Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades

Abstract

Information regarding the spatial extent and timing of flooding in the world's major wetlands is important to a wide range of research questions including global methane models, water management, and biodiversity assessments. The Florida Everglades is one of the largest wetlands in the US, and is subject to substantial development and pressures that require intensive hydrological modeling and monitoring. The Moderate Resolution Imaging Spectrometer (MODIS) is a global sensor with high frequency repeat coverage and significant potential for mapping wetland extent and dynamics at moderate spatial resolutions. In this study, empirical models to predict surface inundation in the Everglades were estimated using MODIS data calibrated to water stage data from the South Florida Water Management District for the calendar year 2004. The results show that hydropatterns in the Florida Everglades are strongly correlated to a Tasseled Cap wetness index derived from MODIS Nadir Bidirectional Reflectance Function Adjusted Reflectance data. Several indices were tested, including the Normalized Difference Wetness Index and the diurnal land surface temperature difference, but the Tasseled Cap wetness index showed the strongest correlation to water stage data across a range of surface vegetation types. Other variables included in the analysis were elevation and percent tree cover present within a pixel. Using logistic regression and ensemble regression trees, maps of water depth and flooding likelihood were produced for each 16-day MODIS data period in 2004. The results suggest that MODIS is useful for dynamic monitoring of flooding, particularly in wetlands with sparse tree cover. © 2008 Elsevier Inc.