The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region
The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region
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DOI：
10.1111/2041-210x.13836
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发表时间：
2022-03-16
影响因子：
6.6
通讯作者：
Brando，Paulo
中科院分区：
文献类型：
--
作者：
Valle，Denis;Silva，Carlos Alberto;Brando，Paulo
激光雷达数据越来越多地被用来提供森林垂直剖面的详细特征。这一特征使人们能够对环境驱动因素和人为干扰对森林结构的影响以及森林结构如何影响重要的生态系统功能和服务产生新的见解。由于LiDAR数据的高维性，从LiDAR数据中提取森林结构及其时间变化的信息是具有挑战性的，我们展示了应用于LiDAR数据的潜在Dirichlet分配模型(LidarLDA)如何用于识别森林结构类型，以及这些森林类型的相对多度如何在整个景观中变化。适用于该模型的代码可通过GitHub中的开源包LidarLDA获得。我们用模拟数据和巴西亚马逊地区大规模火灾实验的数据说明了LidarLDA的使用，并使用模拟数据演示了LidarLDA准确识别森林类型的数量以及它们的空间分布和吸收概率。对于经验数据，我们发现LidarLDA既检测到了森林结构的景观格局，又基于实验火点发现了火灾和森林破碎化对森林结构的强烈交互作用。更具体地说，LidarLDA揭示，靠近森林边缘加剧了火灾的影响，被烧毁的森林在至少7年内仍与未被烧毁的地区在结构上不同，即使只被烧毁一次。重要的是，LidarLDA对森林的3D结构产生了洞察力，这是使用更多的标准方法无法获得的，这些方法只关注树冠顶部的信息(例如，基于LiDAR数据的树冠高度模型)。通过能够绘制森林结构及其时间变化的地图，我们相信LidarLDA将在生态研究领域具有广泛的实用价值。
LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data.We show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open‐sourcerpackage LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large‐scale fire experiment in the Brazilian Amazon region.Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape‐level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top‐of‐the‐canopy information (e.g. canopy height models based on LiDAR data).By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.