Cuestionamiento directo

84 of the 211 wetlands showed a statistically significant increase in NDVI at 95% confidence. Of the 84 wetlands that showed an increase in NDVI, 37 of them had an effect size of greater than or equal to 0.5.

189 of the 211 wetlands showed a statistically significant decrease in LST at 95% confidence. Of the 189 wetlands that showed a decrease in LST, 159 of them had an effect size less than or equal to -0.5.

198 of the 211 wetlands showed a statistically significant increase in ET at 95%

confidence. Of the 198 wetlands that showed an increase in ET, 183 of them had an effect size greater than or equal to 0.5.

The median change in NDVI was 0.004, with a 95% confidence interval of 0.0032 to 0.0051. The median change in LST was -3.636 oC, with a 95% confidence interval of -3.674 to -3.596 oC. The median change in ET was 1.268 mm/day, with a 95% confidence interval of 1.261 to 1.277 mm/day. Overall, most of the wetlands showed both a statistically significant, and practical significant increase in ET, and decrease in LST. Most wetlands did not have a statistical or practical increase in NDVI. The wetlands showed a large amount of variance in response values within their individual boundaries for all three remotely sensed values. Boxplots of 2005 and 2014 spectral responses can be seen in Figure 3, and maps of the wetlands’ p-values

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and effect size seen in Figures 4 - 6. The variance in remotely sense values is seen in Figures A3−A5.

Change in WAP Scores

Of the 211 wetlands, WAP scores from 69 wetlands were examined for change from 2005 to 2014. The net changes between the two years are shown in the Tables 2 - 4.

Overall, the selected wetlands showed a net decrease in WAP score whether they were forested or herbaceous. Groundcover scores had the highest decrease overall, while Tree scores had the lowest decrease. When examined as percentages, the general trend was that a little under half the WAP scores showed no change, around a third showed a decrease in WAP, with the remainder of the scores showing an improvement in WAP for groundcover, shrub, and tree scores. Furthermore, because of the unique compositions of herbaceous vs. forested wetlands, the numbers and percentages were examined where wetland type was taken into account. Herbaceous wetlands should be devoid of trees and most shrubs, with groundcover being the main if not only score. Therefore, herbaceous wetlands’ groundcover WAP scores were further analyzed. Forested wetlands should have all 3 WAP scores, as it is appropriate to have

understory plants in forested wetlands. The focus of this study was on the WAP tree score, as majority of the wetlands were classified specifically as Cypress wetlands, where Cypress trees are the main species. The tree score was also chosen because trees would be the main

component of the remotely sensed values, as the understory reflectance would be blocked by the canopy cover. When broken down by wetland type, the breakdowns were still in line with the general trend, though herbaceous wetlands did have a larger percentage of WAP decline (Figure 7). Location and change in status by wetland type are shown in Figures 8 - 10.

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Figure 3. Boxplots of (a) ET, (b) LST, and (c) NDVI. ET and LST showed significant improvements from 2005 to 2014. ET showed increases from 2005 to 2014, with less areas of deposition (negative ET) and less ET values falling below 2 mm/day. LST declined from 2005 to 2014, and had fewer extreme values in 2014 than they did in 2014. NDVI did not show noticeable improvement.

(a) (b)

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Figure 4. Map of NDVI Pvals and Effect size. The majority of the wetlands failed to show statistical or practical significance improvement in NDVI. (a) Green wetlands are where median NDVI in 2014 was greater than in 2005, and statistically significant at 95%. Red Wetlands are where median NDVI in 2014 was less than than 2005, or the increase from 2005 to 2014 was not statistically significant at 95%. (b) Green wetlands are where median NDVI in 2014 was greater than in 2005, and pratically signifcant at 0.5 or greater. Red Wetlands are where median NDVI in 2014 was less than than 2005, or the increase from 2005 to 2014 was not practically significant at 0.5 or greater.

