, Volume 34, Issue 1, pp 67–77

Recent Trends in Satellite Vegetation Index Observations Indicate Decreasing Vegetation Biomass in the Southeastern Saline Everglades Wetlands


    • Department of Geography and Regional StudiesUniversity of Miami
  • Yu Wang
    • Department of Geography and Regional StudiesUniversity of Miami

DOI: 10.1007/s13157-013-0483-0

Cite this article as:
Fuller, D.O. & Wang, Y. Wetlands (2014) 34: 67. doi:10.1007/s13157-013-0483-0


We analyzed trends in time series of the normalized difference vegetation index (NDVI) from multitemporal satellite imagery for 2001–2010 over the southeastern Everglades where major changes in vegetation structure and type have been associated with sea-level rise and reduced freshwater flow since the 1940s. Non-parametric trend analysis using the Theil-Sen slope revealed that 84.4 % of statistically significant trends in NDVI were negative, mainly concentrated in scrub mangrove, sawgrass (Cladium jamaicense) and spike rush (Eleocharis cellulosa) communities within 5 km of the shoreline. Observed trends were consistent with trends in sawgrass biomass measurements made from 1999 to 2010 in three Long-term Ecological Research (LTER) sites within our study area. A map of significant trends overlaid on a RapidEye high-resolution satellite image showed large patches of negative trends parallel to the shoreline in and around the “white zone,” which corresponds to a low-productivity band that has moved inland over the past 70 years. Significantly positive trends were observed mainly in the halophytic prairie community where highly salt tolerant species are typically found. Taken as a whole, the results suggest that increased saline intrusion associated with sea-level rise continues to reduce the photosynthetic biomass within freshwater and oligohaline marsh communities of the southeastern Everglades.


MODIS NDVI Non-parametric trend analysis Sawgrass marsh Mangroves Above-ground biomass production


Coastal wetlands provide a range of essential ecosystem services such as carbon sequestration, protection from erosion, and maintenance of water quality (Webb et al. 2013). Sea-level rise and associated intrusion of salt-water into oligohaline wetland systems can negatively affect primary productivity of wetland plants such that organic accretion rates in marshes may not keep pace with rising water levels and increased salinities (Neubauer 2008, 2013; Barendregt and Swarth 2013). The inundation of coastal wetlands by rising seas may affect as much as 195,000 km2 of tropical and temperate tidal marshes globally (Greenberg et al. 2006; Spalding et al. 2010). A number of large coastal wetland areas are considered especially vulnerable to increased salinity, subsidence, and reduced plant productivity, including the Nile Delta (Hassaan and Abdrabo 2013), major river deltas of China such as the Pearl and Yangtze (Wang et al. 2012), the Sundarbans of Bangladesh and India (Loucks et al. 2010), as well as large areas of eastern North America, Europe and the Gulf of Mexico (Baldwin and Mendelssohn 1998; Neubauer 2008; Couvillion and Beck 2013).

One of the most well-studied coastal wetland ecosystems is the Everglades located on the southern tip of the Florida Peninsula, USA (Egler 1952; Craighead 1968; Davis et al. 2001; Childers et al. 2003; Foti et al. 2013). Vegetation productivity in this oligotrophic subtropical wetland is closely linked to a variety of factors including salinity, nutrient loading, hydroperiod, and surrounding land uses (Ross et al. 2000; Childers et al. 2006). Since 1930, the rate of sea-level rise in South Florida has increased above historical rates and is expected to result in sea-level rise of approximately 60 cm later this century (Wanless et al. 1994). Over the same time period, South Florida’s water management system has greatly reduced the freshwater flow in the major sloughs through which the bulk of freshwater flows to Florida Bay and the Gulf of Mexico (Fig. 1). The freshwater marshes within and around the sloughs typically contain significant areas of sawgrass (Cladium jamaicense), spike rush (Eleocharis cellulosa), tropical hardwood hammocks, and bayheads, which are comprised mainly of freshwater shrubs such as Annona glabra, Chrysobalanus icaco, and Ilex cassine. In the oligohaline estuarine zone, scrub and tall mangrove communities with red mangroves (Rhizophora mangle), white mangroves (Laguncularia racemosa), and black mangroves (Avicennia germinans), which are interspersed with buttonwood (Conocarpus erectus) stands, salt marshes, and woody hammocks (Sternberg et al. 2007). This mangrove-dominated zone constitutes a significant area of bio-sedimentary substrate accumulation. However, with increasing rates of sea-level rise expected this century, Davis et al. (2005) hypothesized that saline intrusion will result in further inland migration of the so-called “white zone”—a band of diminished vegetation productivity consisting of sparse mixed mangroves and graminoid vegetation that represents the inland edge of the oligohaline ecotone, which runs roughly parallel to the shoreline (Ross et al. 2000). Near shore, elevated salinity in surface and soil water is associated with halophytic prairies dominated by herbaceous species such as Salicornia spp., Batis maritima, and Blutaparon vermiculare, which may also become established when tropical storms have damaged and killed mangrove and buttonwood stands (Armentano et al. 1995; Davis et al. 2005). Further, salinity increases have been noted in the formerly oligohaline mangrove zone and as well as saline intrusion in the former freshwater marshes and ground water of the southern Everglades (Ross et al. 2000, 2002).
Fig. 1

