Full-Waveform Airborne Laser Scanning in Vegetation Studies—A Review of Point Cloud and Waveform Features for Tree Species Classification

Kristina Koenig, Bernhard Höfle
2016 Forests  
In recent years, small-footprint full-waveform airborne laser scanning has become readily available and established for vegetation studies in the fields of forestry, agriculture and urban studies. Independent of the field of application and the derived final product, each study uses features to classify a target object and to assess its characteristics (e.g., tree species). These laser scanning features describe an observable characteristic of the returned laser signal (e.g., signal amplitude)
more » ... signal amplitude) or a quantity of an object (e.g., height-width ratio of the tree crown). In particular, studies dealing with tree species classification apply a variety of such features as input. However, an extensive overview, categorization and comparison of features from full-waveform airborne laser scanning and how they relate to specific tree species are still missing. This review identifies frequently used full-waveform airborne laser scanning-based point cloud and waveform features for tree species classification and compares the applied features and their characteristics for specific tree species detection. Furthermore, limiting and influencing factors on feature characteristics and tree classification are discussed with respect to vegetation structure, data acquisition and processing. The potential applicability of FWF ALS for estimating forest and tree characteristics has been shown for the derivation of forest and tree structure [16] [17] [18] , canopy profile [19] , leaf area index (LAI) [20,21], volume and above ground biomass [9, 22, 23] , as well as for tree species classification [24] [25] [26] [27] . Apart from forestry applications, 3D mapping of vegetation has gained increasing interest in urban data management as well as in ecological studies for monitoring and inventory issues. The richer spatial and radiometric information about the volumetric structure of trees from FWF data and the involvement of taxonomic information can assist in the assessment of biodiversity [2,28,29], animal ecology [30] or in the detection of invasive species [31] . Information on different tree species and their characteristics is also needed in urban environments [32] , where both the presence and spatial variation of vegetation have a direct impact on radiation penetration and solar energy flux [33], evapotranspiration, microclimate and air circulation [34] , and thus influence temperatures and air quality [2, 35] . Many practical applications such as noise mitigation, reduction of air pollution, energy management and management of recreation areas rely on detailed up-to-date data sources. However, capturing detailed and classified data is challenging in urban environments, as these are associated with a high structural complexity involving different object types and a variety of geometric shapes. In contrast to closed forest stands, urban trees are characterized by large species diversity within small areas and a complex (typically anthropogenic) shape [13] . Here, FWF data can support the derivation of urban tree structure and species information [13, 36] . Common to each study and application is the use of laser scanning features to capture the target object and its characteristics (e.g., tree species). Laser scanning features describe an observable characteristic of the returned laser signal (e.g., signal amplitude) or a quantity of an object (e.g., height-width ratio of the tree crown). Such features are available from the waveform and the point cloud directly or need to be derived by further processing (e.g., the backscatter cross-section by radiometric calibration [1]). Several studies and review papers on the use of FWF ALS data have been published and several of them deal with vegetation investigation and tree species classification, discussing the derivation and use of features for this purpose [37] [38] [39] [40] [41] . However, an extensive overview, categorization and comparison of FWF ALS-based point cloud and waveform features for tree species classification and how these features relate to specific tree species is still missing. This review aims to fill this gap by identifying and evaluating frequently used and indicative point cloud and waveform features for tree species classification, which are derived from decomposed and partly radiometrically calibrated FWF ALS data. The focus here is on features, which are derived from data of previously detected/segmented single tree objects. Three key questions are addressed:
doi:10.3390/f7090198 fatcat:rtaau7f5ybftlhp4aazpwp5oju