Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR

Xu, Qing; Man, Albert; Fredrickson, Mark M.; Hou, Zhengyang; Pitkänen, Juho; Wing, Brian M.; Li, Bo; Greenberg, Jonathan A.

doi:10.1016/j.rse.2018.07.022

Cited by 43 publications

(31 citation statements)

References 40 publications

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“…Also, our method for forest thinning relies on tree segmentation that can only distinguish the upper canopy and therefore underestimates smaller trees and the amount of thinning actually required. The dataset employed here estimates that 39% of trees are missed (Xu et al, ). Application of more sophisticated tree segmentation algorithms and denser lidar point clouds could reduce omission errors.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we define “stand” as 30 m × 30 m pixels in consultation with our management partners, as it represents a scale at which decisions on forest thinning are being made. In addition to the products we produced, we also acquired a dataset of individual tree crown perimeters from Xu et al, . These tree crown polygons were derived from the same lidar dataset and include per‐tree estimates of height.…”

Section: Methodsmentioning

confidence: 99%

“…We apply a “virtual thinning” proof of concept to understand the effects of forest canopy removal on snowpack mass and energy budgets. The SnowPALM model was run at 1‐m resolution with individual tree canopies removed based on the Xu et al () dataset. Our method removes trees below four thresholds heights <5, <10, <15, and <20 m, as well as a control with no tree removal, and then simulates the effects on snowpack dynamics using SnowPALM.…”

Section: Methodsmentioning

confidence: 99%

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Increasing the efficacy of forest thinning for snow using high‐resolution modeling: A proof of concept in the Lake Tahoe Basin, California, USA

et al. 2020

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Forest manipulation using forest thinning is one of the few means to manage water resources supplying downstream populations that are derived from snow-covered montane forests. The challenges of simulating processes at the tree scale have precluded generalizable recommendations for removing tree canopy to maximize snow water resources. Here, we apply the high-resolution Snow Physics and Lidar Mapping (SnowPALM) model to simulate snow mass and energy budgets at 1-m scale over a 1,200 by 1,200 m domain on the west shore of Lake Tahoe, Sierra Nevada, USA. The SnowPALM model verifies well against observations of snow depth and snow temperature in open and under canopy locations. This supported the application of SnowPALM under "virtual thinning" experiments, where all trees <5, <10, <15, and <20 m height were removed during a 3-year simulation. Increasing snowpack sublimation losses are smaller than reductions in canopy interception resulting in an overall increase in melt volume. Despite relatively modest melt volume increases of 4-8% across the entire domain, individual 30-m stands couldhave >30% melt volume increases. On average, a 0.5 decrease in lidar-derived leaf area resulted in an~10.5% increase in melt volume, with shorter, denser 30-m forest stands having greater melt volume sensitivity. These dense forest stands, where forest thinning was most effective, were found in valley bottoms and north-facing slopes across the west shore region. This proof of concept supports large domain simulations using high-resolution models to inform landscape-scale restoration decisions. K E Y W O R D Sforest management, forest restoration, forest thinning, hydrological modelling, Lake Tahoe Basin, snow, snow-forest interactions

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Increasing the efficacy of forest thinning for snow using high‐resolution modeling: A proof of concept in the Lake Tahoe Basin, California, USA

et al. 2020

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“…Through, we will use the symbol PQ to symbolize the vector of parameters characterizing λ m (u). It turns out that by setting m = 0 Equation (11) returns the linear regression model of the TAMA protocol.…”

Section: Identification Of Z(u) Involving Polynomial Formsmentioning

confidence: 99%

“…For instance, aboveground tree biomass in forest ecosystems has been estimated through remote sensing protocols [7][8][9][10]. Nevertheless, a number of factors such as sample size, weather, complexity of biophysical settings, study area scale, software, or spatial resolution can induce uncertainty of remote-sensed estimation [11][12][13][14][15]. Allometric methods allow implementation of parallel cost-effective non-destructive estimation of plant biomass units [16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A Generalized Model of Complex Allometry I: Formal Setup, Identification Procedures and Applications to Non-Destructive Estimation of Plant Biomass Units

Echavarría-Heras¹,

Leal-Ramírez²,

Villa-Diharce³

et al. 2019

Applied Sciences

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(1) Background: We previously demonstrated that customary regression protocols for curvature in geometrical space all derive from a generalized model of complex allometry combining scaling parameters expressing as continuous functions of covariate. Results highlighted the relevance of addressing suitable complexity in enhancing the accuracy of allometric surrogates of plant biomass units. Nevertheless, examination was circumscribed to particular characterizations of the generalized model. Here we address the general identification problem. (2) Methods: We first suggest a log-scales protocol composing a mixture of linear models weighted by exponential powers. Alternatively, adopting an operating regime-based modeling slant we offer mixture regression or Takagi–Sugeno–Kang arrangements. This last approach allows polyphasic identification in direct scales. A derived index measures the extent on what complexity in arithmetic space drives curvature in arithmetical space. (3) Results: Fits on real and simulated data produced proxies of outstanding reproducibility strength indistinctly of data scales. (4) Conclusions: Presented analytical constructs are expected to grant efficient allometric projection of plant biomass units and also for the general settings of allometric examination. A traditional perspective deems log-transformation and allometry inseparable. Recent views assert that this leads to biased results. The present examination suggests this controversy can be resolved by addressing adequately the complexity of geometrical space protocols

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The prediction of uneven snowpack response to forest thinning informs forest restoration in the central Sierra Nevada

Lewis,

Harpold,

Krogh

et al. 2023

Ecohydrology

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The Sierra Nevada has experienced unprecedented wildfires and reduced snowmelt runoff in recent decades, due partially to anthropogenic climate change and over a century of fire suppression. To address these challenges, public land agencies are planning forest restoration treatments, which have the potential to both increase water availability and reduce the likelihood of uncontrollable wildfires. However, the impact of forest restoration on snowpack is site specific and not well understood across gradients of climate and topography. To improve our understanding of how forest restoration might impact snowpack across diverse conditions in the central Sierra Nevada, we run the high‐resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model across five 23–75 km2 subdomains in the region where forest thinning is planned or recently completed. We conduct two virtual thinning experiments by removing all trees shorter than 10 or 20 m tall and rerunning SnowPALM to calculate the change in meltwater input. Our results indicate heterogeneous responses to thinning due to differences in climate and wind across our five central Sierra Nevada subdomains. We also predict the largest increases in snow retention when thinning forests with tall (7–20 m) and dense (40–70% canopy cover) trees, highlighting the importance of pre‐thinning vegetation structure. We develop a decision support tool using a random forests model to determine which regions would most benefit from thinning. In many locations, we expect major forest restoration to increase snow accumulation, while other areas with short and sparse canopies, as well as sunny and windy climates, are more likely to see decreased snowpack following thinning. Our decision support tool provides stand‐scale (30 m) information to land managers across the central Sierra Nevada region to best take advantage of climate and existing forest structure to obtain the greatest snowpack benefits from forest restoration.

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Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR

Cited by 43 publications

References 40 publications

Increasing the efficacy of forest thinning for snow using high‐resolution modeling: A proof of concept in the Lake Tahoe Basin, California, USA

Increasing the efficacy of forest thinning for snow using high‐resolution modeling: A proof of concept in the Lake Tahoe Basin, California, USA

A Generalized Model of Complex Allometry I: Formal Setup, Identification Procedures and Applications to Non-Destructive Estimation of Plant Biomass Units

The prediction of uneven snowpack response to forest thinning informs forest restoration in the central Sierra Nevada

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