Comparing methods that quantify forest disturbances in the United States’ national forest inventory

Fitts, Lucia Alejandra; Domke, Grant M.; Russell, Matthew B.

doi:10.1007/s10661-022-09948-z

Cited by 11 publications

(8 citation statements)

References 32 publications

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“…This suggests that human is still the major driver of potential carbon loss/transfer in US with heavy harvesting in the Northwestern and Southeastern US. Our estimation of anthropogenic disturbance is very close to the number reported by Fitts et al (2022) based on the proportion of trees disturbed for the silviculture category. Fitts et al (2022) reported that more than 25% tree disturbance is caused by fire, which is much higher than the percentage of canopy area loss (10.7%) caused by fire reported by our study.…”

Section: Discussionsupporting

confidence: 84%

“…Our estimation of anthropogenic disturbance is very close to the number reported by Fitts et al (2022) based on the proportion of trees disturbed for the silviculture category. Fitts et al (2022) reported that more than 25% tree disturbance is caused by fire, which is much higher than the percentage of canopy area loss (10.7%) caused by fire reported by our study. This could result from the difference in our definition of fire disturbance, as we focused on the canopy tree loss from the RS perspective, while Fitts et al (2022) used all the trees in the inventory and not only included dead trees but also partially damaged trees based on FIA tree variable: DAMAGE_AGENT_CD.…”

Section: Discussionsupporting

confidence: 84%

“…Fitts et al (2022) reported that more than 25% tree disturbance is caused by fire, which is much higher than the percentage of canopy area loss (10.7%) caused by fire reported by our study. This could result from the difference in our definition of fire disturbance, as we focused on the canopy tree loss from the RS perspective, while Fitts et al (2022) used all the trees in the inventory and not only included dead trees but also partially damaged trees based on FIA tree variable: DAMAGE_AGENT_CD. Among natural disturbances, about 19.4% of potential carbon loss/transfer is in the other category, which is of similar magnitude to fire and drought.…”

Section: Discussioncontrasting

confidence: 76%

“…Quantitative attribution studies of tree mortality, particularly those caused by drought, are few and have mostly been limited to relatively fine scales (e.g., plot to watershed) or species-specific responses at larger scales (e.g., Stanke et al, 2021) due to the complexity of forest ecosystem and compound effects among the underlying driving factors. Broad-scale quantitative attribution of such loss at the agent level are rarely found with a few studies focused on single agent (Tyukavina et al, 2022) or empirically attributed disturbance agencies based on forest inventory (Fitts et al, 2022). To better understand how drought impacts the large-scale carbon cycling and associated feedbacks to climate, it is important that we attribute both the area and amount of potential carbon loss to atmosphere or transfer to different products (e.g., wood products for timbers) to different types of disturbances at continental and ultimately global scales.…”

Section: Introductionmentioning

confidence: 99%

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Multi-scale quantification of anthropogenic, fire, and drought-associated forest disturbances across the continental U.S., 2000–2014

Wang

Johnson

et al. 2022

Front. For. Glob. Change

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Our understanding of broad-scale forest disturbances under climatic extremes remains incomplete. Drought, as a typical extreme event, is a key driver of forest mortality but there have been no reports on continental-scale quantification of its impact on forest mortality or how it compares to other natural or anthropogenic drivers. Thus, our ability to understand and predict broad-scale carbon cycling in response to changing climate and extreme events is limited. In this study, we applied an attribution approach based on different sources of data to quantify the area and potential carbon loss/transfer in continental U.S. (CONUS) from four types of disturbance: (1) anthropogenic (especially timber harvest); (2) fire; (3) drought-associated; and (4) other from 2000 to 2014. Our results showed that anthropogenic disturbances, fire, drought-associated disturbances, and other disturbances accounted for 54.3, 10.7, 12.7, and 22.3% of total canopy area loss, respectively. Anthropogenic disturbance was the most important driver contributing to 58.1% potential carbon loss/transfer in CONUS for 2000–2014. The potential carbon loss/transfer from natural disturbances (fire, drought, and other) for the same study period accounted for approximately 41.9% of the total loss/transfer from all agents, suggesting that natural disturbances also played a very important role in forest carbon turnover. Potential carbon loss/transfer associated with drought accounted for approximately 11.6% of the total loss/transfer in CONUS, which was of similar magnitude to potential carbon loss/transfer from fire (∼11.0%). The other natural disturbance accounted for 19.3% of potential carbon loss/transfer. Our results demonstrated the importance of the impacts of various disturbances on forest carbon stocks at the continental scale, and the drought-associated carbon loss/transfer data developed here could be used for evaluating the performance of predictive models of tree mortality under droughts.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionsupporting

