Abstract Precision agriculture (PA) constitutes one of the most critical sectors of remote sensing applications that allow obtaining spatial segmentation and within-field variability information from field crops. In the last decade, an increasing source of information is provided by unmanned aerial vehicle (UAVs) platforms, mainly equipped with optical multispectral cameras, to map, monitor, and analyze, temporal and spatial variations of vegetation using ad hoc spectral vegetation indices (VIs). Considering the centimeter or sub-centimeter spatial resolution of UAV imagery, the geographic object-based image analysis (GEOBIA) approach, is becoming prevalent in UAV remote sensing applications. In the present paper, we propose a quick and reliable semi-automatic workflow implemented to process multispectral UAV imagery and aimed at the detection and extraction of olive and citrus trees’ crowns to obtain vigor maps in the framework of PA. We focused our attention on the choice of GEOBIA data input and parameters, taking into consideration its replicability and reliability in the case of heterogeneous tree orchards. The heterogeneity concerns the different tree plantation distances and composition, different crop management (irrigation, pruning, weeding), and different tree age, height, and crown diameters. The proposed GEOBIA workflow was implemented in the eCognition Developer 9.5, coupling the use of multispectral and topographic information surveyed using the Tetracam µ-MCA06 snap multispectral camera at 4 cm of ground sample distance (GSD). Three different study sites in heterogeneous citrus (Bergamot and Clementine) and olive orchards located in the Calabria region (Italy) were provided. Multiresolution segmentation was implemented using spectral and topographic band layers and optimized by applying a trial-and-error approach. The classification step was implemented as process-tree and based on a rule set algorithm, therefore easily adaptable and replicable to other datasets. Decision variables for image classification were spectral vegetation indices (NDVI, SAVI, CVI) and topographic layers (DSM and CHM). Vigor maps were based on NDVI and NDRE and allowed to highlight those areas with low vegetative vigor. The accuracy assessment was based on a per-pixel approach and computed through the F-score (F). The obtained results are promising, considering that the resulting accuracy was high, with F-score ranging from 0.85 to 0.91 for olive and bergamot, respectively. Our proposed workflow, which has proved effective in datasets of different complexity, finds its strong point is the speed of execution and on its repeatability to other different crops with few adjustments. It appears worth of interest to highlights that it requests a working day of two good skilled operators in geomatics and computer image processing, from the on-field data collection to the obtaining of vigor maps.

中文翻译：

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监测异质柑橘和橄榄园的植被活力。从无人机多光谱图像中提取树冠的多尺度对象方法

摘要 精准农业 (PA) 是遥感应用中最关键的领域之一，它允许从大田作物中获取空间分割和田间变异信息。在过去十年中，越来越多的信息来源是由无人机（UAV）平台提供的，主要配备光学多光谱相机，使用临时光谱植被指数（VI）来绘制、监测和分析植被的时间和空间变化。 ）。考虑到无人机图像的厘米或亚厘米空间分辨率，基于地理对象的图像分析 (GEOBIA) 方法在无人机遥感应用中越来越普遍。在本文中，我们提出了一种快速可靠的半自动工作流程，用于处理多光谱无人机图像，旨在检测和提取橄榄树和柑橘树的树冠，以获得 PA 框架中的活力图。我们将注意力集中在 GEOBIA 数据输入和参数的选择上，考虑到其在异类果园情况下的可复制性和可靠性。异质性涉及不同的树木种植距离和组成、不同的作物管理（灌溉、修剪、除草）以及不同的树龄、高度和树冠直径。提议的 GEOBIA 工作流程在 eCognition Developer 9.5 中实施，结合使用 Tetracam µ-MCA06 捕捉多光谱相机在 4 厘米地面采样距离 (GSD) 处测量的多光谱和地形信息。提供了位于卡拉布里亚地区（意大利）的异质柑橘（佛手柑和克莱门汀）和橄榄园的三个不同研究地点。多分辨率分割是使用光谱和地形带层实现的，并通过应用试错法进行优化。分类步骤是作为过程树实现的，并基于规则集算法，因此很容易适应和复制到其他数据集。影像分类的决策变量是光谱植被指数（NDVI、SAVI、CVI）和地形层（DSM 和 CHM）。活力图基于 NDVI 和 NDRE，可以突出那些植物活力低的区域。准确性评估基于每像素方法并通过 F 分数 (F) 计算。获得的结果是有希望的，考虑到由此产生的准确度很高，橄榄和佛手柑的 F 值分别从 0.85 到 0.91 不等。我们提出的工作流程已被证明在不同复杂性的数据集中有效，它发现其强项是执行速度和对其他不同作物的可重复性，只需少量调整。值得强调的是，从现场数据收集到活力图的获取，它需要两个熟练的地理信息学和计算机图像处理操作员一个工作日的时间。