Spectral Matching Techniques to Determine Historical Land-use/Land-cover (LULC) and Irrigated Areas Using Time-series 0.1-degree AVHRR Pathfinder Datasets

0099-1112/07/7309–1029/$3.00/0 © 2007 American Society for Photogrammetry and Remote Sensing Abstract This study established spectral matching techniques (SMTs) to determine land-use and land-cover (LULC) and irrigated area classes from historical time-series (HTS-LULC) AVHRR 0.1-degree pathfinder satellite sensor data. The approach for HTS-LULC mapping and characterization was to develop “target” spectra from: (a) Recent Time Series for which LULC and irrigated area classes (RTS-LULC) were mapped using extensive ground-truth data, and (b) ideal locations, which are known endmembers even during historical time-periods of interest, as determined based on existing knowledge base including agricultural census data. The HTS-LULC for the period of 1982 to 1985 and RTS-LULC for the period of 1996 to 1999 were established using monthly continuous timeseries AVHRR mega-file data of 192 bands (48 months * 4 AVHRR bands per month) each for the HTS and RTS time periods. The study was conducted in the Krishna river basin (India), which has a large area (267,088 km2) with numerous irrigation projects and high population density. The quantitative and qualitative SMTs were used to identify and label HTS LULC classes. The identification and labeling process begins with qualitative spectral matching technique which visually matches the time-series NDVI spectra of known RTS-LULC classes and/or ideal endmember classes with time-series spectra of HTS-LULC classes. This helps identify classes of similar spectral characteristics in terms of shape and magnitude over time. The quantitative SMTs involved: (a) spectral correlation similarity (SCS), as a shape measure, (b) Euclidian distance (Ed), as distance measure, (c) spectral similarity value (SSV) as a combination of shape and distance measure, and (d) modified spectral angle similarity (MSAS) as a hyperangle measure. The quantitative and qualitative SMT methods and techniques lead to assigning HTS-LULC classes that match RTS-LULC names. In all, an aggregated seven HTS-LULC that were spectrally similar to the seven RTS-LULC classes and/or ideal endmember classes were identified and labeled. The SSV was the best method, followed by SCS. Spectral Matching Techniques to Determine Historical Land-use/Land-cover (LULC) and Irrigated Areas Using Time-series 0.1-degree AVHRR Pathfinder Datasets