Replacing Manual Rising Plate Meter Measurements with Low-cost UAV-Derived Sward Height Data in Grasslands for Spatial Monitoring

Bareth, Georg; Schellberg, J.

doi:10.1007/s41064-018-0055-2

Cited by 32 publications

(48 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the high cost of UAV-based LiDAR system is the main limitation for its further application. To reduce the cost of estimating crop PH, researchers start to use low-cost UAV RGB remote-sensing system [64][65][66][67]. For example, Ziliani et al [68] compared the PH derived from high-resolution UAV RGB imagery against PH derived from LiDAR, and showed a strong correlation between structure-from-motion derived heights and LiDAR scan data with the coefficient of determination (R 2 ) ranged from 0.77 to 0.99.…”

Section: Discussionmentioning

confidence: 99%

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

et al. 2019

View full text Add to dashboard Cite

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

show abstract

Section: Discussionmentioning

confidence: 99%

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Crop biomass and %N are two of the most important traits under investigation in precision agriculture. The usability of TLS-derived crop surface height (CSH) or rather plant height as estimator for biomass has been demonstrated by several authors [57][58][59][60][61][62][63]. An increasing number of studies on estimating biomass highlight the benefits of crop height as structural trait in comparison to spectral measurements [47,59,65].…”

Section: Discussionmentioning

confidence: 99%

“…In a comparative study on different sensors, best results were achieved with laser scanning in comparison to an ultrasonic sensor and drone-based imaging [54]. Several studies showed that dry biomass can be estimated from structural crop traits captured with TLS, drone-based imaging, or oblique stereo image acquisition [55][56][57][58][59][60][61][62][63]. Only a few studies compared biomass estimations based on different sensors so far [64].…”

mentioning

confidence: 99%

Estimating Nitrogen from Structural Crop Traits at Field Scale—A Novel Approach Versus Spectral Vegetation Indices

Tilly

Bareth

2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

A sufficient nitrogen (N) supply is mandatory for healthy crop growth, but negative consequences of N losses into the environment are known. Hence, deeply understanding and monitoring crop growth for an optimized N management is advisable. In this context, remote sensing facilitates the capturing of crop traits. While several studies on estimating biomass from spectral and structural data can be found, N is so far only estimated from spectral features. It is well known that N is negatively related to dry biomass, which, in turn, can be estimated from crop height. Based on this indirect link, the present study aims at estimating N concentration at field scale in a two-step model: first, using crop height to estimate biomass, and second, using the modeled biomass to estimate N concentration. For comparison, N concentration was estimated from spectral data. The data was captured on a spring barley field experiment in two growing seasons. Crop surface height was measured with a terrestrial laser scanner, seven vegetation indices were calculated from field spectrometer measurements, and dry biomass and N concentration were destructively sampled. In the validation, better results were obtained with the models based on structural data (R2 < 0.85) than on spectral data (R2 < 0.70). A brief look at the N concentration of different plant organs showed stronger dependencies on structural data (R2: 0.40–0.81) than on spectral data (R2: 0.18–0.68). Overall, this first study shows the potential of crop-specific across‑season two-step models based on structural data for estimating crop N concentration at field scale. The validity of the models for in-season estimations requires further research.

show abstract

“…> 1ha) and the spatial coverage is limited, TLS approaches are not appropriate for large-scale estimation of grassland yield. To derive point clouds, photogrammetric approaches based on numerous photographs from different viewing angles might be an alternative (Wijesingha et al 2019, Grüner et al 2019, Forsmoo et al 2018, Viljanen et al 2018, Bareth, Schellberg 2018 to TLS measurements. While point clouds created from TLS have a deeper penetration into the grassland canopy and can also be affected by single plant parts sticking out (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…height measurements or the rising plate meter) can also be affected by the canopy structure and are not applicable on large fields. To compensate for this effect, SfM approaches have been proven to deliver equal or even better yield estimations than traditional field methods (Grüner et al 2019, Bareth, Schellberg 2018. For example, Grüner et al 2019 showed an improved prediction quality (i.e.…”

Section: Introductionmentioning

confidence: 99%

The Benefit of Spectral and Point-Cloud Data for Herbage Yield and Quality Assessment of Grasslands

Wachendorf

Astor

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Grassland represents the largest single agricultural vegetation in Germany and provides a multitude of ecosystem services. Timely and accurate information about herbage yield and quality is essential for an efficient use of resources, e.g. to be able to match the actual available feed with a demand of animals or with other industrial uses. Grasslands frequently exhibit small-scale botanical and structural heterogeneity with pronounced spatio-temporal dynamics. These features present particular challenges for sensor applications, which, apart from limitations posed by high costs and low temporal and spatial resolutions of many available remote sensing (RS) systems, may be the reason for so far little commercial applications of RS in practical grassland farming. This paper considers recent developments in the use of spectral and point-cloud data for herbage yield and quality assessment of grasslands. Former research showed that single sensor systems mounted on unmanned aerial vehicles produce similar prediction errors in crude protein or acid detergent fibre concentrations as proximal sensing tools (e.g. field spectroscopy). However, further improvements are needed. Beside improvements of single sensor types, the development of systems with complementary sensors is seen as a promising research area. It will help to overcome the limitations of single sensors and provide better information about herbage yield and quality. From an agronomic point of view, thematic maps of farm fields are suggested as the central outcome of RS and data analysis. These maps are representing the relevant grassland features and therefore can be used as low-cost, appropriate and timely information to support farmers' decision-making.

show abstract

Replacing Manual Rising Plate Meter Measurements with Low-cost UAV-Derived Sward Height Data in Grasslands for Spatial Monitoring

Cited by 32 publications

References 57 publications

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

Estimating Nitrogen from Structural Crop Traits at Field Scale—A Novel Approach Versus Spectral Vegetation Indices

The Benefit of Spectral and Point-Cloud Data for Herbage Yield and Quality Assessment of Grasslands

Contact Info

Product

Resources

About