On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters

Vega, Cédric; Renaud, Jean-Pierre; Durrieu, S.; Bouvier, M.

doi:10.1016/j.rse.2015.12.039

Cited by 56 publications

(34 citation statements)

References 49 publications

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“…Point density metrics (d02, d03, d04, d05, and d08) were also influential variables for M2a. Point density metrics were also used in the final models in [60] for predicting the vertical distribution of heights and penetration depths in an area-based approach. The best independent variables to estimate AGB for a boreal forest showed by [23] were one variable related to canopy height and another one related to canopy density (similar to point density here).…”

Section: Discussionmentioning

confidence: 99%

“…According to previous studies [22,26,[58][59][60], one source of error that may influence the relationship between the lidar-and the field-estimated AGB at different spatial resolutions is the disagreement between the lidar and field plot measurements over which trees or parts of trees are inside the calibration plots. In the lidar measurements, tree crowns are bisected exactly at the plot edge, while in the field plot measurements, an individual is included in the plot if its basal area contributes to the basal area of the plot; in other words, if the DBH trunk point of measure is inside the plot.…”

Section: Lidar Processingmentioning

confidence: 99%

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Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

et al. 2018

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Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km 2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R 2 (calibration) = 0.89, R 2 (validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha −1 , and RMSE(validation) = 14.80 t·ha −1 ). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.

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Section: Discussionmentioning

confidence: 99%

Section: Lidar Processingmentioning

confidence: 99%

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

et al. 2018

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“…Various algorithms have been successfully developed to estimate tree height [26], leaf area index [27,28], aboveground biomass [29], canopy cover [7,30,31] at both individual tree [29,32,33] and forest stand scales [34,35]. However, studies on LiDAR-based CBH estimation are still insufficient, particularly at individual-tree scale.…”

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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“…While progress has recently been made at tree level [56,62], currently such effects remain difficult to assess on area-based models. This would require simulating forest structure and Lidar signals and developing metrics more robust to the variation in both forest structure and terrain conditions [63].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar

et al. 2015

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Light Detection and Ranging (Lidar) is a state of the art technology to assess forest aboveground biomass (AGB). To date, methods developed to relate Lidar metrics with forest parameters were built upon the vertical component of the data. In multi-layered tropical forests, signal penetration might be restricted, limiting the efficiency of these methods. A potential way for improving AGB models in such forests would be to combine traditional approaches by descriptors of the horizontal canopy structure. We assessed the capability and complementarity of three recently proposed methods for assessing AGB at the plot level OPEN ACCESSRemote Sens. 2015, 7 10608 using point distributional approach (DM), canopy volume profile approach (CVP), 2D canopy grain approach (FOTO), and further evaluated the potential of a topographical complexity index (TCI) to explain part of the variability of AGB with slope. This research has been conducted in a mountainous wet evergreen tropical forest of Western Ghats in India. AGB biomass models were developed using a best subset regression approach, and model performance was assessed through cross-validation. Results demonstrated that the variability in AGB could be efficiently captured when variables describing both the vertical (DM or CVP) and horizontal (FOTO) structure were combined. Integrating FOTO metrics with those of either DM or CVP decreased the root mean squared error of the models by 4.42% and 6.01%, respectively. These results are of high interest for AGB mapping in the tropics and could significantly contribute to the REDD+ program. Model quality could be further enhanced by improving the robustness of field-based biomass models and influence of topography on area-based Lidar descriptors of the forest structure.

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On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters

Cited by 56 publications

References 49 publications

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

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

Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar

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