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Figure 5. Map of LST Pvals and Effect size. Majority of the wetlands showed a statistical and pratical significance improvement in LST. (a) Green wetlands are where median LST in 2014 was less than in 2005, and statistically significant at 95%. Red Wetlands are where median NDVI in 2014 was less than than 2005, or the decrease from 2005 to 2014 was not statistically significant at 95%. (b) Green wetlands are where median LST in 2014 was less than in 2005, and pratically signifgant at 0.5 or greater. Red Wetlands are where median LST in 2014 was greater than 2005, or the decrease from 2005 to 2014 was not practically significant at 0.5 or greater.

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Figure 6. Map of ET Pvals and Effect size. Majority of the wetlands showed a statistical and pratical significance improvement in ET. (a) Green wetlands are where median ET in 2014 was greater than in 2005, and statistically significant at 95%. Red Wetlands are where median ET in 2014 was less than than 2005, or the increase from 2005 to 2014 was not statistically significant at 95%. (b) Green wetlands are where median ET in 2014 was greater than in 2005, and pratically signifgant at 0.5 or greater. Red Wetlands are where median ET in 2014 was less than than 2005, or the increase from 2005 to 2014 was not practically significant at 0.5 or greater.

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Table 2. All Wetlands (n = 69)*

Groundcover Shrub Tree Total Net Change in Score -18 -8 -4

# Wetlands No Change 33 32 38

# Wetlands Improvement 12 16 11

# Wetlands Decrease 24 21 20

Max Score Change All +2 +5 +5

Min Score Change All -3 -5 -3

*Overall counts and net changes in WAP scores from 2005 to 2014 for all wetland types, combined, in the NTBA.

Table 3. Herbaceous Wetlands (n = 18)*

Groundcover Shrub Tree Marsh Net Change in Score -15 -5 +12

# Wetlands No Change 7 9 11

# Wetlands Improvement 1 4 5

# Wetlands Decrease 10 5 2

Max Score Change Marsh +1 +4 +5

Min Score Change Marsh -3 -5 -3

*Overall counts and net changes in WAP scores from 2005 to 2014 for herbaceous type wetlands in the NTBA.

Table 4. Forested Wetlands (n = 51)*

Groundcover Shrub Tree Forested Net Change in Score -3 -3 -16

# Wetlands No Change 26 23 27

# Wetlands Improvement 11 12 6

# Wetlands Decrease 14 16 18

Max Score Change Forested +2 +5 +2 Min Score Change Forested -2 -5 -3

*Overall counts and net changes in WAP scores from 2005 to 2014 for forested type wetlands in the NTBA.

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Figure 7. Pie charts showing percentage of wetlands change in status. Pie charts showing percentage of wetlands that improved, declined, or had no change from 2005 to 2014.

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Figure 8. Map of WAP groundcover score changes. Map showing the locations of (a) Herbaceous, (b) Forested wetlands WAP groundcover score changes.

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Figure 9. Map of WAP shrub score changes. Map showing the locations of (a) Herbaceous, (b) Forested wetlands WAP shrub score changes.

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Figure 10. Map of WAP tree score changes. Map showing the locations of (a) Herbaceous, (b) Forested wetlands WAP tree score changes. (b)

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WAP scores showed that there was a net decrease in WAP score during the timeframe between 2005 and 2014, however, Figures 7a−7i showed that just a little under half the wetlands had a net change of zero. A net change of zero could be a lack of improvement from a low score, but it could also be because of wetlands maintaining a high score. More so, the increase or decrease in scores could be as little as going from 4 to 5, so already wet wetlands improving, or decreasing from 5 to 3, healthy wetlands taking a steep drop in hydrologic conditions. To determine which cases the changes fell into, WAP scores were placed in 3 groups: Good (4-5), OK (3), Poor (1-2), and compared to the net change.

