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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Low Orbiting Satellite and Small UAS-Based High-Resolution Imagery Data to Quantify Crop Lodging: A Case Study in Irrigated Spearmint
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Low Orbiting Satellite and Small UAS-Based High-Resolution Imagery Data to Quantify Crop Lodging: A Case Study in Irrigated Spearmint

机译：低轨道卫星和基于小型UA的高分辨率图像数据，以量化作物住宿：灌溉留下的案例研究

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摘要

Conventional methods for crop lodging assessments need accurate ground observations and tend to be laborious. Lodging assessment methods and accuracy can thus be improved using remote sensing data from small unmanned aerial systems (UASs) and low orbiting satellites (LOSs). With such aim, imagery to assess spearmint crop lodging was acquired using a small UAS at two ground sample distances (GSDs) of 0.01 and 0.03 m. Crop surface model (CSM) and six image color features were extracted from small UAS-based data. These features were then classified into not lodged (NL), partially lodged (PL), and lodged (L) groups. Mean and majority feature classes were obtained for 50 regions of interest (ROI) of size 1 m(2) each. Features were compared with visual crop lodging ratings using Pearson correlation (r) and Cohen's kappa (CK) coefficients. CSM showed higher assessment accuracy with r approximate to 0.85 and CK > 0.60. Mean percentage red (%R) was observed to have the strongest correlation with visual ratings (r = 0.75 and CK = 0.40-0.59) followed by mean percentage blue (%B), both at 0.01- m GSD. The percentage of lodging calculated from %R and %B maps was also contrasted with similar estimates from LOS-based imagery at 3.00-m GSD with no statistical differences found at 5% level.
机译：常规方法用于作物汇总评估需要准确的地面观察，往往是费力的。因此，可以使用来自小无人空中系统（uass）和低轨道卫星（损失）的遥感数据来改善住宿评估方法和准确度。利用此类目的，使用0.01和0.03米的两个地样品距离（GSD）的小UA获得了评估留花茶树的图像。从基于UA的数据中提取裁剪表面模型（CSM）和六种图像颜色特征。然后将这些特征分为未加入（NL），部分收入（PL）和提交（L）组。为50个兴趣区（ROI）的均值为1米（2），获得了平均值和多数特征类。使用Pearson相关（R）和Cohen的Kappa（CK）系数与视觉作物住宿评分进行比较了功能。 CSM显示出高度评估准确性，R大于0.85和CK> 0.60。观察到平均百分比红色（％r）与视觉额定值（r = 0.75和ck = 0.40-0.59）具有最强的相关性，然后是平均百分比的蓝色（％b），两者均在0.01-m gsd。从％R和％B映射计算的植入百分比也与3.00 m GSD的基于LOS的图像类似的估计形成对比，没有统计差异在5％水平。

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