Detecting Changes in Forest Structure over Time with Bi-Temporal Terrestrial Laser Scanning Data

Liang, Xinlian; Hyyppä, Juha; Kaartinen, Harri; Holopainen, Markus; Melkas, Timo

doi:10.3390/ijgi1030242

Cited by 59 publications

(49 citation statements)

References 23 publications

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“…Furthermore, not only the spatial constellation of neighboring trees but also the temporal dynamics of individual tree crown structure and canopy space filling are important aspects in forest research. However, such studies with multitemporal point cloud data analyses are rare, mainly focusing on standard tree dendrometrics or wood volume estimations (Liang et al., 2012; Srinivasan et al., 2014). Our new method is, thus, a very promising approach which should lead to important advances in several aspects of forest ecological research, such as investigations on crown growth, morphology and plasticity (Longuetaud, Piboule, Wernsdörfer, & Collet, 2013; Schröter, Härdtle, & von Oheimb, 2012), canopy structures and canopy packing (Jucker, Bouriaud, Coomes, & Baltzer, 2015; Morin, 2015; Pretzsch, 2014), crown‐related tree interactions and competition (Fichtner, Sturm, Rickert, von Oheimb, & Härdtle, 2013; Lang et al., 2012; Potvin & Dutilleul, 2009; Thorpe, Astrup, Trowbridge, & Coates, 2010), and niche differentiation and spatial complementarity (Ishii & Asano, 2010; Sapijanskas, Paquette, Potvin, Kunert, & Loreau, 2014; Williams, Paquette, Cavender‐Bares, Messier, & Reich, 2017), including changes over time.…”

Section: Discussionmentioning

confidence: 99%

“…In ecological studies, however, the temporal dynamics of forests and tree communities are of particular importance. Studies with multitemporal point cloud data from forests are rare and mainly focused on estimating changes in diameter at breast height or aboveground biomass of individual trees (Liang, Hyyppä, Kaartinen, Holopainen, & Melkas, 2012; Srinivasan, Popescu, Eriksson, Sheridan, & Ku, 2014). In addition, some studies used noninteracting trees (no crown overlap) and no real forest examples (e.g., Fernández‐Sarría et al., 2013; Moorthy et al., 2011).…”

Section: Introductionmentioning

confidence: 99%

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A high‐resolution approach for the spatiotemporal analysis of forest canopy space using terrestrial laser scanning data

Hess

Härdtle

Kunz

et al. 2018

Ecology and Evolution

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Forest canopies and tree crown structures are of high ecological importance. Measuring canopies and crowns by direct inventory methods is time‐consuming and of limited accuracy. High‐resolution inventory tools, in particular terrestrial laser scanning (TLS), is able to overcome these limitations and obtain three‐dimensional (3D) structural information about the canopy with a very high level of detail. The main objective of this study was to introduce a novel method to analyze spatiotemporal dynamics in canopy occupancy at the individual tree and local neighborhood level using high‐resolution 3D TLS data. For the analyses, a voxel grid approach was applied. The tree crowns were modeled through the combination of two approaches: the encasement of all crown points with a 3D α‐shape, which was then converted into a voxel grid, and the direct voxelization of the crown points. We show that canopy occupancy at individual tree level can be quantified as the crown volume occupied only by the respective tree or shared with neighboring trees. At the local neighborhood level, our method enables the precise determination of the extent of canopy space filling, the identification of tree–tree interactions, and the analysis of complementary space use. Using multitemporal TLS data recordings, this method allows the precise detection and quantification of changes in canopy occupancy through time. The method is applicable to a wide range of investigations in forest ecology research, including the study of tree diversity effects on forest productivity or growing space analyses for optimal tree growth. Due to the high accuracy of this novel method, it facilitates the precise analyses even of highly plastic individual tree crowns and, thus, the realistic representation of forest canopies. Moreover, our voxel grid framework is flexible enough to allow for the inclusion of further biotic and abiotic variables relevant to complex analyses of forest canopy dynamics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A high‐resolution approach for the spatiotemporal analysis of forest canopy space using terrestrial laser scanning data

Hess

Härdtle

Kunz

et al. 2018

Ecology and Evolution

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“…These studies, which typically focus on measuring variations in tree form [24] or biomass [24][25][26] and the growth of agricultural crops [19][20][21], highlight the accuracy that can be achieved using TLS technology for these purposes. The objective of this study was, to apply similar methods to assess change induced by fire within Australian dry sclerophyll forests.…”

Section: Multi-temporal Monitoring Of Vegetation and Fuel Hazard Chanmentioning

confidence: 99%

“…As such, a similar approach to Liang et al [24], Srinivasan et al [25], and Hoffmeister et al [19] was undertaken which utilised one fixed target and a fixed instrument set up location. The presence of fire resulted in movement of these targets between subsequent surveys and further refinement using the stem objects was undertaken.…”

