Grassland ecosystem services in a changing environment: The potential of hyperspectral monitoring

Obermeier, Wolfgang A.; Lehnert, Lukas W.; Pohl, Marius; Gianonni, S. Makowski; Silva, B.; Seibert, Ruben; Laser, H.; Moser, Gerald M.; Müller, Christoph; Luterbacher, Jürg; Bendix, Jörg

doi:10.1016/j.rse.2019.111273

Cited by 50 publications

(20 citation statements)

References 99 publications

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“…Linear regression was chosen, since the relationship of the variables is best represented by a linear relationship. LOO-CV was chosen over k-fold cross-validation due to the relatively small sample size (Hair et al 2014) and because it is widely applied in remote sensing research (Homolova et al 2014;Ferner et al 2015;Viljanen et al 2018;Obermeier et al 2019). To evaluate the prediction accuracy, the coefficient of determination (R 2 ) and root-mean-square error (RMSE) were calculated from the LOO-CV results.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

Lussem

Schellberg²,

Bareth

2020

PFG

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Monitoring and predicting above ground biomass yield of grasslands are of key importance for grassland management. Established manual methods such as clipping or rising plate meter measurements provide accurate estimates of forage yield, but are time consuming and labor intensive, and do not provide spatially continuous data as required for precision agriculture applications. Therefore, the main objective of this study is to investigate the potential of sward height metrics derived from low-cost unmanned aerial vehicle-based image data to predict forage yield. The study was conducted over a period of 3 consecutive years (2014–2016) at the Rengen Grassland Experiment (RGE) in Germany. The RGE was established in 1941 and is since then under the same management regime of five treatments in a random block design and two harvest cuts per year. For UAV-based image acquisition, a DJI Phantom 2 with a mounted Canon Powershot S110 was used as a low-cost aerial imaging system. The data were investigated at different levels (e.g., harvest date-specific, year-specific, and plant community-specific). A pooled data model resulted in an R2 of 0.65 with a RMSE of 956.57 kg ha−1, although cut-specific or date-specific models yielded better results. In general, the UAV-based metrics outperformed the traditional rising plate meter measurements, but was affected by the timing of the harvest cut and plant community.

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Section: Data Acquisition and Processingmentioning

confidence: 99%

Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

Lussem

Schellberg²,

Bareth

2020

PFG

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“…Demonstrating that methods and findings can be replicated across studies is critical for the self-correcting mechanism of the scientific method to function properly and is imperative for generating solutions that can be applied widely. Hyperspectral data is commonly used in precision agriculture for predicting biochemical properties of vegetation and nutrients [18,26,[51][52][53]], yet the replicability of findings is rarely tested across different study sites. Scalable science is needed to develop solutions that can be applied globally.…”

Section: Replicability Of Scientific Methods and Findingsmentioning

confidence: 99%

“…Precision agriculture is one field where immediate gains can be made toward testing the replicability of methods while also contributing a larger understanding of the extent of R&R issues in remote sensing. Since the overall goal of precision agriculture is to decrease the ambiguity of decisions required on agricultural lands that are often highly variable [17], the ability to transfer methods and findings from one environment or location to another requires them to be replicable [18]. However, most studies capture data in a single region or location (often in a single crop field) under uniform conditions [12,15], thus limiting their generalizability across environmental or geographical contexts.…”

Section: Introductionmentioning

confidence: 99%

Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with Hyperspectral Data and Partial Least Squares Regression: Replicating Methods and Results across Environments

2020

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Achieving reproducibility and replication (R&R) of scientific results is tantamount for science to progress, and it is also necessary for ensuring the self-correcting mechanism of the scientific method. Topics of R&R have sailed to the forefront of research agenda in many fields recently but have received less attention in remote sensing in general and specifically for studies utilizing hyperspectral data. Given the extremely local environments in which many hyperspectral studies are conducted (e.g., agricultural field plots), purposeful attention to the repeatability of findings across study locales can help ensure methods are generalizable. This study undertakes an investigation of the nutrient content of tef (Eragrostis tef), an understudied plant that is growing in importance worldwide due to both food and forage benefits, but does so within the context of the replicability of methods and findings across two study sites situated in different international and environmental contexts. The aims are to (1) determine whether calcium, magnesium, and protein of both the plant and grain can be predicted using hyperspectral data with partial least squares (PLS) regression with waveband selection, and (2) compare the replicability of models across differing environments. Results suggest the method can produce high nutrient prediction accuracy for both the plant and grain in individual environments, but selection of wavebands for nutrient prediction was not comparable across study areas. The findings suggest that the method must be calibrated in each location, thereby reducing the potential to extrapolate methods to different areas. Our findings highlight the need for greater attention to methods and results replication in remote sensing, specifically hyperspectral analyses, in order for scientific findings to be repeatable beyond the plot level.

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“…Compared with traditional panchromatic and multispectral remote sensing images, hyperspectral imagery carry a wealth of spectral information, which enables more accurate discrimination of different objects. Consequently, in recent years, hyperspectral imagery has gained extensive attention for a variety of applications in Earth observations [1,[6][7][8][9][10], such as urban mapping, precision agriculture, and environmental monitoring [11][12][13][14][15]. The hyperspectral image classification is a significant research topic and it centers on assigning class labels to pixels.…”

Section: Introductionmentioning

confidence: 99%

SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification

et al. 2021

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Conventional classification algorithms have shown great success in balanced hyperspectral data classification. However, the imbalanced class distribution is a fundamental problem of hyperspectral data, and it is regarded as one of the great challenges in classification tasks. To solve this problem, a non-ANN based deep learning, namely SMOTE-Based Weighted Deep Rotation Forest (SMOTE-WDRoF) is proposed in this paper. First, the neighboring pixels of instances are introduced as the spatial information and balanced datasets are created by using the SMOTE algorithm. Second, these datasets are fed into the WDRoF model that consists of the rotation forest and the multi-level cascaded random forests. Specifically, the rotation forest is used to generate rotation feature vectors, which are input into the subsequent cascade forest. Furthermore, the output probability of each level and the original data are stacked as the dataset of the next level. And the sample weights are automatically adjusted according to the dynamic weight function constructed by the classification results of each level. Compared with the traditional deep learning approaches, the proposed method consumes much less training time. The experimental results on four public hyperspectral data demonstrate that the proposed method can get better performance than support vector machine, random forest, rotation forest, SMOTE combined rotation forest, convolutional neural network, and rotation-based deep forest in multiclass imbalance learning.

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Grassland ecosystem services in a changing environment: The potential of hyperspectral monitoring

Cited by 50 publications

References 99 publications

Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with Hyperspectral Data and Partial Least Squares Regression: Replicating Methods and Results across Environments

SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification

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