Spatiotemporal dynamics of stress factors in wheat analysed by multisensoral remote sensing and geostatistics

Plant stresses, in particular fungal diseases, basically show a high variability in space and time with respect to their impact on the host. Recent ‘Precision Agriculture’ techniques allow for a spatially and temporally adjusted pest control that might reduce the amount of cost-intensive and ecologically harmful agrochemicals. Conventional stress detection techniques such as random monitoring do not meet demands of such optimally placed management actions. The prerequisite is a profound knowledge about the controlled phenomena as well as their accurate sensor-based detection. Therefore, the present study focused on spatiotemporal dynamics of stress factors in wheat, Europe’s main crop. Primarily, the spatiotemporal characteristics of the fungal diseases, powdery mildew (Blumeria graminis) and leaf rust (Puccinia recondita), were analysed by remote sensing techniques and geo-statistics on leaf and field scale. Basically, there are two different approaches to sensor-based detection of crop stresses: near-range sensors and airborne-/satellite-borne sensors. In order to assess the potential of both approaches, various experiments in field and laboratory were carried out with the use of multiple sensors operated at different scales. Besides the spatial dimension of crop stresses, all studies focussed on the temporal dimension of these phenomena, since this is the key question for an operational use of these techniques. In addition, a comparison between multispectral and hyperspectral data gave an indication of their suitability for this purpose. The results exhibit very high spatiotemporal dynamics for both fungal diseases. However, powdery mildew and leaf rust showed different characteristics, with leaf rust showing a more systematic temporal progress. The physiological behaviours of the phenomena, which are strongly influenced by various environmental factors, define the optimal disease detection date as well as the temporal resolution required for sensor-based disease detection. Due to the high spatiotemporal dynamics of the investigated diseases, a general recommendation of optimal detection periods can not be given, but critical periods are highlighted for each pathogen. The results indicate that multispectral remote sensing data with high spatial resolution shows a high potential for quantifying crop vigour by using spectral mixture analyses. Simulated endmembers for the identification of stressed wheat areas were utilized, whereby promising results could be achieved. However, due to the low spectral resolution of these data, a discrimination of stress factors or early disease detection is not possible. Hyperspectral data was therefore used to point out the potential of early detection of crop diseases, which is a crucial and restrictive factor for Precision Agriculture applications. In a laboratory experiment, leaf rust infections could be detected by hyperspectral data five days after inoculation. In a field experiment with respect to early stress detection, it could be demonstrated that hyperspectral data outperformed multispectral data. High accuracy for the detection of powdery mildew infections in the field was thereby achieved. Due to the fact that typical spatiotemporal characteristics for each pathogen were found, there is a high potential for decision support systems, considering all variables that affect the disease progress. Besides the further analysis of hyperspectral data for disease detection, the development of a decision support system is the subject of the upcoming last period of the Research Training Group 722

Sparsity Invariant CNNs

In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network. To overcome this problem, we propose a simple yet effective sparse convolution layer which explicitly considers the location of missing data during the convolution operation. We demonstrate the benefits of the proposed network architecture in synthetic and real experiments with respect to various baseline approaches. Compared to dense baselines, the proposed sparse convolution network generalizes well to novel datasets and is invariant to the level of sparsity in the data. For our evaluation, we derive a novel dataset from the KITTI benchmark, comprising 93k depth annotated RGB images. Our dataset allows for training and evaluating depth upsampling and depth prediction techniques in challenging real-world settings and will be made available upon publication

Tourism and urban development as drivers for invertebrate diversity loss on tropical islands

Oceanic islands harbour a disproportionately high number of endemic and threatened species. Rapidly growing human populations and tourism are posing an increasing threat to island biota, yet the ecological consequences of these human land uses on small oceanic island systems have not been quantified. Here, we investigated and compared the impact of tourism and urban island development on ground-associated invertebrate biodiversity and habitat composition on oceanic islands. To disentangle tourism and urban land uses, we investigated Indo-Pacific atoll islands, which either exhibit only tourism or urban development, or remain uninhabited. Within the investigated system, we show that species richness, abundance and Shannon diversity of the investigated invertebrate community are significantly decreased under tourism and urban land use, relative to uninhabited islands. Remote-sensing-based spatial data suggest that habitat fragmentation and a reduction in vegetation density are having significant effects on biodiversity on urban islands, whereas land use/cover changes could not be linked to the documented biodiversity loss on tourist islands. This offers the first direct evidence for a major terrestrial invertebrate loss on remote oceanic atoll islands due to different human land uses with yet unforeseeable long-term consequences for the stability and resilience of oceanic island ecosystems

NUTRItion and CLIMate (NUTRICLIM): investigating the relationship between climate variables and childhood malnutrition through agriculture, an exploratory study in Burkina Faso

Malnutrition remains a leading cause of death in children in low- and middle-income countries; this will be aggravated by climate change. Annually, 6.9 million deaths of children under 5 were attributable directly or indirectly to malnutrition. Although these figures have recently decreased, evidence shows that a world with a medium climate (local warming up to 3–4 °C) will create an additional 25.2 million malnourished children. This proof of concept study explores the relationships between childhood malnutrition (more specifically stunting), regional agricultural yields, and climate variable through the use of remote sensing (RS) satellite imaging along with algorithms to predict the effect of climate variability on agricultural yields and on malnutrition of children under 5. The success of this proof of purpose study, NUTRItion and CLIMate (NUTRICLIM), should encourage researchers to apply both concept and tools to study of the link between weather variability, crop yield, and malnutrition on a larger scale. It would also allow for linking such micro-level data to climate models and address the challenge of projecting the additional impact of childhood malnutrition from climate change to various policy relevant time horizons

Earth observation in support of malaria control and epidemiology: MALAREO monitoring approaches

Malaria affects about half of the world's population, with the vast majority of cases occuring in Africa. National malaria control programmes aim to reduce the burden of malaria and its negative, socioeconomic effects by using various control strategies (e.g. vector control, environmental management and case tracking). Vector control is the most effective transmission prevention strategy, while environmental factors are the key parameters affecting transmission. Geographic information systems (GIS), earth observation (EO) and spatial modelling are increasingly being recognised as valuable tools for effective management and malaria vector control. Issues previously inhibiting the use of EO in epidemiology and malaria control such as poor satellite sensor performance, high costs and long turnaround times, have since been resolved through modern technology. The core goal of this study was to develop and implement the capabilities of EO data for national malaria control programmes in South Africa, Swaziland and Mozambique. High-and very high resolution (HR and VHR) land cover and wetland maps were generated for the identification of potential vector habitats and human activities, as well as geoinformation on distance to wetlands for malaria risk modelling, population density maps, habitat foci maps and VHR household maps. These products were further used for modelling malaria incidence and the analysis of environmental factors that favour vector breeding. Geoproducts were also transferred to the staff of national malaria control programmes in seven African countries to demonstrate how EO data and GIS can support vector control strategy planning and monitoring. The transferred EO products support better epidemiological understanding of environmental factors related to malaria transmission, and allow for spatio-temporal targeting of malaria control interventions, thereby improving the cost-effectiveness of interventions

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