Of the herbaceous wetlands that showed no change in their groundcover scores, 29% were in good condition, 42% were in OK condition, and 29% were in poor condition. Of the wetlands that declined, 50% went from good to OK condition, 30% went from good to poor, 10% declined but still stayed in good condition, and 10% went from OK to poor condition. Of the wetlands that improved, 100% went from poor condition to OK condition.

Of the forested wetlands that showed no change in their tree scores, 48% were in good condition, 37% were in OK condition, and 15% were in poor condition. Of the wetlands that declined, 22% declined but still stayed in good condition, 39% went from good to OK condition, 28% went from OK to poor condition, and 11% went from good to poor condition. Of the wetlands that improved, 50% went from OK to good, 16% improved their already good score, 17% went from poor to OK condition, and 17% went from poor to good condition.

Remote Sensing vs. WAP Comparison

The 69 wetlands that were examined for changes in WAP were also assessed against remotely sensed responses of 2005 and 2014. Boxplots of herbaceous wetlands did not show an obvious trend between the groundcover WAP score and NDVI, LST, or ET (Figures 11 – 13),

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with no discernable difference found between scores. Boxplots of the forested wetlands showed similar results, with no real trend of tree WAP score vs. NDVI, LST, and ET (Figures 14- 16). This lack of trend continues, even when just the median values for each wetland are used. While the 2014 herbaceous scores may appear to show a difference for the two extreme scores of 1 and 5, these each consisted of only 1 wetland (Figures 11b&d, 12b&d, 13b&d), and only a total of 18 wetlands were classed as marsh overall. Forested wetlands did not have any wetlands with a tree score below 2, and consisted of a total of 51 wetlands.

Other Analysis

The median change in POT over only NTBA wetlands was -1.1m, with a maximum change of 0.4m and a minimum change of -2.2m. The median change in rainfall 2 days before, and the day of the image acquisition was 13.3 mm/3days, with a maximum change of 57.0 mm/3days, and a minimum change of -8.8 mm/3day. Changes in rainfall and POT for the entire study area are mapped in Figure 17. The change in spectral and thermal responses of the wetlands when linearly regressed against changes in rainfall between the two study years showed little to no fit, with r2 values under 0.1 (Figure 18). The spectral and thermal responses of the wetlands also showed r2 values under 0.1 when linearly regressed against the change in POT of the Upper Floridan Aquifer (Figure 19).

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Figure 11. Boxplots of NDVI vs. Herbaceous wetlands groundcover score. Boxplots of (a) NDVI vs. 2005 herbaceous wetlands WAP groundcover score, (b) NDVI vs. 2014 herbaceous wetlands WAP groundcover score, (c) Median NDVI vs. 2005 herbaceous wetlands WAP groundcover score, (d) Median NDVI vs. 2014 herbaceous wetlands WAP groundcover score.

(a) (b)

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Figure 12. Boxplots of LST vs. Herbaceous wetlands groundcover score. Boxplots of (a) LST vs. 2005 herbaceous wetlands WAP groundcover score, (b) LST vs. 2014 herbaceous wetlands WAP groundcover score, (c) Median LST vs. 2005 herbaceous wetlands WAP groundcover score, (d) Median LST vs. 2014 herbaceous wetlands WAP groundcover score.

(a) (b)

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Figure 13. Boxplots of ET vs. Herbaceous wetlands groundcover score. Boxplots of (a) ET vs. 2005 herbaceous wetlands WAP groundcover score, (b) ET vs. 2014 herbaceous wetlands WAP groundcover score, (c) Median ET vs. 2005 herbaceous wetland WAP groundcover score, (d) Median ET vs. 2014 herbaceous wetland WAP groundcover score.

(a) (b)

(d) (c)

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Figure 14. Boxplots of NDVI vs. Forested wetlands tree score. Boxplots of (a) NDVI vs. 2005 forested wetlands WAP tree score, (b) NDVI vs. 2014 forested wetlands WAP tree score, (c) Median NDVI vs. 2005 forested wetlands WAP tree score, (d) Median NDVI vs. 2014 forested wetlands WAP tree score.

