ESA - RESGROW: Epansion of the Market for EO Based Information Services in Renewable Energy - Biomass Energy sector

Biomass energy is of growing importance as it is widely recognised, both scientifically and politically, that the increase of atmospheric CO2 has led to an enhanced efficiency of the greenhouse effect and, as such, warrants concern for climate change. It is accepted (IPCC 2011 and just recently in the draft version of the IPCC 2013 report) that climate change is partly induced by humans notably by using fossil fuels. For reducing the use of oil or coal, biomass energy is receiving more and more attention as an additional energy source available regionally in large parts of the world. Effective management of renewable energy resources is critical for the European and the global energy supply system. The future contribution of bioenergy to the energy supply strongly depends on its availability, in other words the biomass potential. Biomass potentials are currently mainly assessed on a national to regional or on a global level, with the bulk biomass potential allocated to the whole country. With certain biomass fractions being of low energy density, transport distances and thus their spatial distribution are crucial economic and ecological factors. For other biomass fractions a super-regional or global market is envisaged. Thus spatial information on biomass potentials is vital for the further expansion of bioenergy use. This study, which is an updated version of a study carried out in 2007 in frame of the ENVISOLAR project, analyses the potential use of Earth Observation data as input for biomass models in order to assessment and manage of the biomass energy resources especially biomass potentials of agricultural and forest areas with high spatial resolution (typical 1km x 1km). In addition to a sorrow review of recent developments in data availability and approaches in comparison to its 2007’ version, this study also includes a review on approaches to directly correlate remote sensing data with biomass estimations. An overview of existing biomass models is given covering models using remote sensing data as input as well as models using only meteorological and/or management data as input. It covers the full life cycle from the planning stage to plant management and operations (Figure 1). Several groups of stakeholders were identified

How sensitive are estimates of carbon fixation in agricultural models to input data?

<p>Abstract</p> <p>Background</p> <p>Process based vegetation models are central to understand the hydrological and carbon cycle. To achieve useful results at regional to global scales, such models require various input data from a wide range of earth observations. Since the geographical extent of these datasets varies from local to global scale, data quality and validity is of major interest when they are chosen for use. It is important to assess the effect of different input datasets in terms of quality to model outputs. In this article, we reflect on both: the uncertainty in input data and the reliability of model results. For our case study analysis we selected the Marchfeld region in Austria. We used independent meteorological datasets from the Central Institute for Meteorology and Geodynamics and the European Centre for Medium-Range Weather Forecasts (ECMWF). Land cover / land use information was taken from the GLC2000 and the CORINE 2000 products.</p> <p>Results</p> <p>For our case study analysis we selected two different process based models: the Environmental Policy Integrated Climate (EPIC) and the Biosphere Energy Transfer Hydrology (BETHY/DLR) model. Both process models show a congruent pattern to changes in input data. The annual variability of NPP reaches 36% for BETHY/DLR and 39% for EPIC when changing major input datasets. However, EPIC is less sensitive to meteorological input data than BETHY/DLR. The ECMWF maximum temperatures show a systematic pattern. Temperatures above 20°C are overestimated, whereas temperatures below 20°C are underestimated, resulting in an overall underestimation of NPP in both models. Besides, BETHY/DLR is sensitive to the choice and accuracy of the land cover product.</p> <p>Discussion</p> <p>This study shows that the impact of input data uncertainty on modelling results need to be assessed: whenever the models are applied under new conditions, local data should be used for both input and result comparison.</p

Modeling Carbon Sinks and Sources in semi-arid Environments for a Land Degradation Assessment Approach

Contrary to wetlands or moderate climates, the understanding of carbon exchange between ecosystem and atmosphere in arid and semi-arid environments is more challenging due to the sensible feedback of terrestrial ecosystems to environmental variability. Especially in the savannah regions of South Africa the biomes are strongly affected on the one hand by low and sporadic precipitation and on the other hand by intense land use of livestock farming. This leads to wide degraded areas under the process of desertification and the loss of huge carbon stocks in soil and vegetation. To quantify the carbon sinks and sources in these regions we ran our dynamic vegetation model BETHY/DLR for South Africa which generates maps of Net Primary Productivity, NPP, in spatial resolution of 1 km. These results can help assessing the status and development of land degradation for the whole country of South Africa

