 |
| Titel |
Integrating field sampling, geostatistics and remote sensing to map wetland vegetation in the Pantanal, Brazil |
| VerfasserIn |
J. Arieira, D. Karssenberg, S. M. Jong, E. A. Addink, E. G. Couto, C. Nunes da Cunha, J. O. Skøien |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1726-4170
|
| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 8, no. 3 ; Nr. 8, no. 3 (2011-03-17), S.667-686 |
| Datensatznummer |
250005570
|
| Publikation (Nr.) |
 copernicus.org/bg-8-667-2011.pdf |
|
|
|
|
|
| Zusammenfassung |
| Development of efficient methodologies for mapping
wetland vegetation is of key importance to wetland conservation. Here we
propose the integration of a number of statistical techniques, in particular
cluster analysis, universal kriging and error propagation modelling, to
integrate observations from remote sensing and field sampling for mapping
vegetation communities and estimating uncertainty. The approach results in
seven vegetation communities with a known floral composition that can be
mapped over large areas using remotely sensed data. The relationship between
remotely sensed data and vegetation patterns, captured in four factorial
axes, were described using multiple linear regression models. There were
then used in a universal kriging procedure to reduce the mapping
uncertainty. Cross-validation procedures and Monte Carlo simulations were
used to quantify the uncertainty in the resulting map. Cross-validation
showed that accuracy in classification varies according with the community
type, as a result of sampling density and configuration. A map of
uncertainty derived from Monte Carlo simulations revealed significant
spatial variation in classification, but this had little impact on the
proportion and arrangement of the communities observed. These results
suggested that mapping improvement could be achieved by increasing the
number of field observations of those communities with a scattered and small
patch size distribution; or by including a larger number of digital images
as explanatory variables in the model. Comparison of the resulting plant
community map with a flood duration map, revealed that flooding duration is
an important driver of vegetation zonation. This mapping approach is able to
integrate field point data and high-resolution remote-sensing images,
providing a new basis to map wetland vegetation and allow its future
application in habitat management, conservation assessment and long-term
ecological monitoring in wetland landscapes. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|