(a) (b)

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Figure 15. Boxplots of LST vs. Forested wetlands tree score.Boxplots of (a) LST vs. 2005 forested wetlands WAP tree score, (b) LST vs. 2014 forested wetlands WAP tree score, (c) Median LST vs. 2005 forested wetlands WAP tree score, (d) Median LST vs. 2014 forested wetlands WAP tree score.

(a) (b)

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Figure 16. Boxplots of ET vs. Forested wetlands tree score.Boxplots of (a) ET vs. 2005 forested wetlands WAP tree score, (b) ET vs. 2014 forested wetlands WAP tree score, (c) Median ET vs. 2005 forested wetlands WAP tree score, (d) Median ET vs. 2014 forested wetlands WAP tree score.

(a) (b)

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Figure 17. Maps of change in POT and Rainfall. Maps of (a) change in POT, (b) change in Rainfall in relation to study area and wetlands

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Figure 18. Scatterplots of change in Rainfall vs. change in NDVI, LST, and ET. Scatterplot of change in Rainfall vs. (a) Change in NDVI, (b) Change in LST, and (c) Change in ET

(a) (b)

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Figure 19. Scatterplots of change in POT vs. change in NDVI, LST, ET. Scatterplot of change in POT vs. (a) Change in NDVI, (b) Change in LST, and (c) Change in ET

(a) (b)

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DISCUSSION

The results of the analysis show a mismatch between the remotely sensed apparent recovery and the slight degradation indicated by the WAP scores and POT decreases. The results do not support the hypothesized belief that WAP scores would display a relationship with remotely sensed values. The results do fit the overall narrative of mixed, conflicting results of wetland recovery in the NTBA (Metz, 2011; Haag & Pfeiffer, 2012; Nilsson et al., 2013).

Changes in Spectral and Thermal Properties

The thermal and spectral responses showed an overall increase in wetland status, despite an overall decrease in POT of the Flordian Aquifer. The reasons for this increase in ET are unknown. Examination of one of the most likely reasons, the increase in rainfall, showed low r2 scores, as seen in Figure 19. Another reason examined was the change in the elevation of the Surficial Aquifer (SWL). The SWL of the NTBA wetlands showed an overall increase in

elevation, with a median change of 0.9m, a minimum change of -1.6m and a maximum change of 4.0m, with the change over the entire study area mapped in Figure 20. As the surficial aquifer is closer to land surface and plant root zones, it was believed that the increase in ET would be show a positive relationship with the increase in SWL. However, when changes in SWL were plotted against changes in ET, no trend emerged, as seen in Figure 21.

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Figure 20. Map of change in SWL in relation to study area and wetlands

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Based on these trends, the overall increase in ET cannot be fully explained by the variables examined in this study. The graphs show a large range in ET change regardless of the other variables, a likely indicator of other variables playing a part into water availability. Therefore, while it is likely that at least some of the increase in ET is due to the increase in part SWL and rainfall, based on the conflicting results with POT, the overall increase in ET cannot be fully explained by these two variables alone.

Change in WAP Scores

The overall trend of the wetlands ranged from generally no change, to a slight decline in WAP score from 2005 to 2014, regardless of type. The reasons for the decline in WAP despite decreases in pumping could be for a variety of reasons.

First, the NTBA wellfields began a staggered reduction in groundwater withdrawals in 2002, but the amount reduced varied from wellfield to wellfield (Haag & Pfeiffer, 2012). As the WAP scores showed a range in improvement to decline, it was believed that the amount reduced by nearby wellfields played some role in the trends in WAP between the two study years.

Therefore, the May average MGD of 9 public supply groundwater wellfields within 2 miles of the study areas boundary (Water & Air Research et al., 2006) was analyzed for the two study years, with the results displayed in Figure 22. Furthermore, the annual pumping records of the 9 wellfields were examined for the change in MGD.

