Quantifying moderate resolution remote sensing phenology of Louisiana coastal marshes

Ecological Modelling 312 (2015) 191–199

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Quantifying moderate resolution remote sensing phenology of Louisiana coastal marshes Yu Mo, Bahram Momen, Michael S. Kearney ∗ Department of Environmental Science and Technology, University of Maryland, 1426 Animal Sci./Ag. Engr. Bldg, College Park, MD 20742, USA

a r t i c l e

i n f o

Article history: Received 27 January 2015 Received in revised form 16 May 2015 Accepted 18 May 2015 Available online 14 June 2015 Keywords: Coastal marshes Remote sensing Normalized Difference Vegetation Index (NDVI) Phenology Nonlinear mixed model

a b s t r a c t Coastal ecosystems are under multiple stresses ranging from global climate change to regional hazardous weather and human interventions. Coastal marshes in Louisiana are inherently vulnerable to these threats because they are microtidal and inhabit a narrow portion of the intertidal zone. Phenological dynamics of the marshes offer valuable information on the stressors’ impacts, yet they have rarely been reported or compared. Here, we study the landscape-level phenologies of the marshes under different climatic conditions, using Landsat-derived Normalized Difference Vegetation Index (NDVI) records (30 × 30 m2 spatial resolution) and a nonlinear mixed model that enables a quantitative analysis of nonlinear and piecewise functions involving repeated measures. In 2007 (a normal year), the Gaussian function was the best phenological model for Louisiana coastal marshes (pseudo R2 0.56–0.85), showing that: (1) NDVI of all marshes peaked within one month from late July to mid-August; (2) freshwater marshes had the highest peak NDVI, followed by intermediate, brackish, and saline marshes; and (3) saline marshes had the longest growth duration, followed by brackish, and then intermediate and freshwater marshes. Phenological shifts were found in years featuring extreme weather events: (1) a two-month delay in the peak NDVI day of saline marshes in 1999 (a drought year) compared to 2007; and (2) a shortening in growth duration of all marshes by approximately half in 2005 (a hurricane year). This work presents a methodgology to analyze and predict Louisiana coastal marshes’ phenological dynamics in response to current and future stresses. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Coastal ecosystems face a variety of threats ranging from global stressors like climate change – and its corollary, sea-level rise – to regional hazardous weather and anthropogenic factors like drainage and canals constructions and eutrophication (Charles and Dukes, 2009; Gedan et al., 2009), whose effects are either straightforward or commingled with others’ (Kirwan and Megonigal, 2013). As a crucial component of coastal ecosystems inhabiting a narrow portion of the intertidal zone, salt marshes are inherently vulnerable to sea-level rise (Morris et al., 2002). This is especially true for microtidal marshes – where any rise in sea level represents a larger portion of the total tidal range that defines the elevational growth range of vegetation (Goudie, 2013) – and marshes located in areas experiencing mineral sediment deficits and land subsidence, such as Louisiana coastal marshes (Day et al., 1995; McKee et al.,

∗ Corresponding author. Tel.: +1 301 405 4057; fax: +1 301 314 9023. E-mail addresses: [email protected] (Y. Mo), [email protected] (B. Momen), [email protected] (M.S. Kearney). http://dx.doi.org/10.1016/j.ecolmodel.2015.05.022 0304-3800/© 2015 Elsevier B.V. All rights reserved.

2004). Moreover, the conditions in such environments are so physiologically challenging to marsh plants that even comparatively moderate changes – e.g. drought, increased storminess or nutrient inputs – can affect a marsh plant’s vigor and resilience (Howes et al., 2010; Mendelssohn and McKee, 1988; Visser et al., 2012). Delineating the multitude of stresses is critical for predicting how the ecosystems might accommodate future climates changes; yet it can be difficult because many factors can impinge on marsh plant functioning over the same period (Turner, 1997), and even when ecosystem shifts can be linked to specific factors, the longevity of such changes is uncertain, i.e., whether they represent short-term responses that ultimately leave little trace or long-range trends (Kirwan et al., 2008). Phenological research at the landscape level has been viewed as a key to understand broad patterns of forest ecosystem change due to climatic and other factors (Cleland et al., 2007; Rich et al., 2008; Walther et al., 2002). However, coastal marsh phenology is less studied. In situ phenological studies in southeastern Louisiana have reported seasonal patterns of several species in bloom; as well as of live biomass of Distichlis spicata, Juncus roemerianus, Sagittaria falcata, Phragmites australis, Phragmites communis, Spartina

