July 2017 - Seeing Green - Best in the West


An exceptional amount of precipitation has fallen this water-year across much of the western US from Washington State to central California helping California recover from a multi-year drought. East of the Sierra and Cascades, the onslaught of precipitation has been noted. Some regions including the eastern slopes of the Sierra Nevada and western portions of the Great Basin have seen double the amount of precipitation that is normally received. This has resulted in an impressive growth of grasses across much of the western U.S. which is seen from space as increased greenness of a usually dry region as seen by the green colors on the map above. Note that the abundant snowpack in mountains of the western US has led to delay in greenup, which is shown by the red colors above.  The relationship between interannual variability in spring greenness and precipitation across the Great Basin is quite strong across the semi-arid region that is generally moisture limited. The relatively lush landscape has both upsides, including increased grazing allotments, and downsides, including growth of fine fuels, including cheatgrass, that can carry wildfire once fuels dry out later this summer and into next summer.

 The metric itself:

NDVI (Normalized Difference Vegetation Index) from the MODIS dataset was used to assess relative vegetation vigor or greenness. MODIS uses a ratio of Near Infrared and Red spectral bands to provide a measure for surface greenness at a high spatial resolution (500-m) from 2000-present. NDVI is typically associated with vegetation productivity or vigor. We visualize results as a percent of normal to emphasize relative differences.

 Map/tool explanation:

Climate Engine provides the ability to map climate and remotely sensed datasets globally for several datasets archived and regularly updated through Google’s Earth Engine. Users can select from datasets and variables, as well as choose to view the raw data or data expressed as a deviation from some baseline or expected value (e.g., long term averages). Users can choose from a set of variables and time periods, examine percentiles or anomalies, and customize the maps to their liking.

 Interactive View on Climate Engine: (Click Here)