Einsatzmöglichkeiten der Erdbeobachtung auf dem Weg zur Umsetzung einer nachhaltigen Energieversorgung

Die Erschließung erneuerbarer Energien gehört zu den wichtigsten Herausforderungen der Gegenwart. Von zentraler Bedeutung sind in diesem Zusammenhang die Bewertung von Flächenpotenzialen, die Bestimmung geeigneter Standorte, die Abwägung von Nutzungsinteressen sowie die Erfassung von Trends und Auswirkungen auf die Landschaftsgestaltung. Für diese Anforderungen und Aufgaben sind aktuelle, in ihrer räumlichen und thematischen Güte hochwertige Geodaten ein unverzichtbarer Bestandteil. Mit Blick auf die Erhebung von Geoinformationen auf unterschiedlichen räumlichen und zeitlichen Skalen hat sich die Satellitenfernerkundung zu einem erfolgreichen Werkzeug entwickelt. So bieten die Erdbeobachtung und darauf basierende Geoinformationsdienste vielversprechende Nutzungspotenziale hinsichtlich der Ergänzung bestehender sowie Bereitstellung fehlender Raumdatenbestände für energiebezogene Fragestellungen. Dieser Beitrag stellt beispielhaft Einsatzmöglichkeiten der Fernerkundung sowie entsprechende Techniken und Geoinformationsprodukte zur Unterstützung eines Landmanagements dar, das auf die Erschließung von Potentialen erneuerbarer Energien ausgerichtet ist

Global Gap-Free MERIS LAI Time Series (2002–2012)

This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002–2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS &amp; Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (&lt;10°), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002–March 2012). Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R2 : 0.88) of agreement on a global scale

Modellierung und Validierung land- und forstwirtschaftlicher Biomassepotentiale für Deutschland und Österreich

Das Biosphere Energy Transfer Hydrology Model (BETHY / DLR), ein Prozessmodell, das die Netto-Primärproduktion land- und forstwirtschaftlicher Flächen berechnen kann, wurde verwendet um Biomassepotenziale für Deutschland und Österreich zu berechnen. Das Modell wird von Fernerkundungsdaten und meteorologischen Daten angetrieben. Als Fernerkundungsdatensätze wurden Zeitreihen des Blattflächenindexes (eng. Leaf Area Index, LAI), welcher den Zustand der Vegetation beschreibt, sowie eine Klassifikation der Bodenbedeckung genutzt. LAI und Bodenbedeckungsinformation wurden vom Sensor VEGETATION abgleitet. Beide Datensätze haben eine räumliche Auflösung von etwa 1 km x 1 km und sind frei verfügbar für den Untersuchungsbereich. Die meteorologischen Eingabeparameter sind Lufttemperatur (in 2 m Höhe), Niederschlag, Bewölkung, Windgeschwindigkeit (in 10m Höhe) und Wassergehalt des Bodens (in den vier obersten Bodenschichten), welche vom European Centre for Medium-Range Weather Forecasts bezogen wurden. Ihre räumliche Auflösung beträgt etwa 0,25 ° x 0,25 ° und die zeitliche Auflösung ist bis zu viermal täglich. Die Modellausgabe, die Brutto-Primärproduktion, wird mit täglicher Auflösung berechnet. Durch Subtraktion der kumulativen Instandhaltungs- und Wachstumsatmung, wird die Netto-Primärproduktion bestimmt. Zur Validierung der modellierten Netto-Primärproduktion wurden Ernteertragsschätzungen, sowie mittlere Zuwachsraten der oberirdischen Biomasse aus nationalen Statistiken von Deutschland und Österreich genutzt. Hierzu muss zunächst die oberirdische Biomasse bestimmt werden und anschließend die oberirdischen und der unterirdische Anteil mit Artenspezifischen Konversionsfaktoren (Korn-zu-Stroh und Blatt-zu-Rüben-Beziehungen) berechnet werden. Anschließend wird der Kohlenstoffgehalt der Trockenmasse geschätzt. Zur Korrelierung der Modellergebnisse mit diesen statistischen Daten, wurden die modellierten Daten auf Landkreise! bene aggregiert. Die Ergebnisse zeigen, dass ein mit Fernerkundungsdat en betriebenes Prozessmodell zuverlässige Schätzungen der land- und forstwirtschaftlichen Biomassepotenziale liefern kann und diese sehr gut mit statistisch abgeleiteten Schätzungen der tatsächlichen Biomasse korrelieren. Darüber hinaus wurden theoretische Energiepotenziale aus dem modellierten und validierten NPP Daten unter der Annahme einer nachhaltigen Land-und Forstwirtschaft berechnet. Hierzu wurden Speziesspezifische Heizwerte genutzt. Solche nachhaltigen Biomasse-Energie-Potentiale spielen eine wichtige Rolle bei der nachhaltigen Energie-Debatte. Um das Modell BETHY / DLR weiter zu verbessern wurde ein mehrschichtiges Bodenwasserhaushaltsmodell entwickelt. Es nutzt van Genuchten Parameter, die für 128 weltweit verfügbare FAO Bodenarten berechnet wurden

