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Agrarian landscapes linear features detection from LiDAR: application to artificial drainage networks

机译:LiDAR的农业景观线性特征检测:在人工排水网络中的应用

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摘要

Linear features of agrarian landscapes are the anthropogenic elements such as hedgerows, ditches, and bench terraces that strongly impact agrarian areas' environmental behaviour, especially the ecological and hydrological areas. The need to map these linear elements for environmental impact assessments of agrarian areas is thus increasing since these maps limit the developments of spatial indicators and spatially distributed modelling. Until now, no generic remote sensing methodology has been proposed for such mapping purposes. This research was designed to assess the use of airborne LiDAR data for agrarian landscape linear features mapping. We proposed a methodology that uses LiDAR data in three steps. We first estimated elevation profiles from LiDAR points on a set of pre-located sites. We secondly performed profile shape description with wavelet transform or a watershed algorithm. Finally, we classified the profiles using classification trees with predictors coming from shape analysis. Methodology accuracies were calculated for a ditch network detection problem in a Mediterranean vineyard landscape. LiDAR Toposys data and field survey data for ditch location were collected in June 2002. As ditches are always located on field boundary lattices, elevation profiles were only computed on field boundary sites. Methodologies, using wavelets or the watershed algorithm, gave similar accuracies. Overall accuracy is about 70% with a high ditch omission rate (50%) but low commission rate (15%). The omissions conform to those obtained when performing visual classification of profiles. This high omission rate in ditch detection is therefore due to LiDAR data, not methods. Dense vegetation over ditches during the summertime and the specific LiDAR points spatial sampling design explain these omissions. However, the proposed methodology, especially using wavelets transform, looks transposable for the automatic detection or characterization of other agrarian linear features.
机译:耕地景观的线性特征是人为因素,例如树篱,沟渠和梯田,对耕地地区的环境行为,特别是生态和水文地区产生重大影响。由于这些地图限制了空间指标和空间分布建模的发展,因此绘制这些线性元素以进行农业地区环境影响评估的需求也因此增加。迄今为止,尚未提出用于这种映射目的的通用遥感方法。这项研究旨在评估机载LiDAR数据在农业景观线性特征映射中的使用。我们提出了一种分三步使用LiDAR数据的方法。我们首先从一组预先定位的地点的LiDAR点估计了海拔剖面。其次,我们使用小波变换或分水岭算法进行轮廓形状描述。最后,我们使用分类树对轮廓进行分类,并使用来自形状分析的预测变量。计算了地中海葡萄园景观中沟渠网检测问题的方法学准确性。 2002年6月收集了LiDAR Toposys数据和用于沟渠位置的野外调查数据。由于沟渠始终位于野外边界晶格上,因此仅在野外边界位置计算高程剖面。使用小波或分水岭算法的方法论也给出了类似的准确性。沟漏率高(50%)但佣金率低(15%)时,总体精度约为70%。省略符合对外观进行视觉分类时获得的省略。因此,沟渠检测中的高遗漏率是由于LiDAR数据而不是方法引起的。在夏季,沟渠上的植被茂密,特定的LiDAR点空间采样设计可以解释这些遗漏。但是,所提出的方法,尤其是使用小波变换的方法,似乎可以自动转换或表征其他农业线性特征。

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