Title
The Suitablity of Decadal Image Data Sets for Mapping Tropical Forest Cover Change in the Democratic Republic of Congo: Implications for the Global Land Survey
Document Type
Article
Publication Date
11-7-2008
Description
Landsat remote sensing of the central African humid tropics is confounded by
persistent cloud cover and, since 2003, missing data due to the Landsat-7
Enhanced Thematic Mapper Plus (ETM + ) scan line corrector (SLC) malfunction.
To quantify these limitations and their effects on contemporary forest cover
and change characterization, a comparison was made of multiple Landsat-7
image mosaics generated for a six Landsat path/row study site in central Africa
for 2000 and 2005. Epoch 2000 mosaics were generated by compositing (i) two to
three Landsat acquisitions per path/row, (ii) using the best single GeoCover 2000
acquisition for each path/row. Epoch 2005 composites were generated by
compositing SLC-off data using (iii) five to seven acquisitions per path/row, (iv)
three acquisitions per path/row. Eighty per cent of pixels were of suitable quality
for change detection between (ii) and (iv), emulating that which is possible with
current GeoCover and planned Global Land Survey (GLS) inputs. In a more
data intensive change detection analysis using mosaics (i) and (iii), 96% of pixels
had suitable quality. Compositing more acquisitions per path/row for the study
area systematically reduced the percentage of SLC-off gaps and, when more than
three acquisitions were composited, reduced the percentage of pixels with high
likelihood of cloud, haze or shadow. The results indicate that additional input
imagery to augment both the Geocover and GLS data may be required to enable
forest cover and change analyses for regions of the humid tropics.
Publication Title
International Journal of Remote Sensing
Volume
29
Issue
24
DOI of Published Version
10.1080/01431160802275890
Rights
© 2008 Taylor & Francis
Recommended Citation
E. J. Lindquist , M. C. Hansen , D. P. Roy & C. O. Justice (2008) The suitability of decadal image data sets for mapping tropical forest cover change in the Democratic Republic of Congo: implications for the global land survey, International Journal of Remote Sensing, 29:24, 7269-7275, DOI: 10.1080/01431160802275890