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

Moeckel, Thomas; Safari, Hanieh; Reddersen, Björn; Fricke, Thomas; Wachendorf, M.

doi:10.3390/rs9010098

Cited by 61 publications

(71 citation statements)

References 42 publications

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“…Furthermore, the results indicate that for a successful estimation of biomass, height information alone is not enough. Evolving sensor fusion approaches might improve the model prediction performance, as it has been shown for example for grassland biomass [42,43].…”

Section: Discussionmentioning

confidence: 99%

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

et al. 2018

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3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R 2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).

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Section: Discussionmentioning

confidence: 99%

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

et al. 2018

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“…Knowledge of the spatio-temporal dynamics of plant biomass production is essential to inform the management of natural resources, in conservation areas and in agro-pastoral systems [1][2][3][4]-particularly in the Mediterranean and semiarid regions, where inter-annual changes in precipitation often result in large variations in plant production [5].…”

Section: Introductionmentioning

confidence: 99%

“…They can be highly accurate but often involve intensive field work and destructive methods, which makes them costly and inapplicable to inaccessible or sensitive areas, or when involving endangered species [4,6,7]. Remote sensing constitutes an increasingly used alternative [8], based on the relationship between satellite-derived metrics and primary production [7].…”

Section: Introductionmentioning

confidence: 99%

Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

et al. 2017

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Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources-particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP) derived from MODIS, which we calibrate and validate in the Doñana National Park's marsh vegetation. Out of the different estimators tested, the Land Surface Phenology maximum NDVI (LSP-Maximum-NDVI) correlated best with ground-truth data of biomass production at five locations from 2001-2015 used to calibrate the models (R 2 = 0.65). Estimators based on a single MODIS NDVI image performed worse (R 2 ≤ 0.41). The LSP-Maximum-NDVI estimator was robust to environmental variation in precipitation and hydroperiod, and to spatial variation in the productivity and composition of the plant community. The determination of plant biomass using remote-sensing techniques, adequately supported by ground-truth data, may represent a key tool for the long-term monitoring and management of seasonal marsh ecosystems.

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“…Many studies have outlined the importance of non-spectral predictors such as plant height for non-destructive biomass estimation, with plant height being recorded by different sensors: manually in [20], using ultrasonic measurements in [28], using TLS and time-of-flight cameras in [29], and using TLS in [8]. Other studies derive plant height using UAV-captured imagery for crops such as barley [10], wheat [30] and sorghum [31] with a focus on field-phenotyping.…”

Section: Discussionmentioning

confidence: 99%

Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery

Brocks

Bareth

2018

Remote Sensing

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Non-destructive monitoring of crop development is of key interest for agronomy and crop breeding. Crop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this. In this study, fresh and dry barley biomass per plot are estimated from CSM-derived plot-wise plant heights. The CSMs are generated in a semi-automated manner using Structure-from-Motion (SfM)/Multi-View-Stereo (MVS) software from oblique stereo RGB images. The images were acquired automatedly from consumer grade smart cameras mounted at an elevated position on a lifting hoist. Fresh and dry biomass were measured destructively at four dates each in 2014 and 2015. We used exponential and simple linear regression based on different calibration/validation splits. Coefficients of determination R 2 between 0.55 and 0.79 and root mean square errors (RMSE) between 97 and 234 g/m 2 are reached for the validation of predicted vs. observed dry biomass, while Willmott's refined index of model performance d r ranges between 0.59 and 0.77. For fresh biomass, R 2 values between 0.34 and 0.61 are reached, with root mean square errors (RMSEs) between 312 and 785 g/m 2 and d r between 0.39 and 0.66. We therefore established the possibility of using this novel low-cost system to estimate barley dry biomass over time.

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

Cited by 61 publications

References 42 publications

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery

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