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Agricultural land cover classification using RapidEye satellite imagery in South Korea - First result

  • Hyun Ok Kim*
  • , Jong Min Yeom
  • , Youn Soo Kim
  • *Corresponding author for this work
  • Korea Aerospace Research Institute

Research output: Contribution to conferenceConference paperpeer-review

Abstract

Global climate changes as well as abnormal climate phenomena have affected the agricultural environment on a great scale. Thus, there is a strong need for countermeasures by making full use of agriculture related information. As agricultural lands in South Korea are mostly operated by private farmers on a small parcel level, it is difficult to gather information for an overview on changing crop condition and to construct database necessary for disease management, production estimation and compensation measures on a regional or governmental level. The objective of this study is to evaluate the multispectral reflectance characteristics of RapidEye image data to classify agricultural land cover as well as crop condition in South Korea. As the RapidEye sensor offers the spectral information in red edge range as a first multispectral satellite system, we focus on the usefulness of red edge reflectance for identifying crop species and for interpreting crop growth or stress condition.

Original languageEnglish
Title of host publicationRemote Sensing for Agriculture, Ecosystems, and Hydrology XIII
DOIs
StatePublished - 2011
EventRemote Sensing for Agriculture, Ecosystems, and Hydrology XIII - Prague, Czech Republic
Duration: 2011.09.192011.09.21

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8174
ISSN (Print)0277-786X

Conference

ConferenceRemote Sensing for Agriculture, Ecosystems, and Hydrology XIII
Country/TerritoryCzech Republic
CityPrague
Period11.09.1911.09.21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Agriculture
  • Crop condition
  • Object-based classification
  • RapidEye
  • Red edge
  • Spectral vegetation index

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