Seasonal timing for estimating carbon mitigation in revegetation of abandoned agricultural land with high spatial resolution remote sensing

Ning Liu, Richard J. Harper, Rebecca N. Handcock, Bradley Evans, Stanley J. Sochacki, Bernard Dell, Lewis L. Walden, Shirong Liu

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Dryland salinity is a major land management issue globally, and results in the abandonment of farmland. Revegetation with halophytic shrub species such as Atriplex nummularia for carbon mitigation may be a viable option but to generate carbon credits ongoing monitoring and verification is required. This study investigated the utility of high-resolution airborne images (Digital Multi Spectral Imagery (DMSI)) obtained in two seasons to estimate carbon stocks at the plant- and stand-scale. Pixel-scale vegetation indices, sub-pixel fractional green vegetation cover for individual plants, and estimates of the fractional coverage of the grazing plants within entire plots, were extracted from the high-resolution images. Carbon stocks were correlated with both canopy coverage (R2: 0.76-0.89) and spectral-based vegetation indices (R2: 0.77-0.89) with or without the use of the near-infrared spectral band. Indices derived from the dry season image showed a stronger correlation with field measurements of carbon than those derived from the green season image. These results show that in semi-arid environments it is better to estimate saltbush biomass with remote sensing data in the dry season to exclude the effect of pasture, even without the refinement provided by a vegetation classification. The approach of using canopy cover to refine estimates of carbon yield has broader application in shrublands and woodlands.

Original languageEnglish (US)
Article number545
JournalRemote Sensing
Volume9
Issue number6
DOIs
StatePublished - Jun 1 2017
Externally publishedYes

Keywords

  • Aboveground biomass
  • Atriplex nummularia
  • Carbon inventory
  • Carbon mitigation
  • Forage crops
  • Remote sensing
  • Vegetation index

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