Provenance Information Representation and Tracking for Remote Sensing Observations in a Sensor Web Enabled Environment

Abstract

The provenance of observations from a Sensor Web enabled remote sensing application represents a great challenge. There are currently no representations or tracking methods. We propose a provenance method that represents and tracks remote sensing observations in the Sensor Web enabled environment. The representation can be divided into the description model, encoding method, and service implementation. The description model uses a tuple to define four objects (sensor, data, processing, and service) and their relationships at a time point or interval. The encoding method incorporates the description into the Observations & Measurements specification of the Sensor Web. The service implementation addresses the effects of the encoding method on the implementation of Sensor Web services. The tracking method abstracts a common provenance algorithm and four algorithms that track the four objects (sensor, data, processing, and service) in a remote sensing observation application based on the representation. We conducted an experiment on the representation and tracking of provenance information for vegetation condition products, such as the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI). Our experiments used raw Moderate Resolution Imaging Spectroradiometer (MODIS) data to produce daily NDVI, weekly NDVI, and weekly VCI for the 48 contiguous states of the United States, for May from 2000 to 2012. We also implemented inverse tracking. We evaluated the time and space requirements of the proposed method in this scenario. Our results show that this technique provides a solution for determining provenance information in remote sensing observations.