Säynäjoki Raita, Packalén Petteri, Maltamo Matti, Vehmas Mikko, Eerikäinen Kalle
University of Joensuu, Faculty of Forest Sciences, P.O.Box 111, FI-80101 Joensuu, Finland.
Finnish Forest Research Institute, Joensuu Research Unit, P.O Box 68, FI-80101 Joensuu, Finland.
Sensors (Basel). 2008 Aug 25;8(8):5037-5054. doi: 10.3390/s8085037.
The aim was to use high resolution Aerial Laser Scanning (ALS) data and aerial images to detect European aspen (Populus tremula L.) from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 m size located in the Koli National Park in the North Karelia, Eastern Finland. A Canopy Height Model (CHM) was interpolated from the ALS data with a pulse density of 3.86/m2, low-pass filtered using Height-Based Filtering (HBF) and binarized to create the mask needed to separate the ground pixels from the canopy pixels within individual areas. Watershed segmentation was applied to the low-pass filtered CHM in order to create preliminary canopy segments, from which the non-canopy elements were extracted to obtain the final canopy segmentation, i.e. the ground mask was analysed against the canopy mask. A manual classification of aerial images was employed to separate the canopy segments of deciduous trees from those of coniferous trees. Finally, linear discriminant analysis was applied to the correctly classified canopy segments of deciduous trees to classify them into segments belonging to aspen and those belonging to other deciduous trees. The independent variables used in the classification were obtained from the first pulse ALS point data. The accuracy of discrimination between aspen and other deciduous trees was 78.6%. The independent variables in the classification function were the proportion of vegetation hits, the standard deviation of in pulse heights, accumulated intensity at the 90 percentile and the proportion of laser points reflected at the 60 height percentile. The accuracy of classification corresponded to the validation results of earlier ALS-based studies on the classification of individual deciduous trees to tree species.
目的是利用高分辨率航空激光扫描（ALS）数据和航空影像从其他落叶树种中检测出欧洲山杨（Populus tremula L.）。实地数据由位于芬兰东部北卡累利阿的科利国家公园内14个大小为30米×30米的样地组成。从脉冲密度为3.86/m²的ALS数据中插值生成冠层高度模型（CHM），使用基于高度的滤波（HBF）进行低通滤波并二值化，以创建在各个区域内将地面像素与冠层像素分离所需的掩码。对低通滤波后的CHM应用分水岭分割以创建初步的冠层片段，从中提取非冠层元素以获得最终的冠层分割，即针对冠层掩码分析地面掩码。采用航空影像的人工分类将落叶树的冠层片段与针叶树的冠层片段区分开来。最后，对正确分类的落叶树冠层片段应用线性判别分析，将其分为属于山杨的片段和属于其他落叶树的片段。分类中使用的自变量从首次脉冲ALS点数据中获取。山杨与其他落叶树之间的判别准确率为78.6%。分类函数中的自变量是植被命中比例、脉冲高度标准差、第90百分位数处的累积强度以及第60高度百分位数处反射的激光点比例。分类准确率与早期基于ALS的将单个落叶树分类到树种的研究的验证结果相符。
