Document Type
Journal Article
Publisher
MDPI
School
Office of Research and Innovation
RAS ID
25626
Funders
Chinese Academy of Forestry
Murdoch University Strategy PhD Scholarship
Special Research Program for Public-welfare Forestry (Grant No. 201404201)
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.
DOI
10.3390/rs9060545
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Liu, N., Harper, R. J., Handcock, R. N., Evans, B., Sochacki, S. J., Dell, B., ... & Liu, S. (2017). Seasonal timing for estimating carbon mitigation in revegetation of abandoned agricultural land with high spatial resolution remote sensing. Remote Sensing, 9(6), 545.
https://doi.org/10.3390/rs9060545