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Biomass

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Christopher Conrad – 1st expert on this subject based on the ideXlab platform

  • Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data
    PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science, 2019
    Co-Authors: Nima Ahmadian, Tobias Ullmann, Jochem Verrelst, Erik Borg, Reinhard Zölitz, Christopher Conrad

    Abstract:

    ZusammenfassungBiomassebewertung von landwirtschaftlichen Kulturen mit multitemporalen Methoden basierend auf dual-polarimetrischen TerraSAR-X-Daten . Die Studie zielt auf die Bestimmung von Biomasse dreier landwirtschaftlicher Kulturen, Winterweizen (Triticum aestivum L.), Gerste (Hordeum vulgare L.) und Raps (Brassica napus L.) mit multitemporalen dual-polarimetrischen TerraSAR-X-Daten. Der Radarrückstreuungskoeffizient Sigma Null der beiden Polarisationskanäle HH und VV wurde aus den Satellitenbildern extrahiert. Anschließend wurden Kombinationen von HH- und VV-Polarisationen berechnet (z. B. HH/VV, HH + VV, HH × VV), um Beziehungen zwischen den SAR-Daten und der frischen und der trockenen Biomasse jeder Kulturart unter Verwendung einer multiplen schrittweisen Regression zu bestimmen. Zusätzlich wurde das semi-empirische Water Cloud Model (WCM) verwendet, um die Wirkung von PflanzenBiomasse auf Radarrückstreudaten abzuschätzen. Das Potenzial des maschinellen Lernens mit Random Forest (RF) wurde ebenfalls untersucht. Das Verfahren der geteilten Stichprobe (split sampling, 70% Training und 30% Test) wurde durchgeführt, um die schrittweisen Regressionen, WCM und RF zu validieren. Das multiple schrittweise Regressionsverfahren unter Verwendung von dual-polarimetrischen Daten war in der Lage, die Biomasse der drei Kulturen, insbesondere für trockene Biomasse mit R² > 0,7, ohne weitere externe Eingangsgrößen wie beispielsweise Informationen über die (tatsächliche) Bodenfeuchte zu erfassen. Ein Vergleich der Random Forest (RF) Methode mit dem WCM zeigt, dass die RF Methode das WCM bei der Biomassenabschätzung deutlich übertroffen hat, insbesondere für frische Biomasse. Beispielsweise ergab die RF Methode ein R² > 0,68 für die Schätzung der frischen Biomasse verschiedener Kulturarten, während das WCM nur ein R² > 0,35 zeigte. Andererseits ähnelten sich die Ergebnisse beider Ansätze im Fall der trockenen Biomasse.AbstractThe Biomass of three agricultural crops, winter wheat ( Triticum aestivum L.), barley ( Hordeum vulgare L.), and canola ( Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry Biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop Biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the Biomass of the three crops, particularly for dry Biomass, with R ^2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in Biomass estimation, especially for the fresh Biomass. For example, the R ^2 > 0.68 for the fresh Biomass estimation of different crop types using RF whereas WCM show R ^2 

  • Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data
    PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science, 2019
    Co-Authors: Nima Ahmadian, Tobias Ullmann, Jochem Verrelst, Erik Borg, Reinhard Zölitz, Christopher Conrad

    Abstract:

    The Biomass of three agricultural crops, winter wheat ( Triticum aestivum L.), barley ( Hordeum vulgare L.), and canola ( Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry Biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop Biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the Biomass of the three crops, particularly for dry Biomass, with R ^2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in Biomass estimation, especially for the fresh Biomass. For example, the R ^2 > 0.68 for the fresh Biomass estimation of different crop types using RF whereas WCM show R ^2  0,7, ohne weitere externe Eingangsgrößen wie beispielsweise Informationen über die (tatsächliche) Bodenfeuchte zu erfassen. Ein Vergleich der Random Forest (RF) Methode mit dem WCM zeigt, dass die RF Methode das WCM bei der Biomassenabschätzung deutlich übertroffen hat, insbesondere für frische Biomasse. Beispielsweise ergab die RF Methode ein R² > 0,68 für die Schätzung der frischen Biomasse verschiedener Kulturarten, während das WCM nur ein R² > 0,35 zeigte. Andererseits ähnelten sich die Ergebnisse beider Ansätze im Fall der trockenen Biomasse.

  • Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data
    PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science, 2019
    Co-Authors: Nima Ahmadian, Tobias Ullmann, Jochem Verrelst, Erik Borg, Reinhard Zölitz, Christopher Conrad

    Abstract:

    Biomassebewertung von landwirtschaftlichen Kulturen mit multitemporalen Methoden basierend auf dual-polarimetrischen TerraSAR-X-Daten . Die Studie zielt auf die Bestimmung von Biomasse dreier landwirtschaftlicher Kulturen, Winterweizen (Triticum aestivum L.), Gerste (Hordeum vulgare L.) und Raps (Brassica napus L.) mit multitemporalen dual-polarimetrischen TerraSAR-X-Daten. Der Radarrückstreuungskoeffizient Sigma Null der beiden Polarisationskanäle HH und VV wurde aus den Satellitenbildern extrahiert. Anschließend wurden Kombinationen von HH- und VV-Polarisationen berechnet (z. B. HH/VV, HH + VV, HH × VV), um Beziehungen zwischen den SAR-Daten und der frischen und der trockenen Biomasse jeder Kulturart unter Verwendung einer multiplen schrittweisen Regression zu bestimmen. Zusätzlich wurde das semi-empirische Water Cloud Model (WCM) verwendet, um die Wirkung von PflanzenBiomasse auf Radarrückstreudaten abzuschätzen. Das Potenzial des maschinellen Lernens mit Random Forest (RF) wurde ebenfalls untersucht. Das Verfahren der geteilten Stichprobe (split sampling, 70% Training und 30% Test) wurde durchgeführt, um die schrittweisen Regressionen, WCM und RF zu validieren. Das multiple schrittweise Regressionsverfahren unter Verwendung von dual-polarimetrischen Daten war in der Lage, die Biomasse der drei Kulturen, insbesondere für trockene Biomasse mit R² > 0,7, ohne weitere externe Eingangsgrößen wie beispielsweise Informationen über die (tatsächliche) Bodenfeuchte zu erfassen. Ein Vergleich der Random Forest (RF) Methode mit dem WCM zeigt, dass die RF Methode das WCM bei der Biomassenabschätzung deutlich übertroffen hat, insbesondere für frische Biomasse. Beispielsweise ergab die RF Methode ein R² > 0,68 für die Schätzung der frischen Biomasse verschiedener Kulturarten, während das WCM nur ein R² > 0,35 zeigte. Andererseits ähnelten sich die Ergebnisse beider Ansätze im Fall der trockenen Biomasse. The Biomass of three agricultural crops, winter wheat ( Triticum aestivum L.), barley ( Hordeum vulgare L.), and canola ( Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry Biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop Biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the Biomass of the three crops, particularly for dry Biomass, with R ^2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in Biomass estimation, especially for the fresh Biomass. For example, the R ^2 > 0.68 for the fresh Biomass estimation of different crop types using RF whereas WCM show R ^2 

Suani Teixeira Coelho – 2nd expert on this subject based on the ideXlab platform

  • Renewable energy – Traditional Biomass vs. modern Biomass
    Energy Policy, 2004
    Co-Authors: José Goldemberg, Suani Teixeira Coelho

    Abstract:

    Renewable energy is basic to reduce poverty and to allow sustainable development. However, the concept of renewable energy must be carefully established, particularly in the case of Biomass. This paper analyses the sustainability of Biomass, comparing the so-called “traditional” and “modern” Biomass, and discusses the need for statistical information, which will allow the elaboration of scenarios relevant to renewable energy targets in the world. © 2003 Elsevier Ltd. All rights reserved.

Kartik C. Khilar – 3rd expert on this subject based on the ideXlab platform

  • Pyrolysis characteristics of Biomass and Biomass components
    Fuel, 1996
    Co-Authors: K. Raveendran, Anuradda Ganesh, Kartik C. Khilar

    Abstract:

    Biomass pyrolysis studies were conducted using both a thermogravimetric analyser and a packed-bed pyrolyser. Each kind of Biomass has a characteristic pyrolysis behaviour which is explained based on its individual component characteristics. Studies on isolated Biomass components as well as synthetic Biomass show that the interactions among the components are not of as much significance as the composition of the Biomass. Direct summative correlations based on Biomass component pyrolysis adequately explain both the pyrolysis characteristics and product distribution of Biomass. It is inferred that there is no detectable interaction among the components during pyrolysis in either the thermogravimetric analyser or the packed-bed pyrolyser. However, ash present in Biomass seems to have a strong influence on both the pyrolysis characteristics and the product distribution.

  • Pyrolysis characteristics of Biomass and Biomass components
    Fuel, 1996
    Co-Authors: K. Raveendran, Anuradda Ganesh, Kartik C. Khilar

    Abstract:

    Biomass pyrolysis studies were conducted using both a thermogravimetric analyser and a packed-bed pyrolyser. Each kind of Biomass has a characteristic pyrolysis behaviour which is explained based on its individual component characteristics. Studies on isolated Biomass components as well as synthetic Biomass show that the interactions among the components are not of as much significance as the composition of the Biomass. Direct summative correlations based on Biomass component pyrolysis adequately explain both the pyrolysis characteristics and product distribution of Biomass. It is inferred that there is no detectable interaction among the components during pyrolysis in either the thermogravimetric analyser or the packed-bed pyrolyser. However, ash present in Biomass seems to have a strong influence on both the pyrolysis characteristics and the product distribution. Copyright © 1996 Elsevier Science Ltd.