ABSTRACT

Mapping the structure of forest vegetation with field surveys or high-resolution light detection and ranging (LiDAR) data is costly. We tested whether landscape topography and underlying geology could predict the vegetation density of a 19 km² area of wet eucalypt forest at the Warra Long-Term Ecological Research Supersite, Tasmania, Australia. Using spatial layers for 12 topographic attributes derived from digital terrain models (DTMs) and a geology layer, we predicted the vegetation density of three strata with a high degree of accuracy (validation root mean square error ranged from 9.0% to 13.7%). The DTMs with 30 m resolution provided greater predictive accuracy than DTMs with higher resolution. The importance of different variables depended on spatial resolution and strata. Among the predictor variables, geology generally had the highest predictive importance, followed by solar radiation. Topographic Position Index, aspect, and System for Automated Geoscientific Analyses (SAGA) Wetness Index had moderate importance. This study demonstrates that geological and topographic attributes can provide useful predictions for the density of vegetation layers in a tall wet sclerophyll primary forest. Given the good performance of the model based on 30 m DTM resolution, the predictive power of the models could be tested on a larger geographical area using lower-density LiDAR point clouds combined with medium-resolution satellite data.

摘要

使用实地调查或高分辨率光探测和测距 (LiDAR) 数据绘制森林植被结构图的成本很高。我们测试了景观地形和底层地质是否可以预测 19 km ^{2的植被密度}澳大利亚塔斯马尼亚州 Warra Long-Term Ecological Research Supersite 的湿桉树林区域。使用从数字地形模型 (DTM) 和地质层导出的 12 个地形属性的空间层，我们以较高的准确度预测了三个地层的植被密度（验证均方根误差范围为 9.0% 至 13.7%）。具有 30 m 分辨率的 DTM 提供了比具有更高分辨率的 DTM 更高的预测精度。不同变量的重要性取决于空间分辨率和地层。在预测变量中，地质学通常具有最高的预测重要性，其次是太阳辐射。地形位置指数、方面和自动地球科学分析系统 (SAGA) 湿度指数具有中等重要性。这项研究表明，地质和地形属性可以为高湿硬叶原始林中植被层的密度提供有用的预测。鉴于基于 30 m DTM 分辨率的模型具有良好的性能，模型的预测能力可以使用低密度 LiDAR 点云结合中等分辨率卫星数据在更大的地理区域上进行测试。