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首页> 外文期刊>GIScience & remote sensing >Using GeoEye-1 Imagery for Multi-Temporal Object-Based Detection of Canegrub Damage in Sugarcane Fields in Queensland, Australia
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Using GeoEye-1 Imagery for Multi-Temporal Object-Based Detection of Canegrub Damage in Sugarcane Fields in Queensland, Australia

机译:使用GeoEye-1影像进行基于时间的基于对象的澳大利亚昆士兰州甘蔗田Canegrub损伤检测

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摘要

The greyback canegrub (Dermolepida albohirtum) is the main pest of sugarcane crops in all cane-growing regions between Mossman (16.5 degrees S) and Sarina (21.5 degrees S) in Queensland, Australia. In previous years, high infestations have cost the industry up to $40 million. However, identifying damage in the field is difficult due to the often impenetrable nature of the sugarcane crop. Satellite imagery offers a feasible means of achieving this by examining the visual characteristics of stool tipping, changed leaf color, and exposure of soil in damaged areas. The objective of this study was to use geographic object-based image analysis (GEOBIA) and high-spatial resolution GeoEye-1 satellite imagery for three years to map canegrub damage and develop two mapping approaches suitable for risk mapping. The GEOBIA mapping approach for canegrub damage detection was evaluated over three selected study sites in Queensland, covering a total of 254km(2) and included five main steps developed in the eCognition Developer software. These included: (1) initial segmentation of sugarcane block boundaries; (2) classification and subsequent omission of fallow/harvested fields, tracks, and other non-sugarcane features within the block boundaries; (3) identification of likely canegrub-damaged areas with low NDVI values and high levels of image texture within each block; (4) the further refining of canegrub damaged areas to low, medium, and high likelihood; and (5) risk classification. The validation based on field observations of canegrub damage at the time of the satellite image capture yielded producer's accuracies between 75% and 98.7%, depending on the study site. Error of commission occurred in some cases due to sprawling, drainage issues, wind, weed, and pig damage. The two developed risk mapping approaches were based on the results of the canegrub damage detection. This research will improve decision making by growers affected by canegrub damage.
机译:在澳大利亚昆士兰州Mossman(南纬16.5度)和Sarina(南纬21.5度)之间的所有甘蔗种植区,灰背甘蓝(Dermolepida albohirtum)是甘蔗作物的主要害虫。在过去的几年中,高发病率使该行业损失了高达4000万美元。但是,由于甘蔗作物通常难以穿透,因此很难确定田间的损害。卫星图像通过检查大便倾倒,叶片颜色变化以及受损区域土壤暴露的视觉特征,为实现这一目标提供了可行的方法。这项研究的目的是使用基于地理对象的图像分析(GEOBIA)和高空间分辨率的GeoEye-1卫星图像三年来绘制canegrub破坏图,并开发两种适用于风险图的绘制方法。在昆士兰州的三个选定研究地点,评估了用于canegrub损伤检测的GEOBIA映射方法,覆盖了254 km(2)的总面积,其中包括eCognition Developer软件开发的五个主要步骤。其中包括:(1)甘蔗块边界的初步分割; (2)块边界内的休耕/收割场,径迹和其他非蔗糖烷特征的分类以及随后的遗漏; (3)识别每个块内具有低NDVI值和高图像纹理水平的可能的canegrub损坏区域; (4)进一步将油菜籽皮受损的区域进一步细化为低,中和高可能性; (五)风险分类。根据在实地拍摄时对油菜籽叶损伤的现场观察进行的验证得出生产者的准确度在75%至98.7%之间,具体取决于研究地点。由于蔓延,排水问题,风,杂草和猪的伤害,在某些情况下会发生委托错误。两种已开发的风险映射方法基于canegrub损坏检测的结果。这项研究将改善受到油菜籽损害的种植者的决策。

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