First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe
First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe
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DOI：
10.3390/rs8030166
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发表时间：
2016-03-01
期刊：
影响因子：
5
通讯作者：
Atzberger, Clement
中科院分区：
文献类型：
--
作者：
Immitzer, Markus;Vuolo, Francesco;Atzberger, Clement
该研究介绍了两项分类工作的初步结果，评估了Sentinel-2（S2）数据在绘制作物类型和树种地图方面的能力。在第一个案例研究中，S2图像被用来绘制下奥地利州的六种夏季作物以及冬季作物/裸露土壤。需要作物类型图来说明具体作物的用水情况和农业统计数据。作物类型信息还有助于作物生长模型参数化，以估计产量，以及利用辐射传输模型反演植被生物物理变量。第二个案例研究旨在绘制德国七种不同的落叶和针叶树种的地图。关于树种分布的详细信息对于森林管理和评估气候变化的潜在影响非常重要。在我们的S2数据评估中，通过将10个S2光谱通道与10和20米像素大小相结合，以10米的空间分辨率制作了作物和树种地图。部署了一个有监督的随机森林分类器（RF），并用适当的地面实况进行训练。在这两个案例研究中，S2数据证实了其制作可靠的土地覆盖图的预期能力。交叉验证的总体准确率范围在65%（树种）和76%（作物类型）之间。这项研究证实，红边和短波红外波段对植被测绘具有很高的价值。此外，蓝色条带在两个研究中心都很重要。近红外的S2波段是最不重要的通道之一。基于对象的图像分析（OBIA）和经典的基于像素的分类取得了相当的结果，主要是农田。由于本研究仅可获得单次数据采集，因此无法评估S2数据的全部潜力。未来，两颗双S2卫星将每五天提供全球覆盖，因此可以同时利用前所未有的高空间分辨率光谱和时间信息。
The study presents the preliminary results of two classification exercises assessing the capabilities of pre-operational (August 2015) Sentinel-2 (S2) data for mapping crop types and tree species. In the first case study, an S2 image was used to map six summer crop species in Lower Austria as well as winter crops/bare soil. Crop type maps are needed to account for crop-specific water use and for agricultural statistics. Crop type information is also useful to parametrize crop growth models for yield estimation, as well as for the retrieval of vegetation biophysical variables using radiative transfer models. The second case study aimed to map seven different deciduous and coniferous tree species in Germany. Detailed information about tree species distribution is important for forest management and to assess potential impacts of climate change. In our S2 data assessment, crop and tree species maps were produced at 10 m spatial resolution by combining the ten S2 spectral channels with 10 and 20 m pixel size. A supervised Random Forest classifier (RF) was deployed and trained with appropriate ground truth. In both case studies, S2 data confirmed its expected capabilities to produce reliable land cover maps. Cross-validated overall accuracies ranged between 65% (tree species) and 76% (crop types). The study confirmed the high value of the red-edge and shortwave infrared (SWIR) bands for vegetation mapping. Also, the blue band was important in both study sites. The S2-bands in the near infrared were amongst the least important channels. The object based image analysis (OBIA) and the classical pixel-based classification achieved comparable results, mainly for the cropland. As only single date acquisitions were available for this study, the full potential of S2 data could not be assessed. In the future, the two twin S2 satellites will offer global coverage every five days and therefore permit to concurrently exploit unprecedented spectral and temporal information with high spatial resolution.