Assessing forest disturbance and recovery with spatial and temporal structural morphology

激光雷达的迅速采用和可用性使研究重点重新集中在调查森林的垂直结构上，以获得关于改善森林管理的特定林分条件的关键见解。此外，越来越多的卫星图像数据的时间记录正在促进对森林变化和恢复的时间序列分析的探索。该方案旨在融合这些概念，并通过包括垂直结构或时间，将景观度量的广泛基础工作，特别是结构分割扩展到第三维度。时空结果将使我们能够了解森林的三维空间和结构恢复轨迹，然后向后预测森林干扰的日期，补偿丢失或污染的卫星场景，更具体地说，延伸到1972年以前可用的商业卫星图像时期。北方森林形成了最大的陆地全球生物群系，是由生物和非生物元素组成的复杂环境，这些生物和非生物元素跨尺度相互作用，将化学、生物、气候、物理和人类过程相互联系起来。这些森林容易受到周期性扰动，主要是由火灾和采伐影响其结构。直接估算树高、体积、物种、林下植被、干扰、地形和栖息地的尝试，正在推动着对光谱冠层特征与林分指标简单关联的改进。新的结构轨迹将改善扰动检测、制图、分类和与北方森林景观扰动过程的相关性，或光穿透、湿度分布和气流系统的建模。虽然描述森林景观格局特征的景观指标已经存在、发展或实施了几十年，但它们的使用和解释充满了挑战。度量标准通常限于2D，很少扩展到2.5D特征，并且在尝试统计比较结果时造成困难。在提供基于相对结构关系的景观分割方面，形态空间格局分析已经发展成为一种比景观度量学更直观、更切实的选择。我们将开发明确的逻辑和相应的软件来扩展形态分割，成为真正的景观3D表征。将对方法进行敏感性分析，并用于比较诸如火灾、采伐和森林道路建设活动等景观过程的影响和恢复，并确定它们之间的关键结构差异；改进将有利于评估数据库的准确性、碳核算，并为森林管理规划提供信息。HQP将接受STEM方面的广泛培训，因为我们努力提取存在于3D数据中的大量信息，而不仅仅是单个图像中可能包含的信息。

The rapid adoption and availability of LiDAR has refocused research attention on investigating the vertical structure of forests to gain critical insight about specific stand conditions for improved forest management. Additionally, a growing temporal record of satellite image data is stimulating the exploration of time-series analyses of forest change and recovery. This proposal seeks to fuse these concepts and extend the broad foundational work of landscape metrics, and specifically structural segmentation, into a third-dimension by including either vertical structure or time. The spatial-temporal results will allow us to learn the three-dimensional spatial and structural recovery trajectories of forests and to then backwards-project the dates of forest disturbances, compensating for missing or contaminated satellite scenes and more specifically to extend into the pre-1972 period of available commercial satellite imagery. Boreal forests form the largest terrestrial global biome and are complex environments comprising biotic and abiotic elements that interact across scales to interconnect chemical, biological, climatic, physical, and human processes. These forests are prone to cyclic disturbances, dominated by fire and harvesting that shape their structures. Attempts to directly estimate tree heights, volume, species, understory, disturbances, terrain, and habitats are motivating improvements to simply correlating spectral canopy signatures with stand metrics. The new structural trajectories will improve disturbance detection, mapping, classification, and correlation with landscape disturbance processes in boreal forests or the modelling of light penetration, moisture distribution, and airflow systems. While landscape metrics for characterizing forest landscape patterns have existed for, and been developed or implemented for decades, their use and interpretation are fraught with challenges. Metrics are generally constrained to 2D, and rarely extend to 2.5D characterizations, and pose difficulties when attempting to statistically compare results. Morphological spatial pattern analysis has evolved to provide a more intuitive and tangible alternative to landscape metrics in providing landscape segmentation based on relative structural relationships. We will develop the explicit logic and corresponding software to extend morphological segmentation to become a true 3D characterization of landscapes. Methods will be subject a sensitivity analysis and be used to compare the effects and recovery of landscape processes such as fire, harvesting, and forest road building activities, and to identify critical structural differences among them; improvements will benefit appraisals of database accuracy, carbon accounting, and inform forest management planning. HQP will receive extensive training in STEM as we strive to extract the vast amount of information that exists in 3D data, extending beyond what might be contained in a single image.