Moving Voxel Method for Estimating Canopy Base Height from Airborne Laser Scanner Data

Maguya, Almasi S.; Tegel, Katri; Junttila, Virpi; Kauranne, Tuomo; Korhonen, Markus; Burns, Janice; Leppänen, V.; Sanz, Blanca

doi:10.3390/rs70708950

Cited by 19 publications

(14 citation statements)

References 27 publications

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“…However, studies on LiDAR-based CBH estimation are still insufficient, particularly at individual-tree scale. Most previous researchers investigated the estimation of CBH at plot level [1,7,25,36]. In the recent decade, studies on individual tree-level CBH estimation are increasing to provide more precise forest structural information to support forest management planning and decision making [2,3,37].…”

Section: Introductionmentioning

confidence: 99%

Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data

Lai-ping

Zhai

et al. 2018

Opt. Express

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Abstract:Crown base height (CBH) is an essential tree biophysical parameter for many applications in forest management, forest fuel treatment, wildfire modeling, ecosystem modeling and global climate change studies. Accurate and automatic estimation of CBH for individual trees is still a challenging task. Airborne light detection and ranging (LiDAR) provides reliable and promising data for estimating CBH. Various methods have been developed to calculate CBH indirectly using regression-based means from airborne LiDAR data and field measurements. However, little attention has been paid to directly calculate CBH at the individual tree scale in mixed-species forests without field measurements. In this study, we propose a new method for directly estimating individual-tree CBH from airborne LiDAR data. Our method involves two main strategies: 1) removing noise and understory vegetation for each tree; and 2) estimating CBH by generating percentile ranking profile for each tree and using a spline curve to identify its inflection points. These two strategies lend our method the advantages of no requirement of field measurements and being efficient and effective in mixed-species forests. The proposed method was applied to a mixed conifer forest in the Sierra Nevada, California and was validated by field measurements. The results showed that our method can directly estimate CBH at individual tree level with a root-mean-squared error of 1.62 m, a coefficient of determination of 0.88 and a relative bias of 3.36%. Furthermore, we systematically analyzed the accuracies among different height groups and tree species by comparing with field measurements. Our results implied that taller trees had relatively higher uncertainties than shorter trees. Our findings also show that the accuracy for CBH estimation was the highest for black oak trees, with an RMSE of 0.52 m. The conifer species results were also good with uniformly high R 2 ranging from 0.82 to 0.93. In general, our method has demonstrated high accuracy for individual tree CBH estimation and strong potential for applications in mixed species over large areas. References and links1. E. Naesset and T. Økland, "Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve," Remote Sens. Environ. 79, 105-115 (2002). 2. S. C. Popescu and K. Zhao, "A voxel-based lidar method for estimating crown base height for deciduous and pine trees," Remote Sens.

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Section: Introductionmentioning

confidence: 99%

Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data

Lai-ping

Zhai

et al. 2018

Opt. Express

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“…Using LiDAR pulse densities much higher than those presented here, Andersen et al [77] attained an R 2 of 0.77 when estimating CBH from LiDAR data with a point density of 3.5 pts/m 2 for a small forest in southwestern The accuracies presented in this study offer similar results compared to the few previous studies estimating CBH and CBD using low-density LiDAR (<1 pts/m 2 ). For example, Maguya et al [76] presented R 2 values ranging from 0.46 to 0.75 for CBH at a point density of 0.5 pts/m 2 in conifer-dominated forests of eastern Finland. Also using 0.5 pts/m 2 point density, González-Ferreiro et al [48] generated estimates for CBD (R 2 = 0.44) but also reported CBH accuracy in upwards of R 2 = 0.96 over plantations of young, highly stocked Pinus radiata in northwest Spain.…”

Section: Discussionmentioning

confidence: 99%

Estimating Canopy Fuel Attributes from Low-Density LiDAR

Engelstad

Falkowski

Wolter

et al. 2019

Fire

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Simulations of wildland fire risk are dependent on the accuracy and relevance of spatial data inputs describing drivers of wildland fire, including canopy fuels. Spatial data are freely available at national and regional levels. However, the spatial resolution and accuracy of these types of products often are insufficient for modeling local conditions. Fortunately, active remote sensing techniques can produce accurate, high-resolution estimates of forest structure. Here, low-density LiDAR and field-based data were combined using randomForest k-nearest neighbor imputation (RF-kNN) to estimate canopy bulk density, canopy base height, and stand age across the Boundary Waters Canoe Area in Minnesota, USA. RF-kNN models produced strong relationships between estimated canopy fuel attributes and field-based data for stand age (Adj. R2 = 0.81, RMSE = 10.12 years), crown fuel base height (Adj. R2 = 0.78, RMSE = 1.10 m), live crown base height (Adj. R2 = 0.7, RMSE = 1.60 m), and canopy bulk density (Adj. R2 = 0.48, RMSE = 0.09kg/m3). These results suggest that low-density LiDAR can help estimate canopy fuel attributes in mixed forests, with robust model accuracies and high spatial resolutions compared to currently utilized fire behavior model inputs. Model map outputs provide a cost-efficient alternative for data required to simulate fire behavior and support local management.

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“…CBH-or the height above which there is sufficient fuel to move a fire upward-is a critical component for assessing fire hazard in a given area [38,78,79] yet it is difficult to measure. Lidar-derived CBH involves regressions between point cloud metrics and plot data [20,[27][28][29]49], while the typical measurement for CBH without Lidar involves estimates at the plot scale through allometric equations that consider species, diameter at breast height (DBH), tree height, crown length, height to live crown base, crown ratio, and crown width [38,78,79]. This discrepancy in method (i.e., regression with direct measures vs. modeled estimate) can result in a low correlation between Lidar and field estimates for CBH [20,35,79], and thus inflating the error estimates that are associated with CBH.…”

Section: Discussionmentioning

confidence: 99%

Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA

Kelly

Tommaso

et al. 2017

Remote Sensing

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Light detection and ranging (Lidar) data can be used to create wall-to-wall forest structure and fuel products that are required for wildfire behavior simulation models. We know that Lidar-derived forest parameters have a non-negligible error associated with them, yet we do not know how this error influences the results of fire behavior modeling that use these layers as inputs. Here, we evaluated the influence of error associated with two Lidar data products-canopy height (CH) and canopy base height (CBH)-on simulated fire behavior in a case study in the Sierra Nevada, California, USA. We used a Monte Carlo simulation approach with expected randomized error added to each model input. Model 1 used the original, unmodified data, Model 2 incorporated error in the CH layer, and Model 3 incorporated error in the CBH layer. This sensitivity analysis showed that error in CH and CBH did not greatly influence the modeled conditional burn probability, fire size, or fire size distribution. We found that the expected error associated with CH and CBH did not greatly influence modeled results: conditional burn probability, fire size, and fire size distributions were very similar between Model 1 (original data), Model 2 (error added to CH), and Model 3 (error added to CBH). However, the impact of introduced error was more pronounced with CBH than with CH, and at lower canopy heights, the addition of error increased modeled canopy burn probability. Our work suggests that the use of Lidar data, even with its inherent error, can contribute to reliable and robust estimates of modeled forest fire behavior, and forest managers should be confident in using Lidar data products in their fire behavior modeling workflow.

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Moving Voxel Method for Estimating Canopy Base Height from Airborne Laser Scanner Data

Cited by 19 publications

References 27 publications

Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data

Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data

Estimating Canopy Fuel Attributes from Low-Density LiDAR

Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA

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