Assessment of Pasture Biomass with the Normalized Difference Vegetation Index from Active Ground‐Based Sensors

Flynn, E. Scott; Dougherty, C. T.; Wendroth, Ole

doi:10.2134/agronj2006.0363

Cited by 35 publications

(13 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Normalized Difference Vegetation Index (NDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Renormalized Difference Vegetation Index (RDVI), Red Edge Inflection Point (REIP), and Normalized Difference Red-Edge Index (NDREI) were chosen to be comparable to previous studies. 7,36,[59][60][61] In addition, the VIs listed in Table 3 were calculated from narrowband data acquired by the ASD (with 670 nm as red, 550 nm as green, and 480 nm as blue band) to evaluate the VI RGB obtained by the UAV's camera.…”

Section: Field Spectroradiometer-derived Vegetation Indicesmentioning

confidence: 99%

Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices

Lussem

Bolten

Menne

et al. 2019

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Monitoring grassland biomass throughout the growing season is of key importance in sustainable, site-specific management decisions. Precision agriculture applications can support these decisions. However, precision agriculture relies on timely and accurate information on plant parameters with a high spatial and temporal resolution. The use of structural and spectral features derived from unmanned aerial vehicle (UAV)-based image data from low-cost sensors is a promising nondestructive approach to assess plant traits such as above-ground biomass or plant height. Therefore, the main objectives were (1) to evaluate the potential of low-cost UAV-based canopy surface models to monitor sward height as an indicator of grassland biomass, (2) to evaluate the potential of vegetation indices from low-cost UAV-based red-greenblue (RGB) digital image data, and (3) to compare the mentioned methods with established methods for biomass monitoring such as rising plate meters and spectroradiometer-based narrowband vegetation indices over the growing season in 2017, including three cuts. We compared the accuracy of each single UAV-based height feature and vegetation index using a combined multivariate approach to estimate fresh and dry biomass. The heterogeneous sward structure with high spatiotemporal variability led to varying performance in biomass estimation depending on the growths (time between two cuts) and choice of predictor variable. The results showed that biomass prediction by height features provided moderate-to-good results (cross-validation R 2 ¼ 0.57 to 0.73 for dry biomass and 0.43 to 0.79 for fresh biomass), but reference measurements based on rising plate meters were more robust when estimating biomass. The spectral features (RGB-based vegetation indices and spectroradiometerbased vegetation indices) yielded varying accuracy and suitability for biomass prediction. Despite the variability, our findings indicate a promising approach for grassland biomass monitoring. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Section: Field Spectroradiometer-derived Vegetation Indicesmentioning

confidence: 99%

Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices

Lussem

Bolten

Menne

et al. 2019

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

“…Vegetation biomass estimation based on remote sensing is important for remote sensing applications [44]. Several studies have shown strong correlations between biomass and vegetation indices [45]. In particular, typical steppe vegetation indices, calculated based on the TM red and near-infrared bands, are strongly correlated with aboveground biomass [46,47].…”

Section: Correlation Between Total Aboveground Biomass and Savi In Qumentioning

confidence: 99%

Effects of Different Grazing Systems on Aboveground Biomass and Plant Species Dominance in Typical Chinese and Mongolian Steppes

Hoshino

et al. 2018

Sustainability

View full text Add to dashboard Cite

This study investigated the effects of different grazing systems on plant communities, and examined the causes of Mongolian grassland desertification. The typical steppes near the Chinese-Mongolian border were studied using quadrat sampling and remote sensing methods. Aboveground biomass in the steppe areas differed significantly among the three grazing systems (p < 0.05): Biomass in the grazing-prohibited areas (455.9 g) was greater than that in the rotational-grazing areas (268.4 g) and the continuous grazing areas (122.2 g). Aboveground biomass was well correlated with the Soil Adjusted Vegetation Index (SAVI; y = 5600x 2 + 260x + 110; R 2 = 0.67; p < 0.05). The relative mean deviation between the aboveground biomass was calculated using this regression and the measured biomass was 29.1%. The Soil Adjusted Vegetation Index (SAVI) values for nomadic-grazing areas were greater than those for continuous-grazing areas in 1989, 2005, 2011, and 2016, and were significantly greater in 2011 and 2016. The SAVI values for the continuous-grazing areas were slightly, but not significantly greater, than those for the nomadic-grazing areas in 1993. Plant species that dominated in moderately degraded areas were most dominant in nomadic-grazing areas, followed by continuous-grazing areas and grazing-prohibited areas. Plant species that dominated in lightly and heavily degraded areas were most dominant in continuous-grazing areas, followed by nomadic-grazing areas and grazing-prohibited areas. Generally, continuous grazing caused more serious grassland degradation than did nomadic grazing, and nomadic-grazing areas tolerated more intense grazing than did continuous-grazing areas.

show abstract

“…These sensors have been used to determine biophysical parameters, such as the green BM and leaf area index (LAI) (Flynn et al . ; Fitzgerald ), and the nitrogen (N) status (Hong et al . ; Tremblay et al .…”

Section: Introductionmentioning

confidence: 99%

Evaluating a hand‐held crop‐measuring device for estimating the herbage biomass, leaf area index and crude protein content in an Italian ryegrass field

et al. 2015

View full text Add to dashboard Cite

The objective of this study is to evaluate the ability of a newly developed hand‐held crop‐measuring device and vegetation indices (VIs) to estimate the herbage biomass (BM), leaf area index (LAI) and forage crude protein mass (CPmass) in an Italian ryegrass (Lolium multiflorum Lam.) field, Japan. The device uses bi‐directional passive sensors (550, 650 and 880 nm) upward and downward to overcome the major drawback of optical remote sensing as influenced by weather conditions. The canopy reflectance and plant sample data were collected 11 times during two winter growing seasons in 2010–11 and 2011–12. Seven VIs were compared to estimate the forage parameters in the normal and logarithmic forms. The predictive ability of the VIs was assessed by the cross‐validated coefficient of determination (R2cv) and the residual prediction (RPD) values. Acceptable RPD values (>1.5) were found in most of the log‐transformed forage parameters with all of the VIs but not in most of the normal‐form. The highest R2cv and RPD were obtained in the normalized difference vegetation index (NDVI) for the ln BM (R2cv = 0.76, RPD = 2.04) and ln LAI (R2cv = 0.80, RPD = 2.25), and the highest modified soil‐adjusted vegetation index (MSAVI) was obtained for the ln CPmass (R2cv = 0.78, RPD = 2.16). By evaluating the limitation of NDVI or MSAVI sensitivity with ranges of cumulative data, the log‐transformed forage parameters showed good R2cv values in the ln BM (0.76–0.79), ln LAI (0.80–0.84) and ln CPmass (0.71–0.84) with acceptable RPD values throughout most of the range, whereas in the normal‐form, the NDVI or MSAVI was saturated at moderate‐high BM, LAI or CPmass, causing the R2cv and RPD values to decrease with increasing plant parameters. These results suggest that this device is applicable in log‐transformed BM, LAI and CPmass throughout the growing season without cloud effect.

show abstract

Assessment of Pasture Biomass with the Normalized Difference Vegetation Index from Active Ground‐Based Sensors

Cited by 35 publications

References 22 publications

Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices

Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices

Effects of Different Grazing Systems on Aboveground Biomass and Plant Species Dominance in Typical Chinese and Mongolian Steppes

Evaluating a hand‐held crop‐measuring device for estimating the herbage biomass, leaf area index and crude protein content in an Italian ryegrass field

Contact Info

Product

Resources

About