Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation

Mielcarek, Miłosz; Stereńczak, Krzysztof; Khosravipour, Anahita

doi:10.1016/j.jag.2018.05.002

Cited by 77 publications

(82 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Measuring the height of conifers is easier and more precise due to their compact and cone-shaped crowns. Mielcarek et al (2018) also noted that errors for deciduous trees increased slightly with increasing tree height. Such a tendency was not found in coniferous and alder trees.…”

Section: Tree Heightmentioning

confidence: 90%

“…The accuracy of tree height measurements using ALS data depends not only on data processing methods but also on the tree species, and is also dependent on the quality of field-based measurements (Mielcarek et al 2018). Their results show that when relatively high point density ALS data are used, the error in height estimation is greater for some deciduous species (e.g., oaks) than for conifers and alder.…”

Section: Tree Heightmentioning

confidence: 99%

See 1 more Smart Citation

Accuracy of determining specific parameters of the urban forest using remote sensing

Ciesielski¹,

Stereńczak²

2019

iForest

View full text Add to dashboard Cite

This paper reviews the current state of knowledge in the field of urban forest inventory and specific tree parameters derived by remote sensing. The paper discusses the possibilities and limitations of using remote sensing to determine the following characteristics of individual trees acquired during the inventory: position (coordinates), tree height, breast height diameter, tree crown parameters (crown span, height of tree crown basis, crown projection surface), health condition, and tree species. A total of 543 papers published in scientific databases (Scopus ® and ScienceDirect ® ) from the year 2000 to December 2017 have been analyzed; 86 of them were used for the review. The most important outcomes are: (a) the integration of many datasets, in particular spectral data (aerial images and satellite imageries) and structural data (LIDAR), allows the most complex use of remote sensing data and helps to improve the accuracy of parameter estimations as well as the correct identification of tree species; (b) the highest precision of measurement is characteristic of TLS, while ALS data has the largest operating system; (c) remote sensing data applications are associated with a large number of sophisticated processing on very large datasets using often proprietary elaborations; (d) the use of remote sensing data makes it possible to determine the characteristics of urban vegetation at various levels of detail and at different scales. Citation: Ciesielski M, Sterenczak K (2019). Accuracy of determining specific parameters of the urban forest using remote sensing. iForest 12:

show abstract

Section: Tree Heightmentioning

confidence: 90%

Section: Tree Heightmentioning

confidence: 99%

Accuracy of determining specific parameters of the urban forest using remote sensing

Ciesielski¹,

Stereńczak²

2019

iForest

View full text Add to dashboard Cite

show abstract

“…At the stand level, results from recent research on smallto medium-sized boreal and tropical forests have demonstrated the potential use of UAV-based DAP data for estimating forest biomass [53,71]. On the other hand, ITC has also been applied to DAP point clouds [72] and to ALS clouds ( [73,74]). ITC presents several advantages over ABA for estimation of above-ground biomass because it can be used to derive biomass when an allometric model is available at individual tree level [75].…”

Section: Study Areamentioning

confidence: 99%

“…Finally, ALS and SfM approaches to tree height estimation tend to underestimate tree height [73,74,76,77]. Recent studies [77] have presented a model that explains the observed bias using probability theory, developing methods for correcting several ALS metrics used for ABA prediction of stand structure.…”

Section: Introductionmentioning

confidence: 99%

Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Guerra-Hernández

Cosenza

Cardíl³

et al. 2019

Forests

View full text Add to dashboard Cite

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.

show abstract

“…For example, previous studies have demonstrated that canopy structure provides a distinguishing difference between vegetation cover in riparian zones and other dense vegetation (Laslier et al, 2019;Villarreal et al, 2012), and canopy height is a particularly influential variable (Dowling and Accad, 2003). The ability of lidar metrics such as canopy height to characterize three-dimensional canopy structure provides complementary information that improves land cover classifications (Mielcarek et al, 2018;Roussel et al, 2017). Moreover, lidar data can be used to derive a high spatial resolution digital elevation model (DEM) of the terrain.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Dashtiahangar¹

View full text Add to dashboard Cite

Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation

Cited by 77 publications

References 46 publications

Accuracy of determining specific parameters of the urban forest using remote sensing

Accuracy of determining specific parameters of the urban forest using remote sensing

Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Contact Info

Product

Resources

About