Remote detection of ecosystem degradation in the Everglades ridge-slough landscape

Indicators of landscape condition should be sensitive and specific to environmental change and provide early warning detection of ncipient changes. We assessed a suite of five spectral metrics derived from Landsat 5 TM imagery spanning a decade (1996-2007) to quantify ecosystem condition in the ridge-slough mosaic of the Everglades (South Florida, USA). These included the normalized difference vegetation index (NDVI), the same index using green instead of red band as the visible reference (NDVIg), the normalized difference water index (NDWI), the simple ratio of NIR and red bands (SR) and the moisture stress index (MSI). Mean and variance from pure ridge or slough pixels (i.e., those >30 m from a mapped patch edge) were quantified for twentyfour 2 x 5 km blocks across a gradient of hydrologic and ecological condition. Metrics were compared with field measures of landscape condition from block-scale soil elevation surveys, which capture dramatic spatial gradients between conserved and degraded locations. Elevationbased measures of landscape condition, validated as diagnostic in previous work, included soil elevation bi-modality (BISE), a binary measure of ecosystem condition, and the soil elevation standard deviation (SDSE), a continuous measure of condition. Spectral metric performance was assessed based on the strength (sensitivity) and shape (leading vs.lagging) of the relationship with elevation-based measures. We observed significant logistic regression slopes with BISE for only 3 metrics (mean ridge NDVI and NDVIg, mean ridge SR). In prediction of variance SDSE, the standard deviation of our VIs was more informative than the mean, and measures from sloughs more informative than ridges. The strongest predictions are variance in slough NDVIg and SR, suggesting that spatial heterogeneity in slough biomass is most informative for predicting the status of soil elevation variance. None of the vegetation metrics were leading indicators of change; a multivariate model using three VIs selected based on consistent performance across years (variance in Slough SR, slough NDVIg, and ridge NDWI) substantially improve predictions of SDSE and yielded more plausible prediction maps. These findings suggest that soil elevation changes from altered peat accretion dynamics precede visible changes in vegetation reflectance. While this constrains predictions of incipient ecosystem change, the reasonable performance of spectral metrics indicates that efficient monitoring of ridge-slough landscape health is possible as part of the ongoing Everglades restoration effort.