Integrated remote sensing and GIS-assisted problem solving now supports a remarkable array of domains (e.g., food and agricultural security, climate change, forest management, heritage preservation, and urban and regional planning) and is being configured in a great variety of technical means. Given the sheer quantity of innovations reported in journals and books (including the Remote Sensing Handbook), any one expert may be keenly aware of only a fraction of the detailed remote sensing and related geospatial methods available to address a given problem statement. Regardless of the remote sensing application under study or review, some reliance (whether implied or reported) is always made upon the geoprocesses and workflows associated with any geospatial artifacts produced. In the context of a specific geospatial decision support artifact (e.g., a map of predicted crop yield in kg/ha), a record of the specific geoprocesses may be termed geospatial provenance (or lineage; see Section 19.1.1). This chapter explores how remote sensing-assisted geoprocessing and related GIS workflows have been or may be combined with digital provenance information in order to augment scientific reproducibility, comparison, trust, or to otherwise improve remote sensing-assisted decision support.
|Title of host publication||Remotely Sensed Data Characterization, Classification, and Accuracies|
|Number of pages||21|
|Publication status||Published - Jan 1 2015|
ASJC Scopus subject areas
- Environmental Science(all)
- Earth and Planetary Sciences(all)