Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi
Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi
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DOI：
10.3390/land7040116
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发表时间：
2018-10
期刊：
影响因子：
3.9
通讯作者：
M. Hirschmugl;Carina Sobe;Janik Deutscher;M. Schardt
中科院分区：
文献类型：
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作者：
M. Hirschmugl;Carina Sobe;Janik Deutscher;M. Schardt
卫星数据供应方面的最新发展使热带森林监测得以从两个方面扩展：(1)密集时间序列促进了绘制和监测干燥热带森林的新方法的发展；(2)光学数据和合成孔径雷达（SAR）数据的结合减少了频繁云量造成的问题，并提供了额外的信息。本文通过分析在马拉维使用光学（Sentinel-2）和SAR （Sentinel-1）时间序列数据进行森林和土地覆盖制图的可能性，涵盖了这两个问题，用于REDD+（减少毁林和森林退化排放）应用。挑战在于如何组合这些不同的数据源，以便最佳地利用它们的互补信息内容。我们比较了使用不同输入数据集以及两种数据组合方法的结果。结果表明，时间序列的光学数据比单时间序列的光学数据具有更好的结果（森林制图总体精度+8%）。光学和SAR数据的结合带来了进一步的改进：土地覆盖的总体精度提高了5%，森林制图的总体精度提高了1.5%。对于所测试的组合方法，基于数据的组合比基于结果的贝叶斯组合性能稍好（总体精度+1%）。
Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.