Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data

Yao, Wei; Krzystek, Peter; Heurich, Marco

doi:10.1016/j.rse.2012.03.027

Cited by 286 publications

(235 citation statements)

References 31 publications

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“…The procedure consists of four steps: (1) delineation of individual trees; (2) extraction of tree parameters such as height, species and crown parameters; (3) model calibration of the biophysical parameters: Diameter at Breast Height (DBH), volume and biomass with the help of reference trees measured on the ground; and finally (4) the application of models to predict DBH, volume and biomass for all delineated LiDAR trees. Based on the crown representations basic attributes reflecting tree health such as total volume, crown length, crown area and crown base height can be derived [108,109]. The extracted individual trees also form the basis for identifying the tree species.…”

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

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Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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“…Airborne Laser Scanning (ALS) has become a key tool for gathering information on 3D structure of forests (Wulder et al, 2012). The derived information from ALS data can grant detailed estimations of forest characteristics and single tree analysis (Yao et al, 2012;Maltamo et al, 2012). Previous studies are showed several properties of single trees such as species, height and crown properties which can be measured with high resolution ALS data (Maltamo et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Detection of Single Tree Stems in Forested Areas From High Density Als Point Clouds Using 3d Shape Descriptors

Amiri¹,

Polewski²,

Yao³

et al. 2017

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

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ABSTRACT:Airborne Laser Scanning (ALS) is a widespread method for forest mapping and management purposes. While common ALS techniques provide valuable information about the forest canopy and intermediate layers, the point density near the ground may be poor due to dense overstory conditions. The current study highlights a new method for detecting stems of single trees in 3D point clouds obtained from high density ALS with a density of 300 points/m 2 . Compared to standard ALS data, due to lower flight height (150-200 m) this elevated point density leads to more laser reflections from tree stems. In this work, we propose a three-tiered method which works on the point, segment and object levels. First, for each point we calculate the likelihood that it belongs to a tree stem, derived from the radiometric and geometric features of its neighboring points. In the next step, we construct short stem segments based on high-probability stem points, and classify the segments by considering the distribution of points around them as well as their spatial orientation, which encodes the prior knowledge that trees are mainly vertically aligned due to gravity. Finally, we apply hierarchical clustering on the positively classified segments to obtain point sets corresponding to single stems, and perform 1-based orthogonal distance regression to robustly fit lines through each stem point set. The 1-based method is less sensitive to outliers compared to the least square approaches. From the fitted lines, the planimetric tree positions can then be derived. Experiments were performed on two plots from the Hochficht forest in Oberösterreich region located in Austria. We marked a total of 196 reference stems in the point clouds of both plots by visual interpretation. The evaluation of the automatically detected stems showed a classification precision of 0.86 and 0.85, respectively for Plot 1 and 2, with recall values of 0.7 and 0.67.

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“…Recent advances in LiDAR technology have demonstrated that new full waveform scanners can provide a higher spatial point density especially along the vertical dimension and additional information about the reflectional characteristics and vertical structure of trees [1][2][3][4][5]. As LiDAR instrument technology continues to improve and acquisition costs decrease, the wall-to-wall characterization of large forested areas will become more common.…”

Section: Introductionmentioning

confidence: 99%

“…In the ITD approach, either the canopy height model (CHM) [14,15] or the ALS raw data point clouds [3,16,17] are used to be segmented into single tree objects. Properties at single tree level can subsequently be estimated using the tree segments, such as tree height, crown diameter, tree species, crown base height and stem volume.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

Yao

Krull

Krzystek

et al. 2014

Forests

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Light detection and ranging (LiDAR) sampling or full-area coverage is deemed as favorable means to achieve timely and robust characterizations of forests. Recently, a 3D segmentation approach was developed for extracting single trees from LiDAR data. However, key parameters for modules used in the strategy had to be empirically determined. This paper highlights a comprehensive study for the sensitivity analysis of 3D single tree detection from airborne LiDAR data. By varying key parameters, their influences on results are to be quantified. The aim of the study is to enlighten the optimal combination of parameter values towards new applications. For the experiment, a number of sample plots from two temperate forest sites in Europe were selected. LiDAR data with a point density of 25 pts/m 2 over the first site in the Bavarian forest national park were captured with under both leaf-on and leaf-off conditions. Moreover, a Riegl scanner was used to acquire data over the Austrian Alps forest with four-fold point densities of 5 pts/m 2 , 10 pts/m 2 , 15 pts/m 2 and 20 pts/m 2 , respectively, under leaf-off conditions. The study results proved the robustness and efficiency of the 3D segmentation approach. Point densities larger than 10 pts/m 2 did not seem to significantly contribute to the improvement in the performance of 3D tree detection. The performance of the approach can be further examined and improved by optimizing the parameter settings with respect to different data properties and forest structures. OPEN ACCESSForests 2014, 5 1123

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Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data

Cited by 286 publications

References 31 publications

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Detection of Single Tree Stems in Forested Areas From High Density Als Point Clouds Using 3d Shape Descriptors

Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

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