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:: Structural information of the landscape as ground truth for the interpretation of satellite imagery. M. Antrop

Document type:: Multivolume work

Structure type:: Chapter

ship between land- the image, texels and = hedge- & tree- ds ; M = buildings ; he landscape : otic volume ; BR = ace ; S = biotic cent pixels having ttern of texels. atial pattern of he image correspond P = polderland ; ; G = region of W = Land van Waas. do not contain landscape. Their only by adding con- chieved automatially lowd in visual inter- structural informa- hierarchival rela- extures and struc- landscape. information about the d truth could be me : ification in the classical methods d geographical lands- significant for s of the landscape image for the using mainly tex- te the proportion he different land- 3. - use these image attributes in a further typolo gical classification of the landscapes to ob tain units which are potential homogeneous strata for a statistical sampling of landuse categories using image classification ; 4. - implement these strata in the classification procedure. General purpose landclassification methods have been used since the 1960's (Howard J. & Mitchell C., 1980 ; Webster R. & Beckett P., 1970, Antrop M.,1983) The/are based upon a stereoscopic interpretation of medium to small scale air photographs. Stereoscopic satellite imagery as the one available from SPOT, will allow the use of the same methods. Photomorphic image classification on orbital imagery of a resolution level of Landsat MSS and TM, results in a classification of land units on scale levels of at least a landregion (J.Townsend, 1981 ; M.C. Pirard & C.M.Girard, 1985 ; V.Ackerson & E.Fish, 1980). Geography offers techniques for a regional classi fication of such categorical landunits (A.Kilchen- mann, 1973). Landscape analysis offers a set of methods to des cribe and measure spatial structures in the landsca pe. Important ones are network analysis, shape and directional analysis. The proportion of pure pixels for any kind of ter rain feature can be based upon an estimation using a simulation pixel (M.Vauzelle, 1982 ; V.Aurelio, et.al., 1984 ; M.Antrop, 1985). The use of the 5 groups described is suggested because of their constancy. A topological classification, based upon the attri butes measured, can be achieved by determining si milarities between the sample sites followed by a cluster analysis. In this case a mean squared Euclidian distance was used as similarity index. Finally, the clusters obtained should be related to terrain knowledge of the occuring landscapes. Landscape elements and components which cause im portant changes in the proportion of pure pixels, should be used for a chorological classification of the occuring geographical regions. Across-boundary similarity can be used for this and thus zones may be defined which have a constant structure according to the pixel size used. Although a small country, Belgium possesses great diversity of both natural and cultural landscapes. The long history of the occupation of the land by man, is characterized by : -different social groups and cultures acting in a different manner and different periods for the different natural regions ; -specific and drastic adaptations of the natural environment, which are also reflected in changes of the soil conditions ; -drastic changes occured since the early Industrial Revolution and are still going on today (house building, industrial land use, re-allotment). The high population pressure is illustrated by the average rural population density of 313 inhab./ sq. km, a motorway density of 13 km/100 sq.km, a railway density of 14 km/100 sq.km. The spatial differences in land use patterns between the major administrative regions of Belgium is evident even from general statistics (table 1.) In the regions of Flanders and Brussels resp. 21 % and 77 % of the areas are more or less built- up. Linear constructions such as roads and water ways, occupy about 7 % and 20 % resp. of the area. The utmost splitted character of the land use is obvious. Table 1. Cadastral landuse in Belgium, the region of Flanders and Brussels (1980). Proportion (%) of the area occupied by the main categories (after H.Van der Haegen, 1982). Flanders Brussels Belgium agriculture 67 11 61 woodland, heathland 12 12 23 rurban 4 19 3 built-up areas 10 38 7 roads, waterways 7 20 6 Total area in ha 1351143 16178 3051871 About 12 % of the agricultural land has been refor med by re-allotment, mainly characterized by more open landscapes and larger fields. Traditional landscapes, which are given a large historical and ecological value, still occur more less well con- servated over an area of about 25 %. Although a great diversity in landscape typs exists, it is not possible to distinguish them on Landsat MMS image, not even by greatly deductive visual interpretation (I .Vandecasteele, 1979 ; M.Antrop, 1985). In most cases there is no systematic rela tionship between the image structure formed by the texels and the landscape structures for the whole region of Flanders. Only macrostructures can be deli neated easily by photomorphicimage classification. They are formed by the settlement pattern at the scale level of villages and small towns, the pat terns formed by vast landelements (waterbodies and woods with an area of at least 1 sqkm), the main geomorphological units (valleys, polders, cuesta's, dunes) and the fieldpattern at the scale level of complexes. The orientation of the latter are espe cially important in the recognition process for regions with large fields bordered by ditches and tree- or hedgerows. 4.2.The analysis based upon a simulated pixel samp ling A first analysis was based upon a landscape sampling using a simulated pixel upon orthophotographs on a scale of 1/10.000, according to the method already described. Sample sites have not been selected on a regional basis but on their structural type, especially when the structure is fine and complex (fig.2). Consequentely most sites are taken from the sandy region of Flanders between Ghent and Brussels, where the rural landscapehas been affected strongly by recent unorganized settlement, and which is also called now a 'rurban' landscape. The sampling used a simulated TM pixel size of 30mx30m. The results are given in tables 2 and 3. The great diversity, complexity and the utmost fine structure of the different landscapes is evi dent even when only using the rather general cate gories of biotic volumes (BM, i.e. mainly woods), abiotic volumes (AM, i.e. mainly buildings), biotic space (BR, i.e. all the agricultural land and heathland, abiotic space (AR, i.e. water surfaces and open (industrial) wasteland and biotic screen (S, i.e. tree- and hedgerows). The number of landscape elements/sq.km, is extre mely high (table 2) and varies between about 1700 (openfield and re-allotment landscapes) to about 3500 (traditional enclosed landscapes with small fields and regions with open settlement of a high density). The proportionof estimated pure pixels (size 30mx30m) is accordingly very low : about 50 %-80 % in the large scale open landscapes to only about 12 %-25 % in the fine structured enclosed and rurban landscapes. 4. AN APPLICATION UPON THE REGION OF FLANDERS (BELGIUM) 4.1.The general setting of the landscape 5

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