Projects & Publications
Determine the Effectiveness of Detectability and Scalability of Wetland Plant Communities Using WorldView 2 and Landsat Satellite Data for the Greater Everglades
This research project supports the assessment of landscape patterns in the Greater Everglades wetland domain.
The purpose of this project was to evaluate the use of remote sensing 1) to detect and map Everglades wetland plant communities at different scales; and 2) to compare map products delineated and resampled at various scales with the intent to quantify and describe the quantitative and qualitative differences between such products. We evaluated data provided by Digital Globe’s WorldView 2 (WV2) sensor with a spatial resolution of 2m and data from Landsat’s Thematic and Enhanced Thematic Mapper (TM and ETM+) sensors with a spatial resolution of 30m. We were also interested in the comparability and scalability of products derived from these data sources. The adequacy of each data set to map wetland plant communities was evaluated utilizing two metrics: 1) model-based accuracy estimates of the classification procedures; and 2) design-based post-classification accuracy estimates of derived maps. The following four questions guided this research:
1) What is the overall and class-specific detection accuracy for Greater Everglades freshwater marsh plant communities from medium spectral and high spatial resolution (i.e., World View 2) and from medium spectral and medium spatial resolution (i.e., Landsat) satellite data?
2) How do overall and class-specific classification accuracies differ at different thematic hierarchical levels (i.e., detection at the plant community level vs. structural level) and different spatial resolutions (i.e., WV2 vs. Landsat and WV2 aggregated to Landsat spatial resolution)?
3) How do aggregation algorithms applied to high spatial resolution (detection) maps compare when aggregating to medium resolution maps using a morphological aggregation algorithm versus grid-based (arbitrary origin) majority rules? For the purpose of this project we were interested in two resolutions: 1) 30x30m, the pixel size of Landsat data; and 2) 50x50m, the grid cell size of visually interpreted vegetation maps provided by CERP 2004/2009.
4) How does the heterogeneity of grid-based maps aggregated by a simple majority rule compare to that of maps classified at the same grid-based resolution?
The research and mapping was done in two distinct regions of interest, the Tamiami Trail Bridge area, which included sub-regions north of Tamiami Trail and south of Tamiami Trail, and an area in the western part of Water Conservation Area 3A. To evaluate the suitability of remote sensing to detect plant communities in these landscapes, we established plant community classification schemata; acquired satellite data and performed atmospheric corrections; evaluated different classifiers; classified images using the classifier with the highest model-based accuracy; and assessed post-classification accuracy. To investigate the scalability of plant community maps generated with remote sensing methods, we evaluated scaling using hierarchical thematic aggregation and grid-based vs. morphological spatial aggregation.
Methods and imagery evaluated include supervised classification algorithms, with the use of reflectance and texture variables derived from satellite imagery captured during both the wet and dry seasons. Information gained from this project will provide future guidance relevant to the Comprehensive Everglades Restoration Plan (CERP) structure and implementation. The scope is written as part of the RECOVER Monitoring and Assessment Plan (MAP), Sections 3.1.3 and 3.1.4.
The results of the work performed will be essential to the refinement of existing Greater Everglades project performance measures, as well as linking the existing predictive metrics to the CERP Monitoring and Assessment Plan (RECOVER 2004, 2006b, 2008) and its associated hypothesis clusters.
- Years
- 2011
- Principal Investigator
- Daniel Gann
- Agencies
- South Florida Water Management District
- Project Link
- ftp://gisrsftp.fiu.edu/Share/gann/4500058664_synthesisReport.pdf