Aerial CIR remote sensing for weed density mapping in a soybean field
Aerial CIR remote sensing for weed density mapping in a soybean field
复制标题
DOI：
10.13031/2013.6995
复制
发表时间：
2001-11-01
期刊：
影响因子：
--
通讯作者：
Tian, LF
中科院分区：
文献类型：
--
作者：
Bajwa, SG;Tian, LF
准确的杂草地图对于使用基于地图的可变速率喷雾器在特定地点施用除草剂至关重要。本研究利用机载数字彩色红外（CIR）传感器获取的遥感图像，对大豆田杂草侵染密度的空间分布进行了制图和建模。研究了与数据相关的空间定位误差对分辨率要求和成图精度的影响。三波段CIR影像的营养指标与空间杂草密度有较强的相关性。在4.5 ~ 5.3 m/pixel的空间分辨率下，相关性最好，但低于实际数据分辨率。在较低分辨率下观测到的较高建模精度是由与航空成像数据和地面真值数据相关的定位误差造成的。在此分辨率下，利用人工神经网络建立的杂草密度模型的R-2值分别为0.87和0.83。该模型绘制了未用于建模的农田杂草密度的空间分布，R-2值为0.58。
Accurate weed maps are essential for the success of site-specific herbicide application using map-based variable-rate sprayers. In this study, remotely sensed images acquired using an airborne digital color infrared (CIR) sensor were used for mapping and modeling the spatial distribution of weed infestation density within a soybean field. The effect of spatial positioning error associated with data on resolution requirements and mapping accuracy was also studied. Vegetative indices developed from the three-band CIR image showed strong correlation with spatial weed density. The best correlation was observed at the spatial resolutions of 4.5 m/pixel to 5.3 m/pixel which was lower than the actual data resolutions. Higher modeling accuracies observed at lower resolutions were caused by the positioning error associated with both aerial imaging data and ground-truth data. At this resolution, the weed density models developed using an artificial neural network resulted in R-2 values of 0.87 and 0.83. This model mapped the spatial distribution of weed density with an R-2 value of 0.58 for afield not used in modeling.