Three of the wellfields (Cypress Creek, Cypress Bridge, and Morris Bridge) showed an increase in pumping in 2014 compared to 2005, with Cypress Bridge having an especially large increase in pumping, going from approximately 5.5MGD to approximately 11MGD. The rest of the wellfields showed decreases in MGD. It is important to note that despite the decrease in MGD for majority of the well fields, three, almost four, wellfields had a MGD greater than 10 in

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2014. When the locations of wetlands that showed a decrease in relevant WAP score were compared to wellfields, it was seen that many of the wetlands that declined are located in or near those wellfields that had a MGD of about 10 or more (Figures 8-10). The wetlands that showed declines also tended to be in areas where the change in POT was negative, which also happened to be in the areas where withdrawals were around 10 MGD. Based on these results, it is believed that the decline in WAP scores of the wetlands examined in this study is at least partially a result of the uneven and in some cases lack of reduction in groundwater withdrawals across the study area.

The other factor that might help explain the decline in WAP scores is the weather

experienced in the area between the two years. As it turns out, the weather patterns in the NTBA since the establishment of the new WAP were on the extreme ends, and more than likely affected WAP scores. The 2005 establishment of WAP came at the end period of an extreme rainfall and heavy hurricane activity during 2003 and 2004 (Granville Kinsman, personal communication, 2015). These abnormally heavy precipitation events led to higher than mean groundwater and surface water levels, and much more suitable conditions for wetland flora. These wet conditions were followed by many years of abnormally dry conditions for the majority of the temporal skip from 2005 to 2014 (Granville Kinsman, personal communication, 2015). As 2005 can be seen as the new “baseline” for WAP, it is believed that scores were set high due to the abnormal wet years. It is also reasonable to believe that the excessively dry conditions of the gap years led to decreases in WAP scores.

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Figure 22. Map of wellfields within two miles of the Study area with changes in MGD. Map of wellfields within two a miles of the Study area boundary, and their pumping rates for the two years included in the study.

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Mismatch of Remotely Sensed Responses vs. WAP Scores

Remotely sensed values had large ranges in most WAP scores, leading one to believe that there is no relationship between the two assessment methods. The lack of a relationship also leaves questions as to whether the wetlands of the NTBA recovered after reductions in

groundwater withdrawals.

The first possibility that comes to mind is that the spatial resolution was too coarse for the purposes of this study. ASTER scenes had a spatial resolution of 15 m at the finest, and 90 m at the coarsest. WAP transects are 10 m wide and variable length depending on the size of the wetland. So while the transects are similar to the 15 m pixel size, it could be that the pixel resolutions are still too coarse in differentiating wetlands intermixed or overtaken by exotics and non-wetland plants from those that are mostly obligate and facultative wet species. (WAP: Instruction Manual for Isolated Wetlands, 2005). It is also important to remember that WAP is scored based on presence and location of upland vs. wetland plants in the wetland, not the condition they are in (WAP: Instruction Manual for Isolated Wetlands, 2005). Scores may be high or low due to the location of species in the wetland, but not reflected in the catchall pixel, which only describes the health. There is also the fact that WAP scores are originally scored based off transects, which t are divided into zones. These zones may or may not completely match up with the catchall pixel. If the zones both overlap the same pixel, their results are mixed together for the remote sensing assessment, while they are initially collected and judged

separately using WAP. Furthermore, the transect is at face value a sample of the wetland, albeit one that is supposed to represent the entire wetland. Meanwhile the remotely sensed method used in this study actually utilized the entirety of the wetland. The results of the remotely sensed method showed wetlands had a large variance in pixel values (Figures A3−A5), so it could be

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that areas outside of the transect are showing improvement or decline, but are not represented in the WAP score.

Another thing to consider is the temporal differences between the two methods. Remote sensing methods, while they can be used to study long-term trends, are based off instantaneous data. The remotely sensed values of this study show the hydrologic conditions of