Untitled

Schino, Gabriele; Borfecchia, Flavio; Cecco, L. De; Dibari, Camilla; Iannetta, Massimo; Martini, S.; Pedrotti, Franco

doi:10.1023/a:1026308928874

Cited by 70 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional methods of aboveground green biomass estimation based on destructive sampling are expensive, timeconsuming, and feasible only to small-scale biomass survey. Remote sensing techniques offer an effective solution for accurately estimating green aboveground biomass in grassland [Schino et al, 2003;Liu et al, 2004;Elsfelder et al, 2012]. Vegetation indices calculated from red and near-infrared (NIR) bands are good indicators of vegetation photosynthetic activity [Myneni and Los, 1995;Liu et al, 2013;Marino and Alvino, 2014], and are well correlated to green aboveground biomass in grassland [Schino et al, 2003;Ren et al, 2011].…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing techniques offer an effective solution for accurately estimating green aboveground biomass in grassland [Schino et al, 2003;Liu et al, 2004;Elsfelder et al, 2012]. Vegetation indices calculated from red and near-infrared (NIR) bands are good indicators of vegetation photosynthetic activity [Myneni and Los, 1995;Liu et al, 2013;Marino and Alvino, 2014], and are well correlated to green aboveground biomass in grassland [Schino et al, 2003;Ren et al, 2011]. The well-known vegetation index, now widely used for green aboveground biomass estimation in grassland [Wessels et al, 2006;An et al, 2013;Gao et al, 2013;Jin et al, 2014;Xia et al, 2014], is normalized difference vegetation index (NDVI) [Rouse et al, 1974].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Ren

Zhou

2014

European Journal of Remote Sensing

View full text Add to dashboard Cite

Accurate estimation of green aboveground biomass in arid and semiarid grassland is essential for a variety of studies, such as sustainable grassland management, fire risk assessment, climate change, and environmental degradation. A great need exists for the establishment of robust method for estimating green aboveground biomass in arid and semiarid grassland due to the influences of soil background and litter. In the study, a new index (litter-soil-adjusted vegetation index, L-SAVI) was proposed to estimate green aboveground biomass in arid and semiarid grassland. The L-SAVI was also evaluated based on biomass and spectra in situ measurements in the desert steppe of Inner Mongolia. Results showed that, the performance of the new index was better than that of NDVI (normalized difference vegetation index), SAVI (soil-adjusted vegetation index), MSAVI (modified soil-adjusted vegetation index), OSAVI (optimised soil-adjusted vegetation index), TSAVI (transformed soil-adjusted vegetation index), ATSAVI (adjusted transformed soil-adjusted vegetation index), PVI (perpendicular vegetation index), GSAVI (green-adjusted vegetation index), and L-ATSAVI (litter-corrected ATSAVI) in our study site. The logic behind the L-SAVI was to enable the SAVI to be less sensitive to litter by incorporating the CAI (cellulose absorption index) in the SAVI. In conclusion, the L-SAVI is a suitable predictor for complementing existing vegetation indices on green aboveground biomass estimation in arid and semiarid grassland.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Ren

Zhou

2014

European Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Spectral bands have a low AGB predictive accuracy, when compared to the use of derived indices and a combination of variables. Improved prediction accuracies using indices have been acknowledged in estimating species AGB by previous studies [32,45,50]. These variables have been perceived to be more sensitive to species characteristics, which improve their predictive accuracy, than individual bands.…”

Section: The Performance Of Landsat 8 Sentinel 2 and Worldview-2 Vamentioning

confidence: 99%

Determining Optimal New Generation Satellite Derived Metrics for Accurate C3 and C4 Grass Species Aboveground Biomass Estimation in South Africa

2018

View full text Add to dashboard Cite

While satellite data has proved to be a powerful tool in estimating C3 and C4 grass species Aboveground Biomass (AGB), finding an appropriate sensor that can accurately characterize the inherent variations remains a challenge. This limitation has hampered the remote sensing community from continuously and precisely monitoring their productivity. This study assessed the potential of a Sentinel 2 MultiSpectral Instrument, Landsat 8 Operational Land Imager, and WorldView-2 sensors, with improved earth imaging characteristics, in estimating C3 and C4 grasses AGB in the Cathedral Peak, South Africa. Overall, all sensors have shown considerable potential in estimating species AGB; with the use of different combinations of the derived spectral bands and vegetation indices producing better accuracies. However, WorldView-2 derived variables yielded better predictive accuracies (R 2 ranging between 0.71 and 0.83; RMSEs between 6.92% and 9.84%), followed by Sentinel 2, with R 2 between 0.60 and 0.79; and an RMSE 7.66% and 14.66%. Comparatively, Landsat 8 yielded weaker estimates, with R 2 ranging between 0.52 and 0.71 and high RMSEs ranging between 9.07% and 19.88%. In addition, spectral bands located within the red edge (e.g., centered at 0.705 and 0.745 µm for Sentinel 2), SWIR, and NIR, as well as the derived indices, were found to be very important in predicting C3 and C4 AGB from the three sensors. The competence of these bands, especially of the free-available Landsat 8 and Sentinel 2 dataset, was also confirmed from the fusion of the datasets. Most importantly, the three sensors managed to capture and show the spatial variations in AGB for the target C3 and C4 grassland area. This work therefore provides a new horizon and a fundamental step towards C3 and C4 grass productivity monitoring for carbon accounting, forage mapping, and modelling the influence of environmental changes on their productivity.

show abstract

“…Furthermore it could influence the hydrological status, stability and productivity of the ecosystem (Kirkman & Moore, 1995;O'Connor & Bredenkamp, 1997;Snyman & Fouché, 1991;Snyman 1997Snyman , 1998. Biomass is a good indicator of available forage and the risk of soil erosion (Schino et al, 2003). Biomass is dependent on the root reserves, nutrient and water content of the soil.…”

Section: Effects Of Grazing On Biomassmentioning

confidence: 99%

Impact of Domestic Animals on Ecosystem Integrity of Lesotho High Altitude Peatlands

Preez¹,

Brown²

2011

Ecosystems Biodiversity

View full text Add to dashboard Cite

Untitled

Cited by 70 publications

References 13 publications

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Determining Optimal New Generation Satellite Derived Metrics for Accurate C3 and C4 Grass Species Aboveground Biomass Estimation in South Africa

Impact of Domestic Animals on Ecosystem Integrity of Lesotho High Altitude Peatlands

Contact Info

Product

Resources

About