# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
## 5 Conclusions  
This study provides a comprehensive analysis of the phenological trends of the salt marsh ecosystem of LA, showcasing the efficiency of the MODIS-based biophysical model derived time-series composites of salt marsh GBM. We used TIMESAT to generate noise-free phenology from the GBM time-series composites after determining the SOS and EOS thresholds using derivative analysis. We justified our selection of the best smoothing algorithm (AG) and SOS/EOS thresholds after matching the SOS and EOS dates derived using derivative analysis and TIMESAT thresholds. We analyzed the seasonality in the SOS and EOS dates over a 16-year time period; the effects of the environmental events influencing the deviations from the normal SOS and EOS have been efficiently captured. Finally, the trend analysis demonstrated a reduction in the rates of seasonal amplitudes and small seasonal integrals, and improvement in the levels of GBM base values, indicating the progressive reduction in the levels of photosynthesis and biomass allocation in the salt marsh ecosystem that might be attributed as a combination of environmental effects such as rising atmospheric CO\({}_{2}\) levels and sea level rise. Further, the trend analysis results have been able to capture areas that are demonstrating significant stress in terms of reduced photosynthetic activity and thus require immediate restoration and conservation efforts. The results of this study can be compared with Gross Primary Productivity (GPP) trends as observed from the MODIS 500-m and 1-km GPP products; however, MODIS GPP products for coastal wetlands are derived using very generic grassland light use efficiency (LUE) estimates from a Biome properties look-up table that do not incorporate tidal effects or other conditions specific to coastal marshes. Therefore, caution should be exercised when drawing conclusions derived from GPP trend matching. Although trend analysis using MODIS-derived images can provide a valuable insight into the phenological dynamics at the landscape level, site-specific trend analysis might require a similar analysis using site-specific records of sea level changes, nitrogen enrichment,and salinity, along with finer resolution images. In addition, with finer resolution images, it will be possible to determine the distribution dynamics of the invasive _Phragmites australis_ in an otherwise _Spartina_-dominated landscape, and compare the progressive phenological trends of the two species with respect to elevated CO\({}_{2}\) levels and sea level rise. Such a future study will provide a clearer picture regarding the possible ecological succession of _Spartina_ by _Phragmites_. In addition, due to the coarse nature of the MODIS sensor, the possibility of encountering mixed pixels cannot be ruled out, especially while studying a highly fragmented ecosystem such as a salt marsh. One way to address the issue is to mask out mixed pixels completely from the analysis. However, ignoring or eliminating mixed pixels often poses the problem of eliminating the critical shoreline of salt marsh habitats from the analysis, which are often the most sensitive to environmental changes. Therefore, the selection of the appropriate vegetation index that is less susceptible to signals from water/tides in the shoreline pixels is crucial. This problem of mixed pixels exists even with finer spatial resolution sensors. Further, most fine spatial resolution sensors do not acquire images at such high temporal frequency as MODIS (daily images combined to generate the eight-day surface reflectance product), which is ideal for the analysis of long-term phenological trends. Further, finer spatial resolution images often present the problem of cloud cover that may render images completely unusable for analysis, resulting in frequent data gaps. This problem is particularly evident in the coastal Gulf of Mexico, where cloud presence in images from fine resolution sensors often hinders analysis, especially during the peak growing season.