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:: 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Detection of subpixel woody features in simulated SPOT imagery. Patricia G. Foschi

Document type:: Multivolume work

Structure type:: Chapter

25 Ln a hypothetical training set x 3 window. PIXEL WOODY FEATURES ped for this project ons of feature space, sions of interest in ch was constructed us- ve pixels from each of and squares represent present in adjacent undary between the sen the line joining ing points E and F. i the fields includes e circles represent Lng this boundary are slygon circumscribed lg a pixel in one of classifying it in ei- Les or the class of the study site were sature spaces whose ?al bands. The pan- ial inspection of the l are highly correlat- .on between classes sd. Therefore, only ibsequent processing; redundancy, ition, consisting of trge patch of decidu- was used in all fea- > locate its central s space. As shown in into three parts: two .elds and a central pixels. These two ¡t for woody vegeta- .ture space for each i window were then L = 79 S = 149 Figure 3. Feature space created from window data. classified by their locations in this space. ■ Examples of this classification method are shown in Figures 3-5’ In these figures, squares represent woody vegetation, triangles pointing downward and triangles pointing upward represent the two training sets de rived from the window, and circles represent the cen tral pixels in the window. The circle with the dot in dicates the pixel at the exact centre of the window. L and S refer to the line and sample of the pixel in the centre. In each example, the circles to the left of or below the line are placed in the class of small woody features. 5 5 RESULTS AND DISCUSSION Twenty subpixel woody features and two simple field boundaries were selected from the study site for the initial testing of the method. The woody features greatly vary in width and length and include hedge rows, a hedge with one large tree, single rows of trees, and strips or patches two or more trees wide. All of the woody features selected were found to be incorrectly detected by a standard nearest neighbor classification. Most of the features were also unable to be either detected or adequately interpreted by visual inspection of a colour-composite image. The classification method developed in this project correctly classified all subpixel woody features larg er than hedgerows, properly discriminated the simple field boundaries, and identified about 20 percent of the hedges. Further testing is in progress. Use of the panchromatic band will be incorporated into future classification schemes and is expected to improve classification accuracy for hedgerows. Figures 3-5 illustrate the three possible ways the training sets may be situated in feature space. The roughly triangular shape, suggested by the training sets in Figure 3, is the most typical arrangement. When adjacent farm fields contain the same land cover, the clusters of their training sets overlap as in Fig ure 4. In both of these cases, pixels containing small woody features are likely to be pulled toward the woodland cluster. However, when the three training clusters are positioned along a straight line, as in Figure 5» pixels containing small woody features are less likely to be pulled past the middle cluster into the zone designating woody features. This last case only occurred once in the 22 features tested. Before this method can be completely operational, a L = 134 S = 95 L = 57 S = 171 70 10 *0 ICO 110 120 130 1*0 150 140 170 BAND 2 Figure 5* Feature space created from window data. few practical problems must be addressed. The most important problem is determining an optimal size for the window. The 13 x 3 window is frequently too big to fit into the small comers of farm fields. This inability to fit also contributes to the creation of anomalies in the training sets. Four anomalous train ing pixels are shown in Figure 6; the correct posi tion of the division line in this feature space is also shown. A smaller window, like a 9 x 1 window, would fit into smaller fields and would create fewer anomalies. A smaller window would also be more effi cient since it would less frequently use the same pixels in its calculations. The automated use of this method could provide an effective means of mapping and monitoring the pres ence of subpixel woody features in satellite imagery. Also, the use of this method could provide the basis for determining specific quantitative information of use to ecologists and resource managers.

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