Integrating remotely sensed fires for predicting deforestation for REDD.
Integrating remotely sensed fires for predicting deforestation for REDD.
复制标题
整合遥感火灾来预测 REDD 森林砍伐。
DOI：
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发表时间：
2017
影响因子：
5
通讯作者：
L. Dávalos
中科院分区：
文献类型：
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作者：
D. Armenteras;Cerian Gibbes;J. Anaya;L. Dávalos
火是热带森林管理的一个重要工具，因为它改变了森林的组成、结构和碳收支。联合国减少森林砍伐和退化所致排放计划（REDD+）旨在可持续地管理森林，并保护和增加其碳储量。尽管火灾管理发挥着至关重要的作用，但关于降排+干预措施的决策未能系统地包括火灾。在这里，我们以两种方式解决这一关键的知识差距。首先，我们审查REDD+项目和计划，以评估火灾纳入监测，报告和核查（MRV）系统。其次，我们模拟火灾和森林之间的关系，在哥伦比亚的一个试点网站使用近实时（NRT）火灾监测数据来自中分辨率成像光谱仪（MODIS）。文献综述显示，火灾仍将作为MRV系统的关键组成部分。土地利用变化的空间显式建模表明，随着前一年距离最近火灾的距离增加，森林砍伐的可能性急剧下降（多年模型曲线下面积[AUC] 0.82）。基于该模型的森林砍伐预测比官方的REDD预警系统表现得更好。2013年和2014年的AUC模型为0.81，而2013年预警系统为0.52，2014年为0.68。这表明NRT火灾监测是预测森林砍伐地点的有力工具。应用新的、公开可用的和开放获取的NRT火灾数据应该是早期预警系统的一个基本要素，以检测和防止森林砍伐。我们的研究结果提供了工具，以改善目前的MRV系统，并在哥伦比亚的森林砍伐预警系统。
Fire is an important tool in tropical forest management, as it alters forest composition, structure, and the carbon budget. The United Nations program on Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to sustainably manage forests, as well as to conserve and enhance their carbon stocks. Despite the crucial role of fire management, decision-making on REDD+ interventions fails to systematically include fires. Here, we address this critical knowledge gap in two ways. First, we review REDD+ projects and programs to assess the inclusion of fires in monitoring, reporting, and verification (MRV) systems. Second, we model the relationship between fire and forest for a pilot site in Colombia using near-real-time (NRT) fire monitoring data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). The literature review revealed fire remains to be incorporated as a key component of MRV systems. Spatially explicit modeling of land use change showed the probability of deforestation declined sharply with increasing distance to the nearest fire the preceding year (multi-year model area under the curve [AUC] 0.82). Deforestation predictions based on the model performed better than the official REDD early-warning system. The model AUC for 2013 and 2014 was 0.81, compared to 0.52 for the early-warning system in 2013 and 0.68 in 2014. This demonstrates NRT fire monitoring is a powerful tool to predict sites of forest deforestation. Applying new, publicly available, and open-access NRT fire data should be an essential element of early-warning systems to detect and prevent deforestation. Our results provide tools for improving both the current MRV systems, and the deforestation early-warning system in Colombia.