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

Damen, M. C. J.

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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:: 856343064

Title:: Remote sensing for resources development and environmental management

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

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

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:: 4 Renewable resources in rural areas: Vegetation, forestry, agriculture, soil survey, land and water use. Chairman: J. Besenicar, Liaisons: M. Molenaar, Th. A. de Boer

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: The use of multitemporal Landsat data for improving crop mapping accuracy. Alan S. Belward & John C. Taylor

Document type:: Multivolume work

Structure type:: Chapter

385 data shows :lassification cative of the ogies at this d the oilseed s low and the r, is not yet oung plants. the spring as and beans. emerged in soil class. ve occured in fusion is now ereals, grass nd beans; and racy of 51.5% al confusion, d the oilseed usion between eet, peas and confused with to the lower lanting. The the spring nfusion. The ively limited 11 not found, of the winter ise to some g planted a op growth and However both 1 established lassification and 73.6% for and now has a coincident still showing oodland class he deciduous efined with a now that the paration from esult in good sses. A mean strates this, ique spectral other class, 100%, though hese were all lassification ed the same onfusion was ry pixels was entified with growth and good spectral d beans which anges in crop of confusion the winter accuracy of een the two lassification ing date have n between the assified with ey having the barley has a two woodland 0% accuracy. As expected the multitemporal combination of data from April and May gives the best results. Overall class purity is now 69.78%. Classification purity is greatly increased for the cereal crops. The unique spectral response of the oilseed rape in May plus the reasonable separation of the two winter cereals in April combined with the good separation of the spring crops in May give class purities of 100% for the oilseed rape, 87.4% for spring barley, 82.9% for the winter wheat and 69.7% for the winter barley. The rather low figure for the winter barley highlights the difficulties of accurate separation of crops with closely matched phenologies. Inclusion of Multispectral data from late June and early July would in all probability resolve the winter wheat, winter barley confusion because of the difference in the time of crop scenescence. Comparison of the classification crop area estimates with proportions for the whole county and the parishes show large errors in the classification of the cereal crops. In particular the spring barley is wildly over predicted. Much of the spring barley class seems to be from boundary pixels, and so can be considered to be a mixed pixel class. Chhikara, 1984 has shown that classification of mixed pixels is biased and that bias in the crop proportion estimate is increased by an increase in the relative size of the mixed pixel class. Such bias may account for the over estimation of the spring barley as this would appear to be a mixed pixel class of considerable size. Most of the other crop types also show considerable error in area estimation when compared with the June census data. This tends to show that the classification accuracy as predicted through the confusion analysis of the training areas is rather over optimistic. It must however be remembered that the crop area estimates for the county, the parishes and the independant crop area estimates used to weight the confusion matricies are based on the June census returns. Work as early as 1955 by Coppock highlights the fact that inaccuracies can emerge in such data due to the relationship between parish and farm boundaries, and more recently Wright, 1985, in his work on oilseed rape classification found errors in excess of 100% in the area recorded by the census return and the actual ground conditions. The possible errors from this source may go part of the way towards explaining the rather poor crop area estimations. 5. CONCLUSIONS The thesis that the use of multitemporal Landsat data in crop classification will give greater classification accuracy than single date has been shown to hold for the United Kingdom. The classification accuracies as expressed through the analysis of the training data are generally satisfactory though problems in good discrimination between the winter cereals and grassland remain, even when multitemporal data is used. This is a problem associated with lack of suitable imagery. Landgrebe, 1974, and Baur et al, 1979 both report decreases in classification accuracy through the use of mismatched multitemporal data. However, the data set used shows the clear advantages of using multitemporal data, though image aquisition corresponding to the scenescent phase of the winter cereal crops may well prove to be vital in the successful separation of these closely related crops. Alternative approaches to the analysis of multitemporal data may also improve the discrimination between the various cover types. Work by Richards (1984), has shown the value of using principal components transformation in the analysis of multitemporal data. Regions of localised change in constant cover types were found to be enhanced in the higher components. Simple image addition through the use of multitemporal Landsat colour composites has also been shown to be of value in change detection. Areas of change showing up as a series of colours and areas of no change showing as black and white (Eyton, 1983). Vector change detection has also been sucessfully applied to multitemporal Landsat data. Engvall et al, 1977, showed good proportion estimates for winter wheat through the analysis of the temporal trends in the Landsat mean vectors from training areas. The problems of mixed pixels associated with field boundaries encountered in this study calls for the greater use of geographic information systems. The integration of map data such as field boundaries could considerably reduce the detrimental impact of mixed pixels on classification accuracy. The potential and the problems of crop identification and monitoring from multitemporal satellite imagery are illustrated in this study. With new developments in this area and improved frequency of satellite overpass allowing the aquisition of sufficient cloud free data many of the problems will be overcome and the potential for crop mapping and monitoring will be increased REFERENCES Anderson J.E. (1985), The use of Landsat 4 MSS digital data in temporal data sets and the evaluation of scene to scene registration accuracy. Photogramm. Eng. and Remote Sens.51:457-462. Badwhar, G.D. (1984), Automatic corn-soybean classification using landsat MSS data. 1. Near harvest crop proportion estimation. Remote Sens.Environ.14:15-29. Bauer, M.E. (1975), The role of remote sensing in determining the distribution and yield of crops. In Advances in Agronomy (N.C.Brady,Ed.), Academic Press, New York, 2:271-304. Baur M.E., Cipra J.E.,Anuta P.E. and Etheridge J.B., (1979), Identification and area estimation of agricultural crops by computer classification of LANDSAT MSS data. Remote Sens. Environ.8:77-92. Card D.H. (1982), Using known map category marginal frequencies to improve estimates of thematic map accuracy. Photogramm. Eng. and Remote Sens.48:431-439. Carlson R.E. and Aspiazu C. (1975), Cropland acreage estimates from temporal, multi-spectral ERTS-1 Data. Remote Sens. Environ.4:237-243. Chhikara R.S. (1984), Effect of mixed (boundary) pixels on crop proportion estimation. Remote Sens. Environ.14:207-218. Coppock J.T. (1955), Farm and parish boundaries. Geographical Studies.2:12-26. Engvall J.L., Tubbs J.D. and Holmes Q.A. (1977), Pattern recognition of Landsat data based upon temporal trend analysis. Remote Sens. Environ.6:303-314. Eyton J.R. (1983), Landsat multitemporal color composites. Potogramm. Eng. and Remote Sens.49:231-235. Hay C.M. (1974), Agricultural inventory techniques with orbital and high altitude imagery. Photogr. Eng.40:1283-1293. Landgrebe D.A. (1974), A study of the utilsation of ERTS-1 data from the Wabash river baisin, final report, Purdue University, Purdue, IN, LARS, NASA contract NAS 5-21773. Lindenlaub J.C. and Davis S.M. (1978), Applying the Quantitative approach. In (Swain P.H and Davis S.M. Ed's) Remote sensing:the quantitative approach. McGraw-Hill. pp.291-336. Morain, S.A. and Williams D.L. (1975), Wheat production estimates using satellite images. Agron J. 67:361-364.

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