Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data

Palace, M. W.; Sullivan, F.; Ducey, Mark J.; Treuhaft, R. N.; Herrick, C.; Shimbo, Julia; Mota-E-Silva, Jonas

doi:10.1016/j.rse.2015.01.020

Cited by 95 publications

(73 citation statements)

References 86 publications

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“…Forest structure, defined as the spatiotemporal arrangement of structural components in specific vertical and horizontal spatial patterns within a forest stand [3][4][5], is recognized as both a product and driver of forest biophysical processes [6] …”

Section: Introductionmentioning

confidence: 99%

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

2017

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Accurate and timely estimation of forest structural parameters plays a key role in the management of forest resources, as well as studies on the carbon cycle and biodiversity. Light Detection and Ranging (LiDAR) is a promising active remote sensing technology capable of providing highly accurate three dimensional and wall-to-wall forest structural characteristics. In this study, we evaluated the utility of standard metrics and canopy metrics derived from airborne LiDAR data for estimating plot-level forest structural parameters individually and in combination, over a subtropical forest in Yushan forest farm, southeastern China. Standard metrics, i.e., height-based and density-based metrics, and canopy metrics extracted from canopy vertical profiles, i.e., canopy volume profile (CVP), canopy height distribution (CHD), and foliage profile (FP), were extracted from LiDAR point clouds. Then the standard metrics and canopy metrics were used for estimating forest structural parameters individually and in combination by multiple regression models, including forest type-specific (coniferous forest, broad-leaved forest, mixed forest) models and general models. Additionally, the synergy of standard metrics and canopy metrics for estimating structural parameters was evaluated using field measured data. Finally, the sensitivity of vertical and horizontal resolution of voxel size for estimating forest structural parameters was assessed. The results showed that, in general, the accuracies of forest type-specific models (Adj-R 2 = 0.44-0.88) were relatively higher than general models (Adj-R 2 = 0.39-0.77). For forest structural parameters, the estimation accuracies of Lorey's mean height (Adj-R 2 = 0.61-0.88) and aboveground biomass (Adj-R 2 = 0.54-0.81) models were the highest, followed by volume (Adj-R 2 = 0.42-0.78), DBH (Adj-R 2 = 0.48-0.74), basal area (Adj-R 2 = 0.41-0.69), whereas stem density (Adj-R 2 = 0.39-0.64) models were relatively lower. The combination models (Adj-R 2 = 0.45-0.88) had higher performance compared with models developed using standard metrics (only) (Adj-R 2 = 0.42-0.84) and canopy metrics (only) (Adj-R 2 = 0.39-0.83). The results also demonstrated that the optimal voxel size was 5 × 5 × 0.5 m 3 for estimating most of the parameters. This study demonstrated that canopy metrics based on canopy vertical profiles can be effectively used to enhance the estimation accuracies of forest structural parameters in subtropical forests.

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Section: Introductionmentioning

confidence: 99%

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

2017

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“…The superiority of such data-rich paradigm was exemplified in our physically-based scale-invariant and machine learning biomass models (Zhao et al, 2011b. Despite its great potential (Palace et al, 2015), this paradigm is still underappreciated.…”

Section: Introductionmentioning

confidence: 99%

Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution

Zhao

Garcı́a

Liu

et al. 2015

Agricultural and Forest Meteorology

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“…It is worth mentioning that since the vertical structure index is reflected by the dispersion of the heights of the peaks, it is not significantly affected by changes in the vertical resolution of the system employed. It should be mentioned that similar measures can be found in the literature for waveform Lidar [48,49]. Furthermore, it is important to note here that system and, even more so, seasonal or environmental variability of the forest may affect the tomographic reflectivity and impact the estimation of structure indices.…”

Section: Forest Structure Estimationmentioning

confidence: 56%

Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography

et al. 2017

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Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as forests, thus providing an opportunity to infer structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations of forest structure is addressed using simulated and experimental datasets. First, 3D reflectivity profiles were extracted by means of TomoSAR reconstruction based on a Compressive Sensing (CS) approach. Next, two complementary indices for the description of horizontal and vertical forest structure were defined and estimated by means of the distribution of local maxima of the reconstructed reflectivity profiles. To assess the sensitivity and consistency of the proposed methodology, variations of these indices for different types of forest changes in simulated as well as in real scenarios were analyzed and assessed against different sources of reference data: airborne Lidar measurements, high resolution optical images, and forest inventory data. The forest structure maps obtained indicated the potential to distinguish between different forest stages and the identification of different types of forest structure changes induced by logging, natural disturbance, or forest management.

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Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data

Cited by 95 publications

References 86 publications

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution

Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography

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