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cynosuroides, and Spartina patens from 1973 to 1975, and of D. spicata, J. roemerianus, Spartina alterniflora, and S. patens from 1975 to 1976 (Hopkinson et al., 1978; Penfound and Hathaway, 1938; White et al., 1978). Although the landscape-scale phenological curves of the marshes were recorded, the phenological metrics were not determined as the previous study was not designed for phenological studies (Steyer et al., 2013). Moreover, changes in marshes’ biomass and species composition associated with extreme environmental events were reported (Lucas and Carter, 2013; Mishra et al., 2012; Visser et al., 2002), but the associated phenological variations were not examined. Currently, there are a growing number of phenological studies using optical remote sensing data (e.g. AVHRR, MODIS, and Landsat imagery), focusing on long-term inter-annual change in peak biomass and length of the active growing season (Myneni et al., 1997) or on intra-annual phenological dynamics by fitting various phenological models (Beck et al., 2006). In particular, in the latter studies, the date of phenological events, e.g. green up and senescence, can also be compared spatially (across latitude and longitude) or temporally (across years) (Fisher et al., 2006; Melaas et al., 2013; Zhang et al., 2003). Nonetheless, these studies did not provide statistical comparisons for phenological curves of different vegetation types since they were pixel-based: the curve fitting and parameter estimation were derived for each pixel, not for comparable observational units of vegetation types. In addition, although the piecewise logistic functions have been reported to be the best model in a number of phenological studies of forest ecosystems (Beck et al., 2006; Fisher et al., 2006; Melaas et al., 2013; Zhang et al., 2003), they are complicated (require 6 parameters or more for one annual cycle) and may not provide the best fit for other ecosystems such as marshes. Phenological measurements made on the same observational units over time are best represented by repeated-measure models (Hurlbert, 1984; Motulsky and Ransnas, 1987; Peek et al., 2002). In addition, phenological models are often represented by nonlinear and piecewise functions (Fisher et al., 2006; Melaas et al., 2013; Zhang et al., 2003). In this study, we present the use of a nonlinear mixed model that enables more rigorous statistical analysis of optical remote sensing phenological records, and allows comparisons among different phenological models and among phenological metrics of different vegetations. To achieve parsimony, we compare the performances of the piecewise logistic functions and two less complicated functions, the Gaussian and the piecewise Gaussian functions that require 4 and 5 parameters for one annual cycle, respectively. To gain better understanding of coastal marsh phenological dynamics, we also compare phenological metrics of different marsh types. We used moderate resolution remote sensing data collected by the Landsat series satellites to model landscape-level phenological patterns of Louisiana coastal marshes. A landscape approach is especially beneficial since focusing on changes in one or a few species can overlook factors that can affect larger ecosystem functioning (Walther, 2010). Furthermore, because the effects of different stressors on coastal marshes vary across space and time, it is necessary to view the overall system aside from factors that may be important locally, but blur more general trends that could reveal more systemic coast-wide factors influencing overall regional marsh conditions. We chose marshes in four major coastal basins in Louisiana that: (1) span salinity values from 35 psu (polyhaline) to <5 psu (oligohaline); (2) have different hydrodynamic and soil conditions (Visser et al., 2012); and (3) incorporate a number of anthropogenic impacts that are common for coastal marshes in North America (from large oil spills to extensive canal constructions). They thus are comparable to microtidal marshes in the oligohaline and mesohaline reaches of estuaries elsewhere in the

northern Gulf of Mexico, as well as those in the U.S. middle Atlantic Coast.

