English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A large-scale global canopy height map (GCHM) is essential for global forest aboveground biomass estimation. Four GCHMs have recently been built using data from the Geoscience Laser Altimeter System (GLAS) sensor aboard the Ice, Cloud, and land Elevation Satellite (ICESat), along with auxiliary spatial and climate information. The main objectives of this research were to find out how well a selected GCHM agrees with airborne lidar data from locations in the southern United States and to recalibrate that GCHM to more closely match the forest canopy heights found in the region. The airborne lidar resource was built from data collected between 2010 and 2012, available from in-house and publicly available sources, for sites that included a variety of vegetation types across the southern United States. EPA ecoregions were used to provide ecosystem information for the southern United States. The airborne lidar data were pre-processed to provide lidar-derived metrics, and assigned to four height categories—namely, returns from above 0 m, 1 m, 3 m, and 5 m. The assessment phase results indicated that the 90th and 95th percentiles of the airborne lidar height values were well-suited for use in the recalibration phase of the study. Simple linear regression was used to generate a new, recalibrated GCHM. It was concluded that the characterization of the agreement of a selected GCHM with local data, followed by the recalibration of the existing GCHM to the local region, is both viable and essential for future GCHMs in studies conducted at large scales.

Regionalization of an Existing Global Forest Canopy Height Model for Forests of the Southern United States