Ways forward for Machine Learning to make useful global environmental datasets from legacy observations and measurements

doi:10.1038/s41467-022-32693-3

Cited by 1 publication

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“…Although the EML model deviance can give an indication of the uncertainty of the prediction, the lack of ground-truth data on potential FAPAR requires the results to be interpreted cautiously. A more robust framework is needed to calculate the probability distributions of FAPAR per pixel (Andriuzzi, Hengl, Routh, & van den Hogen, 2022).…”

Section: Limitations Of This Studymentioning

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

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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.

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Section: Limitations Of This Studymentioning

confidence: 99%