Map of the study area showing the location of the Taylor Slough, Everglades National Park, and biomass sites used to assess signifiance of Sen-Theil Slope applied to NDVI images from 2001 to 2010

Thus, the productivity of Everglades marsh and mangrove species is strongly influenced by salinity gradients and flushing by fresh and saltwater over different time scales (Childers et al. 2006; Barr et al. 2013). For example, Macek and Rejmánková (2007) found that plant height and shoot/root biomass decreased in C. jamaicense and Eleocharis cellulosa under conditions of elevated salinity. Barr et al. (2009) showed that carbon assimilation in mangrove leaves was limited when salinity exceeded 35 parts per thousand. In the absence of regular flushing by either fresh or brackish waters, Wanless and Vlaswinkel (2005) observed that marsh communities can collapse—a phenomenon has also been noted in mangrove communities that have been migrating inland in recent decades (Davis et al. 2005). Therefore, even relatively salt-tolerant communities may decline as a result of elevated salinity in the southern Everglades.

Systematic estimates of above-ground biomass and culm density have been made in 16 marsh sites in the southern Everglades since 1998 as part of the Florida Coastal Everglades Long-term Ecological Research (FCE LTER) sampling network (Childers et al. 2006; Ewe et al. 2006). These data provide an important baseline for understanding long-term behavior of sawgrass and spike rush communities in relation to salinity, nutrient loading, and hydrological drivers. For example, Childers et al. (2006) showed that above-ground net primary productivity (ANPP) in these sawgrass-dominated communities was negatively related to surface water salinity measured continuously in the Taylor Slough, while more the more hydric spike rush communities possessed higher biomass in sites with long hydroperiods and elevated water levels.

While site-level monitoring of biomass has provided critical understanding of ecosystem processes that control photosynthesis in the southern Everglades, productivity data from satellite observations may be used to assess recent fluctuations and trends in biomass and vegetation cover. In particular, sums of the normalized difference vegetation index (NDVI) obtained from red and near infrared reflectance provide a direct measurement of the fraction of absorbed photosynthetic activity (Goetz et al. 1999) as well as indirect measures of gross and net primary productivity, biomass, and green leaf area in a variety of grassland and forest ecosystems (Green et al. 1997; Paruelo et al. 1997; Myneni et al. 2001; Pineiro et al. 2006; Wessels et al. 2008; An et al. 2013; Barr et al. 2013). Since mid-2000, global NDVI data have been available at 250 m spatial resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the polar-orbiting Terra Satellite operated by the National Aeronautics and Atmospheric Administration (NASA). As NDVI image archives have grown over the past three decades, various time-series techniques have been applied to these data to identify multi-year trends that may relate to variety of anthropogenic and biophysical factors (Fuller 1998; Herrmann et al. 2005; de Jong et al. 2011, 2013). In this study, we exploit 10 years of MODIS 250 m NDVI imagery covering South Florida to map decadal-scale trends, which we relate to vegetation type and ground-level measurements made in sawgrass sites in the southern Everglades National Park (NP). Our objective, therefore, was to identify statistically significant trends and explain these in terms of current understanding of environmental factors that control ANPP in the southern Everglades ecosystems. Our study area is centered over the Taylor Slough, which is the second-largest flow-way for surface water in the Everglades and stretches approximately 30 km along the eastern boundary of the Everglades NP (Fig. 1).

