首页> 外文期刊>Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic >Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery
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Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery

机译:栖息地监测评估基于多源卫星遥感图像的作物疾病和害虫分布

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Habitat monitoring can be used to evaluate the potential occurrence and distribution of plant diseases and pests in a region. Here, we present a new method for monitoring crop diseases and pests based on Worldview 2 and Landsat 8 satellite data. As a case study, the method was applied to wheat fields in Zhou Jiazhuang, Jinzhou City, Hebei Province. Crop growth indices (GNDVI and VARI d-edge, land environmental features (Wetness, Greenness, and LST) were used describe habitat, and an independent t-test was used to evaluate the performances of these five features in representing crop diseases and pests. Field measurements were used to evaluate the validity of the method. An FLDA model incorporating both vegetation and environmental indices was more accurate for monitoring crop disease and pest occurrence compared with a model based on vegetation indices alone (71% vs. 82% accuracy). Future work should include multiple forms of information (e.g., meteorological data and web sensor networks) to further improve regional-scale monitoring of crop diseases and pests. (C) 2017 Elsevier GmbH. All rights reserved.
机译:栖息地监测可用于评估一个地区植物疾病和害虫的潜在发生和分布。在这里,我们提出了一种基于WorldView 2和Landsat 8卫星数据监测作物疾病和害虫的新方法。以案例研究为例,该方法应用于河北省锦州市周嘉庄的麦田。使用裁剪索引(GNDVI和Vari D-Edge,使用植物栖息地,使用独立的T检验来评估代表作物疾病和害虫的这种五个特征的性能。使用现场测量来评估该方法的有效性。与仅基于植被指数的模型(71%与82%的精度为71%vs.82%)相比,植被和环境指标的FLDA模型更准确地监测作物疾病和害虫发生。未来的工作应包括多种形式的信息(例如,气象数据和网络传感器网络),以进一步改善作物疾病和害虫的区域规模监测。(c)2017年Elsevier GmbH。保留所有权利。

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