May 01, 2020

One of the most common uses of remotely sensed data is to detect change. Google Earth Engine (GEE) provides a rich open-access catalogue of geospatial information, including optical, meteorological and oceanographic datasets.

For forest monitoring, two useful sources of meteorological and optical data are CHIRPS and Sentinel-2, respectively. Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) is a global dataset combining satellite imagery with local rain gauge data, aggregated to daily or five daily precipitation estimates at a resolution of roughly 3 km.

Sentinel-2 (S2) is a pair of optical sensors offering multispectral imagery at a resolution of 10 m, with a 5-daily revisit. Developed specifically to support large-scale land cover mapping, the near-infrared and shortwave infrared bands are ideal for monitoring forest canopy condition.

The NDVI and Precipitation Time Series app allows rapid and straightforward interaction with these datasets. Drawing a polygon over a given area will sample both S2 and CHIRPS for the 2019 year, providing a time series showing NDVI (Normalized Difference Vegetation Index, a measure of vegetation vigour) and precipitation through time. Several pre-selected examples are also available. Below the graph, the available Sentinel images are displayed in a looping animation to assist in highlighting changes.

Clicking a point of the NDVI time-series will load the Sentinel 2 scene from which the given NDVI observation was generated. This assists in identifying the cause of any unexpected deviations present in the time series plot.

The combined use of these datasets allows for a better understanding of ground conditions. For instance, a sharp decline in NDVI can signal a harvest event (NZ example), while a subsequent increase may be explained by grass growth following a rainfall event over the harvested area. Alternatively, a low NDVI over a crop setting (Californian example) during a period of drought, suggests the field is not irrigated.