Forest canopy height variation in relation to topography and forest types in central Japan with LiDAR
Forest canopy height variation in relation to topography and forest types in central Japan with LiDAR
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DOI：
10.1016/j.foreco.2021.119792
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发表时间：
2022-01
影响因子：
3.7
通讯作者：
Mohammad Farhadur Rahman;Y. Onoda;K. Kitajima
中科院分区：
文献类型：
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作者：
Mohammad Farhadur Rahman;Y. Onoda;K. Kitajima
最大冠层高度的空间变化是地上生物量和生产力等许多生态系统过程的重要指标。随着遥感技术的快速发展，如机载激光扫描（ALS），我们现在可以测量冠层高度，树木密度和冠层间隙从局部到大尺度。但这些数据尚未得到充分利用，以更好地了解冠层高度的变化，精细的地形异质性和附近的条件，在景观水平（>100平方公里）。在这项研究中，我们分析了ALS数据，以提取空间变化的冠层高度和地形特征的森林占主导地位的面积为230平方公里的京都，日本。研究区域的海拔范围从70米到1000米，包括四种森林类型，即：雪松或柏树人工林（PL）、天然万年青针叶林（ENF）、落叶阔叶林（DBF）和万年青阔叶林（EBF）。PL的平均冠层高度大于ENF，两者均高于DBF和EBF。各林型冠层高度与海拔高度均呈驼峰状关系，峰值出现在中海拔（461-560 m）。凹谷的冠层高度比凸坡和凸脊高5-10 m。树木较高，在更大的距离，从林隙和较低的树木密度在附近。当地的树木密度，距离最近的林窗，地形曲率是最显着的预测最佳拟合模型（随机森林：R2= 0.41）。在这项研究中开发的分析方法提供了一个有用的工具，规划适当的土地利用和保持一个健康的森林站在区域尺度上。
Spatial variations of maximum canopy height are key indicators of many ecosystem processes such as above-ground biomass and productivity. With rapid advances in remote sensing techniques, such as airborne laser scanning (ALS), we can now measure canopy height, tree density, and canopy gaps from local to large scales. But these data are yet to be fully exploited for a better understanding of canopy height variations in relation to fine topographical heterogeneities and neighborhood conditions at the landscape level (>100 km2). In this study, we analyzed ALS data to extract spatial variations in canopy height and topographic features for a forest-dominated area of 230 km2in Kyoto, Japan. The study area spanned an elevational range from 70 to 1000 m a.s.l and included four forest types, i.e., cedar or cypress plantations (PL), natural evergreen needle leaf forests (ENF), deciduous broadleaved forests (DBF), and evergreen broadleaved forests (EBF). PL had a greater mean canopy height than ENF, both of which were taller than DBF and EBF. Canopy height exhibited a hump-shaped relationship with elevation with the peak value at mid-altitude (461–560 m) in all forest types. Canopy height was greater by 5-10 m in the concave valleys than on the convex slopes and ridges. Trees were taller at greater distances from forest gaps and with lower tree density in the neighborhood. Local tree density, distance from the nearest canopy gap, and topographic curvatures were the most significant predictors in the best-fitted model (Random Forest: R2= 0.41). The analytical approach developed in this study provides a useful tool for planning proper land use and maintaining a healthy forest stand at the regional scale.