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description Publicationkeyboard_double_arrow_right Article , Conference object , Other literature type , Journal 2019 NetherlandsPublisher:Copernicus GmbH Authors:G. T. Alckmin;
G. T. Alckmin;G. T. Alckmin
G. T. Alckmin in OpenAIREL. Kooistra;
L. Kooistra
L. Kooistra in OpenAIREA. Lucieer;
+2 AuthorsA. Lucieer
A. Lucieer in OpenAIREG. T. Alckmin;
G. T. Alckmin;G. T. Alckmin
G. T. Alckmin in OpenAIREL. Kooistra;
L. Kooistra
L. Kooistra in OpenAIREA. Lucieer;
R. Rawnsley; R. Rawnsley;A. Lucieer
A. Lucieer in OpenAIREAbstract. Vegetation indices (VIs) have been extensively employed as a feature for dry matter (DM) estimation. During the past five decades more than a hundred vegetation indices have been proposed. Inevitably, the selection of the optimal index or subset of indices is not trivial nor obvious. This study, performed on a year-round observation of perennial ryegrass (n = 900), indicates that for this response variable (i.e. kg.DM.ha−1), more than 80% of indices present a high degree of collinearity (correlation > |0.8|.) Additionally, the absence of an established workflow for feature selection and modelling is a handicap when trying to establish meaningful relations between spectral data and biophysical/biochemical features. Within this case study, an unsupervised and supervised filtering process is proposed to an initial dataset of 97 VIs. This research analyses the effects of the proposed filtering and feature selection process to the overall stability of final models. Consequently, this analysis provides a straightforward framework to filter and select VIs. This approach was able to provide a reduced feature set for a robust model and to quantify trade-offs between optimal models (i.e. lowest root mean square error – RMSE = 412.27 kg.DM.ha−1) and tolerable models (with a smaller number of features – 4 VIs and within 10% of the lowest RMSE.)
The International Ar... arrow_drop_down The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2019 . Peer-reviewedLicense: CC BYData sources: CrossrefThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticleLicense: CC BYData sources: UnpayWallDANS (Data Archiving and Networked Services)Conference object . 2019Data sources: DANS (Data Archiving and Networked Services)The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2019Data sources: CopernicusThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2019Data sources: DOAJWageningen Staff PublicationsArticle . 2019License: CC BYData sources: Wageningen Staff Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-2-w13-1827-2019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 4 citations 4 popularity Average influence Average impulse Average Powered by BIP!
more_vert The International Ar... arrow_drop_down The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2019 . Peer-reviewedLicense: CC BYData sources: CrossrefThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticleLicense: CC BYData sources: UnpayWallDANS (Data Archiving and Networked Services)Conference object . 2019Data sources: DANS (Data Archiving and Networked Services)The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesOther literature type . 2019Data sources: CopernicusThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesArticle . 2019Data sources: DOAJWageningen Staff PublicationsArticle . 2019License: CC BYData sources: Wageningen Staff Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/isprs-archives-xlii-2-w13-1827-2019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal , Other literature type 2019 NetherlandsPublisher:MDPI AG Authors: ten Harkel, Jelle; Bartholomeus, Harm;Kooistra, Lammert;
Kooistra, Lammert
Kooistra, Lammert in OpenAIREdoi: 10.3390/rs12010017
Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown in Wageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m2. The 3DPI algorithm proved to estimate biomass well for sugar beet (R2 = 0.68, RMSE = 17.47 g/m2) and winter wheat (R2 = 0.82, RMSE = 13.94 g/m2). Also, the height estimates worked well for sugar beet (R2 = 0.70, RMSE = 7.4 cm) and wheat (R2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R2 = 0.50, RMSE = 12 cm) and biomass estimation (R2 = 0.24, RMSE = 22.09 g/m2), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly.
Remote Sensing arrow_drop_down Remote SensingOther literature type . 2019License: CC BYFull-Text: http://www.mdpi.com/2072-4292/12/1/17/pdfData sources: Multidisciplinary Digital Publishing InstituteWageningen Staff PublicationsArticle . 2020License: CC BYData sources: Wageningen Staff Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/rs12010017&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 139 citations 139 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert Remote Sensing arrow_drop_down Remote SensingOther literature type . 2019License: CC BYFull-Text: http://www.mdpi.com/2072-4292/12/1/17/pdfData sources: Multidisciplinary Digital Publishing InstituteWageningen Staff PublicationsArticle . 2020License: CC BYData sources: Wageningen Staff Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/rs12010017&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2017Embargo end date: 07 Feb 2018 NetherlandsPublisher:Dryad Authors: Van Der Meij, Bob;Kooistra, L.;
Kooistra, L.
