Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing.
Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing.
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DOI：
10.1016/j.isprsjprs.2015.09.007
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发表时间：
2015-11
期刊：
影响因子：
--
通讯作者：
Benton TG
中科院分区：
文献类型：
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作者：
O'Connell J;Bradter U;Benton TG
农业景观中的自然和半自然栖息地可能面临越来越大的压力，到2050年全球人口将超过90亿。这些非作物生境主要由树木、树篱和草缘组成，其数量、质量和空间配置对各种生态系统服务的提供和可持续性具有重大影响。在这项研究中，高空间分辨率（0.5米）彩色红外航空摄影（CIR）被用于基于对象的图像分析的分类非作物栖息地在英格兰东南部的10,029公顷的地区。使用4级和9级方案设计了三种分类方案。使用机器学习算法随机森林（RF）将每个分类场景使用的变量数量减少了25.5% ± 2.7%。来自4类层次结构的投票比例可用于9类场景，其中所有情况下排名最高的变量。这种方法允许错误分类的父对象在较低级别被正确分类。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显示平均内部袋外误差和外部验证之间无显著差异。作为这个数据的效用的一个例子，我们评估了栖息地的适合性下降的农田鸟类，黄顶锤（Emberiza香茅），这需要与草缘的灌木树篱。我们发现，0.22%的绿篱是在200米的边缘，面积>183.31平方米。从这种分析的结果可以形成一个关键的信息来源，在环境和政策层面的景观优化粮食生产和生态系统服务的可持续性。
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.