Remotely sensed tree canopy cover-based indicators for monitoring global sustainability and environmental initiatives
Remotely sensed tree canopy cover-based indicators for monitoring global sustainability and environmental initiatives
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DOI：
10.1088/1748-9326/abe5d9
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发表时间：
2021-04-01
影响因子：
6.7
通讯作者：
Nakamura, Shogo
中科院分区：
文献类型：
--
作者：
Estoque, Ronald C.;Johnson, Brian A.;Nakamura, Shogo
随着全球环境变化、可持续性和生物多样性保护的挑战日益严峻，对世界剩余森林的监测变得比以往任何时候都更加重要。如今，地球观测技术，特别是遥感技术，处于全球森林覆盖监测的前沿。鉴于目前对森林概念的理解，冠层覆盖阈值用于根据遥感图像绘制森林覆盖图，并生成分类数据产品，例如森林/非森林 (F/NF) 地图。然而，多时相分类地图产品具有重要的局限性，因为它们不足以代表森林景观的实际状况以及阈值效应导致的森林覆盖变化的轨迹。在这里，我们研究了使用遥感树冠覆盖 (TCC) 数据集（连续数据产品）来补充森林覆盖监测的 F/NF 地图的潜力。我们使用这两种类型的数据产品开发了森林覆盖监测的概念分析框架，并将其应用于东南亚的森林。我们得出的结论是，TCC 数据集和从中得出的统计数据可用于补充分类 F/NF 地图提供的信息。基于TCC的指标（即损失、收益和净变化）不仅可以帮助监测森林砍伐，还可以帮助监测森林退化和森林覆盖增加，所有这些都与2030年可持续发展议程和其他全球森林覆盖监测相关举措高度相关。我们建议未来的研究应重点关注 TCC 数据集的生产、应用和评估，以增进目前对这些产品如何准确地捕捉森林景观在空间和时间上的变化的理解。
With the intensifying challenges of global environmental change, sustainability, and biodiversity conservation, the monitoring of the world's remaining forests has become more important than ever. Today, Earth observation technologies, particularly remote sensing, are at the forefront of forest cover monitoring worldwide. Given the current conceptual understanding of what a forest is, canopy cover threshold values are used to map forest cover from remote sensing imagery and produce categorical data products such as forest/non-forest (F/NF) maps. However, multi-temporal categorical map products have important limitations because they inadequately represent the actual status of forest landscapes and the trajectories of forest cover changes as a result of the thresholding effect. Here, we examined the potential of using remotely sensed tree canopy cover (TCC) datasets, which are continuous data products, to complement F/NF maps for forest cover monitoring. We developed a conceptual analytical framework for forest cover monitoring using both types of data products and applied it to the forests of Southeast Asia. We conclude that TCC datasets and the statistics derived from them can be used to complement the information provided by categorical F/NF maps. TCC-based indicators (i.e. losses, gains, and net changes) can help in monitoring not only deforestation but also forest degradation and forest cover enhancement, all of which are highly relevant to the 2030 Agenda for Sustainable Development and other global forest cover monitoring-related initiatives. We recommend that future research should focus on the production, application, and evaluation of TCC datasets to advance the current understanding of how accurately these products can capture changes in forest landscapes across space and time.