  In the context of global climate change and frequent extreme weather events,
forecasting future geospatial vegetation states under these conditions is of
significant importance. The vegetation change process is influenced by the
complex interplay between dynamic meteorological variables and static
environmental variables, leading to high levels of uncertainty. Existing
deterministic methods are inadequate in addressing this uncertainty and fail to
accurately model the impact of these variables on vegetation, resulting in
blurry and inaccurate forecasting results. To address these issues, we propose
VegeDiff for the geospatial vegetation forecasting task. To our best knowledge,
VegeDiff is the first to employ a diffusion model to probabilistically capture
the uncertainties in vegetation change processes, enabling the generation of
clear and accurate future vegetation states. VegeDiff also separately models
the global impact of dynamic meteorological variables and the local effects of
static environmental variables, thus accurately modeling the impact of these
variables. Extensive experiments on geospatial vegetation forecasting tasks
demonstrate the effectiveness of VegeDiff. By capturing the uncertainties in
vegetation changes and modeling the complex influence of relevant variables,
VegeDiff outperforms existing deterministic methods, providing clear and
accurate forecasting results of future vegetation states. Interestingly, we
demonstrate the potential of VegeDiff in applications of forecasting future
vegetation states from multiple aspects and exploring the impact of
meteorological variables on vegetation dynamics. The code of this work will be
available at https://github.com/walking-shadow/ Official_VegeDiff.
