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:: 856641294

Title:: Remote sensing for resources development and environmental management

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

Scope:: IX Seiten, Seiten 551-956

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A,. A. Balkema

Identifier (digital):: 856641294

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,2)

Language:: English

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

Editor:: Damen, M. C. J.

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

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:: 8 Geo-information systems. Chairman: J. J. Nossin

Write comment:: Wegen zu enger Bindung kommt es teilweise im Original zu Textverlust.

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: How few data do we need: Some radical thoughts on renewable natural resources surveys. J. A. Allan

Document type:: Multivolume work

Structure type:: Chapter

901 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 How few data do we need: Some radical thoughts on renewable natural resources surveys J.A.Allan School of Oriental and African Studies, University of London, UK ABSTRACT: The experience of applying remote sensing techniques to renewable natural resources surveys and evaluation has been to show that the necessary is too expensive and the affordable inadequate. The promise of remote sensing has been that it could provide overviews sufficiently frequently to monitor and predict agricultural change and production. In the event the remotely sensed data sets assembled for predictive purposes, such as that which provided a proportion of the data used in the LACIE experiment proved to be unwieldly even though the samples handled were only a tiny fraction of the data sensed. It is argued here that the time has come for remote sensing specialists to identify economic activities which attract returns to institutions which will benefit by advance warning of agricultural performance at regional level, and then to determine the minimum level of sample coverage and frequency necessary to provide predictive information at a price which the economic activities will bear. The experience of users of the general purpose remotely sensed satellite data available to date for renewable natural resources surveys (viz Landsat data) has led many users, and especially potential users, to acquire extravagant expectations; they have accepted the marvel of the comprehensive high resolution overview enabled by the satellite perspective without recognising the consequences of needing in addition high radiometric and temporal resolution data to detect information of concern. It will be concluded that any economically viable operational system will require economies in spatial cover through sampling, and suggests principles according to which such economies might be decided. The implications for sensor design and data processing are also discussed briefly. Keywords: sampling, remotely sensed data, land cover, crops 1 INTRODUCTION The fifteen years of handling satellite remote sensing data for renewable resources studies have been more remarkable for the research papers generated than for the economic benefit of the activity. Such initial disappointment in a new area of technology is not unusual, however, and there are recent precedents for example in a similar sphere, that of satellite space communication. The 1950s and the 1960s were decades when the optimism held out by the proponents of satellite communication was not confirmed by effective technological innovation. In applying remotely sensed data to renewable natural resources studies we are currently in a similar phase where the up-take of remote sensing data capture and data processing systems by professionals in relevant fields has been very partial. Impediments to the adoption of remote sensing procedures have been partly: • technological, • to do with the inertia of potential adopting institutions t the unreasonable expectations of the users of general purpose satellite systems t the lack of sampling and data reducing procedures for the vast data sets generated in studies of the dynamic environments of concern to renewable natural resource surveyors. This last aspect will be the main concern here; the other issues will be referred to in the discussion but will not be treated systematically. It has been shown elsewhere (Allan 1984) that with the launch of the Landsat satellites in the 1970s it became possible for the first time to address the demanding temporal features which have to be taken into account if land-cover, agriculture and cropping are to be surveyed comprehensively . The same the study emphasised that there was a clear data-volume- threshold at which current computing technology, and even future technology, would be inadequate to handle the huge volumes of digital information generated by sensing systems resolving the multi-dimensional remotely sensed data in the spatial, spectral, radiometric and temporal domains. The study also showed that most of the useful applications in renewable natural resources studies, namely crop area and production monitoring, were on the wrong side of the data volume threshold because relatively high spatial, temporal and radiometric resolutions were necessary to enable the reliable detection of such features as crop extent and condition. The only rational approach to the type of study which will inevitably generate huge data sets is some method of sampling or data reduction. The most obvious method is to relax the spatial resolution reducing the data volume in this domain by acquiring the data at a much larger pixel size and the 1980s have seen some important developments in the use of AVHRR data (one to four kilometres spatial resolution depending on whether captured at the nadir or the swath edge) as described by Justice et al. (1985) At the one kilometre spatial resolution the use of such data represents a reduction of 226 times in the volume of data in the spatial domain compared with Landsat MSS; at the four kilometre level its use represents a 3616 times reduction. Meanwhile

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