ABSTRACT This work assesses the use of aerial imagery for the vegetation cover characterization in cork oak woodlands. The study was conducted in a cork oak woodland in central Portugal during the summer of 2017. Two supervised classification methods, pixel-based and object-based image analysis (OBIA), were tested using a high spatial resolution image mosaic. Images were captured by an unmanned aerial vehicle (UAV) equipped with a red, green, blue (RGB) camera. Four different vegetation covers were distinguished: cork oak, shrubs, grass and other (bare soil and tree shadow). Results have been compared with field data obtained by the point-intercept (PI) method. Data comparison reveals the reliability of aerial imagery classification methods in cork oak woodlands. Results show that cork oak was accurately classified at a level of 82.7% with pixel-based method and 79.5% with OBIA . 96.7% of shrubs were identified by OBIA, whereas there was an overestimation of 21.7% with pixel approach. Grass presents an overestimation of 22.7% with OBIA and 12.0% with pixel-based method. Limitations rise from using only spectral information in the visible range. Thus, further research with the use of additional bands (vegetation indices or height information) could result in better land-cover type classification.

中文翻译：

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栓皮栎林地覆被类型分类：无人机遥感影像与实地调查的比较

摘要 这项工作评估了航空影像在软木橡树林地植被覆盖特征描述中的使用。该研究于 2017 年夏季在葡萄牙中部的软木橡树林地进行。 使用高空间分辨率图像镶嵌测试了两种监督分类方法，即基于像素和基于对象的图像分析 (OBIA)。图像是由配备红、绿、蓝 (RGB) 相机的无人机 (UAV) 捕获的。区分了四种不同的植被覆盖：栓皮栎、灌木、草和其他（裸土和树影）。结果已与通过点截距 (PI) 方法获得的现场数据进行了比较。数据比较揭示了栓皮栎林地航空影像分类方法的可靠性。结果表明，软木橡木被准确分类为 82 级。7% 使用基于像素的方法，79.5% 使用 OBIA 。96.7% 的灌木被 OBIA 识别，而像素方法高估了 21.7%。Grass 使用 OBIA 高估了 22.7%，使用基于像素的方法高估了 12.0%。仅使用可见光范围内的光谱信息会产生局限性。因此，使用附加波段（植被指数或高度信息）的进一步研究可能会导致更好的土地覆盖类型分类。