Everglades National Park (Fla.)--Environmental conditions.

Everglades National Park is a hydro-ecologically significant wetland experiencing salinity ingress over the years. This motivated our study to map water salinity using a spatially weighted optimization model (SWOM); and soil salinity using land cover classes and EC thresholds. SWOM was calibrated and validated at 3-km grids with actual salinity for 1998–2001, and yielded acceptable R2 (0.89-0.92) and RMSE (1.73-1.92 ppt). Afterwards, seasonal water salinity mapping for 1996–97, 2004–05, and 2016 was carried out. For soil salinity mapping, supervised land cover classification was firstly carried out for 1996, 2000, 2006, 2010 and 2015; with the first four providing average accuracies of 82%-94% against existing NLCD classifications. The land cover classes and EC thresholds helped mapping four soil salinity classes namely, the non saline (EC = 0~2 dS/m), low saline (EC = 2~4 dS/m), moderate saline (EC = 4~8 dS/m) and high saline (EC >8 dS/m) areas.

Fire plays a key role in the ecology of the Everglades and is a ubiquitous tool for
managing the structure, function, and ecosystem services of the Greater Everglades
watershed. Decades of hydrologic modifications have led to the alteration of plant
community composition and fire regime in much of the Everglades. To create a better
understanding of post-fire recovery in sawgrass (Cladium jamaicense) communities,
sawgrass marshes in the northern Everglades were studied along a chronosequence of
time since fire and along a nutrient gradient. Areas closer to a water nutrient source and
with fewer mean days dry contained greater total and dead aboveground graminoid
biomass whereas live graminoid biomass was greater in areas with less time since fire
and with fewer days dry. Post-fire characteristics of sawgrass marshes can provide insight
on the effectiveness of fire management practices in the maintenance and restoration of
quality habitat in the northern Everglades.

Knowledge of the geospatial distribution of vegetation is fundamental for resource management. The objective of this study is to investigate the possible use of airborne LIDAR (light detection and ranging) data to improve classification accuracy of high spatial resolution optical imagery and compare the ability of two classification algorithms to accurately identify and map wetland vegetation communities. In this study, high resolution imagery integrated with LIDAR data was compared jointly and alone; and the nearest neighbor (NN) and machine learning random forest (RF) classifiers were assessed in semi-automated geographic object-based image analysis (GEOBIA) approaches for classification accuracy of heterogeneous vegetation assemblages at
Everglades National Park, FL, USA.