Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

damen, m. c. j.

Bibliographic data

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:: 856641294

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: IX Seiten, Seiten 551-956

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A,. A. Balkema

Identifier (digital):: 856641294

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,2)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

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:: 7 Human settlements: Urban surveys, human settlement analysis and archaeology. Chairman: W. G. Collins, Co-chairman: B. C. Forster, Liaison: P. Hofstee

Write comment:: Wegen zu enger Bindung kommt es teilweise im Original zu Textverlust.

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Spectral characterization of urban land covers from Thematic Mapper data. Douglas J. Wheeler

Document type:: Multivolume work

Structure type:: Chapter

66 + + + + 10 16 20 1 notion scatter equentially n image display ity or there is a rials, so play very similar sses contained e than one cover eonfusion caused ielding similar s of the luster diagrams, ial photography, ry was best oteristics of ^tral classes the jure 2 by s. Ground arficial spectral э, represents the Dttom) , and light concrete, metal, er conditions ions in both of 49, 2, and 48 al component st of the light represented a small amount ing. Classes 52, drop between between channels nany of the flat fell as saline or agetation. are soil with the Ef, and were з of dry farms Ltion, usually <rere mixed aleted concrete 3es 50 and 42 construction Many of these m trailer parks scraped land, jht colored soils sand covered Water was expected to have a low spectral response in all four channels, especially in band 5, the water-absorption band. There was difficulty, however, in distinguishing between water and other dark cover categories by four channel mutlispectral classification alone. Class 37 displayed a typical pattern for open water and was found in lakes, sewage treatment ponds, and in other deep ponds. Class 54, though, was comprised of equal proportions of open water and coal piles. By training on individual pixels in both water and coal cover categories it was noted that the spectral responses frcm the visible and near infrared bands were essentially identical, while the middle infrared channel (band 5) showed a slightly lower response for the water. This, however, did not present enough variability to keep the two classes from being merged in the hierarchical clustering process. Classes 64, 28, and 27 also showed a high propensity for water within their spectral classes. The spectral curve for class 43 displayed a rather odd shape for water, yet it characterized the Jordan surplus canal with it silty water and its mix with embankment materials. The very dark materials in Figure 2(b) were primarily considered to be open coal or slag piles, with occasional sites of tar or very black asphalt. As mentioned earlier, there was considerable difficulty in clearly distinguishing these cover types from water surfaces by spectral characteristics alone. Other methods were later tested to better differentiate these cover types. Class 54 represented the darkest coal and slag piles in a smelter location. Lighter colored materials were represented by classes 64 and 28 and were found in scattered coal piles throughout the industrial areas of Salt Lake. Asphalt surfaces cover much of the urban Salt Lake area and are good indicators of cannercial and transportation land uses. A distinction was made between light and dark asphalt surfaces since they often represent different land use activities or have different effects on the urban environment. The dark inert materials cover type mostly included blacktop areas and dark material mixed with soil (such as railroad yards). Classes 12, 38, and 27 represented this cover category although class 27 also characterized some water bodies. The other classes were also occasionally confused with water in the surplus canal. The light asphalt surfaces were generally asphalt mixed with gravel to form roads and parking lots. Several of the pixels in these classes also represented roofing materials in the commercial areas composed of tar and gravel. Classes 58 and 10 especially characterized these areas devoid of vegetation. Classes 59 and 5, on the other hand, had a higher proportion of vegetation cover mixed in, as evidenced by the flatter slopes between channels 4 and 5 in their spectral curves shown in Figure 2(d). Classes 15 and 35 primarily represented parking lots and road networks. Although class 63 was also mostly transportation, there was a little mix with natural grass areas. There were large portions of the study area with mixed pixel responses. This generally occurred in residential areas where a large variety of heterogeneous surface materials were spaced very closely together, usually into an area smaller than the spatial resolution of a pixel. The mixed responses from surfaces such as lawns, concrete, asphalt shingles, trees, metals, etc. made the hybridized signature curves displayed in Figure 2 (e- f). While these classes occupied virtually the same region on the discriminant function scatter diagram as other non-mixed land cover categories, they reflected distinct differences in the shapes of their spectral curves. This was observed by comparing Figure 2 (e-f) with Figure 2 (g-i). Class 56 had a very bright response and was found in many trailer courts. The major contributor to this response was the shiny roofs of trailers with sparse lawns and asphalt mixed in. This class also expressed some confusion with agricultural fields having bare ground and stubble remaining. The shape of the spectral curve for class 36 was almost identical to class 56 yet it was slightly darker in reflectance. Class 36 was also primarily shingle roofs with small mixes of lawns and trees. This class was found among light roofed condominium complexes and was also occasionally confused with stubble fields. Classes 8 and 34 were found in condominium complexes and other high density residential areas fringing the central business district (CBD). There were three distinct cropped fields, however, that were also identified as class 8. Class 45 was not as commonly associated with residential areas. This class more often represented the mixed pixels where asphalt borders on natural grass areas (e.g., along freeways, railroad tracks, and in cortmercial or industrial areas) . Other mixed pixel locations showed a higher percentage of vegetation contributing to the spectral response. Classes 65 and 4 represented surface materials that were approximately 50 percent covered by vegetation (usually lawns and trees). Class 1 contained about 60-70 percent vegetation cover, while classes 14 and were generally over 75 percent healthy vegetation. These mixed pixels with high vegetation components were again found primarily in residential locations where surface cover was very heterogeneous within a small area. These mixed classes also showed a very large within-class spectral variance (over 6.5 times the mean variance for all remaining land cover classes). A large portion of the study area was not developed and was covered by senesced annual grasses and weeds. A wide variety of species and cover densities were grouped within a few signatures for this category. Figure 2(g-h) illustrates two distinct patterns in these natural grasslands. Classes 44, 47, 13, and 30 displayed a pattern of healthier vegetation, as indicated by the steepness in curves between channels 3 and 4. These were usually weedy fields or vacant lots where the plants were not completely senesced. In classes 13 and 30 the soil was often wet, contributing to the darkness of the response. There was seme confusion between this cover category and fields in areas where crops were newly planted and the soil was still contributing a major portion of the response. Classes 33 and 22 still had a considerable amount of vegetative response and were quite reflective, containing a high proportion of light soil. Classes 51 and 57 represented darker colored soils in natural, undeveloped areas, while classes 25, 7, and 11 were often found as idle fields in dry farm or irrigated areas. Cropped or sparsely watered agricultural fields represented in Figure 2(i), were typified by mixed soil and vegetation responses. The surface materials contributing to these spectral curves mostly included cropped fields with seme stubble and sane new growth showing through; newly planted or young crops; pasture areas, irrigated or subirrigated, with weeds or bare patches; and occasionally short cropped grasses with same soil showing through. Since this category was a blend of the vegetation and soil responses there was some confusion with other similar cover categories. Pixels representing these spectral classes were often found in areas classed as lawns, natural grasses, or in residential areas. Classes 23, 55, and 67 were usually correctly identified as pastures or cropped fields, while classes 24 and 9 were frequently confused with sparse lawns. Distinguishing lawns from other vegetative surface materials was also rather difficult. Various lawns were quite different from each other in terms of moisture content, amount of thatch, shortness of the grass, and grass vigor. Fairways of golf courses and school playfields were most characteristic of the lawn cover type. Classes 66 and 17 from Figure 2(j) showed the short cropped, but healthy grass found at parks, playgrounds, and many golf courses. Class 39 identified lawns that often had trees or shrubs nearby. Several other spectral classes were

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