confidence: 84%

Section: Discussioncontrasting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

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Multi-scale quantification of anthropogenic, fire, and drought-associated forest disturbances across the continental U.S., 2000–2014

Wang

Johnson

et al. 2022

Front. For. Glob. Change

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“…Changes vary in magnitude and cause (biotic, abiotic or anthropogenic), from small, natural changes associated with stand development and aging, to moderate disturbances due to pests, wildlife or silviculture, to large disturbances that remove the forest completely such as stand‐replacing disturbance (e.g. wildfire, flooding and windthrow), clear‐cut harvesting and land conversion (Fitts et al, 2022). Depending on the sequence of events (remote sensing followed by disturbance followed by field sampling, or vice versa), forest parameters may be under‐ or over‐estimated when harmonizing remotely sensed and NFI data.…”

Section: Sources Of Outliers Arising From Geospatial Data Harmonizationmentioning

confidence: 99%

Effects of outliers on remote sensing‐assisted forest biomass estimation: A case study from the United States national forest inventory

et al. 2023

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1. Large-scale ecological sampling networks, such as national forest inventories (NFIs), collect in situ data to support biodiversity monitoring, forest management and planning, and greenhouse gas reporting. Data harmonization aims to link auxiliary remotely sensed data to field-collected data to expand beyond field sampling plots, but outliers that arise in data harmonization-questionable observations because their values differ substantially from the rest-are rarely addressed.2. In this paper, we review the sources of commonly occurring outliers, including random chance (statistical outliers), definitions and protocols set by sampling networks, and temporal and spatial mismatch between field-collected and remotely sensed data. We illustrate different types of outliers and the effects they have on estimates of above-ground biomass population parameters using a case study of 292 NFI plots paired with airborne laser scanning (ALS) and Sentinel-2 data from Sawyer County, Wisconsin, United States.3. Depending on the criteria used to identify outliers (sampling year, plot location error, nonresponse, presence of zeros and model residuals), as many as 53 of the 292 Forest Inventory and Analysis plot observations (18%) were identified as potential outliers using a single criterion and 111 plot observations (38%) if all criteria were used. Inclusion or removal of potential outliers led to substantial differences in estimates of mean and standard error of the estimate of biomass per unit area. The simple expansion estimator, which does not rely on ALS or other auxiliary data, was more sensitive to outliers than model-assisted approaches that incorporated ALS and Sentinel-2 data. Including Sentinel-2 predictors showed minimal increases to the precision of our estimates relative to models with ALS predictors alone.

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Tracking 20 years of forest demographics in east Texas, USA, using national forest inventory data

Klockow,

Edgar,

Domke

et al. 2023

Environ Monit Assess

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Comparing methods that quantify forest disturbances in the United States’ national forest inventory

Cited by 11 publications

References 32 publications

Multi-scale quantification of anthropogenic, fire, and drought-associated forest disturbances across the continental U.S., 2000–2014

Multi-scale quantification of anthropogenic, fire, and drought-associated forest disturbances across the continental U.S., 2000–2014

Effects of outliers on remote sensing‐assisted forest biomass estimation: A case study from the United States national forest inventory

Tracking 20 years of forest demographics in east Texas, USA, using national forest inventory data

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