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首页> 外文期刊>The Journal of Agricultural Science >Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK
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Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK

机译：作物型测绘多时间谱指标的分类：英国煤炭煤炭案例研究

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摘要

Remote sensing (RS) offers an efficient and reliable means to map features on Earth. Crop type mapping using RS at various temporal and spatial resolutions plays an important role spanning from environmental to economical. The main objective of the current study was to evaluate the significance of optical data in a multi-temporal crop type classification-based on very high spatial resolution and high spatial resolution imagery. With this aim, three images from WorldView-3 and Sentinel-2 were acquired over Coalville (UK) between April and July 2016. Three vegetation indices (VIs); the normalized difference vegetation index, the green normalized difference vegetation index and soil adjusted vegetation index were generated using red, green and near-infrared spectral bands; then a supervised classification was performed using ground reference data collected from field surveys, Random forest (RF) and decision tree (DT) classification algorithms. Accuracy assessment was undertaken by comparing the classified output with the reference data. An overall accuracy of 91% and κ coefficient of 0·90 were estimated using the combination of RF and DT classification algorithms. Therefore, it can be concluded that integrating very high- and high-resolution imagery with different VIs can be implemented effectively to produce large-scale crop maps even with a limited temporal-dataset.
机译：遥感（RS）为地球上映射特征提供了高效且可靠的手段。在各个时间和空间分辨率下使用卢比的作物类型映射起着从环境到经济学的重要作用。目前研究的主要目的是评估基于非常高空间分辨率和高空间分辨图像的多时间作物类型分类中光学数据的重要性。通过此目的，来自2016年4月至7月至7月的煤炭（英国）获得了来自WorldView-3和Sentinel-2的三张图像。三个植被指数（VI）;使用红色，绿色和近红外光谱带产生归一化差异植被指数，绿色归一化差异植被指数和土壤调整后植被指数;然后使用从现场调查，随机森林（RF）和决策树（DT）分类算法中收集的地参考数据进行了监督分类。通过将分类输出与参考数据进行比较来进行准确性评估。使用RF和DT分类算法的组合估计了91％和κ系数的总精度为0·90。因此，可以得出结论，即使有有限的时间数据集，也可以有效地实现与不同的VIS的非常高和高分辨率的图像集成具有不同的VIS以产生大规模的作物地图。

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