Griffith K T, Ponette-González A G, Curran L M, Weathers K C
Department of Geography, University of North Texas, 1155 Union Circle #305279, Denton, TX, 76203, USA.
Environ Monit Assess. 2015 May;187(5):270. doi: 10.1007/s10661-015-4486-6. Epub 2015 Apr 18.
Atmospheric inputs to forest ecosystems vary considerably over small spatial scales due to subtle changes in relief and vegetation structure. Relationships between throughfall fluxes (ions that pass through the canopy in water), topographic and canopy characteristics derived from sub-meter resolution light detection and ranging (LiDAR), and field measurements were compared to test the potential utility of LiDAR in empirical models of atmospheric deposition. From October 2012 to May 2013, we measured bulk (primarily wet) deposition and sulfate-S, chloride (Cl(-)), and nitrate-N fluxes beneath eight clusters of Douglas fir trees differing in size and canopy exposure in the Santa Cruz Mountains, California. For all trees sampled, LiDAR data were used to derive canopy surface height, tree height, slope, and canopy curvature, while tree height, diameter (DBH), and leaf area index were measured in the field. Wet season throughfall fluxes to Douglas fir clusters ranged from 1.4 to 3.8 kg S ha(-1), 17-54 kg Cl(-) ha(-1), and 0.2-4 kg N ha(-1). Throughfall S and Cl(-) fluxes were highest under clusters with large trees at topographically exposed sites; net fluxes were 2-18-fold greater underneath exposed/large clusters than all other clusters. LiDAR indices of canopy curvature and height were positively correlated with net sulfate-S fluxes, indicating that small-scale canopy surface features captured by LiDAR influence fog and dry deposition. Although tree diameter was more strongly correlated with net sulfate-S throughfall flux, our data suggest that LiDAR data can be related to empirical measurements of throughfall fluxes to generate robust high-resolution models of atmospheric deposition.
由于地形和植被结构的细微变化,森林生态系统的大气输入在小空间尺度上差异很大。比较了穿透雨通量(水中穿过树冠层的离子)、基于亚米分辨率光探测和测距(LiDAR)得出的地形和树冠特征以及实地测量之间的关系,以测试LiDAR在大气沉降经验模型中的潜在效用。2012年10月至2013年5月,我们在加利福尼亚州圣克鲁斯山脉八组不同大小和树冠暴露情况的花旗松树下测量了总(主要是湿)沉降以及硫酸盐-S、氯离子(Cl(-))和硝酸盐-N通量。对于所有采样的树木,利用LiDAR数据得出树冠表面高度、树高、坡度和树冠曲率,同时在实地测量树高、直径(胸径)和叶面积指数。花旗松组在湿季的穿透雨通量范围为1.4至3.8千克硫公顷(-1)、17至54千克氯离子公顷(-1)和0.2至4千克氮公顷(-1)。在地形暴露地点有大树的组下,穿透雨硫和氯离子通量最高;暴露/大树组下方的净通量比所有其他组大2至18倍。树冠曲率和高度的LiDAR指数与净硫酸盐-S通量呈正相关,表明LiDAR捕捉到的小尺度树冠表面特征会影响雾和干沉降。虽然树径与净硫酸盐-S穿透雨通量的相关性更强,但我们的数据表明,LiDAR数据可与穿透雨通量的经验测量相关联,以生成强大的高分辨率大气沉降模型。