ABSTRACT

 Sown biodiverse pastures (SBP) are a pasture system developed in Portugal. Until 2014, farmers were supported in installing and maintaining SBP, but tracking their locations has been lacking. To survey the country, remote sensing tools with machine learning were used. Here, we developed the first algorithm that combines remote sensing data with machine learning algorithms to identify SBP areas. The algorithm combines Landsat-7 and night-light spectral data with terrain and bioclimatic data. Remotely sensed data offer higher spatial resolution compared to bioclimatic data and also cover interannual variability. Gradient-boosted decision trees (XGB) and artificial neural networks (ANN) were the machine learning methods used. The overall classification accuracy, on an independent validation dataset, was 94%, with 82% producer accuracy and 85% user accuracy. The total estimated area of SBP in the Portuguese region of Alentejo region was 1300 km2 in 2013, which is similar to the total known installed area (approximately 1000 km²). The estimated spatial distribution is in accordance with the known distribution at the municipal level. These results are a critical first step towards the future development of remote systems for assessing the state of SBP and for compliance checks of farmer commitments.

摘要

 播种生物多样性牧场（SBP）是葡萄牙开发的牧场系统。直到 2014 年，农民都得到了安装和维护 SBP 的支持，但一直缺乏追踪他们的位置。为了调查该国，使用了具有机器学习功能的遥感工具。在这里，我们开发了第一个将遥感数据与机器学习算法相结合的算法来识别 SBP 区域。该算法将 Landsat-7 和夜间光谱数据与地形和生物气候数据相结合。与生物气候数据相比，遥感数据提供更高的空间分辨率，并且还涵盖年际变化。使用的机器学习方法是梯度增强决策树（XGB）和人工神经网络（ANN）。在独立验证数据集上的总体分类准确度为 94%，生产者准确度为 82%，用户准确度为 85%。2013 年，葡萄牙阿连特茹地区 SBP 总面积估计为 1300 平方公里，与已知总安装面积（约 1000 平方公里）相似。²）。估计的空间分布与市级的已知分布一致。这些结果是未来开发用于评估 SBP 状态和农民承诺合规性检查的远程系统的关键的第一步。