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

538 Table 7. Percent correct classification of nine major cover types by each classification approach. Classi COVER Ì Y P E S Overall Perfor- mance fication Approach Com Forest Grass Industry Roads Soils Soybean Urban Water I 100 a * 100 a 79 a 90 a 84 a 93 a 100 a 97 a 100 a 93.7 0. 0 II 95 a 89 ab 65 ab 83 ab 76 ab 88 ab 99 a 89 ab 100 a 87.1 % III 99 a 98 a 75 a 87 a 81 ab 80 ab 99 a 98 a 100 a 90.8 0. 0 IV 92 a 73 c 63 ab 83 ab 71 ab 73 b 98 a 92 ab 100 a 82.8 0. 0 V 53 be 78 be 22 d 85 a 62 b 80 ab 86 b 64 cd 100 a 70.0 0, 0 VI 49 be 88 abc 50 be 75 ab 74 ab 74 b 86 b 60 d 100 a 72.9 0. 0 VII 64 b 55 d 40 cd 66 b 82 a 73 b 86 b 58 d 100 a 67.4 % Vili 44 c 77 be 67 ab 36 c 38 c 49 c 85 b 78 be 100 a 63.8 a 0 MSE (%) 14 13 21 15 14 17 6 14 — * Within each cover type, approaches followed by the same letter are not significantly different at =0.05 level by the Bonferroni T - test. (Degrees of freedom = 792, Critical value of T = 3.13) 4.12 Computer time evaluation Considering classifiaction approach I as the standard procedure, the CPU time consumed for the Maximum Li kelihood Classifier in this approach (7,783 secs) was considered as the reference time to compare with the other approaches. A reduction in CPU time is result of less channels used in the classifications. Table 8. Computer time (CPU) consumption for each approach. Classification Approach CPU Time Ratio I 1 : 1 II 1 : 1.3 III 1 : 1.3 IV 1 : 1.3 V 1 : 2.5 VI 1 : 2.6 VII 1 : 4.0 Vili 1 : 4.5 5 CONCLUSIONS The four data sets examined in this research provide a method for evaluatting the effect of the TM thermal infrared band in multispectral classifications. A Per Point GAussian Maximum Likelihood classification was performed with eight different approaches. The analysis of the data sets with all seven bands or the six reflective bands (i.e., data sets A and B), pro vided 37 spectrally separable classes. THe use of four or three Principal Components provide fewer spectrally separable classes. The use of the seven TM bands for the analysis pro cedure gave better discrimination among classes and fewer mixed classes. This same situation prevails between data sets C and D where the use of three Principal Components gave more mixed classes than set C The use of the seven TM bands gave the best minimum and average separability values. If the thermal band is not included for multispectral classification, then it is better to generate the training statistics (cluster) without the thermal band. Water features show to be equally discriminated with all the approaches. Soybean and com were better discriminated with classifications of the data set A. Urban classifications using statistics generated with the seven TM bands (data set A) were significantly different from those of the other three data sets. Soils and industrial classes in the approach VIII (Three Principal Components) were significantly di fferent and had the lowest accuracy mean values. Classifications performed with data sets B, C and D provided fores/com mixed classes because of lower separability values between those features. In general, classifiactions using the thermal band were significantly different from classifications without this band. THe separability values between pairs of classes were higher when the thermal band was used. When there is a constraint on computer tine and/or hardware, the use of data compression techniques such as PRincipal Components may be advantageous due to the drastic decrease in CPU time consumed. The thermal band itself has great possibilities for specific types of research, specially in the areas of thermal pollution mapping, detection of vegetation on stress situations and mapping of sea currents. REFERENCES Anderson, J.R.,E.E. Hady, J.T. Roach S R.E. Witnjer 1976. A Land Use and Land Cover Classifiaction Sys tem for USe with REmote Sensor Data. Geological Survey Professional Paper 964. Supt. of Docs. No. 119.16:964, U.S.A. Anuta, P.E., L.A. Bartolucci, M.E. Dean, D.F. Lozano, E. Malaret, C.D. McGillem, J.A. Valdes S C.R. Va lenzuela. 1984. LANDSAT-4 MSS and Thematic Mapper Data Quality and INformation Content Analysis, in IEEE Transactions on Geoscience and Remote Sensing, Special Issue on Landsat-4 , Volume GE-22, No. 3, pp 222-236. Bartolucci, L.A.,M.E. Dean S P.E. Anuta 1983. Evalua tion of the Radiometric Quality of the TM Data Using Clustering, Linear Transformations and Multi spectral Distance MEasures. in Proceedings, Landsat 4 Scientific Characterizarion Early REsults Sympo sium, NASA Goddard Space Flight Center, Greenbelt, MD. February 22-24, 1983. Bauer, M.E. 1977. Crop IDentification and Area Esti mation over Large Geographic AReas Using Landsat MSS Data. LARS Information NOte 012477. LARS/Purdue University, West Lafayette, IN. Bauer, M.E., P.H. Swain, P.R. Mcrozynski, P.E. Anuta 5 B.R. MacDonald 1971. Detection of Southern Com Blight by Remote Sensing Techniques. Proceedings Seventh Symposium on Remote Sensing of Environment. Ann Arbor, Michigan, pp. 693 - 704 Brown, tion mmet 1414 Clum, viro Soci Cris^ Tran Tass Remo GE - Curtis of L 364. Hoffer tent in R Univ of G Kumar, tanc With Mult 0724 Lougea Regi Sens MacDon J.D. the of t Sens Mich MacDon ment 679. Mroczy LARS sity Myers, mote hfeas 7: 1 Myers, mas. vati on F Ann Phi Hi Mara. Price. Date togi 229 Swain. due Tiltor shoj Date due USDA. Stat Ser\ Valdes de / diar espe y de rets Valdes red tisj Agrx yeti Wear, res1 dice Pac, Repc

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