Computer scientist: High Performance Computing for Earth Observation data - OpenGeoHub Foundation: Connect | Create | Share | Repeat

Come to OpenGeoHub and help us improve, optimize and extend HPC infrastructures for Machine Learning so we can contribute together to the global good.

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Join us and learn more about the OEMC Use Cases. Every two months, on the first Thursday of the month, the Open-Earth-Monitor project hosts science webinars, inviting use case leaders and stakeholders to share their research and work on one of the Open-Earth-Monitor use cases.

Post-doctoral researcher: dynamic soil-landscape modeling – OpenGeoHub Foundation: Connect | Create | Share | Repeat

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Global cropland expansion in the 21st century | GLAD

The dataset represents a globally consistent cropland extent time-series at 30-m spatial resolution. Cropland defined as land used for annual and perennial herbaceous crops for human consumption, forage (including hay), and biofuel. Perennial woody crops, permanent pastures, and shifting cultivation are excluded from the definition. The fallow length is limited to four years

Communication officer - OpenGeoHub Foundation: Connect | Create | Share | Repeat

Application deadline: 1st of July 2024Possible start date: 1st of August 2024Duration: 2+ yearsJob location: Waldeck Pyrmontlaan 14, 6965HK Doorwerth, The

OpenLandMap

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

The article 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.