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alternative to relying solely on government statistics in agricultural geography.

The June returns thus have their problems, and in this paper we propose a combination of

satellite remote sensing and fieldwork as a suitable alternative methodology for the

investigation of local variations in arable land-use. The example presented is a study of

farming in County Durham through satellite imagery, in which one of the authors hasproduced a detailed map of land-uses at the scale of an individual pixel (30x30 metres)

(Shueb, 1990). The approach adopted by-passes the spatial complexities which make the

interpretation of a parish-scale map of the agricultural returns so fraught with doubts. It

should be stressed, however, that for technical reasons, (inherent in the digital image

classification process), the reliability of our land-use classification varies from crop to crop.

Background - remote sensing

The first remote sensing satellite sensor system was launched in 1972. Since then there have

been improvements and modifications to remote sensing systems, and the increased

availability of data from the Landsat series, SPOT and others has led to many applications,among which there are important agricultural examples. Using data from the Landsat

Multispectral Scanner System and Thematic Mapper the Large Area Crop Inventory

Experiment (LACIE), the Crop Identification Technology Assessment for Remote Sensing

Project (CITARS) and the Agriculture and Resources Inventory Surveys Through Aerospace

Remote Sensing (AgRISTARS) are all examples of applications in the United States. In

Europe a number of projects have also been carried out, notably AGRESTE, DUTA and

AGRIT in France and Italy, and there is currently a European Community agricultural census

project in collaboration with ISPRA, using a combination of various types of remote sensing

data (for instance SPOT) and agroclimatic models.

County Durham presents a severe test of the robustness of remote sensing for crop

identification and area estimation. It covers a range of markedly different environments, as

influenced by micro-climate and soils, and there are also spatial variations in farm

management regimes. In essence there is a gradually phased transition from arable and mixed

farming on small fields in the eastern lowlands of the county to a dominantly pastoral

economy on the large fields and open commons of the upland west.

Methods

The image chosen was dated 31st May 1985, as close as possible to the agricultural census of

June 4th and with a minimum of cloud cover.

2

Magnetic tapes were obtained containing theLandsat TM digital data for the county, which comprised five bands in the visible and

shortwave infra-red parts of the electromagnetic spectrum. Figure la illustrates a subscene of

the Durham City environs in TM band 4 (near infra-red). Here we reproduce a monochrome

image but it is also possible to create a colour composite by combining data from three

different TM bands. This gives a very impressive and detailed multicoloured agricultural

land-use map on which each pixel is classified according to its dominating land-use.

The first task was to divide the area into five main strata according to criteria such as field

size, cropping regimes noted in previous work, agricultural intensity and soil type. Secondly a

number of sites, such as Durham County Councils Agricultural College at Houghall where a

detailed crop calendar was available, were chosen as representative of the various strata.These were used for obtaining training data which helped to improve the accuracy of a

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supervised maximum likelihood classification. The third stage was the post-classification

data processing, which included median smoothing to produce a less noisy image and a

classification accuracy assessment.3

A field survey was undertaken for the purposes of cross-checking. A stratified random

sample was taken of 3.1 per cent of the countys area, by choosing 44 kilometre grid squares

from the five strata. The sample units were identified from aerial photographs and field visits

made in order to collect information on crop type, field size, natural vegetation and soil

colour. Following the field visit, the crop areas were measured in each sample unit. These

represent the field area measurements. A final crop area estimation was produced from a

regression of the field area measurements on the Landsat area measurements.

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Table 1 shows three comparable estimates of crops grown in the county for the year 1985 and

the standard error (S.E.) of the regression. The Landsat estimates represent the results of the

multispectral classification of the TM data alone. The hybrid method of calibrating satellite

data with an input from fieldwork is considered most likely to yield accurate results. The last

column has data from the MAFFs June census.

As illustrated in the table, the regression estimate was more efficient for some crops than

others. For instance, the low standard error of the regression estimate for oilseed rape was

due to the crop being in its flowering stage, which made it relatively easy to monitor. On the

other hand the reflectance characteristics of winter barley in its early stages of growth are

difficult to distinguish from managed pastures and there will therefore be some overlap

between these categories in the table. Spring barley and crops such as potatoes could not be

clearly identified either because they had not reached a sufficiently advanced stage of growth

by the end of May. Figure lb is reproduced to show that the detailed distribution of a single

crop can be separated out or inserted as an overlay on a background, in this case of TM band

4.

