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

4 Landunits on these scale levels can be recognized and delineated easily by visual interpretation of small scale imanery, such as those produced by satellite remote sensing (V.Ackerson & E.Fish, 1980 ; C.M. Gerards & M.C.Girard, 1973, 1975, 1985 ; H.Antrop & L.Gaels, 1977 ; M.Antrop, 1982). Landelements and -facets can be recognized indivi dually upon a satellite image when their size, shape ant orientation can be resolved and when their spec tral signature at a given time differs sufficiently from the one of the adjacent features. When this is not the case, the landscape structure to which they belong, will be distorted and may not be recognized. Landscape ecology and -planning often group the landscape elements and -facets according to two pro perties : the biotic significance and the importance of their spatial dimensions, i.e. the height and the size of the objects. Thus five groups are defined : biotic and abiotic volumes, biotic and abiotic spaces and biotic screens. Examples of each group are : forests, built-up areas, agricultural land, water- and barren surfaces, hedges and tree- rows. These groups are fairly independent from the phenology and remain constant throughout the year. They also reflect the typological composition of the landscape as well for the content (genotypical composition) as for the physiognomic appearance or the "scenery" (phenotypical composition). For the image interpretation these groups are also signifi cant. Volumes and screens are shadow giving objects. Biotic volumes occupy mostly vast areas and thus are registered with a large proportion of pure pixels. Biotic screens are seldom resolved in the image but cause a lot of interference with the spec tral reflectance of the adjacent fields. Biotic space constists of the vast areas of agricultural land containing complex structures of field patterns and a varying diversity of land uses. Both this group and the one formed by the abiotic volumes give large proportions of mixed pixels. Although these groups are not completely significant for land use interpretation (all agriculture in one group), they have the advantage of being fairly constant in time (no seasonal variation) and being good indicators for the landscape structure. Even with a well differentiated spectral signature, the possibilities of identification of each object category and of inventoring its areal distribution on a remote sensed image depend upon three additio nal factors : 1. - the size of the objects vs. the pixel size ; 2. - its shape (compactness and orientation) vs. the pixel shape ; 3. - its areal proportion in the scene. The first two factors determine the proportion between the pure and mixed pixels for each object. The probability of having pure pixels in a given category increases with the object size, the com pactness of its shape and the proportion of the area it covers. These three parameters may vary a lot in the geographical space. In visual image interpretation, a remote sensed image is to be considered as an holistic feature showing more or less structured image primitives. These are made of aggregates of adjacent pixels having the same photographical density. In analogy with the airphoto-interpretation these primitives form the texture of the image and may be called texels. Their significance is fundamentally diffe rent from the concept of a pixel and from the con cept of texture in digital image processing. They are formed by a real spatial structure and are not mathematical constructions between pixels. The size, shape orientation of the texels as well as the patterns they may form, contain distorted infor mation about the landscape structures. A pixel should be considered as a discrete sample out of the landscape continuum. No classification problems with the pure pixels as far as the diffe rent categories have distinct image properties. Figure 1. Hierarchical relatioship between land scape structure, structure of the image, texels and pixels. Landscape elements : A = hedge- & tree- rows ; B = woodland ; C = fields ; M = buildings ; D = wasteland. Components of the landscape : BM = biotic volumes ; AM = abiotic volume ; BR = biotic space ; AR = abiotic space ; S = biotic screens. Texels formed by adjacent pixels having the same DN. Texture formed by a spatial pattern of texels. Micro-structure formed by a spatial pattern of textures. Macro-structure of the image correspond to the main landscape types : P = polderland ; H = Houtland ; M = Meetjesland ; G = region of Ghent ; L = Land van Lokeren ; W = Land van Waas. On the contrary, mixed pi els do not contain any real information about the landscape. Their classification can be improved only by adding con textual information. This is achieved automatially in the deductive procedure followd in visual inter pretation, using textural and structural informa tion from all over the scene. There is an space-dependent hierarchival rela tion between pixels, texels, textures and struc tures in the image and in the landscape. Fig. 1 illustrates this. 3. THE METHOD The integration of structural information about the landscape with the other ground truth could be achieved in the following scheme : 1. - make a typological classification in the area of study, combining classical methods of landclassification and geographical lands cape analyss. Attributes significant for the structural properties of the landscape should be used ; 2. - sample the remote sensed image for the landscape units obtained using mainly tex tural parameters. Estimate the proportion of the pure pixels for the different land scape components ; 3. - use the aical c tain ur strata categor 4. - imp!erne procedu General pur been used sin 1980 ; Webste The/are based medium to sma Stereoscopic : from SPOT, wi' Photomorphic of a resoluti( in a classific of at least a Girard & C.M.C 1980). Geography of fication of sl mann, 1973). Landscape an cribe and meas pe. Important directional an The proporti rain feature c a simulation p et.al., 1984 ; groups describi constancy. A topologica butes measured mi lari ties bet\ cluster analys Euclidian distc Finally, the to terrain knov Landscape eleme portant changes should be used the occuring ge similarity can be defined whic to the pixel si 4. AN APPLICATI (BELGIUM) 4.1.The general Although a smal diversity of bo The long histor; man, is characti -different soc different mant different natt -specific and c environment, v of the soil cc -drastic change Revolution anc building, indi The high popul the average rura sq..km, a motorw railway density differences in 1 administrative r from general sta In the regions 21 % and 77 % of U P- Linear const ways, occupy aboi The utmost split obvious.

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