Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches
Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches
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DOI：
10.1016/j.rse.2014.05.018
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发表时间：
2014-09-01
影响因子：
13.5
通讯作者：
Cawkwell, Fiona
中科院分区：
文献类型：
--
作者：
Barrett, Brian;Nitze, Ingmar;Cawkwell, Fiona
准确的草原清查对于碳动态研究、生物多样性保护和农业管理十分重要。对于有持续云层覆盖的区域，使用多时合成孔径雷达数据为生成最新的草原清单提供了一个有吸引力的解决办法。考虑到哨兵-1号和高级陆地观测卫星-2号等即将进行的飞行任务将提供的数据，这一点甚至更有吸引力。在这项研究中，三个机器学习算法的性能;随机森林（RE），支持向量机（SVM）和相对未充分使用的极端随机树（ERT）进行评估，区分草地类型超过两个大的异构领域的爱尔兰使用多时相，多传感器雷达和辅助空间数据集。详细的精度评估显示了三种算法对不同类型草地进行分类的有效性。单一频率分类的总体准确度>= 88.7%（kappa系数为0.87），组合频率分类的最大准确度为97.9%（kappa系数为0.98）。对于大多数数据集，ERT分类器优于SVM和RE。(C)2014爱思唯尔公司All rights reserved.
Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RE), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies >= 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RE. (C) 2014 Elsevier Inc. All rights reserved.