Conclusion

In recent years remote sensing has begun to emerge from its exploratory phase in which

methodologies were developed and refined (Curran, 1985). It now offers a cost-effective and

timely alternative to, or cross-check for, sample land use surveys and questionnaire censuses.

Further work on methods of data processing and interpretation will enhance the efficiency ofidentifying crops and estimating their area (Bauer, 1978). Although not part of this project, it

is also possible to estimate yields from biomass and recognise factors such as disease or

drought which may hinder optimum growth. The County Durham image was for late May

and hence ideal for observing oilseed rape which was in flower, but additional pre-harvest

data is desirable to help improve the estimates for spring-sown crops. It is common practice

anyway to use two or more images from the same growing season to enhance mapping

accuracy.

Our summarised conclusions are as follows:

i) The teaching of agricultural geography from the parish-level agricultural returns faces

problems which may be partially overcome by the use of a hybrid method of satellite imageanalysis and fieldwork. It is possible using this technique to produce land-use maps for the

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classroom.

ii) The analysis of Landsat TM data, when combined with the results of fieldwork, provides a

good estimate of the area of certain crops.

iii) oilseed rape and winter wheat were more easily identifiable than spring sown cropsbecause of the date of the image. Several images during the growing season are desirable to

improve the accuracy of interpretation although there are of course resource implications in

the acquisition and analysis of additional data.

Acknowledgement: The authors wish to thank Dr D. N. M. Donoghue for his detailed

comments on an earlier draft of this paper.

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REFERENCES

Bauer, M. E. (1978) Area estimation of crops by digital analysis of Landsat TM data,

Photogrammetric Engineering and Remote Sensing, 44, pp. 1033-43.

Clark, G. and Gordon, D. S. (1980) Sampling for farm studies in geography, Geography,65, pp. 101-6.

Clark, G., Knowles, D. J. and Phillips, H. L. (1983) The accuracy of the agricultural

census, Geography, 68, pp 115-20

Coppock, J. T. (1978) Land use, in Maunder, W. F. (ed.) Reviews of United Kingdom

Statistical Sources Vol. viii, Oxford: Pergamon.

Coppock, J. T. (1984) Mapping the agricultural returns: a neglected tool of historical

geography, in Reed, M. (ed.) Discovering Past Landscapes, London: Croom Helm

pp 8-55.

Curran, P. J. (1985) Principles of Remote Sensing, London: Longman. Ministry of

Agriculture, Fisheries and Food (1968) A Century of Agricultural Statistics, London:

HMSO.Ministry of Agriculture (1985) Agricultural Statistics of England and Wales, London:

HMSO.

Shueb, S.S. (1990) Crop identification and area estimation through the combined use of

satellite and field data for County Durham, northern England, University of Durham,

unpublished PhD thesis.

ENDNOTES

1 The county data are published in the Ministry of Agriculture, Fisheries and Food (Annual)

Agricultural Statistics United Kingdom, London: HMSO. See also Department of Agriculture

and Fisheries for Scotland (Annual) Agriculture in Scotland, Annual Report and Economic

Report on Scottish Agriculture, Edinburgh: HMSO; Department of Agriculture, Northern

Ireland (Annual) Annual General Report and Statistical Review of Northern Ireland, Belfast

HMSO; and Welsh Office (Annual) Welsh Agricultural Statistics, Cardiff: Welsh Office. The

manuscript parish returns may be viewed any time at the Public Record Office, Kew and by

appointment at the Ministry of Agriculture, Block B, Government Buildings, Epsom Road,

Guildford GUI 2LD. They are also available in machine-readable form from the Economic

and Social Research Council's Data Archive, University of Essex, Wivenhoe Park, Colchester

CO4 3SQ.2

Cloud cover disturbance is a major constraint on land use mapping in the United Kingdom.3 The overall map accuracy of the land cover classification produced with median smoothing

was 82.8 per cent, with 95 per cent confidence limits of 80.4 per cent and 85.3 per cent. For

the full technical details see Shueb (1990).