Disentangling Forest Change from Forest Inventory Change: A Case Study from the US Interior West

Goeking, Sara A.

doi:10.5849/jof.14-088

Cited by 22 publications

(38 citation statements)

References 18 publications

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“…Past and current FIA data include two general sets of sampling protocols termed periodic and annual, respectively. These two sampling protocols differ by subplot number and configuration (Figure 1), sampling design, and types of measurement (Goeking 2015;McRoberts et al 2005). Former FIA surveys (before 1998) were termed periodic as return times to a particular state for re-measurement ranged from 6 to 18 years (McRoberts et al 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Former FIA surveys (before 1998) were termed periodic as return times to a particular state for re-measurement ranged from 6 to 18 years (McRoberts et al 2005). The USDA Forest Service recognized issues with the periodic sampling protocol, including bias and uncertainty due to regionally variable sampling protocols and definitions (Goeking 2015), which, when compounded by land-use change over long sample periods, made area and volume estimation increasingly difficult over large regions (McRoberts et al 2005). To alleviate such problems, the 1998 Farm Bill laid out a plan to initiate an annual protocol of FIA sampling (after 1998) that would standardize a national set of core variables.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconstructing past forest composition and abundance by using archived Landsat and national forest inventory data

Thapa

Wolter

Sturtevant

et al. 2020

International Journal of Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstructing past forest composition and abundance by using archived Landsat and national forest inventory data

Thapa

Wolter

Sturtevant

et al. 2020

International Journal of Remote Sensing

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“…Nationally, the United States Department of Agriculture (USDA) Forest Service inventories permanent plots across each state annually, visiting 20% of all plots per state per year [16]. Prior to 1998, data collection protocols for the Forest Service's Forest Inventory and Analysis (FIA) program were not nationally consistent, resulting in variable trends by state across several forest measures [17]. Many studies have shown that inconsistent data collection, observer bias, and varying sampling methods have an effect on data analysis and interpretation [3,10,17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Prior to 1998, data collection protocols for the Forest Service's Forest Inventory and Analysis (FIA) program were not nationally consistent, resulting in variable trends by state across several forest measures [17]. Many studies have shown that inconsistent data collection, observer bias, and varying sampling methods have an effect on data analysis and interpretation [3,10,17,18]. To combat these issues and improve statistical power, researchers recommend extensive training on data collection protocols, specific objectives, consistent observers, and/or quality control data assessments [18].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Inter-Rater Reliability and Statistical Power of Vegetation Measures Assessing Deer Impact

Begley-Miller

Diefenbach

McDill

et al. 2018

Forests

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Long-term vegetation monitoring projects are often used to evaluate how plant communities change through time in response to some external influence. Here, we evaluate the efficacy of vegetation monitoring to consistently detect changes in white-tailed deer browsing effects. Specifically, we compared inter-rater reliability (Cohen’s κ and Lin’s concordance correlation coefficient) between two identically trained field crews for several plant metrics used by Pennsylvania state agencies to monitor deer browsing impact. Additionally, we conducted a power analysis to determine the effect of sampling scale (1/2500th or 1/750th ha plots) on the ability to detect changes in tree seedling stem counts over time. Inter-rater reliability across sampling crews was substantial for most metrics based on direct measurements, while the observational based Deer Impact Index (DII) had only moderate inter-rater reliability. The smaller, 1/2500th ha sampling scale resulted in higher statistical power to detect changes in tree seedling stem counts due to reduced observer error. Overall, this study indicates that extensive training on plant identification, project protocols, and consistent data collection methods can result in reliable vegetation metrics useful for tracking understory responses to white-tailed deer browsing. Smaller sampling scales and objective plant measures (i.e., seedling counts, species richness) improve inter-rater reliability over subjective measures of deer impact (i.e., DII). However, considering objective plant measures when making a subjective assessment regarding deer browsing effects may also improve DII inter-rater reliability.

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“…Beginning in 2000, all states began implementing a nationally consistent, statistically robust sample and plot design [20]. To mitigate for known confounding effects of inventory methods, sample designs, and definitions between the two periods [24], we compared proportions of live and dead trees relative to the total number of whitebark pine trees during the historical period, rather than actual numbers of trees.…”

Section: Number Of Trees By Size Class and Forest Typementioning

confidence: 99%

Pinus albicaulis Engelm. (Whitebark Pine) in Mixed-Species Stands throughout Its US Range: Broad-Scale Indicators of Extent and Recent Decline

Goeking

Izlar

2018

Forests

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Abstract:We used data collected from >1400 plots by a national forest inventory to quantify population-level indicators for a tree species of concern. Whitebark pine (Pinus albicaulis) has recently experienced high mortality throughout its US range, where we assessed the area of land with whitebark pine present, size-class distribution of individual whitebark pine, growth rates, and mortality rates, all with respect to dominant forest type. As of 2016, 51% of all standing whitebark pine trees in the US were dead. Dead whitebark pines outnumbered live ones-and whitebark pine mortality outpaced growth-in all size classes ≥22.8 cm diameter at breast height (DBH), across all forest types. Although whitebark pine occurred across 4.1 million ha in the US, the vast majority of this area (85%) and of the total number of whitebark pine seedlings (72%) fell within forest types other than the whitebark pine type. Standardized growth of whitebark pines was most strongly correlated with the relative basal area of whitebark pine trees (rho = 0.67; p < 0.01), while both standardized growth and mortality were moderately correlated with relative whitebark pine stem density (rho = 0.39 and 0.40; p = 0.031 and p < 0.01, respectively). Neither growth nor mortality were well correlated with total stand basal area, total stem density, or stand mean diameter. The abundance, extent, and relative growth vs. mortality rates of whitebark pine in multiple forest types presents opportunities for management to encourage whitebark pine recruitment in mixed-species stands. The lodgepole pine forest type contained more whitebark pine seedlings (35%) than any other forest type, suggesting that this forest type represents a potential management target for silvicultural treatments that seek to facilitate the recruitment of whitebark pine seedlings into larger size classes. National forest inventories in other countries may use a similar approach to assess species of concern.

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Disentangling Forest Change from Forest Inventory Change: A Case Study from the US Interior West

Cited by 22 publications

References 18 publications

Reconstructing past forest composition and abundance by using archived Landsat and national forest inventory data

Reconstructing past forest composition and abundance by using archived Landsat and national forest inventory data

Evaluating Inter-Rater Reliability and Statistical Power of Vegetation Measures Assessing Deer Impact

Pinus albicaulis Engelm. (Whitebark Pine) in Mixed-Species Stands throughout Its US Range: Broad-Scale Indicators of Extent and Recent Decline

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