Data and Methods

We obtained 2001–2008 version 5 MOD09Q1 250 m 8-day reflectance imagery for MODIS bands 1 (620–670 nm) and 2 (841–876 nm) covering South Florida from the Website https://lpdaac.usgs.gov/get_data/reverb. These data were used to create a temporal sequence of 8-day NDVI images such that each year in our time series contained 46 NDVI composite images, thus providing sufficient temporal resolution to evaluate changes in photosynthetic activity throughout the year. The annual sum of NDVI, which is used as a proxy for ANPP (Wessels et al. 2008), was calculated for each year (as the growing season is 12 months in the Everglades) and the Theil-Sen slope was calculated for each pixel along with slope significance (Theil 1950, Sen 1968). TS slope is the median of slopes calculated between observations X j and X i at pairwise time steps t j and t i
$$ TS\kern0.5em Slope= Median\left(\frac{X_j-{X}_i}{t_{{}_j}-{t}_i}\right) $$
The TS slope is therefore non-parametric and robust against outliers (Neeti and Eastman 2011). We therefore did not attempt to smooth the NDVI time series, which is sometimes done to eliminate high-frequency noise (e.g., de Jong et al. 2011). Further, we calculated trends based on annual sums, which eliminated any serial autocorrelation present in the 8-day time step. To assess the significance of TS slopes, we employed a modification of the Mann-Kendall test, which uses Kendall’s S:
$$ s={\displaystyle \sum_{i=1}^{n-1}{\displaystyle \sum_{j=i+1}^n sign\left({x}_i-{x}_j\right)}} $$
$$ sign\left({x}_i-{x}_j\right)=\left\{\begin{array}{c}\hfill 1\kern0.5em if\kern0.5em {x}_i-{x}_j<0\hfill \\ {}\hfill 0\kern0.5em if\kern0.5em {x}_i-{x}_j=0\hfill \\ {}\hfill -1\kern0.5em if\kern0.5em {x}_i-{x}_j>0\hfill \end{array}\right. $$
where n is the length of the time series and x i and x j are observations at time i and j respectively
The equations for Mann Kendall significance (Z and p) are:
$$ Z=\left\{\begin{array}{l}\frac{S-1}{\sqrt{ Var(S)}} for\kern0.5em S>0\hfill \\ {}0\kern3.5em for\kern0.5em S=0\hfill \\ {}\frac{S+1}{\sqrt{ Var(S)}} for\kern0.5em S<0\hfill \end{array}\right. $$
$$ p=2\left[1-\varnothing \left(\left|Z\right|\right)\right] $$
where ∅ () is the cumulative distribution function of a standard normal variate such that
$$ \varnothing \left(\left|Z\right|\right)=\frac{2}{\sqrt{\pi }}{\displaystyle \underset{0}{\overset{\left|Z\right|}{\int }}{e}^{-{t}^2} dt} $$

Neeti and Eastman (2011) introduced a contextual Mann Kendall (CMK) approach as a way to incorporate local spatial variation of individual pixels with respect to their neighbors. The logic behind contextual analysis is that similar behavior (i.e., spatial autocorrelation) within small neighborhoods of pixels (e.g., 3 x 3) should produce greater confidence in trends. Neeti and Eastman (2011) also showed that this approach increased the number of pixels in satellite time series that have significant slopes.

They define the CMK statistic \( \overline{S}m \) as
$$ Zm=\frac{\overline{S}m-E\left(\overline{S}m\right)}{\raisebox{1ex}{$\sigma $}\!\left/ \!\raisebox{-1ex}{$\sqrt{m}$}\right.} $$
$$ \overline{S}m=\frac{1}{m}{\displaystyle \sum_{j=1}^m Sj} $$
where m is the neighborhood size and Sj is Kendall’s coefficient for the jth neighbor, so that for a 3 × 3 neighborhood used here
$$ Var\left(\overline{S}m\right)=\frac{n\left(n-1\right)\left(2n+5\right)}{18m} $$

Implementation of TS slope and CMK was done in Earth Trends Modeler software, which is part of the Idrisi Selva GIS software (Eastman 2012).

To evaluate trends within different vegetation types, we utilized a highly detailed (1:15,000) digital vegetation map produced by Welch et al. (1999). Within our study area, the map contains 57 different dominant vegetation types, so to simplify the analysis we concentrated on seven major vegetation types that cover approximately 95 % of the terrestrial portion of our study area within Everglades National Park (shown in Fig. 1). These major vegetation types include mangrove forest (trees > 5 m), mangrove scrub (trees and shrubs < 5 m), sawgrass prairie, spike rush prairie, other graminoids, bayheads, and halophytic prairie. GIS software was then used to calculate the number of pixels with significant positive and negative TS slopes within each major vegetation type.

