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TitleSelecting Representative High Resolution Sample Images for Land Cover Studies. Part 1: Methodology
DownloadDownloads (Preprint)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorCihlar, J; Latifovic, R; Chen, J M; Beaubien, J; Li, Z
SourceRemote Sensing of Environment vol. 71, issue 1, 2000 p. 26-42,
Alt SeriesEarth Sciences Sector, Contribution Series 20042689
PublisherElsevier BV
Mediapaper; on-line; digital
File formatpdf
Subjectsremote sensing; LANDSAT; Purposive Selection Algorithm (PSA); AVHRR
Illustrationsflow charts; tables; graphs; satellite images
Released2000 01 01
AbstractThis is the first of two papers which explore the combined use of coarse and fine resolution data in land cover studies. It describes the development and evaluation of an objective procedure to select representative sample of tiles of high resolution images that complements a coarse resolution coverage of an entire region of interest. The second paper explores the use of the procedure for an accurate estimation of cover type composition at the regional scale. The Purposive Selection Algorithm (PSA) assumes that a relationship exists between land cover compositions at the two spatial scales. It selects one tile at a time, seeking the sample which most closely resembles the composition of the coarse resolution map. Two selection criteria were used, fraction of cover types and contagion index. PSA was evaluated using two land cover maps for a 288kmx 165 km area in central Saskatchewan, Canada derived from Landsat Thematic Mapper images (30m pixels) and Advanced Very High Resolution Radiometer (AVHRR, 1000m pixels), each divided into 64 tiles. The performance of an intermediate sensor (480m pixels) was assessed by resampling the TM map. When using cover type composition alone, it was found that the procedure rapidly converges on a representative set of tiles with land cover composition very similar to the full coverage. The match between the domain and sample cover type fractions was very close, with errors less than 0.002% once about 1/5 to 1/3 of the tiles were selected and no discernible bias in the selected sample. Compared to the TM whole area coverage, samples selected with AVHRR classification were as representative as those obtained using the TM map. The performance of samples selected by a combination of cover composition and contagion index responded to the characteristics of individual tiles in terms of the selection criteria. A rigorous application of the algorithm with spatial heterogeneity measures such as the contagion index is computationally very demanding. It is concluded that PSA provides an efficient and effective tool to select a representative sample for land cover studies in which both large area coverage and local detail are desired.

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