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:: 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

897 Figure 2. Continued classified as lawn areas but were primarily drier grasses. There was same class confusion between lawns and vegetation/soil mixes (sparse cover) and healthy vegetation (long, thick grasses). Lawns were also often associated with mixed pixels, either with inert materials in residential areas or with trees and shrubs in parks, cemeteries, or landscaped areas. Healthy, frequently watered vegetation was characterized on the signature plots by a steep rise between channels 3 and 4 and a substantial drop in channel 5, as shown in Figure 2(k). Oi the discriminant function scatter plot (Figure 1) this category was very high on the "greenness axis." Class 40 showed the most vigorous vegetative response of the spectral classes in CLUS67. Most of class 40 involves healthy alfalfa fields in the agricultural areas, although some healthy lawn areas were combined into this class. Classes 20 and 21 also represented alfalfa fields of varying plant densities, while class 46 usually indicated corn fields. Class 18 was on the borderline between very thick and healthy grasses (often found in lawns or golf course roughs) and the slightly drier crops found in some agricultural fields. Trees and shrubs had a very similar spectral pattern to both lawns and moist vegetation, except that the response in the near infrared (channel 4) was usually not as high. Since trees are not as large as the TM sensor's IFOV (30 meters), there was usually some mixing between tree canopies and the understory materials, with both contributing to the pixel's response. Often, the density of tree cover was difficult to observe. Aerial photography that is slightly off nadir will show oblique views of trees, which are in turn hiding other surface cover materials, making tree canopies appear as the predominant land cover. With a completely vertical view it became apparent that tree cover density in the urban setting is actually quite a small percentage, with other cover materials contributing a major proportion of an individual pixel's response. For this reason, the land cover in virtually all of the tree and shrub category was mixed. However, trees with shrubs or dense weedy materials were major contributors to the spectral response. Classes 53 and 60 were the most representative signatures in this category for densely wooded tree cover. Class 19 was a borderline class between trees and other healthy vegetation. It often represented areas where grass was showing through the trees, as in city cemeteries or parks. Class 19 also represented clumps of shrubby trees and marshy weeds. Classes 26 and 62 were primarily located in residential areas and most often represented treelined streets or back yards with large trees. Class 62 contained a slightly higher proportion of inert material than the other classes in this category. 3 USE OF THERMAL CHANNEL SIX DATA IN RECLASSIFICATION In the past, very little use has been made of the Thematic Mappers thermal band (channel 6) in land cover analysis due to its coarser resolution (120 meters) and low range of spectral variation. This is unfortunate, since the two parameters most responsible for variability of surface temperatures are surface moistness (moisture availability) and diurnal heat capacity (Carlson & Boland 1978). These two factors are highly related to the nature of surficial materials in the urban setting. It was observed in this study that many of the land cover categories that were being confused in multispectral classification were actually very different in terms of thermal properties. For example, coal and asphalt were classified interchangeably as water, and cropped agricultural fields were often confused with residential areas or natural grass. For this reason, TM channel 6 was used as an ancillary data layer to set thermal

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