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:: Interpretation of classification results of a multiple data set. Helmut Beissmann, Manfred F. Buchroithner

Document type:: Multivolume work

Structure type:: Chapter

11 '-ESSES > z H i ID O "O ESSES H H ecosystems research complement each discusses the f remote sensing es of thematic erpretations are by the level of rpreter. If the gh, there is gainst. For the oned questions, terpretation is particular this heoretical back- eal contributes fication, and so estigation. It e influence of omputer-assisted e amount of ise. The latter ccount for the like with the by means of the it is formally ATA SETS Austrian Unesco iosphere (MaB)" been transformed fstem, using the Institute for iter Graphics in to compare the itents of the ster on a pixel 3.1 Basic data The following data were available: Airborne multispectral scanner data of the 11 band Bendix Scanner from the German Flugzeugmeßprogramm (FMP), covering an area of 512 x 700 pixels of 2 x 2 to 3 x 3 m2 size; Landsat MSS data (Buchroithner 1982); - Landsat TM data; two colour infrared aerial photographs (film diapositives); a topographic map 1 : 5 000 (Pillewizer 1982) ; a vegetation map 1 : 2 500, only covering part of the study area (Karrer 1980) ; a soil map 1 : 5 000 (Müller 1982); a geological map 1 : 5 000, generated by photographic enlargement of an original map 1 : 25 000 (Cornelius & Clar, 1935); an ecotopic map 1 : 5 000 (Neuwinger in prep . ) . 3.2 Data processing 3.2.1 Digitizing of colour infrared aerial photographs The two CIR aerial photographs available for the study area with a format of 23 x 23 cm2 were digitized with an optoelectronic filmwriter. The measured intensities were quantized in 8 bits (0 - 255) and stored on a magnetic tape. 3.2.2 Geometric rectification of digital imagery In order to create a reference system for the map data available, the CIR images and the airborne scanner data had to be brought into the system of the map data which in our case was the Gauss Krueger System. For this the different geometries of the various data sets had to be taken into consideration. Whereas the aerial photographs display a central projection, the airborne scanner data were generated through scanning of the earth surface. The geometric rectification was done by means of the program system GAMSAD (Kaufmann 1985). The applied interpolation algorithm was the nearest neighbour pixel asignment which prevented a falsification of the original grey values. The identification of the ground control points in the images was rather difficult as the clearly identifiable points were almost exclusively located along a mountain road which more or less runs close to the vertical axis of the imagery. Through this very unfavourable distribution of control points extrapolations along the margins of the images were unavoidable. This led to slight deviations in the peripheral parts of the rectified images. 3.2.3 Digitizing of map information The digitizing of the various maps mentioned in section 3.1. was performed using the geoinformation system DESBOD (Ranzinger , Kainz & HUtter 1985, HUtter 1986). The transformation of map data into the digital form was carried out at a digitizing light table by means of a curser. A special problem for the digital storing was the geological map which had been produced by photographic enlargement of the original map 1 : 25 000. Through the generalization and the thickening of the lines, the correspon dence with the other maps was restricted. Especially along the road the deviations are rather strong (cf. section 5). In order to reach geometric correspondence with the other maps, the road was not digitized and the thematic information was transformed by an affine transformation using some well identifiable points. After the manual digitization the map information was transformed into the image data formats to allow the analysis of the remote sensing data, i.e. a vector- to-raster-convertion was performed. After that digital map information was displayed on a raster screen, and colours were asigned to the various classes. 3.2.4 Generation of a digital terrain model The basic data for the generation of the digital terrain model (DTM) by means of the program system GTM (Leberl & Olson 1982, Hafner & Raetzsch 1985) were the contoyr and drainage lines of the topographic map. The respective film sheets were automatically scanned and the resulting raster data converted to vectors. After that, the data were transformed into the Gauss Krueger System and discretized into a grid of 2x2 m2. This raster contains all points which are located on a control line of the respective height of this line. Eventually the height values for all not yet defined raster points were determined by interpolation between the known points. The resulting raster can then be treated like a digital image. 3.2.5 Information derived from the digital terrain model As an additional information for the pixelwise interpretation of remote sensing data for each picture element of the airborne scanner image slope gradient and aspect were derived by applying the first derivation of the DTM data. In order to prevent a so-called terrace generation which might result from the integration of a too small area and to better adapt those data to the actual terrain, gradients and aspects were calculated using an algorithm which takes all eight neigboured pixels into account. Thus, the desired smoothing effect was reached but, on the other hand, a significantly higher effort in computation had to be accepted. In addition, the influence of the sun elevation and azimuth has been derived from the digital terrain model by applying the method of the horizon. For the moment of the scanner data acquisition, azimuth and sun elevation of those pixels, which were situated in the shadow, were marked. This resulted in a binary image.

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