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:: Classification of land features, using Landsat MSS data in a mountainous terrain. H. Taherkia & W. G. Collins

Document type:: Multivolume work

Structure type:: Chapter

90 5 conclu: This pap Landsat Tektroni> of the r truths, c conventi delineat manually combinat demonstré to classi Figure 3 Location of Table 1 categories. Legend: 1- Veg. 1-Plain Alfalfa farms. 2- Veg. 2-Sunfacing farms and orchards. 3- Veg. 3-Poorly illuminated orchrds. 4- Veg. 4-poorly illuminated wood lands. 5- Sunfacing landslide. I* ! I LANDS J S Figure 4 Diagram of separation of categories using different combinations of Landsat MSS bands. 4.2 Topographic and Atmospheric Effects Topographically the study area is one of the most uneven in the country- The slopes are extremely steep and altitude varies from 200 m to 5670 m within a few tens of kilometers. The time of data collection (10 am) gives rise to long shadows, which, due to the relief obscure a large proportion of the image, making discrimination of features very difficult or even impossible. This problem is exaggerated during classification of the image. A major problem is that shadow is often wrongly classified with low reflective features, particularly with the poorly reflecting coaly layers of the Shemshak formation. General haze effects on the imagery were eliminated by use of conventional image processing techniques. However, an effective method for enhancing the suppressed detail of heavily shadowed valley sides, incorporates a " mirror type principle " which accentuates the subtle differences of shadow areas according to their degree of contrast with brighter adjacent slopes. At the present time software for this process is not commercially available. (b) (c) Figure 5 Classification of the subcene No 18 a) Subtraction of band 5 from band 7 showing vegetation in white and yellow and limestone in blue • b) Addition of band 5 with band 7 indicating lower Jurassics in red and coarse lava in green. c) Vegetation Index classifying scree in purple and coarse lava plus landslide in red. d) A classification of the subscene derived from the all three combinations (a,b and c). Shaded areas indicate unclassified or misclassified regions within the subscene. Overall, is not a area of i The main shadows final re vegetati identifi hazardou quantitat combinat vegetatic S upe rvi£ selectinj pixel va'. The sepai Figure 6 a supervi seen in 1 for the c result th combinat: only a misclass: resolutic difficult: The metho a bigger image. \ imagery c conventi geological example 1: be observ delineate!

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