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:: 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:: Double sampling for rice in Bangladesh using Landsat MSS data. Barry N. Haack

Document type:: Multivolume work

Structure type:: Chapter

462 are either mature (non-green) or harvested in March. Possible spectral confusion classes for boro in March are forested areas, aquatic vegetation such as water hyacinth in ponds or reservoirs and homesteads. The best available Landsat scene of sufficient quality during the time of greatest boro green biomass was 3 March (scene number E-2406-03404). Maps and statistical information on the location and amount of boro rice were obtained from the Landsat MSS data using both digital and visual analysis strategies. 3 DIGITAL PROCESSING A variety of digital processing techniques for mapping boro rice were investigated as a part of this project. These techniques ranged from rather simple and inexpensive, to more complex and/or costly procedures. The objective was to determine the technique with the best combination of accuracy and cost effectiveness. The various techniques examined for mapping the boro are presented following. 3.1 Single date single band level slicing Graymaps from individual MSS bands 5 and 7 produced for the study area using system derived level slicing did not discriminate features of interest. Manual level slicing maps improved on the maps and provided indications of some of the boro locations but also confused water and boro areas. MSS 5 30 I 29 I 28 I 27 I 26 I 25 J 24 ! 23 I 22 I 21 I 20 I 19 I 18 I 17 I 16 I 15 I 14 I 13 I 12 I WWWWWWWW00000000000000000000000000000000 wwwwwwwwoooooooooooooooooooooooooooooooo wwwwwwwwoooooooooooooooooooooooooooooooo wwwwwwwwoooooooooooooooooooooooooooooooo WWWWWWWW00HHHHH0000000000000000000000000 WWWWWWWWWHHHHHH0000000000000000000000000 WWWWWWWWWHHHHHHOBBBBBBBBOOOOOOOOOOOOOOOO WWWWWWWWWHHHHHBBBBBBBBBBBOOOOOOOOOOOOOOO WWWWWWWWWHHBBBBBBBBBBBBBBBOOOOOOOOOOOOOO WWWWWWWWWWBBBBBBBBBBBBBBBBOOOOOOOOOOOOOO WWWWWWWWWWBBBBBBBBBBBBBBBBOOOOOOOOOOOOOO WWWWWWWWWWBBBBBBBBBBBBBBBBOOOOOOOOOOOOOO WWWWWWWWWWBBBBBBBBBBBBBBBBOOOOOOOOOOOOOO WWWWWWWWWWBBBBBBBBBBBBBBOOOOOOOOOOOOOOOO WWWWWWWWWWBBBBBBBBBBBBOOOOOOOOOOOOOOOOOO wwwwwwwwoooooooooooooooooooooooooooooooo wwwwwwwwoooooooooooooooooooooooooooooooo wwwwwwwwoooooooooooooooooooooooooooooooo wwwwwwwwoooooooooooooooooooooooooooooooo 0000000001111111111222222222233333333334 1234567890123456789012345678901234567890 MSS 7 Figure 1. Table look-up classification for boro rice(B;, water (W), homestead (H), and other (0). signature map had a boro recognition similar to the look-up table but with a slightly lower amount of boro acreage. 3.6 Multidate processing 3.2 Ratio Ratio graymaps using bands 5 and 7 provided reasonable recognition of boro pixels but confused other cover types and did not adequately delineate the water features. 3.3 Look-up table A spectral scatter plot of the study area was generated in MSS bands 5 and 7. The plot was spatially divided on the basis of known spectral signatures to recognize four cover types; boro, water, homestead and other, see Figure 1. Emphasis was then placed on examining those boundary pixels between the known signatures. In these spectral boundary areas there were considerable numbers of reflectances involved where the signatures might be from boro, from homesteads or very frequently, mixed pixels. The classified map generated by the look-up table was compared to available ancillary data and ground truth information. A reasonable map showing the four cover types was obtained by this technique. 3.4 Unsupervised classification Unsupervised signature extraction was accomplished with an automatic clustering algorithm that used a sample of the pixels and all four Landsat MSS data channels. Initially 24 clusters were obtained and then reduced to 17. The clusters were identified using available ground truth and a four category map was generated. The percentage of mapped area identified as boro was slightly less in the cluster map than in the table look-up map. 3.5 Supervised classification Twenty-six sites were selected for signature extraction for boro, homestead, water and other to represent the scene classes. Using these signatures, a maximum liklihood classifier was employed to produce a map for comparison to a map generated the look-up table. The supervised Since the classes for possible spectral confusion with boro rice in March, forests, aquatic vegetation and homesteads, are largely spectrally invariant, one way to separate them may be by use of multidate Landsat data in which boro areas appear non-green on one of the dates. Multidate clustering was accomplished on a registered data set including bands 5 and 7 for March and December (when many of the boro fields are non-green) and bands 5,6 and 7 for the same months (Colwell, 1977). Cluster classifications were attemped by use of a zoom transfer scope to merge the cluster maps and available ancillary maps. The cluster spectral signatures were also examined. Multidate clustering and classification indicated an improved recognition of homesteads, but this was accompanied by poorer recognition of other land classes due to decreased resolution caused by slight misregistration of the merged data. Misregistration is particularly troublesome in areas such as Bangladesh where many of the features, such as homesteads and water areas, are small and often linear. For mapping boro rice, a table look-up procedure has the greatest cost-effectiveness. The 3 March Landsat data were used, and Landsat MSS 5 and 7 data space was divided into boro, water, homestead and other categories. The data was then processed using the subjectively established non-linear boundaries between the various classes in this data space to produce maps. By using the table look-up, as in any traditional multivariate recognition processing not based on resolution decomposition, some pixels that contain less than 100 percent boro will be called entirely boro, and some pixels in which a fraction of the pixel is occupied by boro area will be called non-boro. The effects should be partially compensating (Horwitz, 1971). 4 BORO STATISTICS The average field size in Bangladesh is quite small, usually less than the spatial resolution (0.4 h single invento expecte product adjacen hence cluster pumps hectare Howev many pi so sm disting This irrigat methods based would p Boro officer units n Landsat the ver Landsat indeper on Lar maps betweer the Lar Initi indicat boro we not ur factors those small confuse situati process produce boro t small f of the total variab] of it to be It is consist but the aerial of Lar better A c impleme fairly procede inventr estimal estimal within total betweer sampler For which ' the di indica- were i When i perfori was foi constai Therefi assumei estima the doi Landsa determ for ani

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