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首页> 美国卫生研究院文献>Elsevier Sponsored Documents >Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing
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Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing

机译：利用机载遥感对农业景观中的小尺度特征进行广域制图

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Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m2. The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability.
机译：农业景观中的自然和半自然生境可能会承受越来越大的压力，到2050年，全球人口将超过90亿。这些非作物生境主要由树木，树篱和草缘及其数量，质量和空间配置可能会对各种生态系统服务的交付和可持续性产生重大影响。在这项研究中，将高空间分辨率（0.5 m）彩色红外航空摄影（CIR）用于基于对象的图像分析中，以对英格兰东南部10,029公顷的非作物生境进行分类。使用4类和9类方案设计了三种分类方案。机器学习算法随机森林（RF）用于将每个分类方案使用的变量数量减少25.5％±2.7％。来自4类阶层的选票比例适用于9类情景，并且在所有情况下排名最高的变量都可以使用。这种方法允许对错误分类的父对象进行较低级别的正确分类。具有4类投票比例的单个对象层次结构产生了最佳结果（kappa 0.909）。 RF中最佳训练样本量的验证表明，平均内部袋外误差与外部验证之间没有显着差异。作为此数据效用的一个示例，我们评估了下降的农田鸟类Yellowhammer（Emberiza citronella）的栖息地适宜性，该鸟类需要与草缘相关的树篱。我们发现约22％的树篱位于距边缘200 m以内的区域> 183.31 m 2 。此分析的结果可以在环境和政策级别上为食品生产和生态系统服务可持续性的景观优化提供关键信息来源。

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