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

^{*}

## Abstract

**:**

^{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.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area and Site Characteristics

^{−1}; (b) lenient stocking, average 1.8 SLU ha

^{−1}; and (c) very lenient stocking, average 1.3 SLU ha

^{−1}[25]. To ensure extensive sward variation for data assessment, one representative study plot of 30 × 50 m size was selected within each of the three paddocks using a grazed/ungrazed-classified aerial image to obtain comparable surface proportions.

#### 2.2. Field Measurements

^{2}) were chosen within each of the 3 study plots, adding up to a total of 54 samples per date which represented the existing range of available biomass levels and sward structures. To verify a representative biomass range, a stratified random sampling was performed. In each study plot, three levels of sward height (low, medium, and high) were sampled randomly to compile all date-specific biomass levels in the data set. A Trimble GeoXH GPS device (Trimble Navigation Ltd., Sunnyvale, California, USA) with DGPS correction from AXIO-net (Hannover, Germany, PED-RTK ±20 mm) was used to avoid repeated sampling at the same location during the growing season.

#### 2.2.1. Ground-Based Remote Sensing Measurements

#### 2.2.2. Sampling of Reference Data

#### 2.3. Data Analysis

^{−2}), dry matter yield (DMY) (g·m

^{−2}) or dead material proportion (DMP) (% of DMY); $\mathrm{USH}$ = ultrasonic sward height (mm); $\mathrm{NDSI}\text{}$= 2-band combination vegetation index derived from hyperspectral data based on original NDVI formula; and $\mathrm{X}$ = WorldView-2 satellite bands.

^{2}, wavelength selection was first conducted according to Equation (4) and (6) for each target parameter. Thus, all possible 2-band NDSI combinations, in all 633,375 indices, were individually used in linear regression models for each sensor combination. The best fit wavelengths for the full models were then used to develop regression models. According to the rules of hierarchy and marginality [33,34], non-significant effects were excluded from the models using a step-wise approach, but were retained if the same variable appeared as part of a significant interaction at α-level of 5%. In order to reduce the risk of over-fitting, all models were validated by a four-fold cross validation method [35]. The prediction accuracy was evaluated using two measures: (a) the cross-validated squared correlation coefficient (R

^{2}

_{CV}), which describes the linear relation between the measured dependent variables (i.e., FMY, DMY, and DMP) and the values predicted by the linear model; and (b) the cross-validated root mean square error (RMSE

_{CV}), which describes the average deviation of the estimated values from the observed ones.

## 3. Results

#### 3.1. Sward Characteristics

^{−2}and from 29.2 to 691.9 g·m

^{−2}with an overall mean value of 823.9 g·m

^{−2}and 276.4 g·m

^{−2}, respectively, for all sampling dates (Table 1). The sampling date at the beginning of June (Date 2) exhibited the highest biomass (mean value of 1240 g·m

^{−2}and 314.5 g·m

^{−2}for FMY and DMY, respectively), whereas Date 4 showed the lowest biomass (mean value of 567.5 g·m

^{−2}and 237.6 g·m

^{−2}for FMY and DMY, respectively). USH ranged from 7 to 646 mm during the growing season and the lowest sward heights were found at Date 1 (mean value = 136 mm). A wide range of DMP (1.4% to 83.6% of DM; sd = 20.5%) was observed throughout the growing season. The highest variability of DMP was observed at more advanced developmental stages of swards (Date 3 and 4; sd = 18.8% and 17.7% of DMY, respectively) which also delivered the highest mean values of DMP (45.7% and 40% of DMY, respectively). The proportion of grass was always considerably higher than proportions of legumes and herbs. The proportion of moss was negligible (overall mean value 1.9%). In total, 48 species were identified in the sampling plots (Supplementary Table A1). The most important species were Dactylis glomerata (Constancy, C = 89.7%) and Lolium perenne (C = 70.1%) among the grasses, Trifolium repens (C = 39.7%) and Trifolium pratense (C = 17.8%) among the legumes, and Taraxacum officinale (C = 57.5%) and Galium mollugo (C = 40.7%) among the herbs.

