Rainforest-initiated wet season onset over the southern Amazon.

Although it is well established that transpiration contributes much of the water for rainfall over Amazonia, it remains unclear whether transpiration helps to drive or merely responds to the seasonal cycle of rainfall. Here, we use multiple independent satellite datasets to show that rainforest transpiration enables an increase of shallow convection that moistens and destabilizes the atmosphere during the initial stages of the dry-to-wet season transition. This shallow convection moisture pump (SCMP) preconditions the atmosphere at the regional scale for a rapid increase in rain-bearing deep convection, which in turn drives moisture convergence and wet season onset 2-3 mo before the arrival of the Intertropical Convergence Zone (ITCZ). Aerosols produced by late dry season biomass burning may alter the efficiency of the SCMP. Our results highlight the mechanisms by which interactions among land surface processes, atmospheric convection, and biomass burning may alter the timing of wet season onset and provide a mechanistic framework for understanding how deforestation extends the dry season and enhances regional vulnerability to drought.

Remote sensing-based time-series analysis of cheatgrass (Bromus tectorum L.) phenology.

The western United States is under invasion from cheatgrass (Bromus tectorum L.), an annual grass that alters the pattern of phenology in the ecosystems it infests. This study was conducted to investigate methods for monitoring this invasion. As a result of its annual phenology, cheatgrass is not only an extremely competitive invader, it is also detectible from time series of remotely sensed data. Using the MODerate resolution imaging spectro-radiometer (MODIS) normalized difference vegetation index (NDVI) and spatially interpolated precipitation data, we fit splines to monthly observations to generate time series of NDVI and precipitation from 2001 to 2005 in the state of Utah. We generated a variety of existing metrics of phenology and developed several metrics to describe the relationship between the NDVI and the precipitation time series. These metrics not only describe the pattern of response to precipitation in ecosystems of various infestation levels, but they are predictive of cheatgrass infestation. We tested several popular data mining algorithms to investigate the predictive ability of the time series-based metrics. Our results show that presence-absence can be predicted with 90% accuracy, and four categorical levels of infestation can be predicted with 71% accuracy. The results show that time series-based metrics are effective in prediction of cheatgrass abundance levels, are more effective than metrics based only on NDVI, and provide more information that existing approaches to cheatgrass mapping using phenology. These results are important for designing strategies to monitor ecosystem health over long periods of time at a landscape scale.

Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine.

Surface mining for coal has taken place in the Central Appalachian region of the United States for well over a century, with a notable increase since the 1970s. Researchers have quantified the ecosystem and health impacts stemming from mining, relying in part on a geospatial dataset defining surface mining's extent at a decadal interval. This dataset, however, does not deliver the temporal resolution necessary to support research that could establish causal links between mining activity and environmental or public health and safety outcomes, nor has it been updated since 2005. Here we use Google Earth Engine and Landsat imagery to map the yearly extent of surface coal mining in Central Appalachia from 1985 through 2015, making our processing models and output data publicly available. We find that 2,900 km2 of land has been newly mined over this 31-year period. Adding this more-recent mining to surface mines constructed prior to 1985, we calculate a cumulative mining footprint of 5,900 km2. Over the study period, correlating active mine area with historical surface mine coal production shows that each metric ton of coal is associated with 12 m2 of actively mined land. Our automated, open-source model can be regularly updated as new surface mining occurs in the region and can be refined to capture mining reclamation activity into the future. We freely and openly offer the data for use in a range of environmental, health, and economic studies; moreover, we demonstrate the capability of using tools like Earth Engine to analyze years of remotely sensed imagery over spatially large areas to quantify land use change.