Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state‐space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat‐derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra‐annual phenological variation to a model based on Landsat‐derived normalized difference vegetation index (NDVI). The best‐performing model accounted for inter‐ and intra‐annual noise in spectral reflectance and translated NDVI to canopy height via Landsat–lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state‐space models to quantify ecological dynamics from time series of space‐borne imagery. State‐space models also provide a statistical approach well‐suited to fusing high‐resolution data, such as airborne lidar, with lower‐resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.

中文翻译：

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在Landsat时间序列不确定的情况下，以景观尺度监测热带森林演替。

在景观尺度上预测森林演替速度将有助于全球努力，以恢复数百万公顷退化土地的树木覆盖率。虽然光学卫星遥感技术可以检测区域性土地覆被变化，但量化森林结构变化仍具有挑战性。我们开发了一个状态空间建模框架，该框架使用Landsat卫星数据来估计热带景观中站点之间自然再生速率的变化。我们的模型通过将Landsat衍生的光谱反射率中的测量误差与与连续变异性相关的过程误差区分开来来工作。我们应用了建模框架对大约2001年至2015年巴拿马西南部10个自然更新地点之间的森林演替速率进行了排名，并测试了不同的测量误差模型如何影响预测准确性，生态推断，以及站点之间的继承率排名。通过将年内物候变化添加到基于Landsat的归一化差异植被指数（NDVI）的模型中，我们实现了最大的预测准确性提高。表现最佳的模型考虑了光谱反射率的年际和年内噪声，并通过Landsat-lidar融合将NDVI转换为冠层高度。将森林演替作为树冠高度而不是NDVI的函数进行建模，还可以更准确地估计早期演替过程中的森林状况，包括对站点间演替率的等级顺序更有信心。这些结果建立了状态空间模型的可行性，该模型可以从星载图像的时间序列中量化生态动力学。状态空间模型还提供了一种统计方法，非常适合将高分辨率数据（如机载激光雷达）与低分辨率数据（如Landsat卫星记录）融合在一起，从而提供更好的时空覆盖范围。使用卫星图像监测森林演替可能在实现全球恢复目标方面发挥关键作用，包括确定将以最少干预恢复树木覆盖的地点。