Kooistra, L. in OpenAIRESuomalainen, J.M.;
Suomalainen, J.M.
Suomalainen, J.M. in OpenAIREBarel, J.M.;
+1 AuthorsBarel, J.M.
Barel, J.M. in OpenAIREVan Der Meij, Bob;Kooistra, L.;
Kooistra, L.
Kooistra, L. in OpenAIRESuomalainen, J.M.;
Suomalainen, J.M.
Suomalainen, J.M. in OpenAIREBarel, J.M.;
Barel, J.M.
Barel, J.M. in OpenAIREde Deyn, G.B.;
de Deyn, G.B.
de Deyn, G.B. in OpenAIREdoi: 10.5061/dryad.75k1d
Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R2 = 0.79), chlorophyll content (RMSE = 0.11 g m−2, R2 = 0.80), N-content (RMSE = 1.94 g m−2, R2 = 0.68), and fresh biomass (RMSE = 0.72 kg m−2, R2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms. van der Meij et al_Biogeosciences2017_dataThe experimental set-up, treatments, data collection and data analyses are thoroughly described in the Biogeoscience manuscript ‘Remote sensing of plant trait responses to field-based plant-soil feedback using UAV-based optical sensors’ doi:10.5194/bg-2016-452. Therefore we refer to the manuscript for detailed information an here we provide a brief summary to enable readers to follow what the data entail. The data were collected from a 2-year field experiment with plant rotations in a full factorial design. The plant treatments we focused on are legacy effects of the plant treatments (listed below) to the following oat crop. In this oat crop we quantified several plant traits both in situ and via remote sensing by use of UAV and hyperspectral and EGB sensors. The experiment was set-up in five randomized field blocks. We used part of the in situ collected data to parameterize the hyperspectral data based models and we validated these models with the other half of the field plots. Plant treatments Fa= fallow Lp= Lolium perenne Rs= Raphanus sativus Tr= Trifolium repens Vs= Vicia sativa Lp+Tr= 50:50 species mixture (relative to the monoculture seed densities) of the species Lp and Tr Rs+Vs= 50:50 species mixture (relative to the monoculture seed densities) of the species Rs and Vs
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.75k1d&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 24visibility views 24 download downloads 13 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.75k1d&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal , Other literature type 2020 Netherlands, Australia, AustraliaPublisher:Springer Science and Business Media LLC Authors:Gustavo Togeiro de Alckmin;
Gustavo Togeiro de Alckmin
Gustavo Togeiro de Alckmin in OpenAIRELammert Kooistra;
Lammert Kooistra
Lammert Kooistra in OpenAIRERichard Rawnsley;
Richard Rawnsley
Richard Rawnsley in OpenAIREArko Lucieer;
Arko Lucieer
Arko Lucieer in OpenAIREAbstractPasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass (Lolium perenne) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha−1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha−1). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.