2. Methods 2.1. Phenological data Phenological data collected in 2007 were used to illustrate how the records can be modeled using a nonlinear mixed model in order to compare fitness of different phenological models and phenological characteristics of different marshes. Here, we consider 2007 as a “normal” year. Although southeastern Louisiana experienced Hurricane Katrina in 2005 and a severe drought in 2006 (based on Palmer Drought Severity Index, http://www.ncdc.noaa.gov/ temp-and-precip/drought/historical-palmers/), the marsh vegetation had recovered to conditions before 2005 (Day et al., 2013), and was not subjected to other extreme weather conditions such as excess rainfall or drought. In 2007 the northern Gulf of Mexico witnessed only three tropical systems, two of which were tropical storms and one a tropical storm/Category 1 hurricane (Umberto). None of these storms made landfall near the Louisiana coast. In addition, phenological records of 1999 (a moderate drought year, based on Palmer Drought Severity Index) and 2005 (a hurricane year, during which the Category 3 Hurricane Katrina passed over southeastern Louisiana on August 29) were used to study marsh phenology under stresses (Howes et al., 2010; Visser et al., 2002). The phenological data were collected by Landsat 5 and Landsat 7 (Path 22 Row 39 and Path 22 Row 40, Fig. 1C), both having spatial resolution of 30 × 30 m2 and temporal revisit cycle of 16 days. To overcome the limitation of the comparatively low temporal resolution of Landsat, some images with clouds were used (the cloud and cloud shadow cover were masked out, Fig. 1A). There were a total of 20, 24, and 14 sampling dates in 1999, 2005, and 2007, respectively (Fig. 1IC, IIC, and IIIC). 2.2. Study area The study area consists of four major basins in coastal Louisiana, which were considered as four blocks: the Barataria, Breton Sound, Pontchartrain, and Terrebonne basins (Fig. 2). The distribution of the four marsh types (freshwater, intermediate, brackish, and saline marshes) was determined by the U.S. Geological Survey in 2001 (used for year 1999) and 2007 (used for year 2005 and 2007) (Sasser et al., 2008; US Geological Survey, National Wetlands Research Center, 2005). Each and every marsh type is represented within each block, providing four replicates of each marsh type and resulting in a total of 16 observational units. Based on the 2007 survey, there are 8277 km2 of marshland in the study area, and the size of the 16 observational unites ranges from 97 to 1136 km2 . 2.3. Weather and water level records The weather and water level records were collected for the sampling dates to assess the environmental conditions of the three sampling years (Fig. 1B, D, and E). Water level records were obtained from the National Oceanic and Atmospheric Administration Grand Isle tide gauge #8761724 (http://tidesandcurrents.noaa. gov/waterlevels.html?id=8761724). The monthly average temperature and precipitation records were obtained from the National Weather Service (NWS) New Orleans airport station #12916 (http://cdo.ncdc.noaa.gov/qclcd/QCLCD). The long-term 25% and 75% monthly precipitation and long-term monthly maximum and minimum temperature were calculated by the NWS using data from 1981 to 2010.

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Fig. 1. The dates of Landsat imagery used in 1999 (I), 2005 (II), and 2007 (III), as well as atmospheric and meteorological conditions during the sampling periods: (A) percent cloud and cloud shadow cover, (B) water levels in meters (bar), (C) sampling dates in Julian Day of Year (DOY) of Landsat 5 (open circle) and Landsat 7 (filled circle), (D) monthly average temperature (bar) and long-term averages of monthly minimum and maximum temperature (shaded area) in ◦ C, and (E) monthly average precipitation (bar) and 25th and 75th percentiles of monthly precipitation totals (shaded area) in cm.

