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

degradation rphology and for grazing Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 The use of multitemporal Landsat data for improving crop mapping accuracy i microlight itrated with ? use of the /graphs. study also ; time. These photo inter- very clear ilation with , which were /hole system L lot got more irs of flight ¡rtical B&W of which 900 300 oblique .cal photog- id were taken >uld even in >nably well, pith overlap I areas, or flight, the id a width of 1 of areas to ap. ferent land ; these land e surveys it een techni- s well in a capacity of s concluded le platform large wing ird genera- performance utilization odelcalcula- the future. ults lead to nd utiliza il measures :ope of this river regime ;o virtually L of the iterfering 1 on the lat may lead )n schemes j floodplain, utilization results of when land :ms of the Alan S.Belward & John C.Taylor Cmnfield Institute of Technology, Bedfordshire, UK Six Landsat MSS scenes from between October 1979 and August 1980 were located for a test site in southern England. Each scene was independantly registered to the National Grid. Resampling to 50m pixel size was carried out with Bilinear interpolation. Ground error for each scene was less than 100m. These data were then matched to 2000ha of ground information giving the crop type and field boundaries for the 1979 - 1980 growing season. Ten cover classes were identified; Winter Wheat, Winter Barley, Spring Barley, Oilseed Rape, Grassland, Sugar Beet, Peas, Beans, and Deciduous and Coniferous Woodland. Spectral coincident plots were drawn for all cover types from each image, and a decision tree applied to identify key bands / dates for maximum spectral separation of cover types. Supervised maximum likelihood classification was then used to produce crop classifications from both single date and multitemporal data. Classification accuracy was variable for the different cover classes. The multitemporal data gave better overall classification accuracies than the single date images. The best result was from a spring / early summer combination giving a mean classification purity of 70%. This is a 6% increase over the best single date classification from May, and 46% better than the worst from February. 1. BACKGROUND The large area coverage and sequential nature of Landsat Multispectral Scanner (MSS) data and the opportunity for computer data processing offers the potential for relativly cheap, timely and accurate crop inventory (Baur, 1975). The Landsat MSS has been employed sucessfully for example, in crop inventory for land use stratification (Hay, 1974), the identification and area estimation of winter wheat (Morain and Williams, 1975), automatic corn-soya bean classification (Badwhar, 1984) and as an inventory system for agriculture in California (Wall et al, 1984). Such work has involved both single date and multitemporal image sets. The accuracy with which individual crop types can be classified from Landsat data varies widely. Figures range from 80% accuracy of test field recognition reported by Baur et al, 1979, in their work on the identification and area estimation of corn and soya bean, to an overall crop classification accuracy of less than 50% found by Taylor et al, 1983, working on crop classification in the United Kingdom. The work by Baur et al shows that the 80% accuracy of test field recognition was achieved by using Landsat data for a 3 county area of Illinois, United States of America. In this region 81% of the total land area was cropped, and 71% of this crop land was planted with either corn or soybean. The aquisition date of the imagery used was from August, (identified elswhere in the same work as being the best date for spectral separation of the corn and soybeans).In contrast the 50% crop classification accuracy obtained by Taylor et al was from imagery aquired for Feltwell, Suffolk, United Kingdom. This is an area of mixed cropping in which a range of cereal, oilseed and root crops are produced in small, irregularly shaped fields. Image aquisition was also not idealy matched to the crop calendar. Imagery was used from April, where considerable overlap in crop spectral response exists for this region. Experimental proceedure may go part way to explaining the large difference in reported classification performance, but the contrasting agricultural situation and match between image aquisition and agricultural clalendar are probably of greater importance. Carlson and Aspiazu, (1975), endorse this view stating that, "Satellite coverage critically timed with a crop development calendar is noted to improve classifier effectiveness." The potential for further improvements through the use of the temporal dimension in crop surveys from space platforms has long been recognised, (Steiner, 1970), though there are few published cases where the technique has been applied for crop inventory. Those investigators who have used the technique have generally found that improvements in crop classification accuracy are found over the use of single date images. Von Steen and Wigton, (1976), found that overall classification accuracy of grass, cotton, corn and soybeans using three observation dates increased by 88% over the 50.8% classification accuracy from the best single date image. Other work such as that by Carlson and Aspiazu, (1975), shows similar benefits from the use of multitemporal Landsat data for cropland acreage estimates. More recent work (Odenweller and Johnson, 1984) has used Landsat derived temporal-spectral profiles for crop identification, where a temporal-spectral profile is defined as "The multitemporal representation of Landsat data, optimally in the form of a green vegetation indicator, from a single labeling target". In their work Kauth and Thomas greeness component values were calculated for a series of targets and plotted against time as a temporal spectral profile. Individual cover types were then identified within the context of baseline proceedure analysis logic. Using this approach corn and soybean, could be separated from perennial crops and bare soil, then identified individually because of differences in the amplitude and shape of the Temporal - spectral profiles. Badwhar, again working on corn / soybean classification has developed an automatic corn-soybean classification from Landsat MSS using multitemporal data (Badwhar, 1984). This requires at least two image aquisitions which again are transformed to the Kauth and Thomas greeness

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