Sampling Methods for Multiresource Forest Inventory.

Smith, Ron; Schreuder, Hans T.; Gregoire, T. G.; Wood, G. B.

doi:10.2307/2533478

Cited by 13 publications

(21 citation statements)

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“…Practical issues preventing full execution of a designed sample are common in forest inventories, and can in some cases compromise ensuant inferences (estimates of forest attributes and their uncertainties) (Westfall et al 2022). As with any probability sample, it is also possible that a given (relatively small) set of observations may not accurately represent the larger population (Schreuder et al 1993). In either case, model-based methods such as hybrid estimation make fewer assumptions about the empirical distribution of the data used in the analysis; we need only to assume that the model applies to the population of interest (Gregoire 1998).…”

Section: Discussionmentioning

confidence: 99%

“…Quantifying carbon stored in forest biomass is essential for understanding terrestrial carbon fluxes and for achieving the objectives set under the United Nations Framework Convention on Climate Change (UNFCCC). National forest inventories (NFIs) are critical for monitoring aboveground biomass density (AGBD) since they generally deploy probabilitybased sample design strategies that enable straightforward inference about biomass quantities over large areas (hereafter referred to as 'design-based' estimation) (Brown et al 1989, Schreuder et al 1993, Gregoire 1998. However, the global distribution of field plots remains limited, and many forests are not properly represented due to inaccessibility or financial constraints (de Freitas et al 2009, 2013b.…”

Section: Introductionmentioning

confidence: 99%

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Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory

Bullock,

Healey,

Yang

et al. 2023

Environ. Res. Lett.

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Forests are widely recognized as critical to combating climate change due to their ability to sequester and store carbon in the form of biomass. In recent years, the combined use of data from ground-based forest inventories and remotely sensed data from lidar (Light Detection and Ranging) has proven useful for large-scale assessment of forest biomass, but airborne lidar is expensive and data acquisition is infeasible for many countries. By contrast, the spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument has collected freely available data for most of the world’s temperate and tropical forests since 2019. GEDI’s biomass products rely on models calibrated with a global network of field plots paired with GEDI waveforms simulated from airborne lidar to predict biomass. While this calibration strategy minimizes spatial and temporal offsets between field measurements and corresponding lidar returns, calibration data are sparse in many regions. Paraguay’s forests are known to be poorly represented in GEDI’s current calibration dataset, and here we demonstrate that local models calibrated opportunistically with on-orbit GEDI data and field surveys from Paraguay’s national forest inventory can be used with GEDI’s statistical estimators of aboveground biomass density. We specify a protocol for opportunistically matching GEDI observations with field plots to calibrate a field-to-GEDI biomass model for use in GEDI’s hybrid statistical framework. Country-specific calibration using on-orbit data resulted in relatively accurate and unbiased predictions of footprint-level biomass, and importantly, supported the assumption underlying model-based inference that the model must “apply” to the area of interest. Using a locally calibrated biomass model, we estimate that the mean aboveground biomass density in Paraguay is 65.55 Mg/ha, which coincides well with the design-based approach employed by the national forest inventory. The GEDI estimates for individual forest strata range from 52.34 Mg/ha to 103.88 Mg/ha. On average, the standard errors are 47% lower for estimates based on GEDI than the forest inventory, representing a significant gain in precision. Our research demonstrates that GEDI can be used by national forest inventories in countries that seek reliable estimates of aboveground biomass density, and that local calibration using existing field plots may be more appropriate in some applications than using GEDI global models, especially in regions where those models are sparsely calibrated.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory

Bullock,

Healey,

Yang

et al. 2023

Environ. Res. Lett.

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“…These methods can be considered as extensions of Bitterlich sampling. In the first stage of Bitterlich sampling, the probability of selecting trees ( ) 1 π is proportional to their basal area (De Vries, 1986;Gregoire & Valentine, 2007b;Kershaw Jr. et al, 2016a;Overton & Stehman, 1995;Schreuder, Gregoire, & Wood, 1993), 1 g π ∝ where ( ) F F that are used in the two sampling stages,…”

Section: Mathematical Description Of Two Sampling Methodsmentioning

confidence: 99%

“…. In the second stage of the Point-3P method, the probability of the trees' selection is proportional to a prediction of their height (Gregoire & Valentine, 2007b;Kershaw Jr. et al, 2016a;Schreuder et al, 1993), 21 h π ∝ where h is a prediction of the trees' height. The variables that determine the probabilities of selecting trees in the second stage are well correlated with the variable of interest, which is the VBAR.…”

Section: Mathematical Description Of Two Sampling Methodsmentioning

confidence: 99%

Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

Georgakis¹,

Stamatellos²

2019

OJF

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Big Basal Area Factor (Big BAF) and Point-3P are two-stage sampling methods. In the first stage the sampling units, in both methods, are Bitterlich points where the selection of the trees is proportional to their basal area. In the second stage, sampling units are trees which are a subset of the first stage trees. In the Big BAF method, the probability of selecting trees in the second stage is made proportional to the two BAFs' ratio, with a basal area factor larger than that of the first stage. In the Point-3P method the probability of selecting trees, in the second stage, is based on the height prediction and use of a specific random number table. Estimates of the forest stands' volume and their sampling errors are based on the theory of the product of two random variables. The increasing error in the second stage is small, but the total cost of measuring the trees is much smaller than simply using the first stage, with all the trees measured. In general, the two sampling methods are modern and cost-effective approaches that can be applied in forest stand inventories for forest management purposes and are receiving the growing interest of researchers in the current decade.

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“…Untuk mengetahui sebaran dan kelimpahan B. pentamera di TNGP dilakukan studi vegetasi secara terperinci dengan pembuatan sampel plot menggunakan metode systematic sampling (penarikan contoh beraturan) didasarkan pada deskripsi atau pertelaan dan penelaahan komunitas tumbuhan (Mueller-Dombois & Ellenberg, 2014). Pembuatan petak/plot ukur menggunakan metode plot melingkar dengan radius tetap dengan jari-jari 11,28 m. Penggunaan plot yang lebih umum disebut dengan plot area tetap adalah salah satu metode tertua yang masih banyak digunakan hingga saat ini (Schreuder, Gregoire, & Wood, 1993). Plot radius tetap sering digunakan karena bisa diselesaikan oleh tim kecil atau bahkan hanya satu orang, tidak dipengaruhi secara signifikan oleh tumbuhan bawah, dan memungkinkan melakukan perhitungan sederhana untuk meringkas data tegakan (Keene & Barlow, 2018).…”

Section: Metode Penelitianunclassified

Sebaran dan Kelimpahan Jenis Invasif Bellucia pentamera Naudin di Taman Nasional Gunung Palung, Kalimantan Barat

Setiawan

Darnaedi²,

Setia³

et al. 2022

Jur.pen.hut.konservasi

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Sampling Methods for Multiresource Forest Inventory.

Cited by 13 publications

References 0 publications

Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory

Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory

Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

Sebaran dan Kelimpahan Jenis Invasif Bellucia pentamera Naudin di Taman Nasional Gunung Palung, Kalimantan Barat

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