#### 3.2. Exclusive use of Ultrasonic Sward Height

^{2}

_{CV}= 0.73 and 0.80 respectively) when sward heights were much lower than at later dates. The lowest R

^{2}values were found at Dates 3 and 4 (R

^{2}

_{CV}< 0.40). DMP had very weak or no correlation with USH and, thus, data are not shown.

#### 3.3. Exclusive Use of Spectral Data

^{2}

_{CV}of 0.48 for common and 0.15–0.79 for date-specific models) and FMY (0.67 and 0.33–0.86 respectively) (Figure 1 and Figure 2). For DMP the MPLSR prediction was only best for the common model and date 1 (R

^{2}

_{CV}of 0.76 and 0.67), while for the other dates the NDSI showed the best results (R

^{2}

_{CV}between 0.43 and 0.68) (Figure 3). This regression approach integrates spectral information from the whole hyperspectral range and its usefulness for measuring grassland properties has been acknowledged by other studies [36,37,38,39,40]. The predictive power of WorldView2 (WV2) bands (R

^{2}0.13–0.55) was not satisfactory and never outperformed the NDSI or MPLSR approach.

#### 3.4. Sensor Data Fusion Using Combinations of USH and Spectral Variables

^{2}

_{CV}-values for common swards from 0.42 (USH exclusively) to a maximum of 0.52 (NDSI combined with USH) for DMY and from 0.42 (USH exclusively) to a maximum of 0.63 (NDSI combined with USH) for FMY in common swards (Figure 1 and Figure 2). Irrespective of spectral sensor configuration, date-specific calibrations of yield parameters for Dates 1 and 2 performed better than for Dates 3 and 4. The combination of USH and NDSI consistently produced the best results, both in common and date-specific calibrations. Similar to the model findings with exclusive use of NDSI, the dominant bands of NDSI when in combination with USH were mostly located at water absorption bands, i.e., the ascending slop of the first absorption band (between 996 and 1086 nm) and the ascending slope of the second water absorption band (1215 to 1225 nm) as well as the green region in the visible spectrum (521 to 578 nm) (Table 2). Figure 4 shows example plots of fit for DMY prediction based on USH and NDSI and provides a comprehensive insight into the effects of sensor combination. It becomes clear that with exclusive use of sensors, calibration models led to an overestimation at low levels of DMY, whereas higher values were underestimated. An improvement of fit by combining sensors is obvious for all sampling dates (except Date 3), as demonstrated by higher R

^{2}

_{CV}-values and convergence of the regression line to the bisector. Yield predictions in heterogeneous pastures as presented in this study partly show a complex interaction between USH, NDSI and DMP (Figure 5). At higher levels of NDSI (here seen as a measure of, e.g., sward density), DMY and FMY basically follow a linear increase with USH gain (here seen as a measure for sward height), regardless of DMP. In contrast, at low levels of NDSI, DMY and FMY curves show differing trends. While DMY (Figure 5A) just shows a parallel shift to lower yield levels, FMY (Figure 5B) in swards with high DMP shows a saturated curve.

## 4. Discussion

#### 4.1. Exclusive Use of USH

#### 4.2. Exclusive Use of Spectral Data

^{2}

_{CV}= 0.43–0.64) and, to a lesser degree, at Date 1 (R

^{2}

_{CV}= 0.26–0.49) and Date 4 (R

^{2}

_{CV}= 0.39–0.66). In contrast, DMP is much lower at Date 2, which corresponds to lower R

^{2}

_{CV}values for DMP prediction (0.09–0.24) (Figure 3), but allows higher accuracies for yield prediction, as low levels of DMP are inversely related to higher proportions of green plant material. This is consistent with findings by Chen et al. [42], who pointed out that spectral indicators usually collect data over green vegetation rather than mature and dry vegetation.