2.4. Data processing

2.5. Phenological models

The Landsat Surface Reflectance Climate Data Record (Landsat CDR) generated from the Landsat Ecosystem Disturbance Adaptive Processing System atmosphere correction tool was downloaded from the USGS Earth Explorer website (http://earthexplorer.usgs. gov/). Further processing of the data was performed using ENVI 4.8 (ITT Exelis, USA). Surface reflectance was calculated. Clouds, cloud shadows, water, as well as data gaps in Landsat 7 imagery were masked out using the full masks provided in the Landsat CDR. The Normalized Difference Vegetation Index (NDVI) of each pixel was then calculated from the surface reflectance () of Landsat red and near infrared (NIR) bands:

We correlated NDVI as a response variable to Julian Day of Year (DOY) and marsh type as explanatory variables using three nonlinear mixed models representing three mathematical functions. The first function was a Gaussian function:

NDVI =

(NIR band − red band ) (NIR band + red band )

(1)

NDVI strongly correlates with aboveground net primary productivity (“greenness”) and absorbed photosynthetically-active radiation, and thus provides a photosynthetic index (Kerr and Ostrovsky, 2003). An average NDVI value was extracted from each observational unit. Values from observational units with more than 50% cloud and cloud shadow cover (visually estimated) were not used in data analysis.

2

NDVI = B + A e−(X−)

/2 2

(2)

where B is the background NDVI, describing marsh soils covered by brown or dark vegetation in various stages of decomposition during winter; A is the amplitude of NDVI during the growing season, X is DOY; and  is the peak NDVI day.  is the standard deviation of the Gaussian function, describing variability of the NDVIs along time within one year. Here  is used as a measure of the width of the Gaussian function: the length of the growing season, or the growth duration, centering on the peak NDVI day. The second function examined was a piecewise Gaussian function: NDVI = B + A e NDVI = B + A e

−(X−)2 /2 2 1

2

−(X−)

/2 2 2

,

if X < 

,

if X < 

(3)

where B is the background NDVI; A is the NDVI amplitude; X is DOY; and  is the peak NDVI day and the breakpoint of the piecewise function.  1 and  2 describe the variability of NDVIs along time

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Fig. 2. Study area. Each of the four blocks (the Barataria, Breton Sound, Pontchartrain, and Terrebonne basins) contains the four marsh types (freshwater, intermediate, brackish, and saline). Locations of the New Orleans Airport weather station (blue cross) and Grand Isle water gauge (yellow cross) are also illustrated. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

before and after the peak NDVI day, and the growth duration is calculated as 0.5 1 + 0.5 2 . A piecewise function is a function that has multiple subfunctions that apply to different intervals of the main function’s domain (subdomains). In this case,  separates the main function’s domain into two subdomains, before and after the peak NDVI day, where the two subfunctions describe the growth duration using  1 and  2 separately. Lastly, we investigated the suitability of a piecewise logistic function: NDVI = B + A NDVI = B + A

1 1 + eD1 (X−C1 ) 1 1 + eD2 (X−C2 )

,

if X < R

,

if X > R

(4)

where B is the background NDVI; A is the NDVI amplitude; X is DOY; C1 and D1 are the day and the rate of NDVI change at the start of the growing season (the greenup period); C2 and D2 are the day and the rate of NDVI change at the end of the growing season (the senescence period); and R is the day of maximum NDVI and the breakpoint of the piecewise function. The growth duration is calculated as C2 − C1 . 2.6. Analysis of phenological models Statistical analyses were performed using SAS 9.3 (SAS Institute, Cary, NC, USA). PROC NLIN was used to generate seed estimates for parameters involved in each function and their covariance matrix. Subsequently, PROC NLMIXED was used to estimate and compare parameters for different marsh types (Peek et al., 2002). Blocks were treated as a random factor. Appropriate contrasts statements were then written to compare pairwise parameter estimates among the four marsh types. The piecewise Gaussian function and the piecewise logistic function were coded with a broken-line scheme (Robbins et al., 2006). The goodness-of-fit of the models was assessed graphically and via Efron’s pseudo R2 . Pseudo R2 indicates the percent variance

