A comparison of Landsat Thematic Mapper and SPOT multi-spectral imagery for the classification of shrub and meadow vegetation in northern California, U.S.A.

May, A. Michele Basham; Pinder, J.E.; Kroh, Glenn C.

doi:10.1080/014311697216577

Cited by 43 publications

(12 citation statements)

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“…This capability is not present in Quickbird as it does not capture in the mid-infrared range. Many researchers have commented on the advantage provided by the greater spectral resolution of Landsat TM in vegetation classification (Buttner & Csillag 1988;Godard et al 1990;Michele Basham May et al 1997;Gao 1999;Harvey & Hill 2001). When certain vegetation types occur in small patches, then the greater spatial resolution of Quickbird or SPOT may be better suited for vegetation classification (Michele Basham May et al 1997).…”

Section: Introductionmentioning

confidence: 99%

Accuracy comparison of Quickbird, Landsat TM and SPOT 5 imagery forLantana camaramapping

Taylor

Kumar

Reid

2011

Journal of Spatial Science

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The distribution of an invasive species, Lantana camara L., was mapped using Landsat TM, SPOT 5 and Quickbird imagery with the maximum likelihood supervised classification method. The cost-effectiveness of the three different kinds of imagery was also evaluated. Landsat TM multi-spectral (MS) imagery provided an overall accuracy of 85.1 percent and a kappa coefficient of 0.78, while SPOT 5 imagery gave an overall accuracy of 84.9 percent and a kappa value of 0.77. Landsat TM was suitable for detecting dense infestations of lantana. Quickbird showed an overall accuracy of 84 percent and a kappa coefficient of 0.76. Spectral resolution and band position were more important than spatial resolution in classifying lantana accurately and the most cost-effective option was provided by Landsat TM with no significant difference in overall accuracies between the three types of imagery.

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Section: Introductionmentioning

confidence: 99%

Accuracy comparison of Quickbird, Landsat TM and SPOT 5 imagery forLantana camaramapping

Taylor

Kumar

Reid

2011

Journal of Spatial Science

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“…Conese et al 1988, Baker et al 1991, Kanellopoulos et al 1992) have shown the good classi cation and monitoring capabilities of the visible/infrared images acquired by optical sensors. They used SPOT or Landsat images, whose respective performances have been compared (Michele Basham May et al 1997 ). Unlike optical imagery, Synthetic Aperture Radar (SAR) systems allow Earth observation even under cloudy or nighttime conditions.…”

Section: Introductionmentioning

confidence: 99%

Land cover discrimination from multitemporal ERS images and multispectral Landsat images: A study case in an agricultural area in France

Hégarat‐Mascle¹,

Quesney²,

Vidal-Madjar³

et al. 2000

International Journal of Remote Sensing

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More and more remote sensing data corresponding to various wavelength domains is becoming available. Visible/infrared data were rst used for land cover classi cation. However, radar data are becoming more widely used for hydrological and agricultural applications. This paper discusses the performance, for land cover type discrimination, of an optical image acquisition and a multitemporal radar series. For the majority of land cover types existing within the test site (representative of northern European agricultural areas), both ERS multitemporal SAR and Landsat multispectral visible/infrared classi cations lead to good results, with the latter being more robust. For better identi cation of cultures that are less represented, the complementarity of the two datasets may be exploited using an e cient data fusion algorithm based on the DempsterShafer evidence theory. The performance of this combination was veri ed on two successive vegetation cycles.

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“…According to Michele Basham May et al (1997), LANDSAT/TM achieved better classification accuracy than did SPOT/HRV, largely because of its shortwave infra- red (SWIR) region. Although SPOT2/HRV data were used in this research, greater progress in classification accuracy can be expected from the SPOT4/HRVIR and SPOT5/ HRG now under operation, since these also feature SWIR.…”

Section: Discussionmentioning

confidence: 99%

Seasonal variation in classification accuracy of forest-cover types examined by a single band or band combinations

Murakami

2004

Journal of Forest Research

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In vegetation remote sensing, classification accuracy cannot be fixed, due to seasonal variations in spectral reflectance characteristics. This study aims to clarify the seasonal variability of classification accuracy by forestcover type. In particular, this paper describes seasonal variability by each band or band combinations. The study area is located in the vicinities of Hisayama and Sasaguri in Fukuoka Prefecture, Japan. Natural broadleaved, coniferous plantation, and bamboo forests were studied. Supervised classification was applied to six SPOT/HRV images taken in 1997. Kappa analysis was applied to assess the classification accuracy and compare any two error matrices. The results revealed that some single band or two-band combinations were as accurate as, or more accurate than, the full band (all three bands). The disadvantages of using a full band were especially apparent in the season with high classification accuracy. This study indicates that using all given bands does not necessarily result in the highest classification accuracy. This study also suggests that band selection within the scope of forest type and seasonal variability can contribute to better forest-cover-type classifications.

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A comparison of Landsat Thematic Mapper and SPOT multi-spectral imagery for the classification of shrub and meadow vegetation in northern California, U.S.A.

Cited by 43 publications

References 0 publications

Accuracy comparison of Quickbird, Landsat TM and SPOT 5 imagery forLantana camaramapping

Accuracy comparison of Quickbird, Landsat TM and SPOT 5 imagery forLantana camaramapping

Land cover discrimination from multitemporal ERS images and multispectral Landsat images: A study case in an agricultural area in France

Seasonal variation in classification accuracy of forest-cover types examined by a single band or band combinations

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