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:: 3 Spectral signatures of objects. Chairman: G. Guyot, Liaison: N. J. J. Bunnik

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: LANDSAT TM band combinations for crop discrimination. Sherry Chou Chen, Getulio Teixeira Batista & Antonio Tebaldi Tardin

Document type:: Multivolume work

Structure type:: Chapter

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 he CAESAR data for the used for e been he radio- of the ween the igital words the NLR for facility has corrections ric and geo- titude data platform h position R sensor by an inter- ty tape to AR data are .11 radio- up tables f each n software airborne ied in order ¡eometry of of CAESAR. :ent removed m model atform data. :a from ¡nt dates of l registra- ie performed •ol points, is overlays i programme close co- >rogramme is of CAESAR L be investi- Lution CAESAR /ailable high ige approach. 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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