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Application of EO-1 Hyperion Data for Mapping and Discrimination of Agricultural Crops

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Hydrologic Modeling

Part of the book series: Water Science and Technology Library ((WSTL,volume 81))

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Abstract

Remote sensing offers an efficient and reliable means of collecting the information required for mapping, assessing and monitoring of agricultural crop conditions and production. Recent advances in remote sensing technology have led to the development of hyperspectral remote sensing imaging devices which can obtain high-resolution radiance data. This study evaluates the potential of the hyperspectral data in discrimination and mapping of agricultural crops using EO-1 Hyperion hyperspectral image over the Thalasseri Taluk, Kerala, India. Five agricultural crops such as arecanut, banana, cashew, coconut and rubber were considered for the study. The EO-1 was pre-processed using minimum noise fraction (MNF) transform to reduce the atmospheric effects on the imagery. Support vector machine classification and minimum distance classification were applied in order to perform image data classification based on different crops. The optimum wavelengths suitable for crop discrimination were derived by analysing the spectral reflectance curve as well as by using the techniques such as stepwise discriminant analysis and partial least square regression (PLSR). This study establishes that the Hyperion bands 53, 56, 62, 74, 79 and 84 are suitable for crop-type discrimination. The support vector machine classification is suitable for mapping the crops from Hyperion imagery with a higher accuracy of about 80% and above.

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Authors and Affiliations

  1. Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India

    H. Ramesh & P. P. Soorya

Authors
  1. H. Ramesh
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  2. P. P. Soorya
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Correspondence to H. Ramesh .

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Editors and Affiliations

  1. Department of Biological and Agricultural Engineering, and Zachry Department of Civil Engineering, Texas A&M University, College Station, Texas, USA

    Vijay P Singh

  2. Department of Civil Engineering, AISECT University, Bhopal, Madhya Pradesh, India

    Shalini Yadav

  3. AISECT University, Hazaribagh, Jharkhand, India

    Ram Narayan Yadava

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Ramesh, H., Soorya, P.P. (2018). Application of EO-1 Hyperion Data for Mapping and Discrimination of Agricultural Crops. In: Singh, V., Yadav, S., Yadava, R. (eds) Hydrologic Modeling. Water Science and Technology Library, vol 81. Springer, Singapore. https://doi.org/10.1007/978-981-10-5801-1_28

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