Section: Multi-temporal Monitoring Of Vegetation and Fuel Hazard Chanmentioning

confidence: 99%

An Assessment of Pre- and Post Fire Near Surface Fuel Hazard in an Australian Dry Sclerophyll Forest Using Point Cloud Data Captured Using a Terrestrial Laser Scanner

et al. 2016

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Assessment of ecological and structrual changes induced by fire events is important for understanding the effects of fire, and planning future ecological and risk mitigation strategies. This study employs Terrestrial Laser Scanning (TLS) data captured at multiple points in time to monitor the changes in a dry sclerophyll forest induced by a prescribed burn. Point cloud data was collected for two plots; one plot undergoing a fire treatment, and the second plot remaining untreated, thereby acting as the control. Data was collected at three epochs (pre-fire, two weeks post fire and two years post fire). Coregistration of these multitemporal point clouds to within an acceptable tolerance was achieved through a two step process utilising permanent infield markers and manually extracted stem objects as reference targets. Metrics describing fuel height and fuel fragmentation were extracted from the point clouds for direct comparison with industry standard visual assessments. Measurements describing the change (or lack thereof) in the control plot indicate that the method of data capture and coregistration were achieved with the required accuracy to monitor fire induced change. Results from the fire affected plot show that immediately post fire 67% of area had been burnt with the average fuel height decreasing from 0.33 to 0.13 m. At two years post-fire the fuel remained signicantly lower (0.11 m) and more fragmented in comparison to pre-fire levels. Results in both the control and fire altered plot were comparable to synchronus onground visual assessment. The advantage of TLS over the visual assessment method is, however, demonstrated through the use of two physical and spatially quantifiable metrics to describe fuel change. These results highlight the capabilities of multitemporal TLS data for measuring and mapping changes in the three dimensional structure of vegetation. Metrics from point clouds can be derived to provide quantified estimates of surface and near-surface fuel loss and accumulation, and inform prescribed burn efficacy and burn severity reporting.

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“…Their uses are numerous and widely diverse. They cover areas of urban and regional planning [1]- [6], environmental monitoring and assessment [7]- [10], change detection [11]- [17], simulation and prediction of future spatial and man-made phenomena [18]- [21], and others. They are time sensitive as well as content important.…”

Section: Introductionmentioning

confidence: 99%

Developing an Automated Land Cover Classifier Using LiDAR and High Resolution Aerial Imagery

Ayad¹

2016

GEP

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The aim of this project is to create high resolution land cover classification as well as tree canopy density maps at a regional level using high resolution spatial data. Modeling and the data manipulation and analysis of LiDAR LAS point cloud dataset as well as multispectral aerial photographs from the National Agriculture Imagery Program (NAIP) were carried out. Using geoprocessing modeling, a land cover map is created based on filtered returns from LiDAR point cloud data (LAS dataset) to extract features based on their class and return values, and traditional classification methods of high resolution multi-spectral aerial photographs of the remaining ground cover for Clarion County in Pennsylvania. The newly developed model produced 7 classes at 10 ft × 10 ft spatial resolution, namely: water bodies, structures, streets and paved surfaces, bare ground, grassland, trees, and artificial surfaces (e.g. turf). The model was tested against areas with different sizes (townships and municipalities) which revealed a classification accuracy between 94% and 96%. A visual observation of the results shows that some tree-covered areas were misclassified as built up/structures due to the nature of the available LiDAR data, an area of improvement for further studies. Furthermore, a geoprocessing service was created in order to disseminate the results of the land cover classification as well as the tree canopy density calculation to a broader audience. The service was tested and delivered in the form of a web application where users can select an area of interest and the model produces the land cover and/or the tree canopy density results (http://maps.clarion.edu/LandCoverExtractor). The produced output can be printed as a final map layout with the highlighted area of interest and its corresponding legend. The interface also allows the download of the results of an area of interest for further investigation and/or analysis.

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Detecting Changes in Forest Structure over Time with Bi-Temporal Terrestrial Laser Scanning Data

Cited by 59 publications

References 23 publications

A high‐resolution approach for the spatiotemporal analysis of forest canopy space using terrestrial laser scanning data

A high‐resolution approach for the spatiotemporal analysis of forest canopy space using terrestrial laser scanning data

An Assessment of Pre- and Post Fire Near Surface Fuel Hazard in an Australian Dry Sclerophyll Forest Using Point Cloud Data Captured Using a Terrestrial Laser Scanner

Developing an Automated Land Cover Classifier Using LiDAR and High Resolution Aerial Imagery

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