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Timely identification of agricultural crops in the Temelín NPP vicinity using satellite data in the event of radiation contamination

机译:发生辐射污染时,使用卫星数据及时确定TemelínNPP附近的农作物

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Timely identification of agricultural crops in the Temelín NPP vicinity using satellite data in the event of radiation contaminationThe study established the possibility of rapid evaluation of land cover structure and situation using as an example the Temelín NPP (Nuclear Power Plant) emergency zone. The composition, surface representation and spatial distribution of crop species in the area of interest were assessed on the basis of satellite data analysis (Landsat 5 TM).The supervised classification method of Landsat data yielded 92% accuracy of classification into the land cover classes. A comparison of satellite data classification and field investigation (farmers' and LPIS data) showed that the combination of both methods appears to be ideal for the classification of land cover. Analysis of the assessment of Landsat satellite data showed it was possible to process data in a few days. However, obtaining data was problematic; in our case it was 44 days. The results of the classification as well as other outputs (biomass growth model, expense-to-revenue ratio of measures, route network, LPIS database parcel structure, etc.) serve as a basis for the modelling of potential agricultural production contamination. The subsequent model is available for decision making and the selection of a suitable countermeasure in the event of potential radiation contamination.
机译:在发生辐射污染的情况下,使用卫星数据及时识别TemelínNPP附近的农作物该研究以TemelínNPP(核电厂)应急区为例,建立了快速评估土地覆盖结构和状况的可能性。根据卫星数据分析(Landsat 5 TM)对感兴趣区域内农作物的组成,表面表示和空间分布进行了评估.Landsat数据的监督分类方法产生了92%的土地覆被分类精度。卫星数据分类和现场调查(农民和LPIS数据)的比较表明,两种方法的结合对于土地覆盖物的分类似乎是理想的。对Landsat卫星数据评估的分析表明,有可能在几天内处理数据。但是,获取数据是有问题的。在我们的情况下是44天。分类的结果以及其他产出(生物量增长模型,措施的支出/收入比,路线网络,LPIS数据库地块结构等)可作为潜在农业生产污染建模的基础。后续模型可用于决策和在潜在的辐射污染情况下选择合适的对策。

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