Abstract
Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83–90%; 12 species, 46–54%; 4 species, 68–79%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91%; 12 species, 58%; 4 species, 84%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.
Highlights
Introduction iationsForests are important global resources that affect numerous natural cycles as well as contributing to natural biodiversity, i.e., flora and fauna [1]
This processing ranged from the initial data layers, i.e., the point cloud, digital terrain model (DTM), digital surface model (DSM), and canopy height model (CHM), to feature extraction and balanced Random Forest classification [39]
The Random Forest (RF) classification accuracies were compared for four different species groupings, three airborne laser scanning systems (ALS) systems (ASL12, MSL16, SPL18), and four broad feature groupings: 3D only; intensity (I) only; all the features of a given ALS system; and all the features of all the ALS
Summary
Forests are important global resources that affect numerous natural cycles as well as contributing to natural biodiversity, i.e., flora and fauna [1]. Forested lands constitute the largest terrestrial carbon sink on the planet, with approximate relative contributions of 80% being made by above-ground biomass and 40% being made by below-ground biomass [2]. Precise species identification is a crucial variable for forest inventories [3], for the quantification and monitoring of biodiversity [4], and for the study of forest ecosystems and habitats [5]. Accurate tree species identification is the information that is most frequently requested by the forestry industry and by government organisations in the elaboration of forest inventories [6]. It is economically unfeasible to sample large numbers of Licensee MDPI, Basel, Switzerland
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