3D modelling and monitoring of Indonesian peatlands aiming at global climate change mitigation

Tropical peat swamp forests in Indonesia are highly threatened ecosystems. As a result of economic development during the past two decades, they have been subjected to intensive logging, drainage and conversion to plantation estates, especially for oil palm. The Indonesian peatlands are one of the largest near-surface reserves of terrestrial organic carbon. However, ongoing rapid peat decomposition due to drainage and attendant recurrent fires have recently caused the release of huge amounts of this carbon into the atmosphere as carbon dioxide. If these large emissions from degrading peatlands are taken into account, Indonesia is one of the largest emitters of CO2 worldwide. Within the context of the ongoing discussions concerning climate change, the importance of peatlands as carbon stores is increasingly recognised by the public, accompanied by a demand for conservation and restoration. Therefore, this thesis utilises innovative geospatial 3D modelling and remote sensing techniques to study the Indonesian peatlands with the overall aim of global climate change mitigation. Previous estimates of the total amount of carbon stored in the Indonesian peatlands could be improved by applying 3D modelling based on a combined analysis of satellite imagery and in situ peat thickness measurements. At least 55±10 Gt of carbon are stored in Indonesia’s peatlands. With this huge carbon storage and the current rate of degradation, the tropical peatlands of Indonesia have the power to negatively influence the global climate. Large-scale peatland restoration is needed to prevent further greenhouse gas emissions. This thesis shows that successful rewetting of a 590 km² large area of drained peat swamp forest could result in mitigated emissions of 1.4-1.6 Mt CO2 yearly, and can be achieved with relatively little effort and at low costs. Multitemporal radar satellite imagery proved to be capable of monitoring the effect of hydrological restoration measures on peat soil moisture and groundwater levels in Central Kalimantan, Indonesia. Satellite remote sensing allows continuous large-scale tropical peatland monitoring, compared to only punctual, temporally limited field measurements. This is particularly important for initiatives aiming at carbon trading on the voluntary carbon market or under the REDD (Reducing Emissions from Deforestation and Degradation) mechanism, which both constitute significant financing schemes for conservation and rehabilitation of Indonesia’s peatlands

Überwachung isländischer Vulkane mit innovativen Fernerkundungs-Technologien und 3D Visualisierung

Die phreatomagmatische Eruption des isländischen Vulkans Eyjafjallajökull im Frühjahr 2010 hatte durch die Beeinträchtigung des europäischen Luftverkehrs einen enormen wirtschaftlichen Schaden verursacht. Basierend auf diesem Ereignis wird im Projekt IsViews (Iceland subglacial Volcanoes interdisciplinary early warning system) der wenige Kilometer entfernte Mýrdalsjökull mit dem subglazialen Zentralvulkan Katla untersucht. Ein erneuter Vulkanausbruch wird erwartet. Das interdisziplinär ausgerichtete Forschungsprojekt steht im Kontext von GMES. Zur Überwachung stehen eine Zeitreihe von TerraSAR-X (TSX) Daten seit 2010, optische Daten der RapidEye Satelliten sowie höchstaufgelöste HRSC Luftbilddaten zur Verfügung. Unterstützt wird das Monitoring der geodynamischen Prozesse mit kontinuierlichen Höhenmodellen aus der TanDEM-X Mission. Diese werden mit Hilfe von aktuellen LiDAR Daten verifiziert. Eine umfangreiche GIS-Datenbank wird regelmäßig erweitert, u.a. durch near real-time Stripmap TSX Szenen, aktuelle Seismik-und Wetterdaten sowie Informationen über die Gletscherdynamik. Im Sommer 2013 wurden auf dem Mýrdalsjökull zwei automatische GPS Stationen, drei Pegelstationen und am DLR neu entwickelte Top-Hat Reflektoren zur Erfassung der glazialen Bewegungsvorgänge aufgebaut. Die Reflektoren konnten auf experimentellen Staring Spotlight TSX Daten mit einer Bodenauflösung von wenigen Dezimetern identifiziert werden. Eine Höhendifferenz-Analyse von sieben TanDEM-X Szenen, aufgenommen zwischen September 2011 und Juni 2013, zeigt, dass es möglich ist mit diesen innovativen Daten Änderungen des Gletschervolumens sowie Eisdepressionen zu detektieren. Ziel des Projektes ist es Veränderungen der Gletscheroberfläche, ausgelöst durch subglaziale vulkanische Aktivitäten, automatisch zu erfassen. Ein satellitengestütztes near real-time Monitoring zur Früherkennung von Naturereignissen wird entwickelt und in eine internetbasierte 3D Visualisierungsumgebung integriert. Hierfür wurde bereits ein 3D Modell von ganz Island basierend auf 267 RapidEye Bildkacheln aus den Jahren 2011 und 2012 erstellt. Die hohe vulkanische Aktivität auf Island erlaubt, Anwendungen und Methoden mittels near real-time Fernerkundungs-und GIS-Daten für den Katastrophenschutz zu erproben und einzusetzen

