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

535 jri techniqes 3.3 Study data sets 3.4 Spectral analysis procedure is not possible, ;ed to represent jnrponents. ive transforma- ition that corn er dimensions 1 content. This ance or noise to rolucci, et al., generation of a describe the iginal ban-s on lure allows us 3 contains most :ion content for L984) Four data sets of the classified area were used to evaluate the contribution of the thermal data in the multispectral classifiaction. The first data set is the original seven TM bands. The second set is compo sed of the same original TM bands excluding the ther mal band. The third data set is formed by Principal Components loaded from the original seven TM bands. The fourthdata set is also Princiapl Components, but generated from the second data set, i.e. , only the reflective bands. The satistics used in calculating the Principal Com ponents were generated from data samples of the ori ginal TM data set using every fifth line and fifth column. Tables 1 and 2 shpw the statistics for both Principal Components data sets. Tables 3 and 4 list the eigen- vales and the corresponding amount of data variance taht is accounted for by their respective eigenvectors for both data sets. A non-supervised approach (Clustering) was selected to generate the training statistics. This approach groups spectrally similar pixels regardles of their spatial position (Tilton and Bartolucci, 1982). and extracts the maximum quantity of information availa ble in the TM data. Eight classifications were carried out in this study. Only four spectral analysis were conducted, one for each of the data sets, the classifications are results of different channel combinations selected after the analysis procedure (Table 5). To avoid analysis bias in the generation of training statistics, the same eight training areas and number of cluster classes were requested for each of the four data sets. The analysis was performed utilizing a defined thres hold of 1850 for the transformed divergence distance (D.T.),(Swain and Davis, 1982). present project :>er 1982 over ? is 40049-16264 lata used was icted, i.e., isisted of 5,965 The geomtric ires special ition of thermal other TM bands, al data repre- Lts from any of ition of the agistered grid ands of the geo- le same number Table 1. Eigenvector values for by their respective TM band for the original seven TM bands (Data set C). Wavelength Principal Component (Karhunen 6 Loeve) Eigenvector Band 1 2 3 4 5 6 7 1 0.0376 0.4331 0.5665 -0.1086 -0.1359 -0.6781 -0.0092 2 2 0.0377 0.2641 0.2770 -0.0547 -0.1632 0.4311 0.7988 3 3 0.0293 0.4032 0.3564 -0'0806 -0.0598 0.5898 -0.5930 4 4 0.8109 -0.4312 0.3666 0.0817 0.1167 0.0396 -0.0163 5 5 0.5574 0.4391 -0'5642 -0.0719 -0.4097 -0.0659 -0.0275 7 7 0.1670 0.4115 -0.1578 -0.0465 0.8770 -0.0210. 0.0961 6 6 -0.0101 0.1830 0.0285 0.9822 -0.0272 -0.0116 -0.0013 roximatelly ssentative of ■ ar features. i.ch is in south '45" N and W to 93°45' W. to undulating s and rivers. 7 of a Wiscon- 3 underlain by P- was praire gra- grew along the Des Moines River, bodies, agri- old developments) dense road net- ighways). reservation Ser- riculture in al slides for h slide covers the ground, with aerial tory for Appli- rdue University county as re- e present re- software system data is LARSYS nski,1980). Table 2. Eigenvector values for by their respective TM band for the six reflective TM bands (Data set D). Wavelength Principal Component (Karhunen S LOeve) Eigenvector 1 2 3 4 5 6 1 0.0393 0.4400 0.5694 -0.1389 -0.6792 -0.0092 2 0.0388 0.2654 0.2787 -0.1646 0.4307 0.7986 3 0.0309 0.4096 0.3590 -0.0619 0.5888 -0.5932 4 0.8093 -0.4434 0.3638 0.1190 0.0405 -0.0162 5 0.5591 0.4440 -0.5619 -0.4115 -0.0666 -0.0275 7 0.1686 0.4177 -0.1454 0.8754 -0.0220 0.0960 Table 3. Eigenvalue and the corresponding amount of variance that is accounted for by their respective eigenvector for the data set C. Eigenvector Eigenvalue Percent Variance Cumulative Percent Variance 1 795.642 54.449 54.449 2 554.802 37.967 92.416 3 81.346 5.567 97.983 4 14.888 1.019 99.002 5 10.281 0.704 99.706 6 2.818 0.193 99.899 7 1.482 0.101 100.000 Table 4. Eigenvalue and the corresponding amount of variance that is accounted for by their respective eigenvector for the data set D. Eigenvector Eigenvalue Percent Variance Cumulative Percent Variance 1 795.569 55.706 55.706 2 536.714 37.581 93.287 3 81.290 5.692 98.979 4 10.285 0.720 99.699 5 2.820 0.197 99.896 6 1.482 0.104 100.000

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