Precision Agricultur... arrow_drop_down Wageningen Staff PublicationsArticle . 2021License: CC BYData sources: Wageningen Staff PublicationsUniversity of Tasmania: UTas ePrintsArticle . 2021Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s11119-020-09737-z&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 25 citations 25 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Precision Agricultur... arrow_drop_down Wageningen Staff PublicationsArticle . 2021License: CC BYData sources: Wageningen Staff PublicationsUniversity of Tasmania: UTas ePrintsArticle . 2021Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s11119-020-09737-z&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Journal 2021 NetherlandsPublisher:Proceedings of the National Academy of Sciences Funded by:NWO | Perturbations of System E...NWO| Perturbations of System Earth: Reading the Past to Project the Future - A proposal to create the Netherlands Earth System Science Centre (ESSC)Authors:A. Johannes Dolman;
Kevin Devito;A. Johannes Dolman
A. Johannes Dolman in OpenAIREMaarten C. Braakhekke;
Maarten C. Braakhekke; +14 AuthorsMaarten C. Braakhekke
Maarten C. Braakhekke in OpenAIREA. Johannes Dolman;
Kevin Devito;A. Johannes Dolman
A. Johannes Dolman in OpenAIREMaarten C. Braakhekke;
Maarten C. Braakhekke;Maarten C. Braakhekke
Maarten C. Braakhekke in OpenAIREJakob Wallinga;
Jakob Wallinga
Jakob Wallinga in OpenAIREStefan C. Dekker;
Stefan C. Dekker; Merel B. Soons; Jelmer Nijp;Stefan C. Dekker
Stefan C. Dekker in OpenAIRENicholas Kettridge;
Nicholas Kettridge
Nicholas Kettridge in OpenAIREAdriaan J. Teuling;
George A. K. van Voorn;Adriaan J. Teuling
Adriaan J. Teuling in OpenAIREYpe van der Velde;
Ype van der Velde; Carl Mendoza;Ype van der Velde
Ype van der Velde in OpenAIREArnaud Temme;
Arnaud Temme;Arnaud Temme
Arnaud Temme in OpenAIRELammert Kooistra;
Lammert Kooistra
Lammert Kooistra in OpenAIREpmid: 34521751
pmc: PMC8463847
Significance Peatlands are sensitive ecosystems that store carbon and water and support biodiversity. Currently, European peatlands are threatened by climate change and exploitation. In this study, we show that many landscape settings may support both wetland ecosystems on thick peat soils and forest ecosystems on thin organic soils. Both ecosystems have distinctly different water–carbon dynamics that create internal positive feedbacks, allowing both ecosystems to coexist (bistability) but also to shift when critical limits are exceeded. With this new landscape perspective, we find that currently, 20% of European raised bogs are threatened by climate change and drainage. This study demonstrates that a landscape perspective including interactions between peatlands, forests, and rivers is essential to understand and steer the future of peatlands.
Proceedings of the N... arrow_drop_down Proceedings of the National Academy of SciencesArticle . 2021 . Peer-reviewedLicense: CC BY NC NDData sources: CrossrefProceedings of the National Academy of SciencesArticle . 2021License: CC BY NC NDData sources: Pure Utrecht UniversityOpen University of the Netherlands Research PortalArticle . 2021Data sources: Open University of the Netherlands Research PortalWageningen Staff PublicationsArticle . 2021License: CC BY NC NDData sources: Wageningen Staff PublicationsProceedings of the National Academy of SciencesArticle . 2021add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1073/pnas.2101742118&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 21 citations 21 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Proceedings of the N... arrow_drop_down Proceedings of the National Academy of SciencesArticle . 2021 . Peer-reviewedLicense: CC BY NC NDData sources: CrossrefProceedings of the National Academy of SciencesArticle . 2021License: CC BY NC NDData sources: Pure Utrecht UniversityOpen University of the Netherlands Research PortalArticle . 2021Data sources: Open University of the Netherlands Research PortalWageningen Staff PublicationsArticle . 2021License: CC BY NC NDData sources: Wageningen Staff PublicationsProceedings of the National Academy of SciencesArticle . 2021add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1073/pnas.2101742118&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Journal 2020 Netherlands, Australia, Australia, AustraliaPublisher:MDPI AG Authors:Gustavo Togeiro de Alckmin;
Gustavo Togeiro de Alckmin;Gustavo Togeiro de Alckmin
Gustavo Togeiro de Alckmin in OpenAIRELammert Kooistra;
Lammert Kooistra
Lammert Kooistra in OpenAIRESytze de Bruin;
+2 AuthorsSytze de Bruin
Sytze de Bruin in OpenAIREGustavo Togeiro de Alckmin;
Gustavo Togeiro de Alckmin;Gustavo Togeiro de Alckmin
Gustavo Togeiro de Alckmin in OpenAIRELammert Kooistra;
Lammert Kooistra
Lammert Kooistra in OpenAIRESytze de Bruin;
Sytze de Bruin
Sytze de Bruin in OpenAIREArko Lucieer;
Arko Lucieer
Arko Lucieer in OpenAIRERichard Rawnsley;
Richard Rawnsley
Richard Rawnsley in OpenAIREThe use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass (Lolium perenne) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors.