Local surface mass-balance reconstruction from a tephra layer - a case study on the northern slope of Mýrdalsjökull, Iceland

Most Icelandic glaciers show high-accumulation rates during winter and strong surface melting during summer. Although it is difficult to establish and maintain mass-balance programs on these glaciers, mass-balance series do exist for several of the ice caps (Bjornsson and others, 2013). We make use of the frequent volcanic eruptions in Iceland, which cause widespread internal tephra layers in the ice caps, to reconstruct the surface mass balance (SMB) in the ablation zone. This method requires information about surface geometry and ice velocity, derived from remote-sensing information. In addition, the emergence angle of the tephra layer needs to be known. As a proof-of concept, we utilize a prominent tephra layer of the Myrdalsjokull Ice Cap to infer local SMB estimates in the ablation area back to 1988. Using tephra-layer outcrop locations across the glacier at different points in time it is possible to determine local mass changes (loss and redistribution) for a large part of the ablation zone, without the use of historic elevation models, which often are not available

Effect of Long-Term Farming Practices on Agricultural Soil Microbiome Members Represented by Metagenomically Assembled Genomes (MAGs) and Their Predicted Plant-Beneficial Genes

Nelkner J, Henke C, Lin TW, et al. Effect of Long-Term Farming Practices on Agricultural Soil Microbiome Members Represented by Metagenomically Assembled Genomes (MAGs) and Their Predicted Plant-Beneficial Genes. Genes. 2019;10(6): 424.To follow the hypothesis that agricultural management practices affect structure and function of the soil microbiome regarding soil health and plant-beneficial traits, high-throughput (HT) metagenome analyses were performed on Chernozem soil samples from a long-term field experiment designated LTE-1 carried out at Bernburg-Strenzfeld (Saxony-Anhalt, Germany). Metagenomic DNA was extracted from soil samples representing the following treatments: (i) plough tillage with standard nitrogen fertilization and use of fungicides and growth regulators, (ii) plough tillage with reduced nitrogen fertilization (50%), (iii) cultivator tillage with standard nitrogen fertilization and use of fungicides and growth regulators, and (iv) cultivator tillage with reduced nitrogen fertilization (50%). Bulk soil (BS), as well as root-affected soil (RS), were considered for all treatments in replicates. HT-sequencing of metagenomic DNA yielded approx. 100 Giga bases (Gb) of sequence information. Taxonomic profiling of soil communities revealed the presence of 70 phyla, whereby Proteobacteria, Actinobacteria, Bacteroidetes, Planctomycetes, Acidobacteria, Thaumarchaeota, Firmicutes, Verrucomicrobia and Chloroflexi feature abundances of more than 1%. Functional microbiome profiling uncovered, i.a., numerous potential plant-beneficial, plant-growth-promoting and biocontrol traits predicted to be involved in nutrient provision, phytohormone synthesis, antagonism against pathogens and signal molecule synthesis relevant in microbe–plant interaction. Neither taxonomic nor functional microbiome profiling based on single-read analyses revealed pronounced differences regarding the farming practices applied. Soil metagenome sequences were assembled and taxonomically binned. The ten most reliable and abundant Metagenomically Assembled Genomes (MAGs) were taxonomically classified and metabolically reconstructed. Importance of the phylum Thaumarchaeota for the analyzed microbiome is corroborated by the fact that the four corresponding MAGs were predicted to oxidize ammonia (nitrification), thus contributing to the cycling of nitrogen, and in addition are most probably able to fix carbon dioxide. Moreover, Thaumarchaeota and several bacterial MAGs also possess genes with predicted functions in plant–growth–promotion. Abundances of certain MAGs (species resolution level) responded to the tillage practice, whereas the factors compartment (BS vs. RS) and nitrogen fertilization only marginally shaped MAG abundance profiles. Hence, soil management regimes promoting plant-beneficial microbiome members are very likely advantageous for the respective agrosystem, its health and carbon sequestration and accordingly may enhance plant productivity. Since Chernozem soils are highly fertile, corresponding microbiome data represent a valuable reference resource for agronomy in general.</jats:p

