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High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

机译：通过机器学习和交叉验证方法将基于Sentinel-2和Landsat 8的多时相数据集相结合的日本高分辨率植被图

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This paper presents an evaluation of the multi-source satellite datasets such as Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) with different spatial and temporal resolutions for nationwide vegetation mapping. The random forests based machine learning and cross-validation approach was applied for evaluating the performance of different datasets. Cross-validation with the rich-feature datasets?￠????with a sample size of 390?￠????showed that the MODIS datasets provided highest classification accuracy (Overall accuracy = 0.80, Kappa coefficient = 0.77) compared with Landsat 8 (Overall accuracy = 0.77, Kappa coefficient = 0.74) and Sentinel-2 (Overall accuracy = 0.66, Kappa coefficient = 0.61) datasets. As a result, temporally rich datasets were found to be crucial for the vegetation physiognomic classification. However, in the case of Landsat 8 or Sentinel-2 datasets, sample size could be increased excessively as around 9800 ground truth points could be prepared within 390 MODIS pixel-sized polygons. The increase in the sample size significantly enhanced the classification using Landsat-8 datasets (Overall accuracy = 0.86, Kappa coefficient = 0.84). However, Sentinel-2 datasets (Overall accuracy = 0.77, Kappa coefficient = 0.74) could not perform as much as the Landsat-8 datasets, possibly because of temporally limited datasets covered by the Sentinel-2 satellites so far. A combination of the Landsat-8 and Sentinel-2 datasets slightly improved the classification (Overall accuracy = 0.89, Kappa coefficient = 0.87) than using the Landsat 8 datasets separately. Regardless of the fact that Landsat 8 and Sentinel-2 datasets have lower temporal resolutions than MODIS datasets, they could enhance the classification of otherwise challenging vegetation physiognomic types due to possibility of training a wider variation of physiognomic types at 30 m resolution. Based on these findings, an up-to-date 30 m resolution vegetation map was generated by using Landsat 8 and Sentinel-2 datasets, which showed better accuracy than the existing map in Japan.
机译：本文介绍了对具有不同时空分辨率的Sentinel-2，Landsat-8和中等分辨率成像光谱仪（MODIS）等多源卫星数据集进行全国植被映射的评估。基于随机森林的机器学习和交叉验证方法被用于评估不同数据集的性能。与具有390个样本量的功能丰富的数据集的交叉验证表明，与Landsat相比，MODIS数据集提供了最高的分类准确性（总体准确性= 0.80，Kappa系数= 0.77）。 8个（整体准确度= 0.77，Kappa系数= 0.74）和Sentinel-2（整体准确度= 0.66，Kappa系数= 0.61）数据集。结果，发现时间上丰富的数据集对于植被的地貌分类至关重要。但是，在Landsat 8或Sentinel-2数据集的情况下，由于可以在390个MODIS像素大小的多边形中准备大约9800个地面真点，因此样本量可能会过度增加。样本数量的增加使用Landsat-8数据集显着增强了分类（总体准确度= 0.86，Kappa系数= 0.84）。但是，Sentinel-2数据集（总体准确度= 0.77，Kappa系数= 0.74）的性能不如Landsat-8数据集那么大，这可能是由于迄今为止Sentinel-2卫星所覆盖的时间有限。与分别使用Landsat 8数据集相比，结合使用Landsat-8和Sentinel-2数据集可以稍微改善分类（总体准确度= 0.89，Kappa系数= 0.87）。尽管Landsat 8和Sentinel-2数据集的时间分辨率低于MODIS数据集，但由于可以在30 m的分辨率下训练更广泛的生理学类型，因此它们可以增强原本具有挑战性的植被生理学类型的分类。基于这些发现，使用Landsat 8和Sentinel-2数据集生成了最新的30 m分辨率的植被图，显示出比日本现有地图更好的精度。

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