Classification of tree species or species classes is still a challenge for remote sensing-based forest inventory. Operational use of Airborne Laser Scanning (ALS) data for prediction of forest variables has this far been dominated by area-based methods where laser scanning data have been used for estimation of forest variables within raster cells. Classification of tree species has however not been achieved with sufficient accuracy with area-based methods using only ALS data. Furthermore, analysis of tree species at the level of raster cells with typical size of 15 m × 15 m is not ideal in the case of mixed species stands. Most ALS systems for terrestrial mapping use only one wavelength of light. New multispectral ALS systems for terrestrial mapping have recently become operational, such as the Optech Titan system with wavelengths 1550 nm, 1064 nm, and 532 nm. This study presents an alternative type of area-based method for classification of tree species classes where multispectral ALS data are used in combination with small raster cells. In this “mini raster cell method” features for classification are derived from the intensity of the different wavelengths in small raster cells using a moving window average approach to allow for a heterogeneous tree species composition. The most common tree species in the Nordic countries are Pinus sylvestris and Picea abies, constituting about 80% of the growing stock volume. The remaining 20% consists of several deciduous species, mainly Betula pendula and Betula pubescens, and often grow in mixed forest stands. Classification was done for pine (Pinus sylvestris), spruce (Picea abies), deciduous species and mixed species in middle-aged and mature stands in a study area located in hemi-boreal forest in the southwest of Sweden (N 58°27’, E 13°39’). The results were validated at plot level with the tree species composition defined as proportion of basal area of the tree species classes. The mini raster cell classification method was slightly more accurate (75% overall accuracy) than classification with a plot level area-based method (68% overall accuracy). The explanation is most likely that the mini raster cell method is successful at classifying homogenous patches of tree species classes within a field plot, while classification based on plot level analysis requires one or several heterogeneous classes of mixed species forest. The mini raster cell method also results in a high-resolution tree species map. The small raster cells can be aggregated to estimate tree species composition for arbitrary areas, for example forest stands or area units corresponding to field plots.

对于基于遥感的森林清单，树种或物种类别的分类仍然是一个挑战。迄今为止，机载激光扫描（ALS）数据在操作中用于预测森林变量的方法主要是基于区域的方法，在这些方法中，激光扫描数据已用于估计栅格像元中的森林变量。但是，仅使用ALS数据，基于区域的方法还没有以足够的精度实现树种的分类。此外，在混合树种林分的情况下，在典型栅格大小为15 m×15 m的栅格单元水平上对树木树种进行分析也不理想。大多数用于地面测绘的ALS系统仅使用一个波长的光。新的用于地面测绘的多光谱ALS系统最近已经投入使用，例如波长为1550 nm，1064 nm的Optech Titan系统，和532nm。这项研究提出了一种用于树种分类的基于区域的替代方法，其中多光谱ALS数据与小型栅格像元结合使用。在这种“微型栅格单元方法”中，使用移动窗口平均方法从小栅格单元中不同波长的强度得出分类的特征，以允许异类树种的组成。北欧国家最常见的树种是 在这种“微型栅格单元方法”中，使用移动窗口平均方法从小栅格单元中不同波长的强度得出分类的特征，以允许异类树种的组成。北欧国家最常见的树种是 在这种“微型栅格单元方法”中，使用移动窗口平均方法从小栅格单元中不同波长的强度得出分类的特征，以允许异类树种的组成。北欧国家最常见的树种是樟子松和云杉冷杉，约占生长种群的80％。其余的20％由几种落叶树种组成，主要是桦木（Betula pendula）和毛桦（Betula pubescens），它们通常在混交林林中生长。对松树（Pinus sylvestris），云杉（Picea abies）进行了分类在瑞典西南半北方森林的一个研究区域中，落叶松和中，熟种的混合种（北58°27'，东13°39'）。结果在地块级别以树种组成定义为树种类别的基础面积比例进行验证。微型栅格像元分类方法的准确度（总体准确度为75％）比使用基于绘图级区域的方法（总体准确度为68％）的分类略为精确。极有可能的解释是，微型栅格像元方法可以成功地对田间地块内树种类别的同质斑块进行分类，而基于地块级分析的分类则需要一个或几个异类的混合树种森林。微型栅格像元方法还可以生成高分辨率的树种图。