Malaysia typically suffers from frequent cloud cover, hindering spatially consistent reporting of deforestation and forest degradation, which limits the accurate reporting of carbon loss and CO2 emissions for reducing emission from deforestation and forest degradation (REDD+) intervention. This study proposed an approach for accurate and consistent measurements of biomass carbon and CO2 emissions using a single L-band synthetic aperture radar (SAR) sensor system. A time-series analysis of aboveground biomass (AGB) using the PALSAR and PALSAR-2 systems addressed a number of critical questions that have not been previously answered. A series of PALSAR and PALSAR-2 mosaics over the years 2007, 2008, 2009, 2010, 2015 and 2016 were used to (i) map the forest cover, (ii) quantify the rate of forest loss, (iii) establish prediction equations for AGB, (iv) quantify the changes of carbon stocks and (v) estimate CO2 emissions (and removal) in the dipterocarps forests of Peninsular Malaysia. This study found that the annual rate of deforestation within inland forests in Peninsular Malaysia was 0.38% year−1 and subsequently caused a carbon loss of approximately 9 million Mg C year−1, which is equal to emissions of 33 million Mg CO2 year−1, within the ten-year observation period. Spatially explicit maps of AGB over the dipterocarps forests in the entire Peninsular Malaysia were produced. The RMSE associated with the AGB estimation was approximately 117 Mg ha−1, which is equal to an error of 29.3% and thus an accuracy of approximately 70.7%. The PALSAR and PALSAR-2 systems offer a great opportunity for providing consistent data acquisition, cloud-free images and wall-to-wall coverage for monitoring since at least the past decade. We recommend the proposed method and findings of this study be considered for MRV in REDD+ implementation in Malaysia.

中文翻译：

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从PALSAR和PALSAR-2极化数据获得的马来西亚香樟林地上生物量的时间序列图。

马来西亚通常遭受频繁的云层覆盖，阻碍了森林砍伐和森林退化的空间一致性报告，这限制了碳损失和二氧化碳排放的准确报告，从而减少了森林砍伐和森林退化（REDD +）干预的排放。这项研究提出了一种使用单个L波段合成孔径雷达（SAR）传感器系统准确，一致地测量生物质碳和CO2排放的方法。使用PALSAR和PALSAR-2系统对地上生物量（AGB）进行时间序列分析，解决了许多以前尚未回答的关键问题。使用2007年，2008年，2009年，2010年，2015年和2016年的一系列PALSAR和PALSAR-2镶嵌图来（i）绘制森林覆盖图，（ii）量化森林流失率，（iii）建立预测方程对于AGB，（iv）量化碳储量的变化，并（v）估算马来西亚半岛双果林的二氧化碳排放量（和清除量）。这项研究发现，马来西亚半岛内陆森林的年森林砍伐率为0.38％（第1年），随后造成大约900万Mg C（第1年）的碳损失，这相当于第1年（33,000,000 Mg）的二氧化碳排放量，在十年的观察期内。制作了整个马来西亚半岛的双果皮森林上AGB的空间图。与AGB估计相关的RMSE约为117 Mg ha-1，这等于29.3％的误差，因此准确度约为70.7％。PALSAR和PALSAR-2系统为提供一致的数据采集，至少从过去十年开始，就一直使用无云图像和墙到墙覆盖进行监控。我们建议将所建议的方法和本研究的结果用于在马来西亚实施REDD +的MRV中。