Above-ground biomass and culm-density data were obtained from Florida Coastal Everglades LTER site (http://fcelter.fiu.edu/research/working_groups/), which contained sawgrass time series data on biomass for three sites with 10 or more years of temporal overlap with the NDVI time series. The three sites fall along a nutrient and salinity gradient from south to north, with sites TS/Ph-01b and TS/Ph-03 being more typical of marsh-slough habitats with well-developed periphyton mats, and TS/Ph-06b found in the estuarine ecotone (Ewe et al. 2006). Biomass data were derived from bi-monthly estimates of live biomass using a non-destructive phenometric regression model that was calibrated using clipped and dried culms obtained in triplicate 1 m2 permanent plots and cover an area ranging from 0.25 to 1.0 ha. While these sites cover less area than the MODIS sensor field of view (i.e., 6.25 ha), we assumed that the sites were spatially homogeneous with respect to plant diversity and physiognomy as they were selected to be representative of freshwater marshes and mangroves of the larger Everglades landscape (Ewe et al. 2006). Model independent variables included culm diameter, sum of leaf lengths, total culm height, and the height of inflorescences. These four variables in a step-wise regression explained 92 % of the variance in dried, clipped sawgrass samples (Childers et al. 2006). We also used average culm density data collected as part of the same monitoring effort (Childers et al. 2006). These two data sets were used to assess consistency between observed NDVI trends and biomass/cover trends from 2001 to 2010 and thus allow us to evaluate the reliability of estimates of TS slope and p.


Figure 2a shows the TS slope values while Fig. 2b shows the p-values for areas of significant slope (where p < 0.05). These figures reveal spatially comprehensible patterns (i.e., apparently non-random) that appear to relate to vegetation type and distance from shoreline. For example, large areas of significant slope can be seen in Fig. 2a in a zone that runs roughly parallel to the shoreline both within and outside Everglades NP. Moreover, areas of positive slope are also evident in interior locations within Taylor Slough outside the oligohaline zone as well as along the eastern border and to the east of Everglades NP. The mean distance from shoreline is shown in Fig. 3 for polygons that had negative and positive trends. This figure confirms that negative trends were generally found closer to the shoreline (approximately 4.7 km on average), whereas positive trends were more likely to be found inland, on average about 8 km from the shoreline. However, the standard deviations, which are indicated by the error bars in Fig. 3, also suggest that distance from shore was highly variable for pixels with significant positive NDVI trends. Figure 4 shows polygons that depict locations of significant positive and negative slope overlaid on a 2010 RapidEye color composite satellite image (5 m spatial resolution, acquired 20-January-2010), which allows more detailed assessment of spatial patterns noted in Fig. 2a and b. This figure reveals quite clearly the “white zone” as well as the negative trends that were concentrated in and around this band of low productivity scrub-graminoid mix. Further, Fig. 4 also reveals a large area of significant negative NDVI trends in the Taylor Slough where sawgrass and spike rush communities were common prior to 2000 (Welch et al. 1999).
Fig. 2

T-S slope (a) and slope significance (b) for pixels with p < 0.05; c) shows the location of Fig. 2a overlaid on a map to highlight the location of the study area in South Florida

Fig. 3

Plot of the mean distance of pixels from the shoreline with significant negative and postive T-S slopes

Fig. 4

Areas with sigificant positive and negative trend overlaid on a RapidEye color composite image (5 m spatial resolution) acquired 20 January 2010. Polygons with black cross-hatching indicate areas of significant negative trend and those with yellow diagonal lines show areas of significant positive trend

Analysis of slope by major vegetation type (Fig. 5) indicates that communities consisting of relatively salt-intolerant species (bayheads, other graminoids, sawgrass, and spike rush), showed a preponderance of negative NDVI trends. In addition, trends in mangrove forest and scrub mangrove communities were similar to those seen in graminoid communities. For example, 80 % of statistically significant trends in mangrove forest areas within our study area were negative and 91.3 % of significant trends in scrub mangrove were negative as well. In addition, significant trends were largely negative in bayheads (86.7 %), other graminoids (77.7 %), sawgrass (82.6 %), and spike rush (90.78 %) communities. The notable exception was found in halophytic prairies, which are found close to shoreline locations where tidal and storm surges are common. The percentage of significant negative trends in this halophytic community was 16 %, which suggests that primary productivity in this fringe community generally increased from 2001 to 2010. However, in terms of the area, significant changes were observed in 14 MODIS pixels that corresponded to halophytic prairie, which is restricted to coastal areas adjacent to mangroves and mudflats. The largest change area was associated with scrub mangroves (n = 437 pixels), followed by sawgrass (n = 385 pixels), and other graminoids (n = 292 pixels), and spike rush (n = 206 pixels).
Fig. 5

