Data from: Integrated radar and lidar analysis reveals extensive loss of remaining intact forest on Sumatra 2007–2010

Forests with high above ground biomass (AGB), including those growing on peat swamps, have historically not been thought suitable for biomass mapping and change detection using Synthetic Aperture Radar (SAR). However, by integrating L-band (λ = 0.23 m) SAR with lidar data from the ALOS and ICESat earth-observing satellites respectively, and 56 forest plots, we were able to create a forest biomass and change map for a 10.7 Mha section of eastern Sumatra that still contains high AGB peat swamp forest. Using a time series of SAR data we estimated changes in both forest area and AGB. We estimate that there were 274 ± 68 Tg AGB remaining in natural forest (≥ 20 m height) in the study area in 2007, with this stock reducing by approximately 11.4% over the subsequent 3 years. A total of 137.4 kha of the study area were deforested between 2007 and 2010; an average rate of 3.8% yr−1. The ability to attribute forest loss to different initial biomass values allows for far more effective monitoring and baseline modelling for avoided deforestation projects than traditional, optical-based remote sensing. Furthermore, given SAR's ability to penetrate the smoke and cloud which normally obscure land cover change in this region, SAR-based forest monitoring can be relied on to provide frequent imagery. This study demonstrates that even at L-band, which typically saturates at medium biomass levels (ca. 150 Mg ha−1), it is possible to make reliable estimates of not just the area but the carbon emissions resulting from land use change.

地上生物量（above ground biomass, AGB）较高的森林，包括生长于泥炭沼泽的林分，长期以来被认为不适用于合成孔径雷达（Synthetic Aperture Radar, SAR）开展生物量制图与变化检测研究。然而，本研究分别将L波段（L-band，波长λ=0.23 m）合成孔径雷达数据与ALOS、ICESat对地观测卫星搭载的激光雷达（lidar）数据相结合，并辅以56块森林样地的实测数据，成功为苏门答腊东部一片面积达1070万公顷的区域绘制了森林生物量与变化分布图——该区域目前仍保存有地上生物量较高的泥炭沼泽森林。本研究利用合成孔径雷达时间序列数据，同时估算了森林面积与地上生物量的变化情况。经估算，2007年研究区内树高≥20 m的天然林剩余地上生物量为274±68 太克（teragram, Tg），后续3年间该碳库总量减少约11.4%。2007至2010年间，研究区内总计13.74万公顷森林遭砍伐，年均砍伐速率达3.8%·yr⁻¹。相较于传统的基于光学遥感的监测手段，能够依据初始生物量等级对森林砍伐进行归因的方法，可为减缓毁林项目提供更为高效的监测与基线建模方案。此外，鉴于合成孔径雷达可穿透该区域通常会掩盖土地覆盖变化的烟雾与云层，基于合成孔径雷达的森林监测可稳定提供高频次遥感影像。本研究证实，即便对于通常会在中等生物量水平（约150 吨/公顷（Mg·ha⁻¹））出现信号饱和的L波段，依然可以可靠地估算土地利用变化所引发的碳排放，而非仅能估算森林面积变化。