Sensors arrow_drop_down SensorsOther literature type . 2020License: CC BYFull-Text: http://www.mdpi.com/1424-8220/20/24/7192/pdfData sources: Multidisciplinary Digital Publishing InstituteThe University of Melbourne: Digital RepositoryArticle . 2020License: CC BYFull-Text: http://hdl.handle.net/11343/316626Data sources: Bielefeld Academic Search Engine (BASE)Wageningen Staff PublicationsArticle . 2020License: CC BYData sources: Wageningen Staff PublicationsUniversity of Tasmania: UTas ePrintsArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/s20247192&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 3 citations 3 popularity Top 10% influence Average impulse Average Powered by BIP!
more_vert Sensors arrow_drop_down SensorsOther literature type . 2020License: CC BYFull-Text: http://www.mdpi.com/1424-8220/20/24/7192/pdfData sources: Multidisciplinary Digital Publishing InstituteThe University of Melbourne: Digital RepositoryArticle . 2020License: CC BYFull-Text: http://hdl.handle.net/11343/316626Data sources: Bielefeld Academic Search Engine (BASE)Wageningen Staff PublicationsArticle . 2020License: CC BYData sources: Wageningen Staff PublicationsUniversity of Tasmania: UTas ePrintsArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/s20247192&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Journal 2015 Germany, France, Netherlands, United Kingdom, FrancePublisher:Copernicus GmbH Authors:Sarah Carter;
Sarah Carter;Sarah Carter
Sarah Carter in OpenAIREMariana C. Rufino;
Mariana C. Rufino
Mariana C. Rufino in OpenAIRELammert Kooistra;
+3 AuthorsLammert Kooistra
Lammert Kooistra in OpenAIRESarah Carter;
Sarah Carter;Sarah Carter
Sarah Carter in OpenAIREMariana C. Rufino;
Mariana C. Rufino
Mariana C. Rufino in OpenAIRELammert Kooistra;
Lammert Kooistra
Lammert Kooistra in OpenAIREMartin Herold;
K. Neumann;Martin Herold
Martin Herold in OpenAIRELouis V. Verchot;
Louis V. Verchot
Louis V. Verchot in OpenAIREAbstract. Emissions from agriculture-driven deforestation are of global concern, but forest land-sparing interventions such as agricultural intensification and utilization of available land offer opportunities for mitigation. In many tropical countries, where agriculture is the major driver of deforestation, interventions in the agriculture sector can reduce deforestation emissions as well as reducing emissions in the agriculture sector. Our study uses a novel approach to quantify agriculture-driven deforestation and associated emissions in the tropics. Emissions from agriculture-driven deforestation in the tropics between 2000 and 2010 are 4.3 Gt CO2 eq yr−1 (97 countries). We investigate the national potential to mitigate these emissions through forest land-sparing interventions, which can potentially be implemented under REDD+. We consider intensification, and utilization of available non-forested land as forest land-sparing opportunities since they avoid the expansion of agriculture into forested land. In addition, we assess the potential to reduce agriculture emissions on existing agriculture land, interventions that fall under climate-smart agriculture (CSA). The use of a systematic framework demonstrates the selection of mitigation interventions by considering sequentially the level of emissions, mitigation potential of various interventions, enabling environment and associated risks to livelihoods at the national level. Our results show that considering only countries with high emissions from agriculture-driven deforestation, where there is a potential for forest-sparing interventions, and where there is a good enabling environment (e.g. effective governance or engagement in REDD+), the potential to mitigate is 1.3 Gt CO2 eq yr−1 (20 countries of 78 with sufficient data). For countries where we identify agriculture emissions as priority for mitigation, up to 1 Gt CO2 eq yr−1 could be reduced from the agriculture sector including livestock. Risks to livelihoods from implementing interventions based on national level data, call for detailed investigation at the local level to inform decisions. Three case-studies demonstrate the use of the analytical framework. The inherent link between the agriculture and forestry sectors due to competition for land suggests that these cannot be considered independently. This highlights the need to include the forest and the agricultural sector in the decision making process for mitigation interventions at the national level.
