Abstract
This paper investigated the potential of multispectral airborne laser scanning (ALS) data for individual tree detection and tree species classification. The aim was to develop a single-sensorsolution for forest mapping that is capable of providing species-specific information, required for forest management and planning purposes. Experiments were conducted using 1903 ground measured trees from 22 sample plots and multispectral ALS data, acquired with an Optech Titan scanner over a boreal forest, mainly consisting of Scots pine (Pinus Sylvestris), Norway spruce (Picea Abies), and birch (Betula sp.), in southern Finland. ALS-features used as predictors for tree species were extracted from segmented tree objects and used in random forest classification. Different combinations of features, including point cloud features, and intensity features of single and multiple channels, were tested. Among the field-measured trees, 61.3% were correctly detected. The best overall accuracy (OA) of tree species classification achieved for correctly-detected trees was 85.9% (Kappa = 0.75), using a point cloud and single-channel intensity features combination, which was not significantly different from the ones that were obtained either using all features (OA = 85.6%, Kappa = 0.75), or single-channel intensity features alone (OA = 85.4%, Kappa = 0.75). Point cloud features alone achieved the lowest accuracy, with an OA of 76.0%. Field-measured trees were also divided into four categories. An examination of the classification accuracy for four categories of trees showed that isolated and dominant trees can be detected with a detection rate of 91.9%, and classified with a high overall accuracy of 90.5%. The corresponding detection rate and accuracy were 81.5% and 89.8% for a group of trees, 26.4% and 79.1% for trees next to a larger tree, and 7.2% and 53.9% for trees situated under a larger tree, respectively. The results suggest that Channel 2 (1064 nm) contains more information for separating pine, spruce, and birch, followed by channel 1 (1550 nm) and channel 3 (532 nm) with an overall accuracy of 81.9%, 78.3%, and 69.1%, respectively. Our results indicate that the use of multispectral ALS data has great potential to lead to a single-sensor solution for forest mapping.
Highlights
Knowledge of tree species plays an important role in forest management and planning.The optimum output, requested by forest companies from the forest mapping process, is the species-specific size distribution of the trees
Intensity features are more powerful in separating birch from pine and spruce
The results suggest that additional information, provided by multispectral laser scanning, may be a valuable source of information for tree species classification of pine, spruce, and birch, which are the main tree species found in boreal forest zones
Summary
The optimum output, requested by forest companies from the forest mapping process, is the species-specific size distribution of the trees. Remote sensing techniques were introduced, such as the interpretation of large-scale aerial color or infra-red images [1,2]. Remotely-sensed data have been widely used for forest applications, traditional optical remote sensing techniques suffer from a lack of the ability to capture three-dimensional forest structures, in unevenly-aged, mixed species forests with multiple canopy layers [3]. Recent developments in active remote sensing, laser scanning techniques, have shown potential in forest mapping and other applications because of the capability to capture three-dimensional (3D) information of forests [4,5,6,7,8,9,10,11]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have