A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia
A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia
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DOI：
10.1002/rse2.102
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发表时间：
2019-09-01
影响因子：
5.5
通讯作者：
Pettorelli, Nathalie
中科院分区：
文献类型：
--
作者：
Crowson, Merry;Warren-Thomas, Eleanor;Pettorelli, Nathalie
东南亚泥炭沼泽森林的大面积丧失及其对当地和全球环境的负面影响，导致该地区泥炭恢复的兴趣日益增加。卫星遥感有可能提供有关大面积泥炭沼泽森林损失的最新信息，并支持明确的空间保护和恢复规划。在这方面，光学和雷达遥感数据的融合可能特别有价值，因为大多数泥炭沼泽森林位于云层覆盖率高的地区，这限制了光学数据的使用。雷达数据可以“看穿”云层，但迄今为止的经验表明，它不能很好地区分某些类型的土地覆盖。存在各种融合方法，但关于它们如何比较的信息很少。为了评估这一未开发的潜力，我们比较了三种不同的分类方法与哨兵-1和哨兵-2图像映射的剩余分布的泥炭沼泽森林周围地区双溪布鲁保护林，苏门答腊岛，印度尼西亚。结果表明，数据融合提高了整体精度的三种方法之一，相比，仅使用光学数据。当数据融合与使用原始像素值的基于像素的分类一起使用时，总体准确度增加了一个小的但在统计上显著的量。在使用主要组成部分的基于对象的分类或基于像素的分类的情况下，数据融合没有益处。这表明光学数据仍然是该区域土地覆盖物制图的主要信息来源。根据我们的研究结果，我们提供了方法上的建议，以帮助那些参与泥炭地恢复利用大数据的潜力。
The loss of huge areas of peat swamp forest in Southeast Asia and the resulting negative environmental effects, both local and global, have led to an increasing interest in peat restoration in the region. Satellite remote sensing offers the potential to provide up-to-date information on peat swamp forest loss across large areas, and support spatial explicit conservation and restoration planning. Fusion of optical and radar remote sensing data may be particularly valuable in this context, as most peat swamp forests are in areas with high cloud cover, which limits the use of optical data. Radar data can 'see through' cloud, but experience so far has shown that it doesn't discriminate well between certain types of land cover. Various approaches to fusion exist, but there is little information on how they compare. To assess this untapped potential, we compare three different classification methods with Sentinel-1 and Sentinel-2 images to map the remnant distribution of peat swamp forest in the area surrounding Sungai Buluh Protection Forest, Sumatra, Indonesia. Results show that data fusion increases overall accuracy in one of the three methods, compared to the use of optical data only. When data fusion was used with the pixel-based classification using the original pixel values, overall accuracy increased by a small, but statistically significant amount. Data fusion was not beneficial in the case of object-based classification or pixel-based classification using principal components. This indicates optical data are still the main source of information for land cover mapping in the region. Based on our findings, we provide methodological recommendations to help those involved in peatland restoration capitalize on the potential of big data.