reporting subsystem is used to generate reports in tabular form, digital displays, or maps. Because the input data on which a GIS is based becomes obsolete quickly, it is essential to update periodically the GIS with new spatial and thematic data. Remote sensing is often the most cost effective source for these updates. The information content from large quantities of remotely sensed images, such as those derived from LANDSAT and SPOT satellites, is best extracted using computer systems designed for this purpose. Such a system is called an Image Analysis System (IAS). An IAS has five elements: data acquisition, preprocessing, analysis, accuracy assessment, and information distribution. The usual input for a remote sensing IAS is a computer compatible tape containing a digital image acquired at a remote sensing, satellite tracking station. For some agencies, corrections for sensor-related radiometric and geometric errors will have been performed at the tracking station. However, the preprocessing subsystem in an IAS usually can perform additional radiometric, geometric, and atmospheric corrections. These specialized corrections, not normally provided by image production systems, include: corrections for radiometric distortions due to view angle, geometric compensation for terrain relief, projection of imagery to a variety of map projec tions, and refined atmospheric corrections with the aid of meteorological information. The result is to give an image as free from errors as possible. This preprocessed image is then used for training and classification. In a system integrating GIS and IAS one could use the GIS thematic data and attributes to guide the selection of suitable training areas. Often the thematic polygons in a GIS contain several spectral and spatial classes. It may be necessary, therefore, for the user to have manual control of the selection of training areas. After classifi cation, the accuracy is assessed using theoretical estimation based on class statistics or selected test sites derived from ground reference informa tion, or both. The derived information can be dis tributed in the form of tables, maps, computer tapes, or images. The LANDSAT Digital Image Analysis System (LDIAS) of CCRS includes all of these image analysis functions plus the GIS compo nents described above. 1.2 Integration of Remote Sensing and Geocoded Data Figure 1 is a pictorial example of the integration of remote sensing with other geocoded data. Stored in raster or grid form, one has the LANDSAT and aircraft sensor data, digitized aerial photography, and topography. Thematic data are represented by layers or levels of polygons corresponding to a given theme or class. Finally, one has point data corresponding to meteorological measurements. This figure shows that the integration of GIS and IAS must deal with different data representations and must provide geometric correspondence or registra tion between the different data sets. There are two approaches that one might take to integrating a forestry GIS and remote sensing. To simplify the data representation and labelling, one could develop a new GIS having the same raster structure as the image data with classes compatible with those detected spectrally. Given that one LANDSAT Thematic Mapper (TM) image is more than 400 megabytes, this raster-based unification could lead

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