Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux

Zhao, Kaiguang; Suárez, Juan Carlos Pinilla; Garcı́a, Mariano; Hu, Tongxi; Wang, Cheng; Londo, Alexisi

doi:10.1016/j.rse.2017.09.007

Cited by 191 publications

(141 citation statements)

References 75 publications

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“…Tree parameters, such as tree location, tree height and tree crown size, were considered in the tree-to-tree matching procedure of this study. Tree location was considered as an important parameter of the matching in previous studies [2, 51,52]. However, due to the uncertainties of GPS measurements under forest canopy and the bias of LiDAR-derived tree location, the matching would be difficult and problematic if only using tree location.…”

Section: Tree-to-tree Matching Methodsmentioning

confidence: 99%

“…Therefore, as shown in previous studies, a manual check was required to pair more trees [2]. To make the matching more automatic and to reduce the labor costs, tree height [51,52] was also used to pair the trees. Moreover, by trial and error, we found that the diameter of tree crown was an appropriate threshold to refine the distance between paired trees in LiDAR estimates and field measurements.…”

Section: Tree-to-tree Matching Methodsmentioning

confidence: 99%

“…(2)], which considered the location distance and height difference in a weighted combination. The weight for the height difference was set as 0.5, as suggested [52]. Finally, the tree with the smallest D ij among all the potential LiDAR-derived pairing trees was matched to the field measured tree a i .…”

Section: Tree-to-tree Matching Methodsmentioning

confidence: 99%

“…where (x i ,y i )is the location of a field measured tree a i to be paired, (x j ,y j ) is the position of a LiDAR-derived tree b j , d ij denotes the distance between a i and b j , D ij denotes the comprehensive difference index of tree matching, h i denotes the height of a i , h j denotes the height of b j , w is set to 0.5 as the weight of height difference [52].…”

Section: Tree-to-tree Matching Methodsmentioning

confidence: 99%

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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: Tree-to-tree Matching Methodsmentioning

confidence: 99%

Section: Tree-to-tree Matching Methodsmentioning

confidence: 99%

Section: Tree-to-tree Matching Methodsmentioning

confidence: 99%

Section: Tree-to-tree Matching Methodsmentioning

confidence: 99%

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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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“…Future research efforts should focus on reducing the uncertainty in biomass change estimates. Uncertainty in the prediction models can be reduced-for example, by considering data at finer horizontal resolution, the full vertical signal distribution, the multitude of variables that can describe forest structure and individual-tree-based approaches [26]. The uncertainty in height estimations due to the variability in the remote sensing data can be reduced by fitting trends to dense time series of multiple acquisitions [80].…”

Section: Perspectivesmentioning

confidence: 99%

Model-Assisted Estimation of Tropical Forest Biomass Change: A Comparison of Approaches

et al. 2018

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Monitoring of changes in forest biomass requires accurate transfer functions between remote sensing-derived changes in canopy height (∆H) and the actual changes in aboveground biomass (∆AGB). Different approaches can be used to accomplish this task: direct approaches link ∆H directly to ∆AGB, while indirect approaches are based on deriving AGB stock estimates for two points in time and calculating the difference. In some studies, direct approaches led to more accurate estimations, while, in others, indirect approaches led to more accurate estimations. It is unknown how each approach performs under different conditions and over the full range of possible changes. Here, we used a forest model (FORMIND) to generate a large dataset (>28,000 ha) of natural and disturbed forest stands over time. Remote sensing of forest height was simulated on these stands to derive canopy height models for each time step. Three approaches for estimating ∆AGB were compared: (i) the direct approach; (ii) the indirect approach and (iii) an enhanced direct approach (dir+tex), using ∆H in combination with canopy texture. Total prediction accuracies of the three approaches measured as root mean squared errors (RMSE) were RMSE direct = 18.7 t ha −1 , RMSE indirect = 12.6 t ha −1 and RMSE dir+tex = 12.4 t ha −1 . Further analyses revealed height-dependent biases in the ∆AGB estimates of the direct approach, which did not occur with the other approaches. Finally, the three approaches were applied on radar-derived (TanDEM-X) canopy height changes on Barro Colorado Island (Panama). The study demonstrates the potential of forest modeling for improving the interpretation of changes observed in remote sensing data and for comparing different methodologies.

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Monitoring individual tree‐based change with airborne lidar

Duncanson

Dubayah

2018

Ecology and Evolution

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Understanding the carbon flux of forests is critical for constraining the global carbon cycle and managing forests to mitigate climate change. Monitoring forest growth and mortality rates is critical to this effort, but has been limited in the past, with estimates relying primarily on field surveys. Advances in remote sensing enable the potential to monitor tree growth and mortality across landscapes. This work presents an approach to measure tree growth and loss using multidate lidar campaigns in a high‐biomass forest in California, USA. Individual tree crowns were delineated in 2008 and again in 2013 using a 3D crown segmentation algorithm, with derived heights and crown radii extracted and used to estimate individual tree aboveground biomass. Tree growth, loss, and aboveground biomass were analyzed with respect to tree height and crown radius. Both tree growth and loss rates decrease with increasing tree height, following the expectation that trees slow in growth rate as they age. Additionally, our aboveground biomass analysis suggests that, while the system is a net source of aboveground carbon, these carbon dynamics are governed by size class with the largest sources coming from the loss of a relatively small number of large individuals. This study demonstrates that monitoring individual tree‐based growth and loss can be conducted with multidate airborne lidar, but these methods remain relatively immature. Disparities between lidar acquisitions were particularly difficult to overcome and decreased the sample of trees analyzed for growth rate in this study to 21% of the full number of delineated crowns. However, this study illuminates the potential of airborne remote sensing for ecologically meaningful forest monitoring at an individual tree level. As methods continue to improve, airborne multidate lidar will enable a richer understanding of the drivers of tree growth, loss, and aboveground carbon flux.

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Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux

Cited by 191 publications

References 75 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

Model-Assisted Estimation of Tropical Forest Biomass Change: A Comparison of Approaches

Monitoring individual tree‐based change with airborne lidar

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