CGIAR CGSpace (Consu... arrow_drop_down CGIAR CGSpace (Consultative Group on International Agricultural Research)Article . 2018License: CC BYFull-Text: https://hdl.handle.net/10568/93474Data sources: Bielefeld Academic Search Engine (BASE)https://doi.org/10.5194/bgd-12...Article . 2015 . Peer-reviewedLicense: CC BYData sources: CrossrefGFZ German Research Centre for GeosciencesArticle . 2015Data sources: GFZ German Research Centre for GeosciencesWageningen Staff PublicationsArticle . 2015License: CC BYData sources: Wageningen Staff PublicationsGFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam)Article . 2015Data sources: Bielefeld Academic Search Engine (BASE)Lancaster University: Lancaster EprintsArticle . 2015Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 23 citations 23 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert CGIAR CGSpace (Consu... arrow_drop_down CGIAR CGSpace (Consultative Group on International Agricultural Research)Article . 2018License: CC BYFull-Text: https://hdl.handle.net/10568/93474Data sources: Bielefeld Academic Search Engine (BASE)https://doi.org/10.5194/bgd-12...Article . 2015 . Peer-reviewedLicense: CC BYData sources: CrossrefGFZ German Research Centre for GeosciencesArticle . 2015Data sources: GFZ German Research Centre for GeosciencesWageningen Staff PublicationsArticle . 2015License: CC BYData sources: Wageningen Staff PublicationsGFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam)Article . 2015Data sources: Bielefeld Academic Search Engine (BASE)Lancaster University: Lancaster EprintsArticle . 2015Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/bgd-12-5435-2015&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2022 NetherlandsPublisher:Elsevier BV Authors: Togeirode Alckmin, Gustavo;Lucieer, Arko;
Rawnsley, Richard;Lucieer, Arko
Lucieer, Arko in OpenAIREKooistra, Lammert;
Kooistra, Lammert
Kooistra, Lammert in OpenAIREFrequent biomass measurement is a key activity for optimal perennial ryegrass (Lolium perenne) management in intensive forage-based dairy operations. Due to the necessary high-frequency (i.e., weekly or monthly) pasture monitoring and continuous trend of larger dairy farms, such activity is perceived as an operational bottleneck. Consequently, substantial effort is directed to the development of accurate and automated technological solutions for biomass assessment. The popularization of unmanned aerial vehicles (UAVs) combined with multispectral cameras should allow for an optimal observational system able to deploy machine learning algorithms for near real-time biomass dry-matter (DM) mapping. For successful operation, these systems should deliver radiometrically accurate orthomosaics and robust models able to generalize across different periods. Nevertheless, the accuracy of radiometric calibration and generalization ability of these models is seldom evaluated. Also, such pipelines should require minimum processing power and allow for fast deployment. This study has established a two-year experiment comparing reflectance measurements between a handheld spectrometer and a commercial multispectral UAV camera. Different algorithms based on regression-tree architecture were contrasted regarding accuracy, speed, and model size. Model performances were validated, providing error-metrics for baseline accuracy and temporal validation. The results have shown that the standard procedure for multispectral imagery radiometric calibration is sub-optimal, requiring further post-processing and presenting low correlation with handheld measurements across spectral bands and dates. Nevertheless, after post-calibration, the use of spectral imagery has presented better baseline error than the point-based sensors, respectively displaying an average of 397.3 and 464.2 kg DM/ha when employed alongside the best performing algorithm (i.e., Cubist). When trained and validated across different years, model performance was largely reduced and deemed unfit for operational purposes. The Cubist/M5 family of algorithms have exhibited advantageous characteristics such as compact model structure, allowing for a higher level of model interpretability, while displaying a smaller size and faster deployment than the Random Forest, Boosted, and Bagged Regression Trees algorithms.
Computers and Electr... arrow_drop_down Computers and Electronics in AgricultureArticle . 2022 . Peer-reviewedLicense: CC BYData sources: CrossrefWageningen Staff PublicationsArticle . 2022License: CC BYData sources: Wageningen Staff Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.compag.2021.106574&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 20 citations 20 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Computers and Electr... arrow_drop_down Computers and Electronics in AgricultureArticle . 2022 . Peer-reviewedLicense: CC BYData sources: CrossrefWageningen Staff PublicationsArticle . 2022License: CC BYData sources: Wageningen Staff Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.compag.2021.106574&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu