Full text: Remote sensing for resources development and environmental management (Volume 1)

211 
Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 
LANDSAT TM band combinations for crop discrimination 
Sherry Chou Chen, Getulio Teixeira Batista & Antonio Tebaldi Tardin 
Departamento de Sensoriamento Remoto, Instituto de Pesquisas Espaciáis (INPE), Sao José dos Campos, SP, Brasil 
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ABSTRACT: LANDSAT Thematic mapper provides not only more spectral bands but also improved spatial resolutions 
in the visible and infrared wavelenghts as compared to the MSS data. However, problems are encountered by 
analysts in working with the increased number of wavelength bands. In order to learn how to analyze TM data 
for agriculture studies, LANDSAT data of a 15x15m area in Parana State, Brazil, were acquired on Jan. 19, 
1985. The predominant crops in the study area were soybeans, com and sugarcane. To choose the best 
combination of three TM bands, which represents most information of the agricultural scene the entropy 
criterion was used. Once the triplet bands were chosen, the color of green, red and blue were associated to 
them according to the magnitudes of their variances to form the color composite. Interpretability of these color 
images were evaluated visually. For digital analyses the criterion of Jeffreys-Matusita distance was applied 
to verify the best band combination if 2,3,4 or 5 TM bands were used. A classification algorithm based on 
the maximum likelihood decision rule was then employed to classify the study area using the designated TM 
bands. Classification performances were compared pixel-by-pixel on alphanumeric printouts, the computer 
time consumed, the classification matrice and the upper bounds of the probability of error. After these 
analyses, the TM bands which should be used for an effective digital analysis of this agricultural scene 
were decided. 
RÉSUMÉ: Le LANDSAT TM fournit davantage de bandes spectrales et une plus grande résolution spatiale que 
le MSS. Cependant, il existe des problèmes qui apparaissent lors de l'utilisation des sept bandes spectrales. 
Pour tester les données TM dans les applications agricoles, on a utilisé des données TM LANDSAT dans un 
carré de 15x15km dans l'État du Paraná (Brésil) durant le passage du LANDSAT, le 19 janvier 1985. Les 
principales cultures de la région étudiées étaient: soja maïs et canne a sucre. Pour choisir la meilleure 
combinaison des trois bandes TM qui comportent le plus d'informations intéressantes, on a utilisé le critère 
de l'entropie. Après le choix des trois bandes, les couleurs verte, rouge et bleue ont été associées à ces 
bandes selon les grandeurs de leur variance pour former une composition colorée. Lès résultats de L ' interpreta tin 
visualle des images produites ont été compares. Pour vérifier les meilleurs combinaisons de 
bandes pour la classification par ordinateur, on a utilisé le critère de la distance de Jeffreys-Matusita. 
Ensuite, on a utilisé un algorithme de classification qui utilise les meilleures combinaisons choisies 
pour 3,4 ou 5 bandes du TM. Des analyses ont été faites avec quatre types différents de présentation des 
résultats: a) les sorties alphanumériques; b) les matrices de classification; c) les limites supérieures 
de la probabilité d'erreur, et d) le temps d'ordinateur utilisé. D'après ces comparaisons la meilleure 
combinaison de bandes TM pour la classification des cultures a été determinee. 
1. INTRODUCTION 
Since 1982 a new sensor, called the Thematic Mapper 
(TM) , was mounted on the Land Observation Satellite 
(LANDSAT) together with the Multispectral Scanner 
System (MSS). The TM sensor provides data frcm 
seven better selected spectral regions. There are 
three bands frcm the visible, one iron the near 
infrared (NIR) , two frcm the middle infrared and 
one iron the thermal infrared spectrum region. The 
reflected energy frcm the Earth surface is encoded 
into 8 bits per band, with an improved spatial 
resolution of 30m instead of the 6 bits data and 
80m resolution provided by MSS. In short, the TM 
sensor has considerably better spectral, spatial, 
and radiometric resolution than the MSS system; 
consequently a superior data quality and a much 
larger data volume are obtained. This new sensor 
design was mainly for vegetation discrimination 
considering the charecteristic spectral response 
of vegetation of the selected TM bands (Solomonson 
et al. 1980). Thus, TM data are expected to 
improve crop identification and area estimation 
accuracy in Parana State, where the problem of 
strip fields is presented. However, one of the 
problem encountered in the analysis of TM data is 
to decide how to handle this huge data quantity 
efficiently. Experiences of the passed decade 
demonstrated that for MSS data bands 4,5 and 7 are 
used to form false color composite, and for digital 
analysis, normally all four bands are employed, 
event though information contents of the two visible 
bands (band 4 and 5) or infrared bands (band 6 and 
7) are intercorrelated. Now, for the seven TM bands, 
questions arise about which three bands should be 
used for color image production, and how to reduce 
the dimensionality of TM data for digital analysis 
in order to achieve cost-effective results 
considering the crop identification and area 
estimation accuracy and computer time consumed. The 
knowledgement of how to produce color image using 
TM data for visual interpretation is especially 
important for developing countries, where the 
lacking of computer facility and properly trained 
analyzer are limitations for the implementation of 
digital analysis at local government agencies or 
research institutes. On the other hand, in many 
application areas, information contents can only 
be extracted by digital analysis. Thus, there is an 
urgent need for exploiting how to handle and analyze 
TM data both visually and digitally. 
In this study TM band combinations for visual and 
digital analyses in an agricultural scene were 
investigated and the best band combination for crop 
discrimination was selected. Note that band 6 was 
not included in this study due to its low spatial 
resolution (120 m).
	        
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