Quantitative assessment of vegetation structural attributes from aerial photography

Fensham, R. J.; Fairfax, R. J.; Holman, J. E.; Whitehead, Peter

doi:10.1080/01431160110106050

Cited by 55 publications

(45 citation statements)

References 28 publications

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“…Similar relationships between canopy coverage and biomass have also been reported in the semiarid savanna of Sudan [53], and in semi-arid Senegal [54]. However, different vegetation types showed significantly different estimation accuracy [26].…”

Section: Indicators Of Carbon Stocks (C T )supporting

confidence: 59%

“…Estimates of canopy coverage derived from remote sensing images have also been applied as a proxy for calculating individual tree and stand biomass [25,26]. For example, Sousa et al [27] found that the tree canopy horizontal projection derived from QuickBird satellite images produced highly accurate estimates of AGB of Quercus rotundifolia at both individual and plot scales.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

et al. 2017

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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 (R 2 : 0.76-0.89) and spectral-based vegetation indices (R 2 : 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.

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Section: Indicators Of Carbon Stocks (C T )supporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

et al. 2017

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“…Sources of error that influence the user's ability to extract tree canopy cover that should be considered when utilizing aerial imagery to identify P-J woodland tree canopy cover include shadows, terrain effects, quality of imagery (spatial, spectral, and radiometric resolution), and incorrect orthorectification of measurement subplots (Fensham et al 2002;Jensen 2005;Browning et al 2009;Moffet 2009). For this study where we primarily dealt with P-J species, seasonal variations or the collection period of NAIP imagery was of less concern.…”

mentioning

confidence: 99%

Canopy Area and Aboveground Mass of Individual Redberry Juniper (Juniperus pinchotii) Trees

Ansley¹,

Mırık²,

Surber³

et al. 2012

Rangeland Ecology & Management

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“…Several studies have been conducted since 2002 by Fensham and his colleagues on the cover of semiarid eucalyptus in Australian woodlands (e.g., Fensham et al, 2002;Fensham and Fairfax, 2003;Fensham and Fairfax, 2007) using a manual interpretation method with a scaled 10 Â 10 reticule with open-centered crosses to identify sample points on aerial photographs with different scales. Models that describe woody structure from aerial photography to assess woodland vegetation cover changes were suggested.…”

Section: Data Acquisition Aerial Photo Interpretationmentioning

confidence: 99%

Land cover changes in a forested watershed, southern Zagros, Iran

2008

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This study aims to investigate the land cover changes in relation to traditional utilization and livestock grazing, socioeconomic development, and demographic changes during recent decades in a forested watershed in the southern Zagros, Iran. Data on land cover change were collected through stereo interpretation with digital photogrammetric techniques applied to sets of scanned aerial photographs from 1969 and 1993 with a systematic grid of sample plots provided by GIS techniques. Efforts were made to mitigate errors in the interpretation. Field data were gathered in 2003 from woodland inventory, biophysical, and demographic data and in 2006 from interviews.The results show stability in crown cover density (%) and in the number of large trees, and an increase in the woodland class. The results may be related to a change in woodland utilization. Socioeconomic development, decrease in human and livestock population, and a change in the type of livestock have influenced the traditional pattern. Steep slopes partially limit human accessibility to woodland resources. Around 54% of the oak trees are in coppice form and natural regeneration of the trees is rare. Overgrazing and seed gathering have a negative effect on regeneration and endanger the future survival of the woodland.

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Quantitative assessment of vegetation structural attributes from aerial photography

Cited by 55 publications

References 28 publications

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

Canopy Area and Aboveground Mass of Individual Redberry Juniper (Juniperus pinchotii) Trees

Land cover changes in a forested watershed, southern Zagros, Iran

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