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:: Methods of contour-line processing of photographs for automated forest mapping. R. I. Elman

Document type:: Multivolume work

Structure type:: Chapter

erations îtion CPU time (min.) 5 5 3 15 150 125 105 85 65 45 30 15 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede /August 1986 Methods of contour-line processing of photographs for automated forest mapping R.I.Elman All-Union ’Lesprojekt’ Association, Moscow, USSR :eded should be the according to the s no siginficant ,B*R. But AV8B*R spent therefore C8B-R ABSTRACT: Automated extraction of forest boundaries on aerial and space photographs is con sidered» The methods depend on the contour line clearness on photos and differ in the auto mation degree. The contour extraction is followed by restoration aimed at elimination of de fects in contour lines which appeared during the input» The next stage is the random conto ur networks processing» It is meant for the transformation by contour lines from the pattern into the vector format in order to cut the network into elementary unraraified ’threads’, to code threads, etc» The method is realized in the form of the program package for the disp lay processor and is used in the forest mapping automation. find the optimal S data for forest following conclusions own that the past cover type classifi- all wavelength bands , one can find that ns more bands, ring techniques curacy because both pening the images to ) Principal component n improving the lassification because ast enhancement for e also not good enough ation because the e difference in s with different tio images. (5) Six bands are unable to The mixed bands s their appreciation ommittee, Council of for providing the so due to the Center sarch, National cessing services. 1.A. Mead 1983. )n accuracy using statistical tech iring and Remote L983, pp.1671-1678. Computer-aided rational system to isat MSS data, LARS » University, West L-231. Accuracy of forest Compatible tape, itional symposium on PP.1155-1163. ; species mapping sing of Environment 1 CONTOUR LINES EXTRACTION In the forest mapping automation with the use of Information obtained from aerial and space photos one has to do with operations on contour networks formation making up the contour basis of the complied map» These operations may be quite different by the characteristics, labour-consuming nature and complicacy depending on the primary method of contour lines obtaining» In the world practice the semi-automatic Input of the graphical information with the help of digitizers Is widely used» Such in put Is effective when the contour network is not too complex and has a lot of rectilinear segments» The preference is given to it also In case of superimposing several heterogene ous contour networks, the automatic separa tion of which is difficult» However this me thod is slow and axhaustlng as it requires a lot of manual work» A higher degree of automation and accelera tion of the process of contour lines input can be achieved by using television or opti cal-mechanical input devices» However in this case means of input and extraction of conto urs on photographs depend on the kind of units to be delineated» For example, units having a clear, high contrast image on aeri al and space photos are successfully deline ated automatically, with the help of simple threshold procedures or filters described in detail in literature (Prett 1982) (Roberts, Sobel, Kirsch et al»)» Such units in silvi cultural tasks are: forest boundaries, fresh burns, cutovers, etc» But still the major part of the necessary contour lines is left unextracted» That is why this method is used rarely, in particular model tasks» A more complicated but more effective me thod of the extraction of contour lines se parating different natural units from each other is based on the image segmentation with a further contouring of the extracted segments» In our work (Elman, Bakhtinova, Potapov, Sviridova & Bersneva 1984; Elman & Pamorozski 1986) colour and partially textu re features were used for image segmentati on» For example, in (Elman, Bakhtinova, Po tapov, Sviridova & Bersneva 1984) the seg mentation of space scanner photos obtained with the ’Fragment* equipment (Selivanov, Gektin, Panfilov A Fokin 1981) was carried out» In experimental work video information written on the magnetic tape, in three spec tral bands (0»6-0»7, 0»7-0»8 and 0»8-l»l mic rometres) was used» According to the representation of land categories, economic significance, and the r ossibility of the test plot control the fol- owing set of classes was determined: A - pine stands, B - birch stands, C - aspen sta nds, D - water surfaces, E - land non-covered with forest (arable land)» The test plots of all classes were chosen by the forest stands plans at scale 1:25000 with further field investigations» The plots were selected in side homogeneous compartments with the area of 20-30 ha» Minimal plots of the fixed area of about 5 ha were taken as control plots by which the experiment results assessment was made (Table 1)» Table 1» The results of desses extraction by colour features» K Machine solutions Q N P A B C D E number 7. A 63 63 63 100 B 50 13 63 50 79 C 14 33 07 54 33 61 D 54 54 54 100 E 92 07 99 92 93 Mean estimate ~rrr 292 ~S7~ Notes: K - actual classes Q - number of omissions N - total plots P - identified correctly The table analysis shows that classes of pine stands and water surfaces are Identified with the best reliability, and birch and aspen stands are mostly confused» There are omis sions in the arable land class 'which is in-

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