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The scheme of ragweed risk map production. The methodology of ragweed detection with remote sensing The ragweed recognition model, based on the temporal profile of band values and certain vegetation indices extracted from high resolution satellite data series, performs very well. The method is general but the specific application developed by FÖMI is tuned to the ragweed occurrence parameters in Hungary. Although ragweed appears sporadically at numerous places within settlements, the majority of pollen strain comes from large ragweed stands on plough-lands: from contiguous ragweed spots on cereal stubbles and within sunflower parcels. Remote sensing control mainly aims at picking up areas bigger than 0.8 hectares. Due to the above-mentioned spatial characteristics and size distribution of ragweed spots, this trade-off does not mean serious limitation.
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The Institute of Geodesy, Cartography and Remote Sensing (FÖMI) provides services to the Minis-try of Agriculture and Rural Development. FÖMI has a 30 years' experience in the applications of remote sensing. The programmes carried out in this period served as a basis to ragweed control. The allergy induced by ragweed pollen has become an important...
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... Vegetation maps have many applications worldwide: they can be used to provide advice on risk assessments, e.g. on invasive species (for example Csornai et al., 2011); combined with health data to inform research on health impacts such as respiratory hospital admissions caused by exposure of environmental aeroallergens (Bousquet et al., 2007;Newson et al., 2014); combined with weather data to improve pollen forecasting systems (e.g. Zink et al., 2012); or as inputs to dedicated pollen emission models (Zink et al., 2013). ...
Allergenic pollen is produced by the flowers of a number of trees, grasses and weeds found throughout the UK. Exposure to such pollen grains can exacerbate pollen-related asthma and allergenic conditions such as allergic rhinitis (hay fever). Maps showing the location of these allergenic taxa have many applications: they can be used to provide advice on risk assessments; combined with health data to inform research on health impacts such as respiratory hospital admissions; combined with weather data to improve pollen forecasting systems; or as inputs to pollen emission models. In this study we present 1km resolution maps of 12 taxa of trees, grass and weeds found in the UK. We have selected the main species recorded by the UK pollen network. The taxa mapped in this study were: Alnus (alder), Fraxinus (ash), Betula (birch), Corylus (hazel), Quercus (oak), Pinus (pine) and Salix (willow), Poaceae (grass), Artemisia (mugwort), Plantago (plantain), Rumex (dock, sorrels) and Urtica (nettle). We also focus on one high population centre and present maps showing local level detail around the city of London. Our results show the different geographical distributions of the 12 taxa of trees, weeds and grass, which can be used to study plants in the UK associated with allergy and allergic asthma. These maps have been produced in order to study environmental exposure and human health, although there are many possible applications. This novel method not only provides maps of many different plant types, but also at high resolution across regions of the UK, and we uniquely present 12 key plant taxa using a consistent methodology. To consider the impact on human health due to exposure of the pollen grains, it is important to consider the timing of pollen release, and its dispersal, as well as the effect on air quality, which is also discussed here.
... These solutions present a dedicated approach to a specific environment [2,19,32]. In contrary, most spatial data analysis processes performed at organizations such as the Institute of Geodesy, Cartography and Remote Sensing (FÖMI) have their evolved workflows using multiple (proprietary and open-source) software and GIS expertise [13,34]. Most tasks are semi-automatic, involving some manual adjustments and fine tuning and rather work with files instead of databases. ...
Big data and cloud computing are two phenomena, which have gained significant reputation over the last few years. In computer science the approach shifted towards distributed architectures and high performance computing. In case of geographical information systems (GIS) and remote sensing image analysis, the new paradigms have already been successfully applied to several problems, and systems have been developed to support processing of geographical and remote sensing data in the cloud. However, due to different circumstances many previous workflows have to be reconsidered and redesigned. Our goal is to show a way how the existing approaches to remote sensing image analysis can be advanced to take advantages of these new paradigms. The task aiming in shifting the algorithms shall require a moderate effort and must avoid the complete redesign and reimplementation of the existing approaches. We present the whole journey as a case study using an existing industrial workflow for demonstration. Nevertheless, we define the rules of thumb, which can come in hand when shifting any existing GIS workflows. Our case study is the workflow of waterlogging and flood detection, which is an operative task at the Institute of Geodesy, Cartography and Remote Sensing (FOMI). This task in currently operational using a semi-automatic single machine approach involving multiple software. The workflow is neither efficient nor scalable, thus it is not applicable in emergency situations where quick response is required. We present an approach utilizing distributed computing, which enables the automated execution of this task on large input data with much better response time. The approach is based on the well-known MapReduce paradigm, its open-source implementation, the Apache Hadoop framework and the AEGIS geospatial toolkit. This enables the replacement of multiple software to a single, generic framework. Results show that significant performance benefits can be achieved at the expense of minor accuracy loss.
... Се годня дистанционные методы обнаружения ам брозии активно используются исследователями Франции и Австрии (Auda et al., 2011). С 2004 г. при помощи дешифрования космических снимков вы сокого и среднего разрешения ведется мониторинг амброзии в Венгрии (Csornai et al., 2011). ...
We present results of the decoding ofLandsat satellite images that allows identifying patches of common ragweed (Ambrosia artemisiifolia) in agrophytocenoses of the South of Russia, basing on the normalized difference vegetation index (NDVI). The fact of the absence of extensive areas heavily infested by the ragweed confirms the conclusions of the field studies about the long-term efficiency of the introduction from the North America of the ragweed leaf beetle (Zygogramma suturalis) in the period 1978–1990. Retrospective analysis of the archival satellite images of Stavropol region that were taken in the second half of the 1980’s shows the reduction of the weedcovered areas during this period. Most weed patches detected are systematically located on the roadsides and field boundaries.
... Among invasive alien species, ragweed causes serious problems in Hungary, because of the allergenic effect of its pollen. Since 2005, remote sensing highly supports its exemption (see [3]). The majority of pollen strain comes from large ragweed spots situated in agricultural areas. ...
An investigation of segmentation methods is embedded into a classification problem. The segments are homogeneous areas of images consisting of neighboring pixels. The segment membership of pixels conveys valuable geometric information for the classification step. This article gives a summary on several merge-based and cut-based segmentation methods. The application of segmentation is not only an option but a necessity in the processing of very high-resolution images, as their pixels usually cannot be interpreted individually. Segments are assigned with several attributes (e.g., texture) derived from geometrical properties. This leads to an advanced approach called object-based image analysis. As an application, the task of delimiting tree groups and scattered trees in pastures are presented in detail. Three further applications are shortly introduced.
In recent years distributed data processing has reached many areas of computer science including geographic and remote sensing information systems. With the continuing increase of data, existing algorithms and data management need to be moved to new architecture, which may require a great deal of effort.
This paper describes a geospatial data processing framework designed to enable the management and processing of spatial and remote sensing data in distributed environment. The framework is based on the mainstream MapReduce paradigm, and its open-source implementation, the Apache Hadoop library. The primary design goals of this framework are extensibility and adaptability, to enable previously implemented algorithms and existing toolkits to be easily adapted to distributed execution without major effort.
The possibilities of the framework are demonstrated on remote sensing image processing operations in comparison with existing solutions in the Hadoop environment. Results show that this toolkit provides a much broader possibility to advance geospatial data processing than the currently available solutions.
