This study demonstrated the usefulness of very long-range terrestrial laser scanning(TLS) for analysis of the spatial distribution of a snowpack, to distances up to 3000 m, one of the longest measurement range reporte...This study demonstrated the usefulness of very long-range terrestrial laser scanning(TLS) for analysis of the spatial distribution of a snowpack, to distances up to 3000 m, one of the longest measurement range reported to date. Snow depth data were collected using a terrestrial laser scanner during 11 periods of snow accumulation and melting,over three snow seasons on a Pyrenean hillslopecharacterized by a large elevational gradient, steep slopes, and avalanche occurrence. The maximum and mean absolute snow depth error found was 0.5-0.6 and 0.2-0.3 m respectively, which may result problematic for areas with a shallow snowpack, but it is sufficiently accurate to determine snow distribution patterns in areas characterized by a thick snowpack. The results indicated that in most cases there was temporal consistency in the spatial distribution of thesnowpack, even in different years. The spatial patterns were particularly similar amongst thesurveys conducted during the period dominated by snow accumulation(generally until end of April), or amongst those conducted during the period dominated by melting processes(generally after mid of April or early May). Simple linear correlation analyses for the 11 survey dates, and the application of Random Forests analysis to two days representative of snow accumulation and melting periods indicated the importance of topography to the snow distribution. The results also highlight that elevation and the Topographic Position index(TPI) were the main variables explaining the snow distribution, especially during periods dominated by melting. The intra-and inter-annual spatial consistency of the snowpack distribution suggests that the geomorphological processes linked to presence/absence of snow cover act in a similar way in the long term, and that these spatial patternscan be easily identifiedthrough several years of adequate monitoring.展开更多
文摘【目的】削度方程可以很好地描述树干直径随树高变化的情况,基于地基激光雷达(terrestrial laser scanner,TLS)的高精度三维点云数据建立准确的削度方程并进行立木材积估算,对活立木尺度的材积估计具有重要意义。【方法】以江苏省黄海海滨国家森林公园杨树人工林为研究对象,获取4块样地的TLS点云数据,通过MATLAB 2020a软件计算点云平坦度和法向量以提取单木主干,采用圆拟合方法进行不同高度处的直径拟合,利用32株样木的数据,选取6种削度模型进行建模,得到杨树树干削度方程最优拟合模型,并进行材积估算。【结果】利用TLS数据提取的胸径能替代实测胸径,其平均误差小于0.90 cm。通过对6种模型的拟合优度检验,Schumacher and Hall模型为该地区杨树削度方程最优拟合模型,模型的决定系数R2=0.984,均方根误差为1.00 cm,相对百分误差为2.79%,平均预估误差为0.271%。利用Schumacher and Hall削度方程最优拟合模型进行活立木材积的估算,经与二元材积方程估计结果进行对比,其相对差异为3.34%,二者在统计上无显著差异。【结论】该方法可以减少地面调查对树木造成的永久性破坏,为人工林的蓄积量调查提供有效的技术支持。
基金CGL2014-52599-P “Estudio del manto de nieve enla montana espanola y su respuesta a la variabilidad y cambio climatico” funded by the Spanish Ministry of Economy and CompetitivenessEl glaciar de Monte Perdido: estudio de su dinámica actual y procesos criosféricos asociados como indicadores de procesos de cambio global” (MAGRAMA 844/2013).
文摘This study demonstrated the usefulness of very long-range terrestrial laser scanning(TLS) for analysis of the spatial distribution of a snowpack, to distances up to 3000 m, one of the longest measurement range reported to date. Snow depth data were collected using a terrestrial laser scanner during 11 periods of snow accumulation and melting,over three snow seasons on a Pyrenean hillslopecharacterized by a large elevational gradient, steep slopes, and avalanche occurrence. The maximum and mean absolute snow depth error found was 0.5-0.6 and 0.2-0.3 m respectively, which may result problematic for areas with a shallow snowpack, but it is sufficiently accurate to determine snow distribution patterns in areas characterized by a thick snowpack. The results indicated that in most cases there was temporal consistency in the spatial distribution of thesnowpack, even in different years. The spatial patterns were particularly similar amongst thesurveys conducted during the period dominated by snow accumulation(generally until end of April), or amongst those conducted during the period dominated by melting processes(generally after mid of April or early May). Simple linear correlation analyses for the 11 survey dates, and the application of Random Forests analysis to two days representative of snow accumulation and melting periods indicated the importance of topography to the snow distribution. The results also highlight that elevation and the Topographic Position index(TPI) were the main variables explaining the snow distribution, especially during periods dominated by melting. The intra-and inter-annual spatial consistency of the snowpack distribution suggests that the geomorphological processes linked to presence/absence of snow cover act in a similar way in the long term, and that these spatial patternscan be easily identifiedthrough several years of adequate monitoring.