#### 4.3. Sensor Fusion

## 5. Conclusions

## Acknowledgements

## Author Contributions

## Conflicts of Interest

## Appendix A

**Table A1.**List of pasture species identified in 214 sampling plots in 2013 with their minimum, maximum and mean values of dry matter contribution estimated according to the Klapp and Stählin method. Constancy (Const.) refers to the relative proportion of plots containing the respective species.

Species | Min | Max | Mean | Const. (%) | Species | Min | Max | Mean | Const. (%) |
---|---|---|---|---|---|---|---|---|---|

Grasses | Herbs | ||||||||

Agrostis stolonifera | 0.0 | 79.4 | 9.22 | 54.2 | Achillea millefolium | 0.0 | 85.0 | 0.92 | 5.1 |

Alopecurus pratensis | 0.0 | 95.0 | 3.83 | 13.6 | Anthriscus sylvestris | 0.0 | 28.0 | 0.13 | 0.5 |

Arrhenatherum elatius | 0.0 | 1.0 | 0.00 | 0.5 | Bellis perennis | 0.0 | 59.0 | 0.31 | 2.3 |

Bromus mollis | 0.0 | 7.0 | 0.10 | 3.7 | Centaurea jacea | 0.0 | 1.0 | 0.00 | 0.5 |

Cynosurus cristatus | 0.0 | 59.6 | 1.77 | 10.3 | Cerastium holosteoides | 0.0 | 4.0 | 0.23 | 19.6 |

Dactylis glomerata | 0.0 | 94.0 | 25.68 | 89.7 | Cirsium arvense | 0.0 | 40.0 | 1.14 | 9.3 |

Deschampsia caespitosa | 0.0 | 90.0 | 0.59 | 0.9 | Cirsium vulgare | 0.0 | 15.0 | 0.30 | 7.0 |

Elymus repens | 0.0 | 80.0 | 5.82 | 36.9 | Convolvulus arvensis | 0.0 | 28.6 | 0.39 | 6.1 |

Festuca pratensis | 0.0 | 85.0 | 0.71 | 5.6 | Crepis capillaris | 0.0 | 20.0 | 0.38 | 6.1 |

Festuca rubra | 0.0 | 95.4 | 4.85 | 21.0 | Erophila verna | 0.0 | 4.0 | 0.04 | 4.7 |

Lolium perenne | 0.0 | 88.6 | 15.64 | 70.1 | Epilobium spec. | 0.0 | 16.0 | 0.20 | 4.7 |

Phleum pratense | 0.0 | 4.0 | 0.06 | 2.3 | Galium mollugo | 0.0 | 88.0 | 9.67 | 40.7 |

Poa annua | 0.0 | 1.0 | 0.01 | 0.9 | Geranium dissectum | 0.0 | 13.0 | 0.20 | 13.6 |

Poa pratensis | 0.0 | 45.0 | 2.32 | 27.6 | Geum urbanum | 0.0 | 30.0 | 0.19 | 3.3 |

Poa trivialis | 0.0 | 16.0 | 1.28 | 25.2 | Hieracium pilosella | 0.0 | 0.2 | 0.00 | 0.5 |

Lamium purpureum | 0.0 | 38.0 | 0.21 | 2.3 | |||||

Legumes | Leontodon hispidus | 0.0 | 2.0 | 0.02 | 1.9 | ||||

Medicago lupulina | 0.0 | 5.0 | 0.03 | 0.9 | Plantago lanceolata | 0.0 | 35.0 | 0.56 | 10.7 |

Trifolium campestre | 0.0 | 20.0 | 0.17 | 1.9 | Plantago major | 0.0 | 3.0 | 0.01 | 0.5 |

Trifolium dubium | 0.0 | 25.0 | 0.18 | 3.7 | Taraxacum officinale | 0.0 | 83.0 | 5.89 | 57.5 |

Trifolium pratense | 0.0 | 61.0 | 1.50 | 17.8 | Ranunculus acris | 0.0 | 10.0 | 0.20 | 6.5 |

Trifolium repens | 0.0 | 49.6 | 2.49 | 39.7 | Ranunculus repens | 0.0 | 71.8 | 1.35 | 23.8 |

