Assessing forest disturbance and recovery with spatial and temporal structural morphology

通过时空结构形态评估森林干扰和恢复

• 批准号: RGPIN-2021-03645
• 负责人: Remmel, Tarmo
• 金额: $ 1.82万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741849
• 关键词: Assessing; forest; disturbance; recovery; spatial

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.

LiDAR的快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，以获得有关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。该提案旨在融合这些概念，并将景观指标的广泛基础工作，特别是结构分割，扩展到第三维，包括垂直结构或时间。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，然后向后推算森林扰动的日期，弥补缺失或受污染的卫星图像，更具体地说，扩展到1972年以前的可用商业卫星图像时期。 北方森林是全球最大的陆地生物群落，是由生物和非生物元素组成的复杂环境，这些元素在不同尺度上相互作用，使化学、生物、气候、物理和人类过程相互联系。这些森林容易受到周期性干扰，主要是火灾和采伐，塑造了它们的结构。试图直接估计树高，体积，物种，林下，干扰，地形和栖息地的激励改进，简单地关联光谱冠层特征与林分指标。新的结构轨迹将改善干扰检测，绘图，分类，并与景观干扰过程中的北方森林或建模的光穿透，水分分布和气流系统的相关性。 虽然用于描述森林景观格局的景观指标已经存在，并已制定或实施了几十年，但其使用和解释充满了挑战。这些特征通常局限于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。形态空间格局分析已经发展到提供一个更直观和有形的替代景观指标提供景观分割的基础上相对结构关系。 我们将开发显式逻辑和相应的软件，以扩展形态分割，成为一个真正的三维表征景观。方法将进行敏感性分析，并用于比较火灾，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，并告知森林管理规划。HQP将接受STEM方面的广泛培训，因为我们努力提取3D数据中存在的大量信息，这些信息超出了单个图像中可能包含的信息。