摘要翻译：
绘制森林AGB（地上生物量）图对于估算与热带森林砍伐相关的碳排放量至关重要。本文提出了一种利用GLAS（地球科学激光高度计系统）星载激光雷达数据生成的修正因子图来克服现有AGB图（Vieilledent的AGB图）在高AGB值时饱和的方法。Vieilledent的马达加斯加AGB地图是用光学图像建立的，参数是从SRTM数字高程模型、气候变量和野外清单中计算出来的。在本研究中，首先利用GLAS激光雷达数据获得了马达加斯加森林地区AGB（GLAS AGB）的空间分布（GLAS足迹地理定位）估计，其密度为0.52足迹/km2。其次，计算了每个GLAS足迹位置上Vieilledent AGB地图上的AGB与GLAS AGB之间的差值，并获得了额外的空间分布校正因子。第三，通过考虑这些附加校正因子的空间结构来执行普通的克里格插值，以提供连续的校正因子图。最后，对现有的和修正因子图进行了总结，对Vieilledent的AGB图进行了改进。结果表明，GLAS数据的集成使Vieilledent的AGB图的精度提高了约7t/ha。通过综合GLAS数据，对AGB估计的RMSE从81t/ha(R2=0.62)降低到74.1t/ha(R2=0.71)。最重要的是，我们表明，使用激光雷达数据的这种方法避免了低估高生物量值（新的最大AGB为650吨/公顷，而第一种方法为550吨/公顷）。
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英文标题：
《Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation
  of Biomass in High Biomass Forested Areas》
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作者：
Mohammad El Hajj (UMR TETIS), Nicolas Baghdadi (UMR TETIS), Ibrahim
  Fayad (UMR TETIS), Ghislain Vieilledent (CIRAD, JRC), Jean-St\'ephane Bailly
  (LISAH), Dinh Ho Tong Minh (UMR TETIS)
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最新提交年份：
2017
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Mapping forest AGB (Above Ground Biomass) is of crucial importance to estimate the carbon emissions associated with tropical deforestation. This study proposes a method to overcome the saturation at high AGB values of existing AGB map (Vieilledent's AGB map) by using a map of correction factors generated from GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data. The Vieilledent's AGB map of Madagascar was established using optical images, with parameters calculated from the SRTM Digital Elevation Model, climatic variables, and field inventories. In the present study, first, GLAS LiDAR data were used to obtain a spatially distributed (GLAS footprints geolocation) estimation of AGB (GLAS AGB) covering Madagascar forested areas, with a density of 0.52 footprint/km 2. Second, the difference between the AGB from the Vieilledent's AGB map and GLAS AGB at each GLAS footprint location was calculated, and additional spatially distributed correction factors were obtained. Third, an ordinary kriging interpolation was thus performed by taking into account the spatial structure of these additional correction factors to provide a continuous correction factor map. Finally, the existing and the correction factor maps were summed to improve the Vieilledent's AGB map. The results showed that the integration of GLAS data improves the precision of Vieilledent's AGB map by approximately 7 t/ha. By integrating GLAS data, the RMSE on AGB estimates decreases from 81 t/ha (R 2 = 0.62) to 74.1 t/ha (R 2 = 0.71). Most importantly, we showed that this approach using LiDAR data avoids underestimating high biomass values (new maximum AGB of 650 t/ha compared to 550 t/ha with the first approach).
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PDF链接：
https://arxiv.org/pdf/1703.03432