Percent of pixels in different major vegetation vegetation types that showed either significantly (p < 0.05) negative or significantly positve T-S slopes. Numbers in parentheses indicate the number of significant pixels found in each vegetation class

Biomass data from the FCE LTER reveal patterns consistent with statistically significant and insignificant trends shown in Fig. 2b. In particular, TS slope for sites TS/Ph-01b and TS/Ph-06b were not significant, whereas TS slope for TS/Ph-03 was strongly negative (TS slope = -0.51). This site falls within a sawgrass community in the Taylor Slough that showed significant negative trends in above-ground biomass from 2001 to 2010 (Fig. 6). Consistent with Fig. 2b, the ordinary least squares (OLS) slope, obtained from linear regression with time as the independent variable, showed that for TS/Ph-03 the T-S slope was significantly negative (b = −116.78, t = −3.88, p < 0.05). However, OLS slope values were not significant (p > 0.05) for sites TS/Ph-01b and TS/Ph-06b. Sums of annual NDVI tracked fairly closely biomass sums for TS/Ph-01b and TS/Ph-03 (Fig. 6). The Pearson correlation coefficient (r) between summed NDVI and summed biomass for TS/Ph-01b and TS/Ph-03 was 0.58 (p < 0.05, two-tailed) and 0.84 (p < 0.05, two-tailed), respectively; whereas r was not significant for TS/Ph-06b (r = −0.045, p > 0.05). Relationships between culm density and summed NDVI were similar as those for above-ground biomass and summed NDVI (Fig. 7). Pearson correlation coefficients for average culm density and summed NDVI for sites TS/Ph-01b, TS/Ph-03, and TS/Ph-06b were 0.38 (p > 0.05, two-tailed), 0.82 (p < 0.05, two tailed), and 0.23 (p > 0.05, two-tailed). This result suggests that summed NDVI provided a slightly better estimate of above-ground biomass than average culm density in sawgrass stands.
Fig. 6

Time series of annual NDVI sums plotted against annual sums of above-ground biomass collected at three sawgrass sites in the Taylor Slough for 2001-2010. Top panel is site TS/Ph-01b, middle panel is TS/Ph-03, and the bottom panel is TS/Ph-06b

Fig. 7

Time series of annual NDVI sums plotted against mean culm density data for three sawgrass sites in the Taylor Slough for 2001-2010. Top panel is site TS/Ph-01b, middle panel is TS/Ph-03, and the bottom panel is TS/Ph-06b


Our analysis supports the conceptual model of ecological interactions in the Everglades estuaries advanced by Davis et al. (2005), who postulated that coastal transgression will continue to outpace deposition in coastal marl and mangrove environments in South Florida and that the low-productivity white zone (Fig. 4) will continue to move inland over time. These processes were first observed by Egler (1952) in the 1940s and thus are part of a multi-decadal trend that is driven by reinforcing factors related to global climate change and water management practices within South Florida. Specifically, construction of canals has reduced water levels in the peat mantle and underlying aquifer and withdrawal of groundwater has resulted in lateral saltwater intrusion along the east coast of South Florida. While the process may be reversible through artificial recharge of the aquifer (Barlow and Reichard 2010), continued sea-level rise this century will most likely negate attempts to limit future saline intrusion.

Many of the patterns of significant TS slope in Fig. 2b are difficult to explain based on salinity changes alone. For example, Fig. 4 shows large areas of positive NDVI slope to the north of the white zone and two major areas of significant positive slope along the eastern boundary of Everglades NP and approximately 10 km east of the Park are likely the result of decisions to manage surface water levels for seasonal flood control (Van Lent et al. 1993; Armentano et al. 2006). While increased salinity is a major driver of change in vegetation productivity in sawgrass and spike rush wetlands, shifts in species composition have occurred within 3–4 years in the Taylor Slough as a result of changes in water levels and hydroperiod from 1979 to 2003 (Armentano et al. 2006). For example, increased water levels in portions of the Taylor Slough have been associated with loss of muhly grass (Muhlenbergia capillaris var. filipes) and sawgrass and an increase in spike rush, which is common in long hydroperiod (6–9 month) marshes (Armentano et al. 2006). These observations are consistent with a recent modeling study that suggests major decreases in the area of tall sawgrass vegetation as a result of changes in hydroperiod and land use in the Everglades (Foti et al. 2013). Thus, the changes in sawgrass biomass observed in site TS/Ph-3, which is well outside the oligohaline zone, are likely unrelated to salinity increases. However, it is unclear whether major changes in biomass and ANPP inferred using NDVI time series can be partially ascribed to water level or hydroperiod in areas unaffected by saline intrusion.