explained by the nonlinear models, and is a statistic similar to R2 in linear regression (Hardin et al., 2007). Nonlinear regression estimation is based on the maximum likelihood method, not on ordinary least squares calculations as in linear regression, thus R2 is not relevant to nonlinear regression. Unlike R2 that ranges from 0 to 1, pseudo R2 ranges from −∞ to 1; and like R2 , a pseudo R2 closer to 1 indicates more explained variability in the data by the model. We also considered the Akaike Information Criterion (AIC), the Akaike Information Criterion Correction (AICC), and the Bayesian Information Criterion (BIC) in accessing the fitness of the models examined. These indices are based on the principle of parsimony, i.e. a model with smaller values for these indices can explain more variation in the data with fewer variables, and is considered a better fit (Boyce et al., 2002; Richards, 2005).

3. Results 3.1. Model selection For 2007 (a normal year), the Gaussian function had the smallest AIC, AICC, and BIC (Table 1). The Gaussian function also had the highest pseudo R2 for intermediate, brackish, and saline marshes, and the second highest for freshwater marshes. Moreover, the predicted NDVIs from the Gaussian function were closest to the observed values, and their confidence intervals were the narrowest (the predicted NDVIs versus the observed values of brackish marshes were plotted in Fig. 3, similar results were obtained for the other three marsh types). Therefore, the Gaussian function reflected the actual observations best, accounting for 56–85% of the variation of the four marsh types. The unaccounted variation might be due to variations in phenological patterns and leaf optical properties of different marsh species, interference from water and soil beneath or immediately adjacent to the vegetation, as well as particular atmospheric conditions on the sampling dates (Hopkinson

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Table 1 Goodness-of-fit of the three models for the 2007 phenological record. Phenological models

Gaussian function

Piecewise Gaussian function

Piecewise logistic function

Fit Statistics (from SAS) −2 Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better)

−686.3 −654.3 −651.7 −642.0

−644.4 −604.4 −600.2 −588.9

−613.9 −557.9 −549.5 −536.2

Pseudo R2 (coefficient of determination) (calculated in Microsoft Excel) Freshwater Marshes Intermediate Marshes Brackish Marshes Saline Marshes

0.85 0.82 0.75 0.56

0.90 0.82 0.75 −0.14

0.52 0.62 0.61 0.49

in Section 3.1 (Tables 3 and 4), and their phenological patterns differed from those for 2007 (Fig. 4). The best model for year 1999 was also the Gaussian function, which explained 89–94% of the variation in the phenological data of the four marsh types. Although the shapes of the phenological curves in 1999 were similar to those in 2007, the peak NDVI day was delayed by one month for brackish marshes, and by two months for saline marshes. The growth duration of saline marshes was shortened to less than two months. The effects of Hurricane Katrina on the marshes’ phenological metrics were also substantial. Due to a change in the shape of the phenological curves, a different model, the piecewise Gaussian function, provided the best fit for the 2005 data, explaining 83–94% of the variation of the four marsh types. The most striking features of the curves were the dramatic decrease in NDVI for the intermediate and brackish marshes immediately after Hurricane Katrina crossed over the marshes on August 29, 2005 (Julian day 242), and the shortening in the growth duration by approximately half for all four marsh types compared to 2007.

4. Discussion 4.1. Constructing a phenological record using remote sensing data

Fig. 3. Graphs of observed NDVIs (open circle), and predicted values (solid line) and confidence intervals (˛ = 5%) (dashed line) of brackish marshes from the three models: (A) Gaussian function, (B) piecewise Gaussian function, and (C) piecewise logistic function.

et al., 1978; Kearney et al., 2009; Pettorelli et al., 2005; Ramsey and Rangoonwala, 2004). 3.2. Phenological metrics of different marsh types The background NDVI values (B) of the four marsh types did not differ significantly, but the other three parameters did (Table 2). The peak NDVI day () occurred within one month from late July to mid-August. Freshwater marshes had the highest peak NDVI (A + B), followed by intermediate, brackish, and saline marshes. Saline marshes were characterized by the longest growth duration (), followed by brackish, and then intermediate and freshwater marshes. 3.3. Phenology of marsh under stresses The phenological metrics of 1999 (a drought year) and 2005 (a hurricane year) were analyzed using the same method as described

