Hyper-temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests
Article first published online: 3 AUG 2012
© 2012 International Association for Vegetation Science
Journal of Vegetation Science
Volume 24, Issue 2, pages 214–226, March 2013
How to Cite
Jiang, Y., de Bie, C.A.J.M., Wang, T., Skidmore, A.K., Liu, X., Song, S., Shao, X. (2013), Hyper-temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests. Journal of Vegetation Science, 24: 214–226. doi: 10.1111/j.1654-1103.2012.01453.x
- Issue published online: 24 JAN 2013
- Article first published online: 3 AUG 2012
- Manuscript Accepted: 15 JUN 2012
- Manuscript Received: 7 SEP 2011
- NDVI ;
- Remote sensing;
Is there, at the macro-habitat scale, a relationship between the fraction of evergreen forests and the presence probability of epiphyllous liverworts? Can these two parameters be estimated and mapped using an NDVI-based indicator that is derived from time-series of SPOT-VGT imagery?
Applying the ISODATA algorithm, we classified SPOT-VGT NDVI time-series imagery and produced an NDVI map at 1-km2 resolution containing 128 NDVI classes. That map and the National Land Cover Map of China (2000) were used to prepare a scheme for field sampling. For 537 1-km2 areas, located in 19 blocks of 100 km × 100 km, field data were collected. They represented 51 preselected NDVI classes that were assumed to contain evergreen forests. Data on the fractions of forest and evergreen forest, the fraction of evergreen forest in forest present and presence probability of epiphyllous liverworts were regressed against different NDVI class-specific indicators by means of weighted least-squares regression (WLSR).
The SPOT-VGT NDVI for March was found to best explain the variation between 1-km2 areas in the presence probability of epiphyllous liverworts (R2 = 0.64; linear relationship; RMSE = 0.38) as the area fraction (%) of evergreen forest (R2 = 0.90; exponential relationship; RMSE = 6.1%). Epiphyllous liverworts were only found within 1-km2 areas when the SPOT-VGT NDVI value for March was more than 0.50 (model estimate: 0.43 ± 0.20) and the fraction of evergreen forest in 1 km2 was above 14% (model estimate: 5 ± 35%). The estimation errors (with 95% confidence interval) of these two relationships were calculated using a bootstrap resampling method with 1000 replications; they were, respectively, 0.49–0.76 and 0.85–0.94 for R2, and 0.11–0.23 and 5.2–11.9% for RMSE. Other positive relationships were found between the presence probability of epiphyllous liverworts and the fraction of evergreen forest (R2 = 0.64, linear relationship; RMSE = 0.38) and between the fraction of evergreen forest and forest within 1-km2 areas (R2 = 0.80, linear relationship; RMSE = 29%).
We show that for southern China, the fraction of evergreen forest and the presence probability of epiphyllous liverworts can directly be inferred by making use of SPOT-VGT NDVI imagery. The findings are fully consistent with earlier reported hotspots for epiphyllous liverworts. At the macro-habitat scale, the presence probability of epiphyllous liverworts proved quantitatively related to the fraction of evergreen forest. This suggests causal relationships with patch size and/or rainfall patterns. We believe that the derived maps may serve as a foundation for a range of further studies.