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 SPOT simulation data in forestry mapping. S. J. Dury, W. G. Collins & P. D. Hedges

Document type:: Multivolume work

Structure type:: Chapter

lission on Correct Total % 141 26 50 27 139 73 45 69 104 74 192 74 183 64 149 82 85 69 11 6 207 89 185 48 119 79 91 87 277 85 705 93 295 41 / + I V X / / nd 3 I9-0'89(u»w) :he sixteen From inspection of Fig 1, one would also expect misclassification between the spectrally-similar values of: European Larch (1933) and (1949); Oak (1845) and (1915); Douglas Fir (1966) and Norway Spruce/Scots Pine (1966); and finally Corsican Pine (1965/6) and Norway Spruce (1940). This is borne out by reference to the confusion matrix. The classification of Norway Spruce (1940) would at first seem abnormal with a classification accuracy of just 6%. In fact 93% of the training area has been misclassified as Corsican Pine. A possible explanation could be the result of the close juxtaposition between the spectral values (See Fig 1) but the higher variance in all three bands of the Corsican Pine (See Table 2) , such that in the classification process most pixels are classified as the latter. At this stage it was decided to merge the classes Oak (1845) and Oak (1915) . By treating the two as one class, the classification accuracy increases as shown at the foot of Table 1. The European/Hybrid Larch (1981) class would be more appropriately labelled 'clearcut' as the very young, well-spaced trees would contribute a negligible proportion of reflectance within a single pixel. Table 2. Standard deviations about mean for the 16 classes. Band 1 Band 2 Band 3 Oak 1845 2.1 1.7 0.4 If 1915 2.2 1.4 0.5 II 1947/9 3.4 2.7 0.4 SwCh 1961 1.9 1.1 0.4 EL 1933 1.9 1.0 0.4 " 1949 2.6 1.0 0.4 EL/HL 1981 5.4 1.7 0.5 HL 1971 2.4 0.7 0.3 DF 1966 2.1 1.4 0.5 NS 1940 1.7 1.1 0.3 If 1971/2 2.8 0.9 0.4 NS/SP 1966 1.9 1.0 0.4 SP 1928 2.2 0.9 0.4 CP 1965/6 3.1 2.5 0.4 URBAN 10 8.7 0.8 AGRIC 8.2 5.3 0.6 Table 3. Summary of Commis s ion/Oruission Errors for Evaluation Areas. Class Omission Errors Total % Commission Errors Total % Correct Total % Oak 1845/ 1915 218 81 15 23 51 19 " 1947/9 72 61 89 65 47 39 SW CH 1961 88 100 3 100 0 0 E L 1933 170 81 11 22 40 19 ** 1949 50 51 33 41 48 49 EL/HL 1981 34 55 7 20 28 45 H L 1971 67 66 3 8 34 34 D F 1966 84 47 67 41 96 53 N S 1940 101 99 0 0 1 1 ” 1971/2 96 55 8 9 79 45 NS/SP 1966 150 69 17 20 67 31 S P 1928 42 51 4 9 41 49 C P 1965/6 24 21 200 68 92 79 URBAN 6 4 641 81 149 96 AGRIC 69 15 1 0 390 85 A more credible accuracy assessment is based upon sites of known identity not used in the training procedure. These were the evaluation areas, and a summary of the errors is listed in Table 3. As expected, the overall accuracies have decreased though once again misclassification of urban is the main source of error. The total percentage of pixels correctly classified is 42.9%. A smoothing filter applied to the classification slightly increased this to 43.6%. One would normally expect a greater increase in accuracy than this, since the smoothing filter eliminates stray, erronously classified pixels within a homogenous stand of trees. However, where certain stands have been predominatly classified as urban, this misclassification only becomes magnified. The major problem to be addressed is the high misclassification errors resulting from the highly textured urban class. Simply omitting this class form the training data would only result in the misclassification of urban areas as woodland. The drawback with the classification algorithm is that it is based on the spectral analysis of pixels purely on an individual basis. Work is currently underway in analysing the effect of applying a textural classifier to the imagery. This method has been shown to result in increased classification accuracies in highly textured regions. (Isako, 1979) .The texture band is created by using a 'split and merge' technique developed by Cross and Mason (1985) . Preliminary results suggest a significant increase in classification accuracy. An alternative approach would be to apply a 'layered' classification. By using vegetation indices, vegetated and non-vegetated regions are separated early in the classification, preventing confusion later in the classification process. However, if errors are made at the first stage they are carried on to lower levels, regardless of the soundness of the later decisions. If the urban areas can be sufficiently well recognized in the enhanced false colour composite, it is also possible that they could be simply 'masked' from the classification. Given the hypothetical situation whereby all the pixels misclassified as urban are correctly classified, there is a drastic increase in the proportions correctly classified (See Table 4) . The effect of the smoothing filter is also shown. With reference back to Fig 1, the distribution of the spectral curves appears to agree with the findings of Mayer and Fox III (1981), and later Kachhwaha (1983) . Mayer and Fox concluded that band 5,6 and 7 Landsat MSS digital numbers have strong correlation to the size and density of the (coniferous) trees. High digital numbers tended to be indicative of poor stocking in band 5 (similar to SPOT band 2) and younger trees in band 7 (closest to SPOT band 3) . From Fig 1, the well-stocked stands of Norway Spruce, Norway Spruce/Scots Pine, Corsican Pine and Douglas Fir all have very similar reflectance in band 2, but the younger stands of Douglas Fir and Norway Spruce/Scots Pine have a much higher reflectance in band 3. An outlier is Norway Spruce (1971/2). According to Mayer and Fox, this is typical for young plantations of small numerous trees. One might expect a lower overall band 2 digital value due to the high tree density. However, the small crown size and high percentage of exposed bare soil probably causes the high digital counts.

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