Monitor Forest height growth using Light Detection and Ranging (LiDAR) canopy height models from 2005 to 2018 at the Petawawa Research Forest

Monitoring the growth of trees is important for sustainable forest management. The traditional method of monitoring forest growth at a broad level is timber cruising by humans, while modern remote sensing technology, especially Lidar, is usually used for monitoring at the single-tree level or stand-level. This paper uses airborne Light Detection and Ranging (airborne LiDAR, i.e. airborne laser scanning (ALS)) data from 2005, 2012, 2016, and 2018 and forest survey data in 2007 from the Petawawa research forest (Ontario, Canada) to monitor the tree height growth through a time series and build relationships between stand ages and dominant tree heights (DHs). On the area covered by all the data obtained, the entire forest can be divided into 4 landcover types (Early Seral, Mid-Seral, Mature and Old Growth) based on stand ages and 3 classification types (Grown, Disturbed, and Misclassified) based on the variation of DHs in the time series. 61% of the forest is grown and only 0.97% is misclassified, which proves this methodology a valid tool for land cover classification. The relationships between stand ages and DH for each landcover type and the whole grown forest are very reliable because their adjusted R2s are greater than 0.88.树木生长监测对于森林可持续经营具有重要意义。传统的大范围森林生长监测方法为人工林木测积调查，而现代遥感技术尤其是激光雷达（Lidar）则多用于单木尺度或林分尺度的监测工作。本研究采用加拿大安大略省佩塔瓦瓦研究林2005、2012、2016、2018年的机载光探测与测距（机载激光雷达（airborne LiDAR），即机载激光扫描（ALS））数据，以及2007年的森林调查数据，通过时间序列开展树高生长监测，并构建林分年龄与优势木高（dominant tree heights, DHs）之间的关联关系。在所有获取数据覆盖的研究区域内，可依据林分年龄将整片森林划分为4种土地覆盖类型（早期演替（Early Seral）、中期演替（Mid-Seral）、成熟林（Mature）与过熟林（Old Growth）），同时基于时间序列下优势木高的变化特征划分为3种分类类型（生长型（Grown）、受干扰型（Disturbed）与误分类型（Misclassified））。其中61%的森林属于生长型，仅0.97%为误分类型，这表明该方法可作为有效的土地覆盖分类工具。各土地覆盖类型以及整体生长型森林的林分年龄与优势木高之间的关联关系均具备较高可靠性，其调整决定系数（adjusted R²）均大于0.88。