Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years

Silleos, N.; Alexandridis, Thomas; Gitas, Ioannis Z.; Perakis, Κonstantinos

doi:10.1080/10106040608542399

Cited by 248 publications

(137 citation statements)

References 25 publications

Supporting

Mentioning

113

Contrasting

Unclassified

Order By: Relevance

“…This similarity may be because of their use of the same spectral bands (Table 1). Both NDVI and RVI have been widely used for estimating AGB [17,23,43,55,56].…”

Section: Indicators Of Carbon Stocks (C T )mentioning

confidence: 99%

“…The SAVI was selected to reduce the impact of soil in the scene, as the extent of the canopy coverage of saltbush in our study area was relatively small due to the sparse spacing of the shrubs. The RVI was selected to capture the contrast between the red and infrared bands for vegetated pixels [17], and for its use of only two spectral bands. The green chromatic coordinate (GCC) was used in this study to test the performance of general optical bands for estimating saltbush biomass.…”

Section: Vegetation Index Formula Referencementioning

confidence: 99%

“…Although the use of pixel-based vegetation indices as proxies for estimating vegetation biomass is well established [17,18], including examples such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) [19], and the ratio vegetation index (RVI) on forests [20], grass [21] and woodland [22,23], these techniques have been infrequently used to estimate carbon stocks in shrublands. However, the broader use of remote sensing for calibrating vegetation indices to biomass (e.g., Asner [24]) shows that the relationship between vegetation indices and biomass can differ between species, season, and the scale of the vegetation and pixels.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

“…This similarity may be because of their use of the same spectral bands (Table 1). Both NDVI and RVI have been widely used for estimating AGB [17,23,43,55,56].…”

Section: Indicators Of Carbon Stocks (C T )mentioning

confidence: 99%

Section: Vegetation Index Formula Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2017

View full text Add to dashboard Cite

show abstract

“…This approach provides a clear way to understand the impact of flooding on wetland landscape patterns. NDVI is the most commonly used vegetation index with a range from −1 to 1 [70,71]. This index can be calculated as follows:…”

Section: Changes In Wetland Landscape Patterns After the Flood Periodmentioning

confidence: 99%

Monitoring Spatial and Temporal Dynamics of Flood Regimes and Their Relation to Wetland Landscape Patterns in Dongting Lake from MODIS Time-Series Imagery

Huang

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

Dongting Lake, the second largest freshwater lake in China, is well known for its rapid seasonal fluctuations in inundation extents in the middle reach of the Yangtze River, and it is also the lake most affected by the Three Gorges Project. Significant inter-annual and seasonal variations in flood inundations were observed from Moderate Resolution Imaging Spectroradiometer (MODIS) time-series imagery between 2000 and 2012 in the Dongting Lake. Results demonstrated that temporal changes in inundation extents derived from MODIS data were accordant with variations in annual and monthly precipitation and runoff data. Spatial and temporal dynamics of some related parameters of flood regime were analyzed as well, which included flood inundation probability, duration and start/end date of the annual largest flood. Large areas with high flood inundation probability were identified in 2000 and 2002, but relatively small regions with great flood inundation probability occurred in

show abstract

“…One of the major applications of remote sensing in the field of environmental resources management and decision-making is the detection and quantitative assessment of green vegetation (Silleos et al 2006). Measuring the biophysical parameters of rangeland vegetation condition for accurate prediction of its productivity and management is essential as these ecosystems provide a steady supply of goods and services.…”

Section: Introductionmentioning

confidence: 99%

Evaluating derived vegetation indices and cover fraction to estimate rangeland aboveground biomass in semi-arid environments

Fajji¹,

Palamuleni²,

Mlambo³

2017

SA J of Geomatics

View full text Add to dashboard Cite

show abstract

Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years

Cited by 248 publications

References 25 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

Monitoring Spatial and Temporal Dynamics of Flood Regimes and Their Relation to Wetland Landscape Patterns in Dongting Lake from MODIS Time-Series Imagery

Evaluating derived vegetation indices and cover fraction to estimate rangeland aboveground biomass in semi-arid environments

Contact Info

Product

Resources

About