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Mapping temperate forest tree species using dense Sentinel-2 time series
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-10-13 , DOI: 10.1016/j.rse.2021.112743
Jan Hemmerling 1 , Dirk Pflugmacher 1 , Patrick Hostert 1, 2
Affiliation  

Precise information on tree species composition is critical for forest management and conservation, but mapping tree species with satellite data over large areas is still a challenge. Since 2017, Sentinel-2A/B provide multi-spectral time series with global coverage at an unprecedented spatial and temporal resolution. This is a new opportunity for mapping tree species over large areas that has not yet been fully explored. Because of the high spatial and temporal resolution, Sentinel-2 time series improve the characterization of vegetation phenology and canopy structure, parameters that are intrinsically linked to tree species. The objective of this study was to test the utility of a Sentinel-2 time-series based approach for mapping tree species in a temperate forest region in Central Europe. Using stand-wise forest inventory data for single species stands we assess how well main and minor tree species can be mapped, and if the addition of environmental variables and spatial texture metrics improves the classification accuracy. Our time series approach utilizes all available Sentinel-2 observations and an ensemble of radial basis convolution filters to build cloud-free 5-day time series for each spectral band. The time series are then used as input features to classify seventeen tree species.

Our results show the potential of Sentinel-2 time-series based classification, but they also show the challenges associated with mapping a diverse portfolio of tree species. Accuracy of the nine main species, with an area proportion greater than 0.5%, ranged between 98.9% and 66.8%, which is promising for a large area. Adding detailed environmental data and texture metrics to the spectral model only marginally increased the accuracy of a few minor tree species. Overall, the eight minor tree species with area proportions less than 0.5% were most strongly affected by classification errors. Although the absolute mapped area of minor species correlated well with the estimated reference area, the small class areas of minor species lead to high classification errors in relative terms. Mapping minor tree species is challenging for statistical reasons (i.e., class imbalance, small sample size and class variance). Using all available Sentinel-2 data allows building dense time series at high spatial resolution that are mandatory for improved tree species mapping. We were able to show that the spectral time series is the prime explanatory information, even when complementing our analyses with texture information and various environmental data. The results suggest that with the applied data harmonization approach precise regional tree species mapping is feasible.



中文翻译:

使用密集的 Sentinel-2 时间序列绘制温带森林树种图

关于树种组成的精确信息对于森林管理和保护至关重要,但利用卫星数据绘制大面积树种仍然是一个挑战。自 2017 年以来,Sentinel-2A/B 以前所未有的空间和时间分辨率提供全球覆盖的多光谱时间序列。这是绘制尚未完全探索的大面积树种的新机会。由于空间和时间分辨率高,Sentinel-2 时间序列改进了植被物候和冠层结构的特征,这些参数与树种有内在联系。本研究的目的是测试基于 Sentinel-2 时间序列的方法在中欧温带森林地区绘制树种图的实用性。使用单一物种林分的独立森林清单数据,我们评估主要和次要树种的映射程度,以及环境变量和空间纹理度量的添加是否提高了分类精度。我们的时间序列方法利用所有可用的 Sentinel-2 观测值和一组径向基卷积滤波器为每个光谱带构建无云的 5 天时间序列。然后将时间序列用作输入特征来对 17 种树种进行分类。我们的时间序列方法利用所有可用的 Sentinel-2 观测值和一组径向基卷积滤波器来为每个光谱带构建无云的 5 天时间序列。然后将时间序列用作输入特征来对 17 种树种进行分类。我们的时间序列方法利用所有可用的 Sentinel-2 观测值和一组径向基卷积滤波器来为每个光谱带构建无云的 5 天时间序列。然后将时间序列用作输入特征来对 17 种树种进行分类。

我们的结果显示了基于 Sentinel-2 时间序列的分类的潜力,但它们也显示了与映射不同树种组合相关的挑战。面积比例大于0.5%的9个主要物种的准确率在98.9%至66​​.8%之间,具有大面积发展前景。将详细的环境数据和纹理指标添加到光谱模型中只会略微提高一些小树种的准确性。总体而言,面积比例小于 0.5% 的 8 个小树种受分类错误的影响最大。虽然次要物种的绝对映射面积与估计的参考面积相关性很好,但次要物种的小类面积导致相对较高的分类误差。由于统计原因,绘制次要树种具有挑战性(即。例如,类别不平衡、样本量小和类别方差)。使用所有可用的 Sentinel-2 数据可以构建高空间分辨率的密集时间序列,这对于改进树种制图是必不可少的。我们能够证明光谱时间序列是主要的解释信息,即使在用纹理信息和各种环境数据补充我们的分析时也是如此。结果表明,采用应用数据协调方法进行精确的区域树种制图是可行的。即使用纹理信息和各种环境数据补充我们的分析。结果表明,采用应用数据协调方法进行精确的区域树种制图是可行的。即使用纹理信息和各种环境数据补充我们的分析。结果表明,采用应用数据协调方法进行精确的区域树种制图是可行的。

更新日期:2021-10-13
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