Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests

Pirotti, Francesco; Laurin, Gaia Vaglio; Vettore, Antonio; Masiero, Andrea; Валентини, Р.

doi:10.3390/rs6109576

Cited by 36 publications

(27 citation statements)

References 47 publications

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“…Such enriched point clouds offer an added value to features from discrete ALS for vegetation and forest mapping and for individual tree identification and classification [2,3]. Since 2004, FWF ALS systems are increasingly used for object detection and classification [4,5], and for the derivation of vegetation characteristics in forestry [6][7][8][9][10][11], agriculture [12], and in urban areas [13]. Research efforts focusing on vegetation are expanding due to an increasing need to quantify vegetation characteristics and to model environmental dynamics [14].…”

Section: Introductionmentioning

confidence: 99%

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

Koenig

Höfle

2016

Forests

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Abstract: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) 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.

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Section: Introductionmentioning

confidence: 99%

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

Koenig

Höfle

2016

Forests

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“…treeline, location properties and climate conditions was given by Körner C. entitled A bioclimatic stratification of mountain biota anchored at treeline (see e.g. Körner, 2012 Pirotti, 2010;Pirotti et al, 2014). It illustrated the technological progress of sensors in terms of lower weight and costs, along with higher accuracy and efficiency in terms of battery duration.…”

Section: Related Vegetation and Forestry Lecturesmentioning

confidence: 99%

Close-Range Sensing Techniques in Alpine Terrain

Rutzinger¹,

Höfle²,

Lindenbergh³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Early career researchers such as PhD students are a main driving force of scientific research and are for a large part responsible for research innovation. They work on specialized topics within focused research groups that have a limited number of members, but might also have limited capacity in terms of lab equipment. This poses a serious challenge for educating such students as it is difficult to group a sufficient number of them to enable efficient knowledge transfer. To overcome this problem, the Innsbruck Summer School of Alpine Research 2015 on close-range sensing techniques in Alpine terrain was organized in Obergurgl, Austria, by an international team from several universities and research centres. Of the applicants a group of 40 early career researchers were selected with interest in about ten types of specialized surveying tools, i.e. laser scanners, a remotely piloted aircraft system, a thermal camera, a backpack mobile mapping system and different grade photogrammetric equipment. During the one-week summer school, students were grouped according to their personal preference to work with one such type of equipment under guidance of an expert lecturer. All students were required to capture and process field data on a mountain-related theme like landslides or rock glaciers. The work on the assignments lasted the whole week but was interspersed with lectures on selected topics by invited experts. The final task of the summer school participants was to present and defend their results to their peers, lecturers and other colleagues in a symposium-like setting. Here we present the framework and content of this summer school which brought together scientists from close-range sensing and environmental and geosciences.

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“…in forestry (Hyyppä et al, 2012;Naesset, 2007), plantation (Fieber et al, 2013;Görgens et al, 2015) and urban areas (Höfle et al, 2012;Richter et al, 2013;Yan et al, 2015). The estimation of tree characteristics like the structure and canopy profile (Latifi et al, 2015;Leiterer et al, 2012;Lindberg et al, 2012), the leaf area index (Alonzo et al, 2015;Fieber et al, 2014), the volume and above ground biomass (Allouis et al, 2013;Cao et al, 2014;Pirotti et al, 2014)as well as the classification of tree species (Ghosh et al, 2014;GuangCai et al, 2012;Heinzel and Koch, 2011;Hollaus et al, 2009;Holmgren and Persson, 2004;Hovi et al, 2016) have been applied, especially in forested areas. In contrast to closed forest stands, urban trees are characterized by large species diversity within small areas and a complex (typically anthropogenic) shape.…”

Section: Introductionmentioning

confidence: 99%

Urban Tree Classification Using Full-Waveform Airborne Laser Scanning

Koma

Koenig

Höfle

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Vegetation mapping in urban environments plays an important role in biological research and urban management. Airborne laser scanning provides detailed 3D geodata, which allows to classify single trees into different taxa. Until now, research dealing with tree classification focused on forest environments. This study investigates the object-based classification of urban trees at taxonomic family level, using full-waveform airborne laser scanning data captured in the city centre of Vienna (Austria). The data set is characterised by a variety of taxa, including deciduous trees (beeches, mallows, plane trees and soapberries) and the coniferous pine species. A workflow for tree object classification is presented using geometric and radiometric features. The derived features are related to point density, crown shape and radiometric characteristics. For the derivation of crown features, a prior detection of the crown base is performed. The effects of interfering objects (e.g. fences and cars which are typical in urban areas) on the feature characteristics and the subsequent classification accuracy are investigated. The applicability of the features is evaluated by Random Forest classification and exploratory analysis. The most reliable classification is achieved by using the combination of geometric and radiometric features, resulting in 87.5% overall accuracy. By using radiometric features only, a reliable classification with accuracy of 86.3% can be achieved. The influence of interfering objects on feature characteristics is identified, in particular for the radiometric features. The results indicate the potential of using radiometric features in urban tree classification and show its limitations due to anthropogenic influences at the same time.

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Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests

Cited by 36 publications

References 47 publications

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

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

Close-Range Sensing Techniques in Alpine Terrain

Urban Tree Classification Using Full-Waveform Airborne Laser Scanning

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