Assessment of Fire Effects Based on Forest Inventory and Analysis Data and a Long-Term Fire Mapping Data Set

Shaw, John D.; Goeking, Sara A.; Menlove, James; Werstak, Charles E.

doi:10.5849/jof.2016-115

Cited by 15 publications

(14 citation statements)

References 12 publications

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“…The results presented here represent only the effects of fire on residual stem densities and do not include resprouting or seed regeneration which occur during a later process in LANDIS PRO. This is comparable to the observations of fire effects from FIA plots, which are resampled at five year intervals, and typically would not sample post-fire regeneration if the fire occurred within five years or less since there is a 2.54 cm DBH threshold for inclusion [60]. In addition, the observations of fire disturbance within FIA do not include information regarding the cause of fire which makes differentiating between wildfire and prescribed fire in the data difficult.…”

Section: Discussionsupporting

confidence: 53%

“…Likewise, McNab [58] found that low-intensity fires reduced stem densities in loblolly pine stands in Georgia and that mortality was limited to trees 10 cm DBH and smaller. Some studies have examined individual fire effects using FIA data in the western United States [59,60], although to our knowledge there have been no studies in the eastern United States.…”

Section: Discussionmentioning

confidence: 99%

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Modeling Post-Fire Tree Mortality Using a Logistic Regression Method within a Forest Landscape Model

Fraser

Wang

et al. 2019

Forests

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Fire is a multi-scale process that is an important component in determining ecosystem age structures and successional trajectories across forested landscapes. In order to address questions regarding fire effects over large spatial scales and long temporal scales researchers often employ forest landscape models which can model fire as a spatially explicit disturbance. Within forest landscape models site-level fire effects are often simplified to the species, functional type, or cohort level due to time or computational resource limitations. In this study we used a subset of publicly available U.S. Forest Service forest inventory data (FIA) to estimate short-term fire effects on tree densities across multiple stem diameter classes in two ecological sections in the central and southern United States. We found that FIA plots where low-intensity fires occurred within the preceding five years in the Ozark Highlands ecological section had significantly reduced stem densities in the two smallest diameter classes and in the Gulf Coastal Plains and Flatwoods fire reduced stem densities in the three smallest diameter classes. Using an independent subset of FIA plots we then parameterized and calibrated a forest landscape model to simulate site-level fire effects using a logistic regression based method and compare the results to previous methods of modeling fire effects. When representative landscapes from both study areas were simulated under a low-intensity fire regime using a forest landscape model the logistic regression probability method of modeling fire effects produced a similar reduction in stem densities while the previous age-cohort method overestimated density reductions across diameter classes. A more realistic representation of fire effects, particularly in low intensity fire regimes, increases the utility of forest landscape models as tools for planning and management.

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Section: Discussionsupporting

confidence: 53%

Section: Discussionmentioning

confidence: 99%

Modeling Post-Fire Tree Mortality Using a Logistic Regression Method within a Forest Landscape Model

Fraser

Wang

et al. 2019

Forests

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“…It is also consistent with the impacts of fire. Across the Interior West, inventory data has shown that mortality is complete on only about 25 percent of burned conditions, and that less than 10 percent of burned conditions have no regeneration tallied at the time of plot visit (Shaw et al 2017). The conclusion drawn from this analysis is that while disturbances such as drought, fire, and insects have slightly reduced the overall density of Arizona's forests, the impacts have not been catastrophic at the population scale.…”

Section: Stand Density Index (Sdi)mentioning

confidence: 77%

Arizona’s forest resources, 2001-2014

Shaw¹,

Menlove²,

Witt³

et al. 2018

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“…FIA scientists have likewise used LANDFIRE and MTBS products in their research. For example, Whittier and Gray [102] and Shaw et al [103] intersected FIA plot data with MTBS maps in order to characterize the effects of fire on forests. Dugan et al [27] used FIA data, Landsat imagery, MTBS, and other data to characterize the disturbances associated with carbon loss on USFS NFS land.…”

Section: Landsatmentioning

confidence: 99%

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

Lister

Andersen

Frescino

et al. 2020

Forests

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Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

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Assessment of Fire Effects Based on Forest Inventory and Analysis Data and a Long-Term Fire Mapping Data Set

Cited by 15 publications

References 12 publications

Modeling Post-Fire Tree Mortality Using a Logistic Regression Method within a Forest Landscape Model

Modeling Post-Fire Tree Mortality Using a Logistic Regression Method within a Forest Landscape Model

Arizona’s forest resources, 2001-2014

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

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