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:: 4 Renewable resources in rural areas: Vegetation, forestry, agriculture, soil survey, land and water use. Chairman: J. Besenicar, Liaisons: M. Molenaar, Th. A. de Boer

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Assessment of TM thermal infrared band contribution in land cover/land use multispectral classification. José A. Valdes Altamira, Marion F. Baumgardner, Carlos R. Valenzuela

Document type:: Multivolume work

Structure type:: Chapter

related soil moisture stress with temperature differen ces in cotton and potatoes. Wear (1966) found an in-’ — crease in temperature in forest trees with roots dama ged by insects. Myers and Allen (1968) realted soil — salinity with high cotton leaf temperatures. The Corn Blight Watch Experiment, demonstrated that use of infrared remote sensing has possitive effects in stress levels determinations (MacDonald, et al., 1972; Kumar and Silva, 1973). 2.3 Airbone multispectral scanning thermography Myers, et al. (1966) made use of pictorial and thermal infrared data to determine differences in the tempera ture of plants as an indicator of the relative subsur face salinity and moisture conditions affecting crop - production. They stated that the temperature contrasts between salt affected and unaffected cotton plants are likely to be greater than the temperature contrasts between moisture stressed and unstressed cotton. Wiegand, et al. (1968), using the UNiversity of Mi chigan airborne thermal scanner in Texas, studied the thermal behavior of several variables such as crop species, plant spacing, tillage, irrigation regime and special features, such as highways and water reservoirs. They found that irrigated crops tend to be cooler that non irrigated at midday conditions, but the opposite results were obtained at early morning hours. Thermal differences related to tillage were minimal. The feasibility of using thermal imagery for land use land cover studies has been demonstrated. Brown and Holz (1976) following Anderson^s classification system (Anderson, et al. ,1976), produced a land use/land cover map of Oak Creek Lake, West Texas. 2.4 Thermal band of Landsat 3 The Landsat 3 MSS characteristics are in sense the same as those of the previous Landsats, except that Landsat 3 acquired additional data in the thermal infrared por tion of the spectrum (10.4 to 12.6 um) with a ground resolution of 237 m. As a result, a single thermal band measurements corresponds to an area represented by nine measurements in each of the four reflective spectral bands, a 9 to 1 ratio (Price, 1981). The Landsat 3 thermal band did not function properly due to several unexpected causes. The problems asso ciated with the thermal sensing system were reflected in the quality of the imagery. Both thermal and spa tial resolution were affected and the thermal imaging system was eventually turned off in the spring of 1979 (Price,1981; Lougeay,1982). Despite the problems associated with the thermal band, some analysis was performed to evaluate the contribu tion and usefullness of this band. Price (1981), using Principal Components analysis, assessed the statistical correlation between the emissive band, and the four reflective bands. He found that the thermal data ei ther were not useful or were associated with a physical parameter that is not directly related to surface type. He found that thermal data made a limited contribution to multispectral classifications. He concluded that its use for classification is subject to ambiguities and prone to error: "...an indiscrimante use of the thermal data appears to be undesirable because of many possi bilities for misinterpretation and the fact that the thermal ’signature' is not a direct indicator of sur face type." Lougeay (1982) compared the Landsat 3 MSS band 5 (0.6 to 0.7 um) and the thermal MSS band 8 (10.4 to 12.6 um). He found the thermal imagery of MSS band 8 to be of limited use by itself due to its coarse spa tial and thermal resolution. However it did provide a rendition of gross topographic structure which was not readily available from the other MSS spectral bands. 2.5 Classifiaction and data compression techniqes If the use of all available channels was not possible, data compression techniqes have been used to represent the large content of data into fewer components. Principal Components or Karhunem - Loeve transforma tion is an orthogonal linear transformation that com presses multidimensional data into fewer dimensions without significant loss of information content. This transformation assigns the random variance or noise to eigenvectors with lowest variance (Bartolucci, et al., 1983). Data compression is one result of the generation of principal components. It is possible to describe the relative influence or "pull" of the original ban-s on each of the new components. This procedure allows us to evaluate which of the original bands contains most of the significant variance or information content for a particular data set (ANuta, et al. , 1984) 3 METHODOLOGY 3.1 Landsat TM characteristics The TM data utilized to carry out the present project were gathered by Landsat 4 on 3 September 1982 over the central Iowa. The NASA scene number is 40049-16264 accesion 182, path 27, row 31. The TM data used was radiometrically and geometrically corrected, i.e. , P-tape or fully processed tape, and consisted of 5,965 scan lines with 6,976 pixels per line. The geomtric correction of the TM thermal data requires special consideration, since the spatial resolution of thermal data is 129 m compared to 30 m for the other TM bands. One image sample or pixel of raw thermal data repre sents an area equivalent to 16 area units from any of the reflective bands. The coarse resolution of the thermal data is resaimpled to forma a registered grid of 28.5 m by 28.5 m pixels. Thus all bands of the geo metrically corrected TM data contain the same number of pixels per unit area. 3.2 Description of the study area A study area of 10 by 10 sections (approximatelly 26,000 hectares), was selected as representative of • a great diversity of land use/land cover features. This area is located in Polk County which is in south central Iowa. The area lies between latitudes 41°37'45" N and 41°46’15" N, and from longitude 93°37’ W to 93°45_' W. The general topography is nearly level to undulating with some steep areas along the streams and rivers. The geology of the area consists mainly of a Wiscon- sonian glacial till. The entire area is underlain by a shale bedrock of the Des Moines Group. The native vegetation of Polk County was praire gra sses and hardwood forests. The forests grew along the major streams, particularly along the Des Moines River. The cover types in this area are water bodies, agri cultural fields, urban areas (new and old developments) industrial and commercial parks, and a dense road net work (from gravel roads to four lane highways). The Agricultural Stabilization and Conservation Ser vice (ASCS) of the US Department of Agriculture in Polk County collected 35 mm color aerial slides for the entire county in August 1982. Each slide covers two sections (approximately 520 ha) on the ground. These slides were used in conjunction with aerial infrared slides obtained by the Laboratory for Appli cations of Remote Sensing (LARS) of Purdue University in May 1983 over selected sites in the county as re ference data. The hardware and software used for the present re- searcg resided at LARS/Purdue U. The software system for digital analysis of multispectral data is LARSYS (Phillips, 1973) and LARSYSDV (Mrcoczynski,1980). "

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