Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches

Costanza, Jennifer K.; Faber-Langendoen, Don; Coulston, John W.; Wear, David N.

doi:10.1186/s40663-017-0123-x

Cited by 13 publications

(6 citation statements)

References 58 publications

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“…We used an Ivan damage boundary map from the Alabama Forestry Commission [ 47 ] to select FIA plots located inside the damage zone. The FIA data for the plots in the area were downloaded and extracted from the Microsoft Access State database applications (USDA Forest Service FIA Datamart webpage ( https://apps.fs.usda.gov/fia/datamart/datamart.html ) [ 45 , 54 ]. We selected our sample from post Ivan plots that were collected between 2006–2012 (Post-Ivan_1 event).…”

Section: Methodsmentioning

confidence: 99%

Long-term effects of catastrophic wind on southern US coastal forests: Lessons from a major hurricane

et al. 2021

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Threats posed by windstorms are an increasing concern to forest managers in the southern United States (US). Studies suggest that the southern US will experience an increase in the occurrence as well as the intensity of windstorms, such as hurricanes, in the future. However, forest managers may have difficulty preparing for this future because there is limited understanding of how windstorms affect the structure and composition of forests over the long term. In this study, we evaluated the impacts of Hurricane Ivan, which made landfall in September 2004 near Gulf Shore, Alabama, impacting forests in the western Florida Panhandle and southwestern Alabama. We acquired the United States Department of Agriculture Forest Inventory and Analysis (FIA) plot data available for the period from 2002 to 2018 for the Ivan-affected area and classified the plots into 4 categories: (1). ND (No Disturbance), (2). NDBH (No Disturbance but Harvested), (3). ID (Disturbance caused by Hurricane Ivan), and (4). IDAH (Disturbance caused by Hurricane Ivan and Harvested). The plots that were damaged by Hurricane Ivan (ID and IDAH plots) had significantly (α = 0.05) (1) higher basal area, (2) higher quadratic mean diameter and height, (3) more diverse tree species composition (species richness and Shannon diversity index), (4) denser stocking of seedling and saplings, (5) lower proportion of dead trees or saplings, and (6) higher live aboveground biomass than the plots that were not damaged by the hurricane (ND and NDBH plots). Diverse stands were not necessarily more windstorm resistant. Species diversity in the overstory may not improve forest resistance to hurricane damage but may improve its resilience following the hurricane. The study suggests that managing stand structure through density management and stand improvement could be critical to windstorm resilience and resistance in the southern US forests.

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Section: Methodsmentioning

confidence: 99%

Long-term effects of catastrophic wind on southern US coastal forests: Lessons from a major hurricane

et al. 2021

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“…The comparison proceeds in most cases until a plurality of a forest type is identified." Each of the forest types are associated with one higher level forest type-group (n = 31 including "Nonstocked" forest) that represents an aggregation of like forest types (Costanza et al, 2018). We used the forest type-group classification rather than individual forest types to account for differences in species composition in a tractable number of classes while maintaining a sufficient sample size to analyze effects of site quality in most forest type-groups.…”

Section: Developing Plot-level Estimates Of Forest Carbon Density Sta...mentioning

confidence: 99%

Classifying, inventorying, and mapping mature and old-growth forests in the United States

Barnett¹,

Aplet²,

Belote³

2023

Front. For. Glob. Change

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Because of its importance to both carbon storage and biodiversity, old forest has regained currency as a focus of forest management and policy. However, absence of a systematic approach to classifying stages of forest development across all forest types precludes our understanding of the current distribution of the mature and old-growth forest estate. Here, we propose functional definitions of mature and old-growth forests consistent with theories of forest stand development and evaluate the implications for assessing their spatial distribution nationwide. Using plot data from a national forest inventory and assuming space-for-time substitution, we modeled forest carbon accumulation over time using saturating, non-linear growth models. We define the onset of old-growth characteristics as occurring at the age when the density of total forest carbon stored in live and dead biomass reaches 95% of the empirically derived maximum, and the mature forest stage as occurring between the peak average carbon increment and the age of onset of old growth. We fit models within unique forest type-groups and, where possible, accounted for differences in site productivity. Population-level estimates of the mature and old-growth forest estate were calculated using sample design-based estimators. Across forest type-groups, the age at onset of old growth varied from 34 to 577 years, and the onset of mature forest conditions ranged between 16 and 313 years. Within forest type-groups, the effect of site quality on the age at onset of mature and old-growth forest varied but generally supported our hypothesis that high site quality accelerates forest development and increases forest carbon storage in old forests. We classified 6.3% of current forested lands in the United States as old growth and almost one-third as mature. Of the current old-growth forest estate, approximately 46% is found on federal public lands, and 11% is currently in congressional reserves. We posit that continued improvements to modeling the dynamic process of forest development and integration with structural definitions of old growth will be needed to ensure targets for old-growth retention and development are achieved.

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“…This dataset is a 250-m resolution map of the USA forest types generated from MODIS imagery and Forest Inventory and Analysis (FIA) plot data (Ruefenacht et al 2008). Forest types are assigned to FIA plots using a decision tree based on the relative stocking values of tree species in the plot, which are primarily a function of basal area (Arner et al 2003;Costanza et al 2018). To provide additional validation for this map, we selected the most abundant forest types within the study area (Table 1), and used two field plot datasets: 6068 FIA plots from Montana and Idaho, and 39,852 Gradient Nearest Neighbor (GNN) plots from Washington and Oregon.…”

Section: Existing Forest Type Mapmentioning

confidence: 99%

Using a trait-based approach to asses fire resistance in forest landscapes of the Inland Northwest, USA

et al. 2022

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Context Several plant traits are associated with resistance to fire, thus fire-resistant species may give rise to more fire-resistant landscapes. However, up-scaling from plant traits to landscape- and regional-scale fire effects remains a challenge. Objectives We test two hypotheses: (1) forests composed of fire-resistant species experience lower fire severity than forests composed of less fire-resistant species; and (2) wildfires affecting forests with greater fire resistance experience smaller patches of high-severity fire. Methods We used a predictive map of existing forest types (major tree species dominating forest composition) and a trait-based map of fire resistance. We examined large-scale spatial patterns of fire severity derived from Landsat imagery in 611 wildfires across the range of western larch in the Inland Northwest USA (1985–2014). We then applied structural equation modeling to study complex relationships between fire resistance and high-severity fire in each wildfire. Results Forest types dominated by fire-resister species (e.g., ponderosa pine) experienced lower fire severity than forest types dominated by non-resister species such as lodgepole pine (fire-embracer) and subalpine fir (fire-avoider). We found a strong negative correlation between the fire resistance index and average values of the relative differenced normalized burn ratio, as well as an indirect relationship between fire resistance and high-severity patch size. Conclusions The large-scale differences in fire severity among forest types generally reflect the degree of fire resistance that fire-related traits confer to individual trees species, providing evidence that incorporating plant traits has the potential to assist in assessing fire resistance at large spatial scales.

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Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches

Cited by 13 publications

References 58 publications

Long-term effects of catastrophic wind on southern US coastal forests: Lessons from a major hurricane

Long-term effects of catastrophic wind on southern US coastal forests: Lessons from a major hurricane

Classifying, inventorying, and mapping mature and old-growth forests in the United States

Using a trait-based approach to asses fire resistance in forest landscapes of the Inland Northwest, USA

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