Land degradation in the European Union—Where does the evidence converge?

Gianoli, Federico; Weynants, Mélanie; Michael, Cherlet

doi:10.1002/ldr.4606

Cited by 12 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shao et al (2024), for example, made use of specific variables, such as wind speed, known to drive land degradation in their local study area. Our analysis of land productivity could be used as one of the variables in such frameworks that make use of the "convergence of evidence" to indicate potential degradation areas (Gianoli, Weynants, & Cherlet, 2023).…”

Section: /40mentioning

confidence: 99%

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

Hackländer,

Parente,

et al. 2024

Preprint

View full text Add to dashboard Cite

The paper presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short–term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (Extremely Randomized Trees, Gradient Descended Trees and Artificial Neural Network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.

show abstract

Section: /40mentioning

confidence: 99%

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

Hackländer,

Parente,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: /40mentioning

confidence: 99%

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

Hackländer,

Parente,

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The paper presents results of using remote sensing time series and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Monthly aggregated FAPAR time series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8–day GLASS FAPAR V6 product for 2000–2021 and used to determine long–term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio–physical variables representing climate, terrain, land form, and vegetation cover, as well as several variables related to human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (Extremely Randomized Trees, Gradient Descended Trees and Artificial Neural Network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs potential FAPAR and long–term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes urban, sparse vegetation and rainfed cropland. On the other hand, classes irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.

show abstract

Mapping lock-ins and enabling environments for agri-food sustainability transitions in Europe

Williams,

Bürgi,

Debonne

et al. 2024

Sustain Sci

View full text Add to dashboard Cite

European agri-food systems must overcome structural lock-ins to achieve more sustainable modes of production and consumption. Yet European regions are highly diverse, and we lack understanding of how different regional characteristics may enable or inhibit sustainability transitions. This hinders the development of context-tailored governance strategies. In this paper, we identify and apply sets of spatial indicators to map the regional potentials for agri-food transitions. We first analyse the strength of lock-in to the incumbent agro-industrial paradigm. We then map the enabling environments for two alternative agri-food networks—multifunctional value chains and civic food networks—that each embed distinct social–ecological qualities of agriculture and food. Results demonstrate a large spatial diversity in transition potential, with stronger lock-ins throughout North and Western Europe and stronger enabling environments for agri-food transitions in Italy, France, Switzerland, and Southwest Germany. We find that lock-ins are strongest in livestock-dominated regions and are associated with higher GHG emissions and excess nitrogen levels. Our study demonstrates the need for coordinated public policies that (1) leverage region-specific transition potentials and (2) enable complementary innovations in market-based and community-led networks.

show abstract

Land degradation in the European Union—Where does the evidence converge?

Cited by 12 publications

References 51 publications

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

Mapping lock-ins and enabling environments for agri-food sustainability transitions in Europe

Contact Info

Product

Resources

About