BigData Earth Completes Project on High-resolution (1 meter) Vegetation Mapping for 48 Contiguous U.S. States
13 June 2016:
Through wider collaborations in acquisition, storage and processing, BigData Earth Pty Ltd have access to the comprehensive, orthorectified digital aerial imagery from the USDA National Agriculture Imagery Program (NAIP) and performed advanced imagery feature extraction. The latest NAIP input imagery acquired in 2015, 2014 and 2013 growing seasons (at 1m-resolution and for 48 contiguous U.S. States) has been analyzed. This is the first time that large-scale vegetation mapping is done at such a detailed level.
A number of key features of the NAIP aerial imagery are important for the classification of vegetation, including (1) consistent 1m high resolution; (2) new acquisitions in 2015, 2014, and 2013 growing seasons; (3) including R/G/B/NIR bands; and (4) virtually zero or minimized cloud covers. By comparison, it is hard to find high-resolution satellite imagery sources that can offer all of these advantages, especially for a very large territory.
Both pixel- and object-based image classification techniques, vegetation indices, and newly developed machine learning and classification methods have been actively explored for this feature extraction task. Also leveraging affordable data storage (more than a petabyte – 1,000 terabytes – in terms of the size of all temporary files during image processing) and high-performance computing, the BigData Earth team has achieved a very high accuracy of vegetation classification so that an objective, visual assessment becomes possible.
Unique data sets on the percentage (%, or the density) of vegetation across various geographical areas, such as 5-Digit ZIP Code, Census Block Group, and Census Track, are produced. In addition, 1m-resolution state-level imagery mosaics or basemaps (both natural-color and color infrared composites) are being offered. This project demonstrates excellent application opportunities using modern Earth Observation and geospatial big data analytics.