Our results show that broad marsh types, even those along a coast with diverse hydrodynamic, geomorphic, and floristic characteristics, do exhibit distinct phenologies that can be delineated and modeled using remote sensing data. Moreover, the method proposed provides the basis for long-term monitoring of the marsh systems that can be used to determine the effects of natural and anthropogenic stressors at the coast-wide level. The Landsat series satellites, with the first deployment of the Thematic Mapper instrument in 1984 with the launch of Landsat 5, are best suited for this task as they have collected the world’s longest continuouslyacquired, space-based, moderate resolution land remote sensing data and they, thus, offer a highly valuable record for long-term ecosystem changes (Kennedy et al., 2014). We have described a new method for developing phenological trends from Landsat imagery that: (1) is appropriate for nonlinear and piecewise models involving repeated measures; (2) incorporates a large number of images and thus is less sensitive to one or more “bad” data sets; and (3) is applicable to certain cloudy or hazy images with areas of cloud or haze – not uncommon in marine situations – masked out from the analysis. This last characteristic is especially advantageous for analysis of images collected during the summer growing season, when haze and clouds are more serious problems than usual due to high evapotranspiration rates from the marshes, sharp increases in water vapor, and thus more cloud formation over the marshes.

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Table 2 Means (± standard error) of phenological metrics of the four marsh types in 2007. Means within rows followed by different superscripts differ significantly (P < 0.05). Metrics

Background NDVI (B) NDVI Amplitude (A) Peak NDVI (A + B) Peak NDVI Day () Equivalent date Growth Duration () Equivalent time

Marsh type Freshwater

Intermediate

Brackish

Saline

0.18 ± 0.06a 0.49 ± 0.13a 0.67 0.20 ± 0.01a 19 Jul 2007 0.14 ± 0.03a 143 days

0.21 ± 0.10a 0.36 ± 0.10b 0.57 0.21 ± 0.01b 27 Jul 2007 0.14 ± 0.03b 143 days

0.17 ± 0.16a 0.36 ± 0.16c 0.53 0.21 ± 0.01c 1 Aug 2007 0.18 ± 0.06c 179 day

−0.01 ± 0.48a 0.47 ± 0.48d 0.46 0.23 ± 0.02d 13 Aug 2007 0.30 ± 0.18d 296 days

Table 3 Means (± standard error) of phenological metrics of the four marsh types in 1999. Means within rows followed by different superscripts differ significantly (P < 0.05). Metrics

Background NDVI (B) NDVI amplitude (A) Peak NDVI (A + B) Peak NDVI Day () Equivalent Date Growth duration () Equivalent time

Marsh type Freshwater

Intermediate

Brackish

0.21 ± 0.07 0.48 ± 0.06a 0.69 0.21 ± 0.01a 29 Jul 2007 0.11 ± 0.01a 112 days

0.24 ± 0.05 0.39 ± 0.05b 0.63 0.21 ± 0.01b 31 Jul 2007 0.11 ± 0.01b 112 days

0.24 ± 0.07 0.30 ± 0.07c 0.54 0.24 ± 0.11c 29 Aug 2007 0.11 ± 0.03c 115 day

a

b

Saline c

0.39 ± 0.16d 0.08 ± 0.16d 0.47 0.28 ± 0.01d 8 Oct 2007 0.04 ± 0.01d 41 days

Table 4 Means (± standard error) of phenological metrics of the four marsh types in 2005. Means within rows followed by different superscripts differ significantly (P < 0.05). Metrics

Background NDVI (B) NDVI amplitude (A) Peak NDVI (A + B) Peak NDVI day () Equivalent date Growth Duration ( 1 ) ( 2 ) Equivalent time

