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  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
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  • ENGLISH
  • P-ISSN2287-8327
  • E-ISSN2288-1220
  • SCOPUS, KCI

ISSN : 2287-8327

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Vol.41 No.2

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Comparison of field- and satellite-based vegetation cover estimation methods

Journal of Ecology and Environment / Journal of Ecology and Environment, (P)2287-8327; (E)2288-1220
2017, v.41 no.2, pp.34-44
https://doi.org/10.1186/s41610-016-0022-z
고동욱 (국민대학교)
김다솜 (국민대학교)
Amartuvshin Narantsetseg (Mongolian Academy of Sciences)
강신규 (강원대학교)
고동욱, 김다솜, Amartuvshin, N. , & 강신규. (2017). . Journal of Ecology and Environment, 41(2), 34-44, https://doi.org/10.1186/s41610-016-0022-z

Abstract

Background: Monitoring terrestrial vegetation cover condition is important to evaluate its current condition and to identify potential vulnerabilities. Due to simplicity and low cost, point intercept method has been widely used in evaluating grassland surface and quantifying cover conditions. Field-based digital photography method is gaining popularity for the purpose of cover estimate, as it can reduce field time and enable additional analysis in the future. However, the caveats and uncertainty among field-based vegetation cover estimation methods is not well known, especially across a wide range of cover conditions. We compared cover estimates from point intercept and digital photography methods with varying sampling intensities (25, 49, and 100 points within an image), across 61 transects in typical steppe, forest steppe, and desert steppe in central Mongolia. We classified three photosynthetic groups of cover important to grassland ecosystem functioning: photosynthetic vegetation, non-photosynthetic vegetation, and bare soil. We also acquired normalized difference vegetation index from satellite image comparison with the field-based cover. Results: Photosynthetic vegetation estimates by point intercept method were correlated with normalized difference vegetation index, with improvement when non-photosynthetic vegetation was combined. For digital photography method, photosynthetic and non-photosynthetic vegetation estimates showed no correlation with normalized difference vegetation index, but combining of both showed moderate and significant correlation, which slightly increased with greater sampling intensity. Conclusions: Results imply that varying greenness is playing an important role in classification accuracy confusion. We suggest adopting measures to reduce observer bias and better distinguishing greenness levels in combination with multispectral indices to improve estimates on dry matter.

keywords
Point intercept, Digital photography, Land cover estimate, NDVI, Photosynthetic vegetation, Greenness

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