A modeling and spatio-temporal analysis framework for monitoring environmental change using NPP as an ecosystem indicator

摘要

We present and describe a modeling and analysis framework for monitoring protected area (PA) ecosystems with net primary productivity (NPP) as an indicator of health. It brings together satellite data, an ecosystem simulation model (NASA–CASA), spatial linear models with autoregression, and a GIS to provide practitioners a low-cost, accessible ecosystem monitoring and analysis system (EMAS) at landscape resolutions. The EMAS is evaluated and assessed with an application example in Yellowstone National Park aimed at identifying the causes and consequences of drought. Utilizing five predictor covariates (solar radiation, burn severity, soil productivity, temperature, and precipitation), spatio-temporal analysis revealed how landscape controls and climate (summer vegetation moisture stress) affected patterns of NPP according to vegetation functional type, species cover type, and successional stage. These results supported regional and national trends of NPP in relation to carbon fluxes and lag effects of climate. Overall, the EMAS provides valuable decision support for PAs regarding informed land use planning, conservation programs, vital sign monitoring, control programs (fire fuels, invasives, etc.), and restoration efforts.