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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Fractional vegetation cover (FVC) and aboveground biomass (AGB) are critically important for monitoring grassland degradation, and their accurate estimation can be used as key proxies for assessing land degradation. The main purpose of this study was to estimate the FVC and AGB in the eastern Mongolian steppe using remote sensing and machine learning. In this context, spectral bands and vegetation indices were extracted from the processed Sentinel-2 data and used as predictors. The field vegetation data were derived from the Mongolian pasture-monitoring database, which consisted of 256 plots with FVC and AGB measurements. Consequently, we derived FVC and AGB from Sentinel-2 imagery using 256 field vegetation measurements in the vast eastern Mongolian steppe as a reference for random forest (RF) models (R 2FVC  = 0.81, R² AGB  = 0.76). Among the variables, the predictor variables derived from spectral vegetation and soil indices, especially NDVI, Simple Ratio (SR), and OSAVI, were highly important for predicting FVC and AGB. As expected, a comparison among the map values showed that the spatial distribution of FVC and AGB was consistent with the landscapes and ecoregions in the study area. As the FVC and AGB maps only showed the current condition of vegetation cover, we also analysed NDVI trends to explain vegetation cover changes. We tested temporal trends in vegetation using Landsat NDVI time series data and the Mann-Kendall trend test. This revealed that in 7.3% of the area, the NDVI significantly increased, whereas a significant decrease was observed in 58% of the area.

international journal of remote sensing

Estimating fractional vegetation cover and aboveground biomass for land degradation assessment in eastern Mongolia steppe: combining ground vegetation data and remote sensing