A spatially varying model for small area estimates of biomass density across the contiguous United States

May, Paul; McConville, Kelly S.; Moisen, Gretchen G.; Bruening, J. M.; Dubayah, Ralph

doi:10.1016/j.rse.2022.113420

Cited by 15 publications

(7 citation statements)

References 30 publications

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“…All four models included a spatial random effect to account for spatial variability in occurrence probability, and given the likely small changes in the forest bird distributions from 2019 (the last year in the modelled data set) to 2021, they all had similar abilities to predict occurrence probabilities across the study region. SVC models in other ecological and natural resource applications have shown mixed results regarding their predictive benefits compared to models with only a spatially varying intercept; some studies found improved predictive performance of SVC models (May et al, 2023;Sultaire et al, 2022), while others showed improvements that vary depending on the species (Doser et al, 2024; or region (Babcock et al, 2015). Regardless, we echo the statements of Thorson et al (2023) that the primary benefits of SVC SDMs relate to their improved ability to test and generate hypotheses as well as answer relevant ecological questions regarding spatial variability in species-environment relationships and trends.…”

Section: Discussionmentioning

confidence: 99%

Guidelines for the use of spatially varying coefficients in species distribution models

Doser,

Kéry,

Saunders

et al. 2024

Global Ecol Biogeogr

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AimSpecies distribution models (SDMs) are increasingly applied across macroscales using detection‐nondetection data. These models typically assume that a single set of regression coefficients can adequately describe species–environment relationships and/or population trends. However, such relationships often show nonlinear and/or spatially varying patterns that arise from complex interactions with abiotic and biotic processes that operate at different scales. Spatially varying coefficient (SVC) models can readily account for variability in the effects of environmental covariates. Yet, their use in ecology is relatively scarce due to gaps in understanding the inferential benefits that SVC models can provide compared to simpler frameworks.InnovationHere we demonstrate the inferential benefits of SVC SDMs, with a particular focus on how this approach can be used to generate and test ecological hypotheses regarding the drivers of spatial variability in population trends and species–environment relationships. We illustrate the inferential benefits of SVC SDMs with simulations and two case studies: one that assesses spatially varying trends of 51 forest bird species in the eastern United States over two decades and a second that evaluates spatial variability in the effects of five decades of land cover change on grasshopper sparrow (Ammodramus savannarum) occurrence across the continental United States.Main conclusionsWe found strong support for SVC SDMs compared to simpler alternatives in both empirical case studies. Factors operating at fine spatial scales, accounted for by the SVCs, were the primary divers of spatial variability in forest bird occurrence trends. Additionally, SVCs revealed complex species–habitat relationships with grassland and cropland area for grasshopper sparrow, providing nuanced insights into how future land use change may shape its distribution. These applications display the utility of SVC SDMs to help reveal the environmental factors that drive species distributions across both local and broad scales. We conclude by discussing the potential applications of SVC SDMs in ecology and conservation.

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Section: Discussionmentioning

confidence: 99%

Guidelines for the use of spatially varying coefficients in species distribution models

Doser,

Kéry,

Saunders

et al. 2024

Global Ecol Biogeogr

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“…The graphs, as shown below in Figure 8, display the frequency distribution of the research articles in terms of datasets, regression models used, and article frequency per year and country/continent-wise. These investigations were distributed geographically as follows: China (19), India (10), South Africa (4), Brazil (4), Italy (2), and Vietnam (2). Other countries were the USA, Pakistan, Japan, Mozambique, Madagascar, Iran, Germany, Gabon, Colombia, Canada, Zimbabwe, Tanzania, and Australia.…”

Section: Spaceborne Datasetsmentioning

confidence: 99%

“…It tracks disturbance and regeneration in boreal, temperate, and tropical forests and provides insights into forest structure characteristics [18]. The other dataset is a spaceborne LiDAR imager, the Global Ecosystem Dynamics Investigation (GEDI), that estimates aboveground biomass and provides accurate information about forest structure [19]. In this case, obtaining comprehensive mapping of forest stand volume using GEDI data without optical sensor images will still be difficult because of spatial and temporal limitations [20].…”

Section: Introductionmentioning

confidence: 99%

Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches

Khan,

Tan,

Gul

et al. 2024

Forests

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Remote sensing datasets offer robust approaches for gaining reliable insights into forest ecosystems. Despite numerous studies reviewing forest aboveground biomass estimation using remote sensing approaches, a comprehensive synthesis of synergetic integration methods to map and estimate forest AGB is still needed. This article reviews the integrated remote sensing approaches and discusses significant advances in estimating the AGB from space- and airborne sensors. This review covers the research articles published during 2015–2023 to ascertain recent developments. A total of 98 peer-reviewed journal articles were selected under the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Among the scrutinized studies, 54 were relevant to spaceborne, 22 to airborne, and 22 to space- and airborne datasets. Among the empirical models used, random forest regression model accounted for the most articles (32). The highest number of articles utilizing integrated dataset approaches originated from China (24), followed by the USA (15). Among the space- and airborne datasets, Sentinel-1 and 2, Landsat, GEDI, and Airborne LiDAR datasets were widely employed with parameters that encompassed tree height, canopy cover, and vegetation indices. The results of co-citation analysis were also determined to be relevant to the objectives of this review. This review focuses on dataset integration with empirical models and provides insights into the accuracy and reliability of studies on AGB estimation modeling.

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“…One notable challenge associated with the utilization of forest biomass to obtain energy generation through direct burning is the geographical distance between these waste materials and industrial as well as residential regions (Yana et al, 2022;Al-Bawwat et al, 2023). In addition, it should be noted that forests encompass extensive areas, and the process of collecting biomass presents intricate challenges (May et al, 2023). Consequently, the absence of consistent availability of biomass is a significant issue in ensuring the long-term viability of utilizing forest biomass for direct energy generation (Al-Bawwat et al, 2023;Raihan & Tuspekova, 2022a).…”

Section: Direct Use Of Forest Biomassmentioning

confidence: 99%

A concise review of technologies for converting forest biomass to bioenergy

Raihan

2023

JTIE

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The use of biomass is vital in reducing the negative effects of rising fossil fuel consumption. Given its quantity and diversity, forest biomass has garnered a lot of interest among the many kinds of biomass. This study evaluates the various strategies for transforming woody waste into usable biofuels. Carbon dioxide emissions from traditional energy generation systems could be mitigated through the direct utilization of forest biomass. Low energy conversion rates, as well as soot emissions and residues, are some of the problems that come up when directly using forest biomass. The sustainability of direct energy generation from forest biomass is also seriously threatened by the lack of constant access to biomass. Co-combustion with coal and pelletizing biomass is two solutions proposed for this issue. Co-combustion of forest biomass with coal has the potential to lower the process's emissions of carbon monoxide, nitrogen oxides, and sulfides. This article reviews and discusses the biochemical and thermochemical mechanisms that can transform forest biomass into a variety of liquid and gaseous biofuels. Future research using cutting-edge sustainability assessment tools like life cycle assessment, exergy, etc. should investigate the sustainability of forest biomass conversion processes to bioenergy further.

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A spatially varying model for small area estimates of biomass density across the contiguous United States

Cited by 15 publications

References 30 publications

Guidelines for the use of spatially varying coefficients in species distribution models

Guidelines for the use of spatially varying coefficients in species distribution models

Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches

A concise review of technologies for converting forest biomass to bioenergy

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