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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）为绘制地球特征提供了一种高效可靠的方法。利用不同时空分辨率的遥感进行作物类型制图在从环境到经济的各个方面都发挥着重要作用。本研究的主要目的是评估光学数据在基于超高空间分辨率和高空间分辨率图像的多时相作物类型分类中的意义。为了实现这一目标，2016年4月至7月期间，在科勒维尔（英国）上空获得了来自WorldView-3和Sentinel-2的三张图像。三个植被指数（VIs）；利用红、绿和近红外光谱带生成归一化差分植被指数、绿色归一化差分植被指数和土壤调节植被指数；然后，使用从实地调查、随机森林（RF）和决策树（DT）分类算法中收集的地面参考数据进行监督分类。通过将分类输出与参考数据进行比较，进行准确性评估。通过结合RF和DT分类算法，估计总体准确率为91%，κ系数为0.90。因此，可以得出结论，即使在时间数据集有限的情况下，也可以有效地将具有不同视觉的超高分辨率和高分辨率图像集成，以生成大规模作物地图。

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