Vicia cracca | 0.0 | 1.0 | 0.00 | 0.5 | Rosa spec. | 0.0 | 5.0 | 0.04 | 0.9 |

Rumex acetosa | 0.0 | 4.0 | 0.03 | 1.4 | |||||

Urtica dioica | 0.0 | 84.0 | 1.09 | 2.8 | |||||

Veronica chamaedrys | 0.0 | 4.0 | 0.03 | 1.9 | |||||

Veronica serpyllifolia | 0.0 | 35.0 | 0.19 | 1.9 |

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**Figure 1.**Cross-validation (CV) results for a range of sensor models used for prediction of fresh matter yield (FMY), including exclusive use of ultra-sonic sward height (USH), all hyperspectral wavebands using modified partial least squares regression (MPLSR), normalized difference spectral index (NDSI), and multispectral representation of WorldView-2 wavebands (WV2), as well as models formed from combinations of these sensors.

**Figure 2.**Cross-validation (CV) results for a range of sensor models used for prediction of dry matter yield (DMY), including exclusive use of ultra-sonic sward height (USH), all hyperspectral wavebands using modified partial least squares regression (MPLSR), normalized difference spectral index (NDSI), and multispectral representation of WorldView-2 wavebands (WV2), as well as models formed from combinations of these sensors.

**Figure 3.**Cross-validation (CV) results for a range of sensor models used for prediction of dead material proportion (DMP), including exclusive use of all hyperspectral wavebands using modified partial least squares regression (MPLSR), normalized difference spectral index (NDSI), and multispectral representation of WorldView-2 wavebands (WV2) as explanatory variables.

**Figure 4.**Plots of fit between measured and predicted dry matter yield (DMY) for exclusive use of ultrasonic sward height (USH

_{exclusive}) and the best fit normalized difference spectral index (NDSI

_{exclusive}) as well as a combination of USH and NDSI (USH + NDSI) applied in date-specific swards.

**Figure 5.**Predictions of dry matter yield (DMY) (

**A**); and fresh matter yield (FMY) (

**B**) in common swards based on ultrasonic sward height (USH) and the Normalized Difference Spectral Index (NDSI) as influenced by dead material proportion (DMP) in the range of ± standard deviation (SD). NDSI represents narrow-band reflection values selected in combination with USH for each parameter.

**Table 1.**Descriptive statistics of dry matter yield, fresh matter yield, ultrasonic sward height and proportion of mosses, grasses, legumes, herbs and dead materials for common and date-specific swards.

N | Min | Max | Mean | Sd | Min | Max | Mean | Sd | |
---|---|---|---|---|---|---|---|---|---|