Clusters of Organic Operations and their Impact on Regional Economic Growth in the United States

The purpose of this paper is to examine the impact of clusters of certified organic operations on county-level general economic indicators in order to assess the potential of organic clustering as an economic development tool. We first identify clusters of organic operations using the Local Moran’s I test statistic, which tests the null hypothesis of no spatial autocorrelation, and data from the National Organic Program and the U.S. Census. We then use these spatially defined clusters, as well as county-level data from publicly available sources such as the U.S. Census, the Bureau of Labor Statistics, the USDA’s Census of Agriculture, and the USDA’s Agricultural Resource Management Survey (ARMS), to analyze the impact of clustering on county-level economic indicators. To do this, we use a treatment effects model in which the dependent variable is a county-level economic indicator and the treatment is being in a cluster of organic operations. For comparison, we also perform these analyses for general agricultural establishments

Agglomeration and Spatial Dependence in Certified Organic Operations in the United States

The purpose of this paper to provide added insight into clustering as it pertains to the United States organic sector. I identify clusters of United States certified organic operations by showing how a formal definition of spatial clusters can emerge from an estimated model that accounts for spatial dependency. I also analyze how county-level variables impact the distribution of certified organic operations while controlling for spatial autocorrelation. My results indicate that the spatial distribution of certified organic operations displays statistically significant spatial autocorrelation as well as spatial heterogeneity. The results also indicate that county-level factors related to policy, economics, demographics, and land assets impact the distribution of certified organic operations. As research on firm and industry agglomeration, in general, typically finds that clustering benefits economic development, the results of this paper provide motivation for further research on the formation and impact of clustering in the U.S. organic sector

