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

.cut River. Photo- 686-692. & J.C. van Huis .1 dune manage- ITC Journal C. Davis 1969. 'hotograiranetric Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Spectral characterization of urban land covers from Thematic Mapper data iy of recreational :higan Academy of XVII, 1962, lman 1973. :ries. Tijdschrift rrafie, 64, no 1: and) 1977. Friese meren. onderzoek. h to flourishing rphotostudy of nor peninsula, pretation, work- rzeewerken 1977. ek naar patronen evaart op het aan de hand van >ta nr 291, Lely- rzeewerken 1979. n van een lucht- op de randmeren ion geography of iew, vol LXII, no urist market re- erkehr, no 3: 89- ion and Resources 9, part 3s 467- s in the uplands. York Moors. ng Institute, onderzoek Hel- cten. Afdeling hogeschool, en vanuit vaste vincien Gebied. universiteit f recreational ial photographs. ties and their of the islands, erference. In: Flora and vege- and coastal erdam. nvironmental Con- c lecture given e 20th, 1985 at Douglas J.Wheeler Utah State University, Logan, USA ABSTRACT: Using Salt Lake City, Utah, as a test case, this study evaluates the capabilities of Landsat~~b Thematic Mapper (TM) digital data for distinguishing urban land cover materials. This was accempfShed by using a newly developed hierarchical clustering algorithm which statistically derived spectral classes from TM channels 2, 3, 4, and 5 (visible, near infrared and middle infrared). The relationships between spectral groups were further analyzed using three statistical evaluations: principal components analysis, cluster analysis, and discriminant analysis. Through the use of component scores, cluster linkage diagrams, and canonical discriminant function scatter plots; as well as TM spectral curves, aerial photography, and ground investigation; the spectral classes were grouped into twelve predetermined land cover categories. The accuracy of classification was assessed at approximately 80 percent (0.05 significance level). A significant improvement in classification accuracy (91.5 percent) was achieved by stratifying the multispectral classification with thresholds from the TM thermal channel introduced as ancillary data. INTRODUCTION—DERIVING SPECTRAL CLASSES FROM TM With a high proportion of the world's population living in cities it is increasingly important to understand the very complex ecological interactions that are taking place within the urban environment. One key to better understanding these relationships is to characterize the land cover materials that influence radiational and micro-climatological balances. By monitoring successional changes in land cover materials one may observe its effect on urban ecosystem processes. The value of Landsat's multispectral scanner (MSS) in detecting land cover has been established (Todd 1978). Landsat 5's advanced multispectral scanner called Thematic Mapper (TM) , with improved spatial and spectral resolution over MSS, could be an even better tool for detecting land cover characteristics in complex urban environments. Although very little is published at the present time on the use to TM data in urban analysis, preliminary studies using Thematic Mapper Simulator (IMS) data flown from aircraft indicated that the 30 meter pixel size of TM might be ideal for mapping urban land cover elements (Clark 1980, Welch 1982). Two studies using TM data for Mobile, Alabama, show promising results (Quattrochi 1983, Wang 1985). The urban region of Salt Lake County, Utah, was chosen as the study area for this evaluation of TM digital data due to the diversity of land activity found within a relatively small area. The majority of Salt Lake County's 1984 population of 650,000 is found within a 15 kilometer wide strip stretching north-south along the base of the Wasatch Mountains. Salt Lake City contains the diversity of land activities usually associated with metropolitan areas of a much larger size. There are many heavy and light industrial and commercial activities; diverse multifamily, single-family, and rural residential areas; extensive irrigated and nonirrigated agricultural lands; as well as natural vegetation and water features distributed throughout the county. The central valley portion of the county consists of several urban areas that are coalescing due to ongoing urban development and suburbanization. Even within the city limits of the communities in the Salt Lake valley there are many agricultural and natural areas that are being filled in with urban land use activities. A strip 15 kilometers wide and 25 kilometers long was selected for the study area, extending through the urban corridor of the valley. From within this urban study area, 10 test windows were selected to be used for generating spectral signatures from the raw TM data. The principal data source for analysis wa c the digital tape of a Thematic Mapper scene dated July 27, 1984. Using test windows to break up the study area into manageable size units made the field investigation and computer processing more cost effective. The 10 windows were carefully chosen from aerial photography to represent the broadest spectrun of cover materials found within the Salt Lake urban environment. The 10 test windows accounted for approximately 16 percent of the study area. After the test windows were selected, several trips to the field were made to identify land cover materials. At that time, ocular estimates were made of the percentages of surficial materials found in association with one another, and which combinations comprised various cover types. It was decided that 12 particular land cover classes would be desirable to detect from the TM data of Salt Lake City. The 12 preliminary land cover classes decided upon for this study included: (1) open water, (2) light inert materials (e.g., bare soil, concrete, and reflective metals and glass), (3) coal and slag, (4) dark inert materials (e.g., railroads and blacktop surfaces), (5) light asphalt-gravel surfaces, (6) mixed pixels with mostly inert cover and little vegetation cover, (7) mixed pixels with high vegetation cover, (8) senesced weeds and natural grass, (9) healthy moist vegetation, (10) drier or sparse vegetation with soil showing, (11) short cropped grasses (lawns), and (12) trees and shrubs. A large portion of the computer processing of TM data was accomplished using ELAS digital image processing software obtained from NASA's Earth Resources Laboratory. The TM data tape was reformatted into an ELAS data file format to be processed on a Prime 400 computer. Of the seven original TM channels it was decided to generate statistic files from data in spectral bands 2, 3, 4, and 5, giving representation from the visible, near infrared, and middle infrared wavelengths. The thermal channel (band 6) was not used in determining spectral classes because of its lower spatial resolution (120 meters compared to 30 meters) and the lack of differentiation in spectral values. While there appear to be many opinions on which TM bands are optimum for processing, there is considerable 893

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