Marsh type Freshwater

Intermediate

Brackish

0.40 ± 0.03 0.32 ± 0.04a 0.72 0.18 ± 0.01a 30 Jun 2007 0.05 ± 0.01a 0.06 ± 0.01a 60 days

0.35 ± 0.03 0.28 ± 0.03b 0.63 0.22 ± 0.01b 4 Aug 2007 0.12 ± 0.02a 0.03 ± 0.01b 73 days

0.34 ± 0.03 0.19 ± 0.03c 0.53 0.22 ± 0.01c 5 Aug 2007 0.13 ± 0.02c 0.03 ± 0.01c 77 day

a

4.2. Marsh NDVI and its ecological properties We limited our discussion here to the results from 2007, which as noted is considered “normal” inasmuch as it lacked a major disaster event in the study area. NDVI strongly correlates with green biomass (Gamon et al., 1995), and the correlation between saline marshes’ NDVI and their ecological properties, e.g. biomass and leaf area index, is well documented (Gross et al., 1987, 1993; Kearney et al., 2009; Zhang et al., 1997). Field studies indicate that the timing of maturity of the dominate marsh species in Louisiana varies among species and occurs between June to November (Hopkinson et al., 1978; Penfound and Hathaway, 1938; White et al., 1978). Our results suggest that at a landscape level biomass in all marsh types peaked within one month from late July to mid-August. The supports the notion that peak vegetation biomass coincides with the yearly maximum temperature and solar radiation (Gosselink, 1984). Generally freshwater marshes have a higher primary production than saline marshes, because freshwater marshes receive the energy, nutrient subsidy, and flushing of toxic materials from vigorous tidal fluxes, but avoid the stress of saline soils that saline marshes experience (Good et al., 1978; Waide et al., 1999). Employing a landscape-level perspective, this study also shows this trend. The decline in the length of seasonal cycle of photosynthetic activity, i.e. the growth duration, with decreasing salinity in these basins likely has many causes that are not immediately evident.

b

Saline c

0.32 ± 0.05d 0.12 ± 0.04d 0.44 0.18 ± 0.03d 24 Jun 2007 0.15 ± 0.05d 0.07 ± 0.04d 106 days

A factor that may explain this phenomenon is that as salinities decrease, species diversity increases (Visser et al., 2012). The initiation of “green up” period might be more complex in high species-diversity, oligohaline and freshwater marshes, where the increase in species diversity comes from C3 annuals, compared to the saline marshes, where mainly dominated by perennial C4 plants. In previous phenological studies, which focused on the forest ecosystems, the piecewise logistic function was considered the best model (Beck et al., 2006; Zhang et al., 2003). In this study, the Gaussian function is found to be a better model for marsh ecosystems, as it is more parsimonious and fits better compared to the piecewise logistic function. Fitting of the Gaussian function results in lower values of AIC, AICC, and BIC, suggesting that it explains greater variation in the data with fewer parameters, i.e. greater parsimony. The Gaussian function requires estimation of 4 parameters for one annual cycle, while the piecewise logistic function requires estimation of 7 parameters. Moreover, the Gaussian function has higher pseudo R2 , showing that it fits the marshes’ phenological curves better. These results suggest that the phenological cycle of the marshes – similar to grassland ecosystems (Gamon et al., 1995) – is characterized by a short peak in NDVI, not a plateau that is the characteristic of forest phenological cycles. This finding is not surprising since the dominant marsh plants are generally graminoids or sedges, even in the species diverse freshwater marshes where

