A spatially explicit model of tree leaf litter accumulation in fire maintained longleaf pine forests of the southeastern US

Sánchez‐López, Nuria; Hudak, Andrew T.; Boschetti, Luigi; Silva, Carlos Antônio da; Robertson, Kevin; Loudermilk, E. Louise; Bright, Benjamin C.; Callaham, Mac A.; Taylor, Melanie K.

doi:10.1016/j.ecolmodel.2023.110369

Cited by 10 publications

(1 citation statement)

References 94 publications

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“…Although previous research at these sites did not detect differences in some fire-related characteristics of basal and open locations after one fire (Kreye et al, 2020), the occurrence of certain species close to and away from pines could reflect the longterm fire history at each point (Brewer, 2023). In addition, they could result from spatial patterns of pine litter deposition, which has been shown to vary with time since fire, decomposition rate, stand structure, and direction of prevailing winds (Blaydes et al, 2023;Sánchez-López et al, 2023). Fine-scale fuel heterogeneity results in spatial variation in plant survival and establishment through aboveground fire intensity or soil heating (Platt et al, 2016;Kennard & Outcalt 2006), which ultimately manifests in differential community assembly over time (Mugnani et al, 2019;Robertson et al, 2019).…”

Section: Discussionmentioning

confidence: 93%

Pine trees structure plant biodiversity patterns in savannas

Crandall,

Chew,

Fill

et al. 2024

Ecology and Evolution

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Overstory trees serve multiple functions in grassy savannas. Past research has shown that understory species can vary along gradients of canopy cover and basal area in savannas. This variation is frequently associated with light availability but could also be related to other mechanisms, such as heterogeneity in soil and litter depth and fire intensity. Several savanna studies have found differences in understory plant functional groups within the local environment near trees versus away from them in canopy openings. Although small‐scale variation is known to be high in southeastern U.S. pine savannas, patterns in understory species diversity have not been examined at the scale of individual overstory pine trees in this system. We conducted an observational study of the relationship between understory plant communities and proximity to individual pine trees in xeric and mesic pine savannas in frequently burned sites (1–3 year intervals). We recorded the plant community composition in plots adjacent to tree boles (basal) or outside crown driplines (open). Within each environment, raw species richness was significantly greater in open locations, where light transmittance was greater. In contrast, rarified species richness did not differ. Multivariate analyses showed that community composition differed significantly between basal and open plots. One native, woody species in each environment, Serenoa repens (W. Bartram) Small in mesic and Diospyros virginiana L. in xeric, was more abundant in basal plots. In mesic environments, eight species had greater occurrence in open plots. In xeric environments, four understory forbs were more abundant in open plots. Our results support previous research indicating that individual pine trees are associated with significant variation in understory vegetation in pine savannas.

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Section: Discussionmentioning

confidence: 93%

Pine trees structure plant biodiversity patterns in savannas

Crandall,

Chew,

Fill

et al. 2024

Ecology and Evolution

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Leveraging the next generation of spaceborne Earth observations for fuel monitoring and wildland fire management

Leite,

Amaral,

Neigh

et al. 2024

Remote Sens Ecol Conserv

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Managing fuels is a key strategy for mitigating the negative impacts of wildfires on people and the environment. The use of satellite‐based Earth observation data has become an important tool for managers to optimize fuel treatment planning at regional scales. Fortunately, several new sensors have been launched in the last few years, providing novel opportunities to enhance fuel characterization. Herein, we summarize the potential improvements in fuel characterization at large scale (i.e., hundreds to thousands of km2) with high spatial and spectral resolution arising from the use of new spaceborne instruments with near‐global, freely‐available data. We identified sensors at spatial resolutions suitable for fuel treatment planning, featuring: lidar data for characterizing vegetation structure; hyperspectral sensors for retrieving chemical compounds and species composition; and dense time series derived from multispectral and synthetic aperture radar sensors for mapping phenology and moisture dynamics. We also highlight future hyperspectral and radar missions that will deliver valuable and complementary information for a new era of fuel load characterization from space. The data volume that is being generated may still challenge the usability by a diverse group of stakeholders. Seamless cyberinfrastructure and community engagement are paramount to guarantee the use of these cutting‐edge datasets for fuel monitoring and wildland fire management across the world.

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Characterizing heterogeneous forest structure in ponderosa pine forests via UAS-derived structure from motion

Hanna,

Tinkham,

Battaglia

et al. 2024

Environ Monit Assess

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Increasingly, dry conifer forest restoration has focused on reestablishing horizontal and vertical complexity and ecological functions associated with frequent, low-intensity fires that characterize these systems. However, most forest inventory approaches lack the resolution, extent, or spatial explicitness for describing tree-level spatial aggregation and openings that were characteristic of historical forests. Uncrewed aerial system (UAS) structure from motion (SfM) remote sensing has potential for creating spatially explicit forest inventory data. This study evaluates the accuracy of SfM-estimated tree, clump, and stand structural attributes across 11 ponderosa pine-dominated stands treated with four different silvicultural prescriptions. Specifically, UAS-estimated tree height and diameter-at-breast-height (DBH) and stand-level canopy cover, density, and metrics of individual trees, tree clumps, and canopy openings were compared to forest survey data. Overall, tree detection success was high in all stands (F-scores of 0.64 to 0.89), with average F-scores > 0.81 for all size classes except understory trees (< 5.0 m tall). We observed average height and DBH errors of 0.34 m and − 0.04 cm, respectively. The UAS stand density was overestimated by 53 trees ha−1 (27.9%) on average, with most errors associated with understory trees. Focusing on trees > 5.0 m tall, reduced error to an underestimation of 10 trees ha−1 (5.7%). Mean absolute errors of bole basal area, bole quadratic mean diameter, and canopy cover were 11.4%, 16.6%, and 13.8%, respectively. While no differences were found between stem-mapped and UAS-derived metrics of individual trees, clumps of trees, canopy openings, and inter-clump tree characteristics, the UAS method overestimated crown area in two of the five comparisons. Results indicate that in ponderosa pine forests, UAS can reliably describe large- and small-grained forest structures to effectively inform spatially explicit management objectives.

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A spatially explicit model of tree leaf litter accumulation in fire maintained longleaf pine forests of the southeastern US

Cited by 10 publications

References 94 publications

Pine trees structure plant biodiversity patterns in savannas

Pine trees structure plant biodiversity patterns in savannas

Leveraging the next generation of spaceborne Earth observations for fuel monitoring and wildland fire management

Characterizing heterogeneous forest structure in ponderosa pine forests via UAS-derived structure from motion

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