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

Aboveground biomass (AGB) is one of the key indicators of aboveground net primary productivity (ANPP). The aim of this study is to demonstrate the potential of hyperspectral remote sensing techniques to predict AGB in grasslands. In order to reach this goal, biomass properties with different ecological features and altitudes of 550 m, 1200 m, and 1400 m above sea level were investigated. Twenty-one biomass samples and hyperspectral measurements were collected from each region and a total of 63 samples were analyzed. Linear and nonlinear regression models were generated to analyze the relationships between biomass and hyperspectral vegetation indices (VIs). The results showed strong relationships between VIs and biomass variations. However, dense biomass samples indicated weaker relationships with VIs due to saturation phenomena. Findings based on the measured data showed that AGB (except dense biomass) can be estimated with high accuracy using hyperspectral vegetation indices.

Ratio-based vegetation indices for biomass estimation depending on grassland characteristics