Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest-Agriculture Mosaics in Temperate and Tropical Landscapes
Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest-Agriculture Mosaics in Temperate and Tropical Landscapes
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DOI：
10.3390/rs11080979
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发表时间：
2019-04
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通讯作者：
Audrey Mercier;J. Betbeder;F. Rumiano;J. Baudry;V. Gond;L. Blanc;C. Bourgoin;Guillaume Cornu;C. Ciudad;M. Marchamalo;R. Poccard-Chapuis;L. Hubert‐Moy
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作者：
Audrey Mercier;J. Betbeder;F. Rumiano;J. Baudry;V. Gond;L. Blanc;C. Bourgoin;Guillaume Cornu;C. Ciudad;M. Marchamalo;R. Poccard-Chapuis;L. Hubert‐Moy
监测森林-农业镶嵌对于了解景观异质性和管理生物多样性至关重要。从遥感图像绘制这些镶嵌图仍然具有挑战性，因为从森林到农业地区的生态梯度使植被特征更加困难。最近的合成孔径雷达Sentinel-1（S-1）和光学Sentinel-2（S-2）时间序列提供了一个很好的机会，监测森林-农业镶嵌，因为它们的高空间和时间分辨率。然而，虽然有几项研究仅使用S-2时间序列的时间分辨率来绘制耕地和/或森林地区的土地覆盖和土地利用图，但尚未为此目的单独调查S-1时间序列。S-1和S-2时间序列的组合使用只评估了一个或几个土地覆盖类。在这项研究中，我们评估了S-1数据单独，S-2数据单独，以及它们的组合使用映射森林农业马赛克在两个研究领域：温带山区景观在Cantabrian范围（西班牙）和热带森林景观Paragominas（巴西）。卫星图像分类使用增量程序的基础上的重要性排名的输入功能。仅用S-2数据获得的分类（平均kappa指数= 0.59-0.83）比仅用S-1数据获得的分类（平均kappa指数= 0.28-0.72）更准确。当结合S-1和2数据时，准确度增加（平均kappa指数= 0.55-0.85）。该方法能够定义的数量和类型的功能，区分土地覆盖类在一个最佳的方式，根据考虑的景观类型。西班牙和巴西研究区的最佳配置仅S-2数据分别包括5个和10个地物，仅S-1数据分别包括10个和20个地物。短波红外和VV和VH偏振分别是S-2和S-1数据的关键特征。此外，该方法能够根据所使用的图像类型定义区分土地覆盖类别的关键时期。例如，在坎塔布连山脉，冬季和夏季是S-2时间序列的关键，而春季和冬季是S-1时间序列的关键。
Monitoring forest–agriculture mosaics is crucial for understanding landscape heterogeneity and managing biodiversity. Mapping these mosaics from remotely sensed imagery remains challenging, since ecological gradients from forested to agricultural areas make characterizing vegetation more difficult. The recent synthetic aperture radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series provide a great opportunity to monitor forest–agriculture mosaics due to their high spatial and temporal resolutions. However, while a few studies have used the temporal resolution of S-2 time series alone to map land cover and land use in cropland and/or forested areas, S-1 time series have not yet been investigated alone for this purpose. The combined use of S-1 & S-2 time series has been assessed for only one or a few land cover classes. In this study, we assessed the potential of S-1 data alone, S-2 data alone, and their combined use for mapping forest–agriculture mosaics over two study areas: a temperate mountainous landscape in the Cantabrian Range (Spain) and a tropical forested landscape in Paragominas (Brazil). Satellite images were classified using an incremental procedure based on an importance rank of the input features. The classifications obtained with S-2 data alone (mean kappa index = 0.59–0.83) were more accurate than those obtained with S-1 data alone (mean kappa index = 0.28–0.72). Accuracy increased when combining S-1 and 2 data (mean kappa index = 0.55–0.85). The method enables defining the number and type of features that discriminate land cover classes in an optimal manner according to the type of landscape considered. The best configuration for the Spanish and Brazilian study areas included 5 and 10 features, respectively, for S-2 data alone and 10 and 20 features, respectively, for S-1 data alone. Short-wave infrared and VV and VH polarizations were key features of S-2 and S-1 data, respectively. In addition, the method enables defining key periods that discriminate land cover classes according to the type of images used. For example, in the Cantabrian Range, winter and summer were key for S-2 time series, while spring and winter were key for S-1 time series.