Special UNISPACE III volume

Marsteller, Deborah

Bibliographic data

Monograph

Persistent identifier:: 856485039

Author:: Marsteller, Deborah

Title:: Special UNISPACE III volume

Sub title:: including: ISPRS Workshop on "Resource Mapping from Space", ISPRS-EARSeL Workshop on "Remote Sensing for the Detection, Monitoring and Mitigation of Natural Disasters", ISPRS-NASA Seminar on "Environment and Remote Sensing for Sustainable Development", July 1999, Vienna, Austria

Scope:: IV, 170 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856485039

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

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

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:: Monograph

Collection:: Earth sciences

Chapter

Title:: ISPRS Workshop on "Resource Mapping from Space"

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: THE ROLE OF REMOTE SENSING IN NATURAL RESOURCE MANAGEMENT. Andrew K. Skidmore

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing. Voi. XXXII Part 7C2, UNISPACE III, Vienna 1999 10 I5PR5 UNISPACE III - ISPRS Workshop on “Resource Mapping from Space” 9:00 am -12:00 pm, 22 July 1999, VIC Room B Vienna, Austria ISPRS 1979; Lees and Ritman 1990; Olsson 1989; Skidmore et al. 1997; Zhou and Garner 1990). The difference between image processing (of remotely sensed data) and analysis using geographic information systems becomes blurred when remotely sensed and other geographically registered data are merged in a (raster) GIS data model. There are many common processing techniques including geometric correction, cartographic output, and classification methods. One processing technique that incorporates remote sensing and is executed in a GIS, is the expert system. Expert knowledge from farmers, agronomists, soil scientists, foresters, indigenous people or others interested in land resource management is an important (and sometimes the only) source of information. Participatory techniques have been adapted to extract information through interviews and discussion groups, particularly for land use planning. Expert systems can incorporate such knowledge into a GIS, and then automatically map areas and features of interest. An example is the Land Classification and Mapping Expert System (LCMES) (Skidmore et al., 1997). Forest soils were mapped using terrain parameters such as slope and aspect, remote sensing, and a knowledge base generated by interviewing soil scientists. Interestingly, there was no difference between the accuracy of the expert system map and a map prepared by a soil scientist. Expert systems retain the robustness of the scientific method, but allow knowledge and other ‘soft’ data to be used in analysis, as well as mapping more rapidly and with low'er input of labour. Despite these successes with expert systems, as w'ell as the rapid growth of GIUS described above, satellite remote sensing has played a relatively minor role in the commercial success of GIS. There are a number of reasons for the poor uptake of remote sensing, but five appear to dominate. Firstly, the coarse spatial resolution of satellite images is fine for regional scale studies but most GIS analysis is at the local scale. In addition a limited suite of wavelength bands have been available, and these have usually been rather broad. Secondly, the primary data input into GIS has been conventional cartographic maps in the form of vector data layers, which are expensive to convert to a digital (vector) format. The new high spatial resolution imagery described in the previous section will probably become the primary data model, as well as providing the primary data source. Thirdly, there is a mismatch between the vector data structure (based on points, lines and polygons) used in GIS, and the raster (pixel) based data structure used in remote sensing. There are difficulties in converting between these two data models. In addition, users have been conditioned to accept line work on maps as representing a boundary', when in fact most boundaries of natural resources features (such as soils, vegetation species mixes, or wildlife habitat suitability') are gradients. ‘Cartographic conditioning’ results in most users delineating homogeneous areas (polygons) on maps, instead of mapping variables as a continuum, such as the density' of a species, or the concentration of soil nutrients. The fourth reason for the slow uptake of remote sensing in GIS is poor accessibility to data due to the expense of higher resolution imagery, poor delivery systems for the data, or data being in the wrong format. Tills problem is particularly acute for environmental monitoring where budgets are often tight. Fifthly, there is a lack of expertise to extract information available in remotely sensed images. Key issues in the field of integrating GIS and remote sensing are scale and accuracy. Scale of the imagery, or the map data with which the imageiy is being fused, may be mismatched to each other. The scale may alos be inappropriate to the task. Detailed local information may be viewed as noise in a regional scale, whilst important local detail may be lost when working at regional scales. The second key issue is accuracy'. It is still rare to find maps, or other spatial products, with a quantitative statement of accuracy’, even though accuracy assessment techniques are by now well known. CONCLUSIONS In this presentation, it is shown that remote sensing has not penetrated the natural resource management arena as deeply, nor as quickly, as predicted 20 years ago. This is despite rapid changes to a service economy, and associated demand for information, including spatial information. In less developed countries. GIS and remote sensing offer regional and local mapping and monitoring, which may be used to assist in sustainable development. Growth of GIS has remained very high for the last 10 years, but the remote sensing industry' has not capitalised on this market, and a number of reasons for this are discussed. Some new remote sensing systems may offer solutions, including high spatial resolution imageiy, high spectral resolution imagery and to a lesser extent radar. REFERENCES Aspinall, R. and N. Veitch (1993). “Habitat mapping from satellite imagert and wildlife survey using a Bayesian modeling procedure in a GIS.” Photogrammetric Engineering and Remote Sensing 59(4): 537-543. Britannica. E. (1989). The New Encyclopaedia Brittanica. Chicago. Encyclopaedia Britannica. Burrough, P. A. (1993). Modelling land resource scenarios with field data, remote sensing and process models in GIS is easy. NARGIS 93, Darwin, AGPS: Canberra. Campbell, H. and I. Masser (1992). GIS in British Local Government: An Overview of Take-Up and Implementation. Proceedings of EGIS '92, Munich, EGIS Foundation. Drucker, P. (1991). “The new' productivity challenge.” Harvard Business Review! November-December). Fairall, J. (1995). ESR1 still ahead says report. Spatial Business. 1: 1. Green, R. (1990). “Geographic information systems in Europe.” Cartographic Journal 27: 40-42. Hoffer, R. M. (1975). Natural resource mapping in mountainous terrain by computer analysis of ERTS-1 satellite data, Purdue University: Indiana.

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