ABSTRACT

Although estimating forest disturbance area is essential in the context of carbon cycle assessments and for strategic forest planning projects, official statistics are currently not available in several countries. Remotely sensed data are an efficient source of auxiliary information for meeting these needs, and multiple algorithms are commonly used worldwide for this purpose. However, both more accurate maps and precise area estimates are strongly required, especially in Mediterranean ecosystems, and scientific research in this topic area is anything but concluded.

In this study, we present the new Three Indices Three Dimensions (3I3D) algorithm for the automated prediction of forest disturbances using statistical analyses of Sentinel-2 data. We tested 3I3D in Tuscany, Italy, for the year 2016, and we compared the results to those obtained using the Global Forest Change Map (GFC), LandTrendr (LT), and the Two Thresholds Method (TTM). The 3I3D map was the most accurate (omissions = 27%, commissions = 30%) followed by TTM (omissions = 35%, commissions = 39%), LT (omissions = 41%, commissions = 43%) and lastly GFC with slightly fewer omissions than LT (39%) but with many more commissions (69%). We also presented a probability sampling framework to estimate the forest harvested area using a model-assisted estimator that can be used at an operational level to produce large-scale statistics. 3I3D and TTM produced the smallest standard errors of the area estimates (8%) followed by LT (13%) and GFC (17%).

摘要

尽管在碳循环评估和森林战略规划项目中，估计森林扰动面积至关重要，但目前尚无几个国家提供官方统计数据。遥感数据是满足这些需求的有效辅助信息来源，为此，全世界普遍使用多种算法。但是，强烈需要更精确的地图和准确的面积估计，尤其是在地中海生态系统中，在该主题领域的科学研究几乎没有结论。

在这项研究中，我们提出了使用Sentinel-2数据的统计分析来自动预测森林扰动的新的三个指标三维（3I3D）算法。我们在2016年在意大利的托斯卡纳测试了3I3D，并将结果与​​使用全球森林变化图（GFC），LandTrendr（LT）和两个阈值方法（TTM）获得的结果进行了比较。3I3D地图是最准确的（遗漏 = 27％，佣金 = 30％），其次是TTM（遗漏 = 35％，佣金 = 39％），LT（遗漏 = 41％，佣金 = 43％），最后是GFC更少的遗漏超过LT（39％），但更多的佣金（69％）。我们还提出了一种概率采样框架，可以使用模型辅助估计器来估计森林砍伐面积，该估计器可以在操作级别上用于生成大规模统计数据。3I3D和TTM产生的面积估计值的标准误差最小（8％），其次是LT（13％）和GFC（17％）。