The role of US organic certifiers in organic hotspot formation

Because consumer demand for organic food products has seen a recent surge in momentum, the question of what factors encourage development of (domestic) organic supply has become increasingly important. Consumer interest in organic products has increased in the past decades, with growth rates upwards of 20 percent per year before the 2008 recession, and above 10 percent in the most recent years. This growth, however, has led to the concern that the demand for organic ingredients is out-pacing domestic supply. As a response to this possible shortage, organizations such as Stonyfield, Organic Valley, the Organic Trade Association, and other groups have taken an interest in promoting the organic sector and recruiting new organic operators. This situation, along with the fact that research has linked organic agriculture to regional development, makes it important to analyze and understand the factors associated with the development of geographic areas that have a high presence of organic operations. Existing literature addresses both factors affecting the formation of hotspots in general, and factors affecting the development of the organic industry. For example, factors such as proximity to urban centers and receptiveness to organic are mentioned as factors associated with increased organic operations. Papers discussing the formation of general hotspots also mention lower transportation costs, more labor market pooling, and knowledge spillovers as reasons for hotspot formation. However, none of the literature addressing the organic industry, to our knowledge, discusses the role of the organic certifying agent in the formation of hotspots of organic operations. Examining this role is important because the services provided by the organic certifying agent may be indicative of the level of communication among organic operations, and between organic operations and their communities, which may further indicate networking and knowledge spillover opportunities. The purpose of this paper is to investigate organic hotspot formation, paying particular attention to the role of the organic certifying agent. To do this, we determine how county-level certifier characteristics are associated with the probability that a county is in a hotspot of organic operations. We first identify hotspots (counties with positively correlated, high numbers of organic operations), cold-spots (counties with positively correlated, low numbers of organic operations), and outliers (counties with negatively correlated numbers of organic operations) of organic operations using data from the National Organic Program and the Local Moran’s I test statistic, which tests the null hypothesis of no spatial autocorrelation. We then use a logistic regression to analyze the association that county-level factors related to policy, economics, demographics, and organic certifiers have with the probability that a county is in one of the identified hotspots (or coldspots) of organic operations. We estimate the logit model for several different variations of dependent variables (organic hotspots, organic-production hotspots, organic-handling hotspots, and organic coldspots), but the same specification of influencing factors. By comparing results across these four models, we can focus on the roles of organic certifiers in the different models, rather than the individual results. As a robustness check, we also perform a secondary analysis using a subset of the data where all included counties have at least one certified organic operation. Specifically addressing organic certifiers, we create an indicator variable that takes a value of 1 if more than a certain percentage of certified organic operations in the county are certified by agents who publicly note outreach services, and 0 otherwise. This variable is included as an independent variable in our logit regressions. To examine the robustness of this variable, we examine variations where the thresholds for county-level percentage of organic operations certified by outreach-oriented certifiers are 30, 50, and 70 percent. We use this same technique for government-run certifications. That is, we create include a dummy variable that takes a value of 1 if the more than a certain percentage (again, 30, 50, or 70 percent) of organic operations in a county are certified by state or local government agencies. The results suggest that a high presence of organic certifying agents who provide outreach services, as well as a high presence of government run organic certifying agents, are both positively associated with the probability that a county belongs to a hotspot. Other factors, such as the level of property taxes and the distance of the county from the nearest interstate, are also significantly correlated with the probability that a county is in a hotspot. The results of this paper may encourage public institutions associated with organic certifiers to provide incentives for offering outreach services, and private institutions interested in promoting organic to work more closely with certifying agents as a means to boost organic hotspots. In the future, it would be interesting to quantify the economic impact of organic hotspots and coldspots, and then isolate the indirect economic impact of characteristics related to the organic certifiers

Slotting Fees for Organic Retail Products: Evidence from a Survey of U.S. Food Retailers

This paper investigates the prevalence of slotting fees in organic packaged and prepared products, and identifies the factors that influence the relative size of slotting fees. Based on a 2009 survey of U.S. food retailers, we find that 31 percent of surveyed retailers accept slotting fees for organic packaged and prepared products. Previous literature on slotting fees provides arguments for two rationales, one focused on the role slotting fees play in establishing an efficient allocation of shelf space for new products and the other focused on how slotting fees can be used strategically to price discriminate or otherwise increase rents to parties with more bargaining power. Using an ordered logit regression of the relative magnitude of slotting fees on retailer characteristics, we estimate coefficients that are mostly consistent with the economic efficiency rationale, with a few being consistent with the market power/strategic behavior rationale. More specifically, we find that the magnitude of slotting fees for organic products, relative to their non-organic counterparts, depends on a number of retailer characteristics, including the number of stores in the retailer’s chain, a retailer’s total sales, and the size of its organic marketing budget