Trends in biomass measurements made in sawgrass communities from the FCE LTER support the results obtained from NDVI time series. In particular, the consistency between steep declines in above-ground biomass and culm density at site TS/Ph-03 and NDVI sums for that site provides confidence in our maps of significant trends (Figs. 2b and 4). Lack of significant trends at the other two sites was also consistent with trends in NDVI sums. However, certain limitations in our study should be noted, including lack of concomitant salinity data at the three FCE LTER biomass sites and no biomass time series for major vegetation types other than sawgrass prairies. However, salinity time series from other FCE LTER sites in the Taylor Slough suggest that multi-year trends in this variable are difficult to discern (e.g., Childers et al. 2006). Despite these limitations, the spatial patterns observed in Figs. 2b and 4 reveal changes consistent with general understanding of the long-term (>70 year) trends that have affected vegetation in the southeastern saline Everglades. Therefore, our analysis supports further application of the TS slope and CMK significance to investigate long-term changes in wetland biomass and productivity. While the spatial resolution of MODIS 250 m NDVI may be viewed as somewhat coarse for ecological monitoring, the sensor’s field of view represents a trade-off between temporal and spatial resolution, and was designed to be optimal for detecting land cover changes from space (Justice and Townshend 1988).

The difference in scale between the biomass samples collected in the FCE LTER sites (1 m2 plots) and the MODIS NDVI imagery may partly explain low correlations between biomass and culm density as well. Data on spatial variability at scales comparable to the MODIS sensor resolution were not available to determine if patchiness (i.e., low spatial autocorrelation) may have influenced the results. Moreover, many other factors may influence NDVI including soil background reflectance, atmospheric effects, and view angle effects. Further, the low correlation between biomass, culm density and summed NDVI at site TS/Ph-06b may have been a result of tidal inundation as this location, which may have introduced soil and water background effects on NDVI. Ewe et al. (2006) noted that the low substrate organic content and long hydroperiod may explain the relatively low productivity and low biomass at this particular site. In addition, spatial heterogeneity of plant communities within 250 m MODIS pixels may have resulted in the inclusion of species that were not sampled for biomass as part of the FCE LTER. Thus, as vegetation cover and biomass decline, the soil will play greater role in the radiance received at the satellite sensor. The use of this index is therefore likely to introduce noise in satellite time series and future work using MODIS imagery to isolate significant trends may benefit from use of the Enhanced Vegetation Index (EVI) or EVI-2 indices, which are less sensitive than NDVI to background and atmospheric effects (Jiang et al. 2008).


Many questions remain about how wetland species and communities in the Everglades will respond to the continued sea-level rise and saltwater intrusion in coming decades. The past 70 years of vegetation monitoring and analysis in the southern Everglades suggest that saline communities consisting of salt-tolerant species will continue to expand while oligohaline and salt-intolerant species will decline in productivity, biomass, and density. Significant positive NDVI trends observed in halophytic marshes observed in this study coupled with large areas of negative significant NDVI trends in oligohaline and salt-intolerant vegetation types proximate to the coast such as sawgrass, spike rush, scrub mangrove, and mangrove forest are consistent with long-term (70 year) trends and observed patterns. The white zone, therefore, is a highly sensitive indicator of coastal change and that can be readily monitored using multitemporal vegetation index imagery such as used here. We suggest that studies are needed to remotely monitor change in this sensitive ecotone using similar vegetation indices such as EVI derived from orbital platforms and that further analysis of trends in vegetation indices be evaluated using data on biomass, salinity, water level, and hydroperiod to advance understanding of how saltwater intrusion and inundation will affect the coastal wetlands of the southeastern Everglades.


The authors wish to thank Raymond Turner of Center for Southeast Tropical Advanced Remote Sensing (CSTARS) for providing the RapidEye image. We are grateful to the many scientists of the FCE LTER who have made their field data publicly available. Support for this research was provided by NASA WaterSCAPES (Science of Coupled Aquatic Processes in Ecosystems from Space) Grant NNX08BA43A.

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© Society of Wetland Scientists 2013