Estimation of Fractional Plant Lifeform Cover for the Conterminous United States Using Landsat Imagery and Airborne LiDAR

Parra, A. S.; Greenberg, Jonathan A.

doi:10.1109/tgrs.2022.3199156

Cited by 3 publications

(3 citation statements)

References 57 publications

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“…We used the available data from the “Vegetative Lifeform Cover from Landsat SR for CONUS” product (Parra & Greenberg, 2021) publicly available in the ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) to evaluate the changes in vegetation fractional cover. This dataset includes annual growing season maps of fractional cover (between 0% and a 100%) for each of three lifeform classes (tree, shrub, and herbaceous vegetation), and one additional class of non‐vegetation cover, which groups any surface without vegetation, such as urban impervious surfaces, bare soil, rock outcrops, or permanent snow.…”

Section: Methodsmentioning

confidence: 99%

“…To process the climate data, we defined a year time step as the months from July of one year to July of the next year. We established this time step taking into consideration that the annual lifeform cover maps identify the maximum vegetation cover during the growing season (May–August) (Parra & Greenberg, 2022); therefore, we wanted to make sure that the values of fractional cover were correctly paired with the climatic conditions leading to the growing season. Following this definition, we constructed annual maps of each climate variable for the years 1971 to 2018 (see Figure S1).…”

Section: Methodsmentioning

confidence: 99%

“…This dataset includes annual growing season maps of fractional cover (between 0% and a 100%) for each of three lifeform classes (tree, shrub, and herbaceous vegetation), and one additional class of non‐vegetation cover, which groups any surface without vegetation, such as urban impervious surfaces, bare soil, rock outcrops, or permanent snow. The maps were derived from a fusion of airborne LiDAR data and Landsat imagery and have a spatial resolution of 30 m (Parra & Greenberg, 2022). Although the product covers the years 1984–2018, there are large missing areas in the years 1984 and 1985.…”

Section: Methodsmentioning

confidence: 99%

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Climate‐limited vegetation change in the conterminous United States of America

Parra,

Greenberg

2024

Global Change Biology

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The effects of climate change on vegetation composition and distribution are evident in different ecosystems around the world. Although some climate‐derived alterations on vegetation are expected to result in changes in lifeform fractional cover, disentangling the direct effects of climate change from different non‐climate factors, such as land‐use change, is challenging. By applying “Liebig's law of the minimum” in a geospatial context, we determined the climate‐limited potential for tree, shrub, herbaceous, and non‐vegetation fractional cover change for the conterminous United States and compared these potential rates to observed change rates for the period 1986 to 2018. We found that 10% of the land area of the conterminous United States appears to have climate limitations on the change in fractional cover, with a high proportion of these sites located in arid and semiarid ecosystems in the Southwest part of the country. The rates of change in lifeform fractional cover for the remaining area of the country are likely limited by non‐climate factors such as the disturbance regime, land management, land‐use history, soil conditions, and species interactions and adaptations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Climate‐limited vegetation change in the conterminous United States of America

Parra,

Greenberg

2024

Global Change Biology

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LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

Balestra,

Marselis,

Sankey

et al. 2024

Curr. For. Rep.

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Purpose of the Review Many LiDAR remote sensing studies over the past decade promised data fusion as a potential avenue to increase accuracy, spatial-temporal resolution, and information extraction in the final data products. Here, we performed a structured literature review to analyze relevant studies on these topics published in the last decade and the main motivations and applications for fusion, and the methods used. We discuss the findings with a panel of experts and report important lessons, main challenges, and future directions. Recent Findings LiDAR fusion with other datasets, including multispectral, hyperspectral, and radar, is found to be useful for a variety of applications in the literature, both at individual tree level and at area level, for tree/crown segmentation, aboveground biomass assessments, canopy height, tree species identification, structural parameters, and fuel load assessments etc. In most cases, gains are achieved in improving the accuracy (e.g. better tree species classifications), and spatial-temporal resolution (e.g. for canopy height). However, questions remain regarding whether the marginal improvements reported in a range of studies are worth the extra investment, specifically from an operational point of view. We also provide a clear definition of “data fusion” to inform the scientific community on data fusion, combination, and integration. Summary This review provides a positive outlook for LiDAR fusion applications in the decade to come, while raising questions about the trade-off between benefits versus the time and effort needed for collecting and combining multiple datasets.

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Estimating fractional vegetation cover from multispectral unmixing modeled with local endmember variability and spatial contextual information

Zhang,

Liu

2024

ISPRS Journal of Photogrammetry and Remote Sensing

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Estimation of Fractional Plant Lifeform Cover for the Conterminous United States Using Landsat Imagery and Airborne LiDAR

Cited by 3 publications

References 57 publications

Climate‐limited vegetation change in the conterminous United States of America

Climate‐limited vegetation change in the conterminous United States of America

LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

Estimating fractional vegetation cover from multispectral unmixing modeled with local endmember variability and spatial contextual information

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