A multi-sensor approach for predicting biomass of extensively managed grassland

Reddersen, Björn; Fricke, Thomas; Wachendorf, M.

doi:10.1016/j.compag.2014.10.011

Cited by 51 publications

(42 citation statements)

References 67 publications

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“…Of most importance might be the fact that the relation between plant height and biomass in the two regarded periods seems to be best represented by different models. Overall, the results support the applicability of BRMs for biomass estimations based on TLS-derived spatial plant height data and substantiate the potential of ground-based plant parameter measurements as input for biomass estimation models [7,17].…”

Section: Discussionsupporting

confidence: 49%

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Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data

et al. 2015

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It is known that plant height is a suitable parameter for estimating crop biomass. The aim of this study was to confirm the validity of spatial plant height data, which is derived from terrestrial laser scanning (TLS), as a non-destructive estimator for biomass of paddy rice on the field scale. Beyond that, the spatial and temporal transferability of established biomass regression models were investigated to prove the robustness of the method and evaluate the suitability of linear and exponential functions. In each growing season of two years, three campaigns were carried out on a field experiment and on a farmer's conventionally managed field. Crop surface models (CSMs) were generated from the TLS-derived point clouds for calculating plant height with a very high spatial resolution of 1 cm. High coefficients of determination between CSM-derived and manually measured plant heights (R 2 : 0.72 to 0.91) confirm the applicability of the approach. Yearly averaged differences between the measurements were ~7% and ~9%. Biomass regression models were established from the field experiment data sets, based on strong coefficients of determination between plant height and dry biomass (R : 0.60 to 0.90 and 0.56 to 0.85 for linear and exponential models, respectively). Hence, the suitability of TLS-derived spatial plant height as a non-destructive estimator for biomass of paddy rice on the field scale was verified and the transferability demonstrated.

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Section: Discussionsupporting

confidence: 49%

“…However, therein, plant height was manually measured, which is prone to selection bias. A ground-based multi-sensor approach showed good results for predicting biomass of grassland [17]. For biomass estimation of paddy rice, in-situ approaches with hand-held sensors for measuring canopy reflectance provided good results [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data

et al. 2015

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“…This finding does not match that of Fricke et al [4] and Adamchuk et al [41] who reported that exclusive use of USH achieved better results than exclusive use of narrow or broad band spectral vegetation indices for prediction of biomass in more homogeneous grasslands. Contrary to yields, separation of the common dataset into date-specific subsets did not improve prediction accuracy for DMP (Figure 3).…”

Section: Exclusive Use Of Spectral Datacontrasting

confidence: 56%

Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

et al. 2017

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An accurate estimation of biomass is needed to understand the spatio-temporal changes of forage resources in pasture ecosystems and to support grazing management decisions. A timely evaluation of biomass is challenging, as it requires efficient means such as technical sensing methods to assess numerous data and create continuous maps. In order to calibrate ultrasonic and spectral sensors, a field experiment with heterogeneous pastures continuously stocked by cows at three grazing intensities was conducted. Sensor data fusion by combining ultrasonic sward height (USH) with narrow band normalized difference spectral index (NDSI) (R 2 CV = 0.52) or simulated WorldView2 (WV2) (R 2 CV = 0.48) satellite broad bands increased the prediction accuracy significantly, compared to the exclusive use of USH or spectral measurements. Some combinations were even better than the use of the full hyperspectral information (R 2 CV = 0.48). Spectral regions related to plant water content were found to be of particular importance (996-1225 nm). Fusion of ultrasonic and spectral sensors is a promising approach to assess biomass even in heterogeneous pastures. However, the suggested technique may have limited usefulness in the second half of the growing season, due to an increasing abundance of senesced material.

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“…As both studies applied airborne methods, the spatial resolution was low. A ground-based multi-sensor approach for predicting biomass of grassland based on measurements of plant height, leaf area index (LAI), and spectral reflectance showed that combining multiple sensors can improve the estimation [34]. However, in that study, spectral data were not well-suited.…”

mentioning

confidence: 87%

“…saturation effects [13,34,35], plant height may reach limitations when differences in plant height are low. Consequently, the fusion of multiple parameters should be examined to enhance estimations.…”

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confidence: 99%

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

2015

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Plant biomass is an important parameter for crop management and yield estimation. However, since biomass cannot be determined non-destructively, other plant parameters are used for estimations. In this study, plant height and hyperspectral data were used for barley biomass estimations with bivariate and multivariate models. During three consecutive growing seasons a terrestrial laser scanner was used to establish crop surface models for a pixel-wise calculation of plant height and manual measurements of plant height confirmed the results (R 2 up to 0.98). Hyperspectral reflectance measurements were conducted with a field spectrometer and used for calculating six vegetation indices (VIs), which have been found to be related to biomass and LAI: GnyLi, NDVI, NRI, RDVI, REIP, and RGBVI. Furthermore, biomass samples were destructively taken on almost the same dates. Linear and exponential biomass regression models (BRMs) were established for evaluating plant height and VIs as estimators of fresh and dry biomass. Each BRM was established for the whole observed period and pre-anthesis, which is important for management decisions. Bivariate BRMs supported plant height as a strong estimator (R 2 up to 0.85), whereas BRMs based on individual VIs showed varying performances (R 2 : 0.07-0.87). Fused approaches, where plant height and one VI were used for establishing multivariate BRMs, yielded improvements in some cases (R 2 up to 0.89). Overall, this study reveals the potential of remotely-sensed plant parameters for estimations of barley biomass. Moreover, it is a first step towards the fusion of 3D spatial and spectral measurements for improving non-destructive biomass estimations. OPEN ACCESSRemote Sens. 2015, 7 11450

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A multi-sensor approach for predicting biomass of extensively managed grassland

Cited by 51 publications

References 67 publications

Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data

Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data

Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

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