Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory

Ferraz, António; Saatchi, Sassan; Mallet, Clément; Jacquemoud, Stéphane; Gonçalves, Gil; Silva, Carlos Alberto; Soares, Paula; Tomé, Margarida; Pereira, L. Gomes

doi:10.3390/rs8080653

Cited by 50 publications

(35 citation statements)

References 38 publications

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“…For field-measured trees, the average CBH was 7.63 m with a standard deviation of 4.51 m, the average tree height was 23.97 m with a standard deviation of 9.31 m and the average DBH was 44.22 cm with a standard deviation of 21.38 cm. For LiDAR-derived trees, the average crown diameter was 8.98 m with a standard deviation of 3.14 m; the average tree height was 22.35 m with a standard deviation of 9.29 m. The average height of LiDAR-derived trees was 1.62 m (6.76%) lower than that of field-measured trees, which indicated that LiDAR-derived tree height was slightly lower than the field-measured ones likely for two reasons: 1) it is often difficult for LiDAR laser beam to hit the exact tops of trees [51], and 2) it is often difficult to measure tree height accurately in the field [54]. Table 2 shows the summary of the field measurements for matched trees.…”

Section: Field Measurement Results and Tree-to-tree Matchingmentioning

confidence: 93%

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: Field Measurement Results and Tree-to-tree Matchingmentioning

confidence: 93%

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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“…Figure 7a,b provide a perspective view of a single acquisition dataset and the high-density point cloud calculated by merging the registered individual acquisitions, respectively. The latter represents the forest structure at a finer resolution in both horizontal and vertical dimensions and enables the lidar forest inventory at the individual tree level, which is crucial for the monitoring of tree mortality as well as for tree biomass estimates over areas with non-existent field sampling [20,21]. Furthermore, the merging of multiple acquisitions improves mapping of the surface vegetation characteristics that highly impact the wildfire hazard and behavior [37,38].…”

Section: Resultsmentioning

confidence: 99%

“…On the contrary, high density point clouds (>8 pt/m 2 ) allow direct extraction of forest metrics alleviating the need for costly and time-consuming field work [20,21], reduce uncertainty in forest metrics estimation (e.g., maximum and mean height of canopy) [22], and better represent understory and near-surface vegetation [23]. Therefore, we have the opportunity to markedly improve the forest structure characterization by coherently fusing the weekly ASO measurements (~1 pt/m 2 ) in order to significantly increase the point density (up to~12 pt/m 2 ) and provide a multi-year dataset that is then optimal for any type of lidar-based assessment of vegetation.…”

Section: Introductionmentioning

confidence: 99%

Fusion of NASA Airborne Snow Observatory (ASO) Lidar Time Series over Mountain Forest Landscapes

et al. 2018

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Mountain ecosystems are among the most fragile environments on Earth. The availability of timely updated information on forest 3D structure would improve our understanding of the dynamic and impact of recent disturbance and regeneration events including fire, insect damage, and drought. Airborne lidar is a critical tool for monitoring forest change at high resolution but it has been little used for this purpose due to the scarcity of long-term time-series of measurements over a common region. Here, we investigate the reliability of on-going, multi-year lidar observations from the NASA-JPL Airborne Snow Observatory (ASO) to characterize forest 3D structure at a fine spatial scale. In this study, weekly ASO measurements collected at~1 pt/m 2 , primarily acquired to quantify snow volume and dynamics, are coherently merged to produce high-resolution point clouds (∼12 pt/m 2 ) that better describe forest structure. The merging methodology addresses the spatial bias in multi-temporal data due to uncertainties in platform trajectory and motion by collecting tie objects from isolated tree crown apexes in the lidar data. The tie objects locations are assigned to the centroid of multi-temporal lidar points to fuse and optimize the location of multiple measurements without the need for ancillary data or GPS control points. We apply the methodology to ASO lidar acquisitions over the Tuolumne River Basin in the Sierra Nevada, California, during the 2014 snow monitoring campaign and provide assessment of the fidelity of the fused point clouds for forest mountain ecosystem studies. The availability of ASO measurements that currently span 2013-2017 enable annual forest monitoring of important vegetated ecosystems that currently face ecological threads of great significance such as the Sierra Nevada (California) and Olympic National Forest (Washington).

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“…From a LiDAR point cloud, certain forest parameters can be determined, such as: aboveground forest biomass [7][8][9], Leaf Area Index (LAI) [10][11][12][13][14] or canopy density [15,16]. Furthermore, LiDAR covered by dense forest vegetation).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover

Cățeanu

Ciubotaru

2020

IJGI

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A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by interpolation, a process which is affected by various errors. Using LiDAR data to collect ground surface data for forestry applications is a challenging scenario because the presence of forest vegetation will hinder the ability of laser pulses to reach the ground. The density of ground observations will be therefore reduced and not homogenous (as it is affected by the variations in canopy density). Furthermore, forest areas are generally present in mountainous areas, in which case the interpolation of the ground surface is more challenging. In this paper, we present a comparative analysis of interpolation accuracy for nine algorithms, which are used for generating Digital Terrain Models from Airborne Laser Scanning (ALS) data, in mountainous terrain covered by dense forest vegetation. For most of the algorithms we find a similar performance in terms of general accuracy, with RMSE values between 0.11 and 0.28 m (when model resolution is set to 0.5 m). Five of the algorithms (Natural Neighbour, Delauney Triangulation, Multilevel B-Spline, Thin-Plate Spline and Thin-Plate Spline by TIN) have vertical errors of less than 0.20 m for over 90 percent of validation points. Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs.data can be used for tree inventories [17][18][19][20], monitoring vegetation health [21][22][23][24] or generating Canopy Height Models (CHMs) [16,25].The main advantage of LiDAR, with regard to forestry, is the fact that laser pulses can penetrate the canopy cover, with some of the pulses reaching the ground level. Therefore, geomorphological data is collected even if the ground is covered by forest vegetation, allowing the generation of a detailed and accurate ground surface model.In general, a data structure that models the elevation over an area is called a Digital Elevation Model (DEM). In short, a DEM is simply a surface that represents the elevation of a certain area in reference to a common vertical datum. When this type of model is used for a representation of the bare-earth surface, it can also be termed as Digital Terrain Model (DTM). In this paper, we opted for the term DTM, in order to emphasize the fact that the models we analyse, while generally speaking classify as DEMs, are meant specifically to represent the surface of the bare-earth.To obtain such a representation of the ground surface, a LiDAR point cloud must initially be filtered-a process which involves the separation of ground-level points from the orig...

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Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory

Cited by 50 publications

References 38 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

Fusion of NASA Airborne Snow Observatory (ASO) Lidar Time Series over Mountain Forest Landscapes

Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover

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