首页> 美国卫生研究院文献>Springer Open Choice >Detecting new Buffel grass infestations in Australian arid lands: evaluation of methods using high-resolution multispectral imagery and aerial photography
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Detecting new Buffel grass infestations in Australian arid lands: evaluation of methods using high-resolution multispectral imagery and aerial photography

机译:在澳大利亚干旱地区发现新的Buffel草侵扰:使用高分辨率多光谱图像和航空摄影对方法进行评估

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摘要

We assess the feasibility of using airborne imagery for Buffel grass detection in Australian arid lands and evaluate four commonly used image classification techniques (visual estimate, manual digitisation, unsupervised classification and normalised difference vegetation index (NDVI) thresholding) for their suitability to this purpose. Colour digital aerial photography captured at approximately 5 cm of ground sample distance (GSD) and four-band (visible–near-infrared) multispectral imagery (25 cm GSD) were acquired (14 February 2012) across overlapping subsets of our study site. In the field, Buffel grass projected cover estimates were collected for quadrates (10 m diameter), which were subsequently used to evaluate the four image classification techniques. Buffel grass was found to be widespread throughout our study site; it was particularly prevalent in riparian land systems and alluvial plains. On hill slopes, Buffel grass was often present in depressions, valleys and crevices of rock outcrops, but the spread appeared to be dependent on soil type and vegetation communities. Visual cover estimates performed best (r2 0.39), and pixel-based classifiers (unsupervised classification and NDVI thresholding) performed worst (r2 0.21). Manual digitising consistently underrepresented Buffel grass cover compared with field- and image-based visual cover estimates; we did not find the labours of digitising rewarding. Our recommendation for regional documentation of new infestation of Buffel grass is to acquire ultra-high-resolution aerial photography and have a trained observer score cover against visual standards and use the scored sites to interpolate density across the region.
机译:我们评估了在澳大利亚干旱土地上使用航空影像进行Buffel草检测的可行性,并评估了四种常用的影像分类技术(视觉估计,手动数字化,无监督分类和归一化植被指数(NDVI)阈值化)是否适用于此目的。在我们研究站点的重叠子集中(2012年2月14日),获取了大约5厘米的地面样本距离(GSD)和四波段(可见-近红外)多光谱图像(25厘米GSD)捕获的彩色数字航空摄影。在野外,收集了Buffel草投影覆盖率估计值(直径10 m)的四边形,然后将其用于评估四种图像分类技术。水牛草被发现遍布我们的整个研究地点。它在河岸土地系统和冲积平原尤为普遍。在山坡上,Buffel草经常出现在岩石露头的洼地,山谷和缝隙中,但其扩散似乎取决于土壤类型和植被群落。视觉覆盖估计效果最好(r 2 0.39),而基于像素的分类器(无监督分类和NDVI阈值)表现最差(r 2 0.21)。与基于现场和基于图像的视觉覆盖估计相比,手动数字化始终代表不足的Buffel草覆盖;我们没有找到奖励数字化的工作。对于区域性新发现的Buffel草的建议,我们的建议是获取超高分辨率的航空摄影,并按照视觉标准对训练有素的观察者分数进行掩盖,并使用计分的位置插值整个区域的密度。

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