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Fig. 4. Graphs of observed NDVIs (open circles), the predicted values by the best fit models (solid line), and their confidence intervals (˛ = 5%) (dashed line) for the four marsh types: (A) freshwater, (B) intermediate, (C) brackish, and (D) saline marshes, in the three years with variable weather conditions: (I) 1999, (II) 2005, (III) 2007. Peak NDVI day in 1999 (red line) and senescence rates in 2005 (yellow shading) are highlighted. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

differences in plant height are greatest, there are few instances where a real overtopping upper canopy occurs. This is especially true of salt marshes where only a few species (mostly of the genus, Spartina) dominate the marsh. 4.3. Marsh phenological patterns under stresses The main phenological shift in 1999, a drought year, was the delay of the peak NDVI day for the brackish and saline marshes. Since in 1999 the northern Gulf of Mexico had a weather pattern of low precipitation and high temperature and the discharge of Mississippi River was extremely low, freshwater input into Louisiana’s estuaries was very low (Visser et al., 2002). The physiological impact of these coincident meteorological conditions on the marshes could be manifested as an increase in soil salinities and hydrogen sulfide contents that are inimical to the vigor of many marsh plants (Webb et al., 1995). Salt marshes, being closest to the ocean, are more continuously exposed to such conditions, and the diminished and very short growth duration shown in Fig. 4 appears to bear this out. The main phenological change in 2005 was the shortening of the growth duration of all four marshes. This might result from direct damage brought by Hurricane Katrina that eradicted plants or buried plants under sediment. It is also possible that some of the rapid declines in NDVI values after Katrina were due to rapid

onset of the annual senescence period, but the Landsat data are not capable of distinguishing this factor versus the direct plant damage. The other phenological changes in 2005 was the alteration of the shape of the curves that resulted in a different best model, the piecewise Gaussian function. The Gaussian function provided a better fit in 2007 and 1999, suggesting the curves were relatively symmetric (i.e. rates of growth and senescence were similar), and thus the extra parameter separately describing the senescence rate in the piecewise Gaussian function was not necessary. In contrast, to account for the impact of Hurricane Katrina in 2005, an extra parameter was required and the piecewise Gaussian function provided a better fit. Saline marshes in the Barataria and Breton Sound basins laid directly in the path of Katrina, and saline marshes in all four basins suffered the greatest inundation brought by Hurricane Katrina’s storm surges as they are closest to the open water in the lower bays. However, the saline marshes did not show the deformation of the phenological curve that intermediate and brackish marshes did, or even the level of response of the freshwater marshes. The reasons for the very limited impact on the salt marsh phenological trend may lie in the fact that the few species that dominate these marshes have relatively deep rhizomes, and a culm architecture that resists wave or wind damage: a stiff, thin stem with respect to the co-dominant, J. roemerianus, or a flexible low culm with respect to the other co-dominant, a dwarf form of S. alterniflora (Visser et al., 2012). Incidentally, on September 24, 2005,

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a Category 3 hurricane, Rita, passed western Louisiana. However, as Rita occurred toward the end of the growing season with a track considerably west of the study area, its impacts were not reflected in our result.

5. Conclusions This paper describes a new method for quantifying Louisiana coastal marsh phenologies using moderate resolution remote sensing data and a nonlinear mixed model. We found that, the Gaussian function was the best phenological model for Louisiana coastal marshes in 2007 (a normal year, pseudo R2 0.56–0.85), and phenological metrics of the four marsh types were significantly different. We also found that records from years with extreme meteorological events showed significant phenological shifts. In the drought year of 1999, the peak NDVI day of brackish and saline marshes were delayed for one and two months, respectively. In the hurricane year of 2005, the NDVIs for the intermediate and brackish marshes dramatically decreased immediately after Hurricane Katrina passed over the marshes on August 29, 2005, and the growth duration of all marshes was shortened by approximately half. To be certain, a long-term detailed phenological study is required before the effects of any stressor on the marshes’ phenology can be addressed precisely. Nevertheless, this study indicates that phenology varies both among marsh types and among marshes under various conditions, and these variations can be quantified using the method presented. The proposed method provides a way to analyze the coast-wide phenological dynamics of the marshes, helping to forecast the ecosystem’s responses to future climate challenge.

Acknowledgements This research was made possible in large part by a grant from BP/The Gulf of Mexico Research Initiative, and with financial support from the National Aeronautics and Space Administration (NASA). The authors are particularly grateful for the assistance given by Dr. J. C. Alexis Riter of the University of Maryland and Dr. R. E. Turner of Louisiana State University.

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