The impact of large scale biomass production on ozone air pollution in Europe

Tropospheric ozone contributes to the removal of air pollutants from the atmosphere but is itself a pollutant that is harmful to human health and vegetation. Biogenic isoprene emissions are important ozone precursors, and therefore future changes in land use that change isoprene emissions are likely to affect atmospheric ozone concentrations. Here, we use the chemical transport model LOTOS-EUROS (dedicated to the regional modeling of trace gases in Europe) to study a scenario in which 5% of the crop- and grass-land in Europe is converted into poplar plantations to be used for biofuel production. Although this scenario is rather conservative, our simulations project that isoprene emissions are substantially increased by an average of 45% over the simulated domain. As a consequence, ozone peak values are expected to increase by up to 6%, and ozone indicators for damage to human health and vegetation by up to 25% and 40%, respectively. Finally, we show that after the change in land use NOx emission reductions of 15 e20% in Europe would be required to restore the ozone levels to current values. Because biomass production is expected to increase throughout Europe in the coming decades, we conclude that careful consideration of the tree types and regions to be used is required to constrain the concomitant air pollution to a minimum

Comparing results of a remote sensing driven interception-infiltration model for regional to global applications with ECMWF data

We present results of a remote sensing based modelling approach to simulate the 1D water transport in the vadose zone of unsaturated soils on a daily basis, which can be used for regional to global applications. To calculate the hydraulic conductivity our model is driven by van Genuchten parameters, which we calculated for Bavaria (South-East-Germany), which we choose as area of investigation, using the ISRIC-WISE Harmonized Global Soil Profile Dataset Ver. 3.1 and the Rosetta programme. Soil depth and layering of up to six layers were defined independently for each soil. Interception by vegetation is also considered by using Leaf Area Index (LAI) time series from SPOT-VEGETATION. Precipitation is based on daily time series from the European Centre for Medium-Range Weather Forecasts (ECMWF). The model was applied to the Biosphere Energy Transfer Hydrology (BETHY/DLR) vegetation model, driven at the German Aerospace Center (DLR), to discuss the possibility of regionalization of a global model concept, regarding the soil water budged. Furthermore we compare our results with ECMWF data and discuss the results for the state of Bavaria. We found a good agreement for the general characteristics of our results with this dataset, especially for soils which are close to the standard characteristics of the ECMWF. Disagreements were found for shallow soils and soils under stagnant moisture, which are not considered in the ECMWF modelling scheme, but are distinguished in our approach.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only