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Biomass Productivity-Based Mapping of Global Land Degradation Hotspots

TLDR
Using the long-term trend of biomass productivity as a proxy of land degradation at global scale, the degradation hotspots in the world across major land cover types were identified in this article.
Abstract
Land degradation affects negatively the livelihoods and food security of global population. There have been recurring efforts by the international community to identify the global extent and severity of land degradation. Using the long-term trend of biomass productivity as a proxy of land degradation at global scale, we identify the degradation hotspots in the world across major land cover types. We correct factors confounding the relationship between the remotely sensed vegetation index and land-based biomass productivity, including the effects of inter-annual rainfall variation, atmospheric fertilization and intensive use of chemical fertilizers. Our findings show that land degradation hotpots cover about 29 % of global land area and are happening in all agro-ecologies and land cover types. This figure does not include all areas of degraded lands, it refers to areas where land degradation is most acute and requires priority actions in both in-depth research and management measures to combat land degradation. About 3.2 billion people reside in these degrading areas. However, the number of people affected by land degradation is likely to be higher as more people depend on the continuous flow of ecosystem goods and services from these affected areas. Land improvement has occurred in about 2.7 % of global land area during the last three decades, suggesting that with appropriate actions land degradation trend could be reversed. We also identify concrete aspects in which these results should be interpreted with cautions, the limitations of this work and the key areas for future research.

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Citations
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Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

TL;DR: In this article, the authors examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA.
Journal ArticleDOI

Unpacking the concept of land degradation neutrality and addressing its operation through the Rio Conventions

TL;DR: A portfolio of blended interventions is presented that seeks to address the aspirations of the UNCCD, UNFCCC and CBD in the LDN space, identifying synergistic options for national actions to move towards LDN.
Book ChapterDOI

Global Cost of Land Degradation

TL;DR: In this paper, the authors adopt the Total Economic Value (TEV) approach to determine the costs of land degradation and use remote sensing data and global statistical databases in their analysis.
Journal ArticleDOI

Impact of human activities and climate change on the grassland dynamics under different regime policies in the Mongolian Plateau.

TL;DR: Although human activity was the dominant factor on grassland degradation, it has a positive effect on most of the grassland NPP in the MP, and policy measures and ecological projects in IM brought a more positive effect compared with that in MG.
Journal ArticleDOI

The role of Remote Sensing in land degradation assessments: opportunities and challenges

TL;DR: In this paper, a logical synthesis of the role of Remote Sensing for land degradation assessment is provided, and the authors argue for multi-scale and cross-scale LD assessments calling for application-oriented solutions and highlighting the need of in situ data for validation of remote sensing-based LD maps.
References
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Journal ArticleDOI

A soil-adjusted vegetation index (SAVI)

TL;DR: In this article, a transformation technique was presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths, which nearly eliminated soil-induced variations in vegetation indices.
Journal ArticleDOI

An improved method of constructing a database of monthly climate observations and associated high-resolution grids

TL;DR: In this paper, a database of monthly climate observations from meteorological stations is constructed and checked for inhomogeneities in the station records using an automated method that refines previous methods by using incomplete and partially overlapping records and by detecting inhomalities with opposite signs in different seasons.
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