Improving lidar-based forest structure mapping with crown-level pit removal

Shamsoddini, Ali; Turner, Russell; Trinder, John C.

doi:10.1080/14498596.2012.759092

Cited by 30 publications

(44 citation statements)

References 36 publications

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“…They used morphological crown control to recover crown coverage. Shamsoddini et al [21] employed an adaptive mean filter with 3 × 3 and 5 × 5 kernels to detect and fill data pits. Liu and Dong [22] used a selecting and sorting scheme to select data points during CHM construction.…”

Section: Related Workmentioning

confidence: 99%

Robust and Parameter-Free Algorithm for Constructing Pit-Free Canopy Height Models

Chen

Wang

et al. 2017

IJGI

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Data pits commonly appear in lidar-derived canopy height models (CHMs) owing to the penetration ability of airborne light detection and ranging (lidar) into tree crowns. They have a seriously negative effect on the quality of tree detection and subsequent biophysical measurements. In this study, we propose an algorithm based on robust locally weighted regression and robust z-scores for the construction of a pit-free CHM. A significant advantage of the new algorithm is that it is parameter free, which makes it efficient and robust for practical applications. Simulated and airborne lidar-derived data sets are employed to assess the performance of the new method for CHM construction, and its results are compared to those of three classical methods, namely the natural neighbor (NN) interpolation of the highest point method (HPM), mean filter, and median filter. The results from the simulated data set demonstrate that our algorithm is more accurate compared to the three classical methods for generating pit-free CHMs in the presence of data pits. CHM construction using the lidar-derived data set shows that, compared to the classical methods, the new method has a better ability to remove data pits as well as preserving the edges, shapes, and structures of canopy gaps and crowns. Moreover, the proposed method performs better compared to the classical methods in deriving plot-level maximum tree heights from CHMs. Thus, the new method shows high potential for pit-free CHM construction.

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Section: Related Workmentioning

confidence: 99%

Robust and Parameter-Free Algorithm for Constructing Pit-Free Canopy Height Models

Chen

Wang

et al. 2017

IJGI

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“…The lidar-derived CHM suffers from pit phenomenon which is defined as randomly-distributed pixels whose values are lower than their neighbouring pixels within a crown (Shamsoddini et al, 2013b). Prior to calculation of statistical metrics, it is required to reduce the effect of pits on the CHM (Shamsoddini et al, 2013b).…”

Section: Pre-processingmentioning

confidence: 99%

“…Prior to calculation of statistical metrics, it is required to reduce the effect of pits on the CHM (Shamsoddini et al, 2013b). Pit removal was conducted using adaptive mean filter (AMF) with 7×7 window size developed by Shamsoddini et al (2013b).…”

Section: Pre-processingmentioning

confidence: 99%

“…The launch of new optical satellites such as WorldView-2 (WV-2) offering simultaneously new spectral bands and high resolution data calls for the investigation of the capabilities of these new data and spectral bands for predicting lidar metrics. Among different types of lidar metrics, the prediction of lidar-derived mean and maximum canopy heights has been investigated more frequent than the others using multiple-linear regression as a common modelling method (Hilker et al, 2008;Pascual et al, 2010); However, the other lidar-derived height metrics, such as variance (Zimble et al, 2003), skewness, and percentiles of ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013ISPRS Workshop Laser Scanning 2013, 11 -13 November 2013, Antalya, Turkey lidar-derived heights (Shamsoddini et al, 2013b), are also useful for estimating forest structural parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Considering these difficulties, earth observation technologies have been exploited as an economic source of data for extracting more current and accurate structural parameters at global to local scales than can be achievable by in situ methods (Le Toan et al, 2004). Among different types of remotely sensed data, discrete lidar data has been used to estimate and calculate different structural parameters including tree height (Popescu et al, 2002;Dean et al, 2009), biomass and biomass-related variables such as stand volume, and basal area (Means et al, 1999;Smart et al, 2012;Shamsoddini et al, 2013b). Lidar data can provide a direct measure of tree or stand height (Hyde et al, 2006;Erdody and Moskal, 2010) and is arguably superior in this regard compared to other remote sensing options.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Relating WorldView-2 data to pine plantation lidar metrics

Trinder

Shamsoddini

Turner³

2013

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

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ABSTRACT:Over last decades, different types of remotely sensed data including lidar, radar and optical data were investigated for forest studies. Undoubtedly, lidar data is one of the promising tools for these purposes; however, the accessibility and cost of this data are the main limitations. In order to overcome these limitations, optical data have been considered for modelling lidar metrics and their use for inferring lidar metrics over areas with no lidar coverage. WorldView-2 (WV-2) data as a high resolution optical data offer 8 bands including four traditional bands, blue, green, red, and infrared, and four new bands including coastal blue, yellow, red edge and a new infrared band whose relationships with lidar metrics were investigated in this study. For this purpose, band reflectance, band ratios, and principal components (PCs) of WV-2 multispectral data along with 23 vegetation indices were extracted. Moreover, the grey level co-occurrence matrix (GLCM) indices of bands, band ratios and PCs were also calculated for different window sizes and orientations. Spectral derivatives and textural attributes of WV-2 were provided for a stepwise multiple-linear regression to model 10 lidar metrics including maximum, mean, variance, 10 th , 30 th , 60 th and 90 th height percentiles, standard error of mean, kurtosis and skewness for a Pinus radiata plantation, in NSW, Australia. The results indicated that the textural-based models are significantly more efficient than spectral-based models for predicting lidar metrics. Moreover, the integration of spectral derivatives with textural attributes cannot improve the results derived from textural-based models. The study demonstrates that WV-2 data are efficient for predicting lidar metrics.

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Using canopy height model derived from UAV imagery as an auxiliary for spectral data to estimate the canopy cover of mixed broadleaf forests

Miraki

Sohrabi

2021

Environ Monit Assess

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Improving lidar-based forest structure mapping with crown-level pit removal

Cited by 30 publications

References 36 publications

Robust and Parameter-Free Algorithm for Constructing Pit-Free Canopy Height Models

Robust and Parameter-Free Algorithm for Constructing Pit-Free Canopy Height Models

Relating WorldView-2 data to pine plantation lidar metrics

Using canopy height model derived from UAV imagery as an auxiliary for spectral data to estimate the canopy cover of mixed broadleaf forests

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