000			041960000a22005410004500
999			_c114588 _d114588
003			CR-TuBCO
005			20221110063640.0
007			ta
008			151019b xxu||||| |||| 00| 0 eng d 2000
040			_aCR-TuBCO _cCR-TuBCO _bspa
041	0		_aspa
090			_aThesis _bP372
100	1		_9104021 _aPedroni, Lucio
110			_aCATIE - Centro Agronómico Tropical de Investigación y Enseñanza _cTurrialba, Costa Rica _93977
245	1	0	_aEstimation and use of modified prior probabilities for digital classification improvement of tropical forests
260			_aTurrialba (Costa Rica) _c2000 _bCATIE
300			_a111 páginas _bIlus. Tab
502			_aTesis (Ph. D.)-- CATIE. Escuela de Posgrado, Turrialba (Costa Rica), 2000
504			_aReferencias inician en la página 31
520			_aThis dissertation addresses the problem of tropical forest classification using remotely sensed data. Traditional remote sensing methods have had problems for discriminating tropical secondary and disturbed forests. As a consequence, important information is lacking, that is required for research in biogeography and for a complete assessment of carbon dioxide flows from land-use and land-cover change. To improve the discrimination of tropical secondary and logged forests using remotely sensed data, a Bayesian classification approach was investigated. The prior probabilities were modified as a function of the pixel's geographical context, which is a non-parametric strategy to incorporate information obtained from ancillary data into the maximun likelihood classification. The method has been proposed before, but found little application, because it presented practical problems for obtaining prior probability estimates. The dissertation describes and tests a data analysis procedure that generates prior probability estimates from class frequencies modeled with ancillary data and a Mahalanobis Distance threshold of previously classifies pixels. The method produces a pixel sample size that is large enough to estimates class prior pprobabilities in numerous geographies strata, which is particularly desirable for the study of large and complex landscapes, in which stratified random sampling for obtaining class frequency estimates is economically prohibitive. An experiment is presented, in which the procedure made it possible to estimate 537 sets of prior probabilities for an entire Landsat TM scene of central Costa Rica. After modifying the prior probabilities, the overall classification consistency of the training sites improved from 74.6 por ciento (traditional equal priors maximun likelihood classification) to 91.9 por ciento, while the overall classification accuracy of sites controlled in the field by independent studies improved from 68.7 por ciento to 89.0 por ciento. The classification accuracy was most improved for the spectrally similar forest categories. The usefulness of spectral enhancement using the Normalized Difference Vegetation Index (NDVI) and Tasseled Cap features were also investigated. The results of spectral analysis and of 18 classification experiments using different band and index combination are presented. Weak evidence was found to support the hypothesis that spectral enhancement might help the discrimination of tropical secondary, logged, and undisturbed forest categories.
650	1	4	_9138300 _aBOSQUE TROPICAL
650	1	4	_9140654 _aCLASIFICACION
650	1	4	_9166337 _aTELEDETECCION
650	1	4	_9164850 _aSISTEMAS DE INFORMACION GEOGRAFICA
650	1	4	_9164341 _aSENSORES REMOTOS
650	1	4	_9150873 _aIMAGENES
650	1	4	_92064 _aCOSTA RICA
690			_aTROPICAL FORESTS _9341801
690			_aCLASSIFICATION _9140678
690			_aREMOTE SENSING _9335816
690			_aGEOGRAPHICAL INFORMATION SYSTEMS _9149089
690			_aIMAGERY _9322629
690			_aCOSTA RICA
691			_aTurrialba _xCRI
856			_updf _qhttp://hdl.handle.net/11554/4972 _yspa
901			_aU40
902			_aK10
903			_aE
903			_aU
903			_aV
903			_aZ
904			_aBCO
905			_aC
906			_a20010101
908			_aB
942			_cDIG _2ddc