Dry matter yield (g·m^{−2}) | Fresh matter yield (g·m^{−2}) | ||||||||

Common | 214 | 29.2 | 691.9 | 276.4 | 145.5 | 68.8 | 3207.0 | 823.9 | 554.6 |

Date 1 | 54 | 51.9 | 612.1 | 248.8 | 130.0 | 140.0 | 1883.0 | 739.6 | 416.9 |

Date 2 | 54 | 31.9 | 691.9 | 314.5 | 180.2 | 107.2 | 3207.0 | 1240.0 | 785.6 |

Date 3 | 52 | 68.2 | 654.8 | 305.7 | 138.1 | 148.0 | 1822.0 | 745.4 | 337.0 |

Date 4 | 54 | 29.2 | 468.8 | 237.6 | 112.7 | 68.8 | 1325.0 | 567.5 | 281.7 |

Ultrasonic sward height (mm) | Grass proportion (% of DM) | ||||||||

Common | 214 | 7 | 646 | 252 | 151 | 8.0 | 93.7 | 50.6 | 23.9 |

Date 1 | 54 | 7 | 438 | 136 | 99 | 12.9 | 81.1 | 44.9 | 16.8 |

Date 2 | 54 | 31 | 646 | 364 | 174 | 8.2 | 93.7 | 72.2 | 19.0 |

Date 3 | 52 | 105 | 615 | 268 | 119 | 8.8 | 92.9 | 41.9 | 24.8 |

Date 4 | 54 | 48 | 576 | 240 | 107 | 8.0 | 85.3 | 43.1 | 20.6 |

Legume proportion (% of DM) | Moss proportion (% of DM) | ||||||||

Common | 214 | 0.0 | 39.6 | 2.9 | 6.8 | 0.0 | 27.5 | 1.9 | 4.4 |

Date 1 | 54 | 0.0 | 36.4 | 4.7 | 8.2 | 0.0 | 21.3 | 4.9 | 6.1 |

Date 2 | 54 | 0.0 | 39.6 | 4.1 | 9.0 | 0.0 | 14.7 | 0.7 | 2.4 |

Date 3 | 52 | 0.0 | 31.2 | 1.9 | 5.0 | 0.0 | 27.5 | 1.6 | 4.4 |

Date 4 | 54 | 0.0 | 7.1 | 0.6 | 1.6 | 0.0 | 5.8 | 0.3 | 0.9 |

Herb proportion (% of DM) | Dead material proportion (% of DM) | ||||||||

Common | 214 | 0.0 | 63.7 | 13.1 | 12.9 | 1.4 | 83.6 | 31.6 | 20.5 |

Date 1 | 54 | 0.0 | 44.6 | 13.6 | 12.7 | 2.5 | 70.3 | 31.9 | 14.9 |

Date 2 | 54 | 0.0 | 63.7 | 13.9 | 15.0 | 1.4 | 37.6 | 9.2 | 6.4 |

Date 3 | 52 | 0.0 | 47.5 | 14.6 | 12.8 | 3.9 | 76.3 | 40.0 | 18.8 |

Date 4 | 54 | 0.0 | 42.1 | 10.3 | 10.8 | 10.5 | 83.6 | 45.7 | 17.7 |

**Table 2.**Wavelength position of best-fit band combination (b1, b2) for the normalized difference spectral index (NDSI) exclusively and in combination with ultrasonic sward height (USH) predicted target parameter.

Common (n = 214) | Date 1 (n = 54) | Date 2 (n = 54) | Date 3 (n = 52) | Date 4 (n = 54) | ||||||
---|---|---|---|---|---|---|---|---|---|---|

b1 | b2 | b1 | b2 | b1 | b2 | b1 | b2 | b1 | b2 | |

Dry matter yield (g·m^{−2}) | ||||||||||

NDSI | 1035 | 1051 | 389 | 609 | 1097 | 1139 | 1122 | 1128 | 769 | 778 |

USH + NDSI | 521 | 578 | 1215 | 1225 | 1024 | 1031 | 1116 | 1118 | 1622 | 1633 |

Fresh matter yield (g·m^{−2}) | ||||||||||

NDSI | 1117 | 1134 | 1040 | 1073 | 1080 | 1104 | 1122 | 1128 | 751 | 782 |

USH + NDSI | 1077 | 1086 | 996 | 1005 | 536 | 564 | 1122 | 1135 | 1621 | 1633 |

Dead material proportion (% of dry matter yield) | ||||||||||

NDSI | 1242 | 1305 | 1231 | 1285 | 1188 | 1202 | 1236 | 1281 | 1187 | 1206 |

© 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Moeckel, T.; Safari, H.; Reddersen, B.; Fricke, T.; Wachendorf, M. Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure. *Remote Sens.* **2017**, *9*, 98.
https://doi.org/10.3390/rs9010098

**AMA Style**

Moeckel T, Safari H, Reddersen B, Fricke T, Wachendorf M. Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure. *Remote Sensing*. 2017; 9(1):98.
https://doi.org/10.3390/rs9010098

**Chicago/Turabian Style**

Moeckel, Thomas, Hanieh Safari, Björn Reddersen, Thomas Fricke, and Michael Wachendorf. 2017. "Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure" *Remote Sensing* 9, no. 1: 98.
https://doi.org/10.3390/rs9010098