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Monitoring rapid vegetation succession in estuarine wetland using time series MODIS- based indicators: an application in the River Delta area. Ecol Indic

ARTICLE in ECOLOGICAL INDICATORS · MARCH 2009 Impact Factor: 3.44 · DOI: 10.1016/j.ecolind.2008.05.009

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Monitoring rapid vegetation succession in estuarine wetland using time series MODIS-based indicators: An application in the Yangtze River Delta area

Bin Zhao *, Yaner Yan, Haiqiang Guo, Meimei He, Yongjian Gu, Bo Li

Coastal Ecosystems Research Station of Yangtze River Estuary, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, 200433, article info abstract

Article history: Frequent and continuous time series is required for the detection of plant phenology and Received 17 October 2007 vegetation succession. The launch of novel remote sensor MODIS (moderate resolution Received in revised form imaging spectroradiometer) provided us with an opportunity to make a new trial of studying 14 May 2008 the rapid vegetation succession in estuarine wetlands. In this study, the spatiotemporal Accepted 21 May 2008 variations of vegetation cover and tidal flat elevation along a transect (covering 6 pixels of MODIS) of an estuarine wetland at Dongtan, Chongming Island, in Yangtze River estuary, China were investigated to assess its rapid vegetation succession and physical conditions. Keywords: By combining the field data collected, the time series of MODIS-based VIs (vegetation Chongming Dongtan indices), including NDVI (normalized difference vegetation index), EVI (enhanced vegetation Coastal zone index) and MSAVI (modified soil adjusted vegetation index), and a water index, LSWI (land Ecological succession surface water index) were utilized to characterize the rapid vegetation succession between Remote sensing 2001 and 2006. We found that NDVI, EVI and MSAVI exhibited significant spatial and Time series MODIS temporal correlations with vegetation succession, while LSWI behaved in a positive manner with surface water and soil moisture along with the successional stages. In order to take the advantages of both VIs and water index, a composite index of VWR (vegetation water ratio) combining LSWI and EVI or MSAVI was proposed in this paper. This index facilitates the identification of vegetation succession by simply comparing the values of VWR at different stages, and therefore it could track vegetation succession and estimate community spread rate. Additionally, this study presented an attempt of using MODIS datasets to monitor the change of tidal flat elevation, which demonstrated a potential remote sensing application in geodesy of coastal and estuarine areas. # 2008 Elsevier Ltd. All rights reserved.

1. Introduction for vegetation features on the earth surface through using different band combinations. Vegetation’s response to the Since the first launch of man-made earth resources observa- electromagnetic spectrum is influenced by factors such as the tion satellites, scientists have studied and established various differences in chlorophyll content, nutrient levels, water approximate relationships between spectral response and content, and underlying soil characteristics (Sivanpillai and vegetation coverage (Banari et al., 1995). VIs (vegetation Latchininsky, 2007). The band combinations follow a basic indices) are simply effective and experiential measurements criterion—a band centered in a reflectance peak and another

* Corresponding author. Tel.: +86 21 65642263 2; fax: +86 21 65642468. E-mail address: [email protected] (B. Zhao). 1470-160X/$ – see front matter # 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2008.05.009 ecological indicators 9 (2009) 346–356 347

in a specific absorption valley. This is a very effective mental sciences. For example, Gu et al. (2006) developed an technique, for example, the band combinations covering red enhanced estimation of LAI (leaf area index) of vegetation and infrared wavelengths of the electromagnetic spectrum through smoothing the original time series MODIS LAI data, offer a very effective approach to studying vegetation because and generated the LAI data used for numerical weather these bands include more than 90% information of the prediction. Kang et al. (2003) developed a regional phenology vegetation (Baret et al., 1989). However, observations over a model for detecting the onset of vegetation greenness through relatively short time span do not often reflect vegetation using year 2001 MODIS land products in Korea. Similarly, features meaningfully, because different plant species may Zhang et al. (2003) identified phenological dates by finding have similar spectral properties in a growing season. Further- points on the time course of the VI where the rate of curvature more, leaf structure and chemistry vary seasonally, resulting is the highest. Obviously, these studies have shown the in seasonal dynamics of spectral properties (Zhang et al., multiple uses of time series MODIS datasets. 2006b). Monitoring seasonal changes in vegetation features Estuaries are the important interfaces between marine, and plant phenologies over a long time is essential for many freshwater and terrestrial ecosystems, commonly regarded as applications (Anderson et al., 2006; Sakamoto et al., 2005). intermediate transitional zones where various ecosystems Space-borne remote sensing is not suitable for monitoring join and interact (Attrill and Rundle, 2002). The relatively rapid phenological events at the level of individual plant because of change in estuarine areas produces a narrow ecological zone the coarse ground resolution, although it is a useful tool for between two types of plant communities. Estuarine wetlands characterizing ecosystems (Churkina et al., 2005). Identifying experience frequent flood and ebb. Sediment accumulation is phenological stages of plants with remote sensing may enable controlled by estuarine processes that are highly dynamic and us to distinguish vegetation types or determine the stages of usually unstable, which may increase tideland elevation and ecological succession (sere). Over the last decade, different result in a zone of environmentally stochastic stresses techniques have been developed to determine vegetation (Grosholz, 2002) or sometimes lead to a considerable sediment dynamics through using the time series NDVI (normalized accretion and rapid vegetation succession (Chen et al., 2005a). difference vegetation index) from AVHRR (advanced very high One of the features of estuarine vegetation (salt marshes, resolution radiometer). However, because AVHRR was not brackish and tidal freshwater marshes) is the zonal distribu- designed for land application, these data are generally not tion of plant communities. For this reason, the vegetation in suitable for monitoring vegetation dynamics (Zhang et al., these areas is an ideal setting for studying vegetation 2003). Additionally, Fensholt (2004) found that the MODIS- succession (Wang et al., 2007a). based VI was better correlated with LAI (leaf area index) than The wetland in the Yangtze River Mouth, China is rapidly AVHRR-based VI. growing through the deposition of sand, silt and mud carried by MODIS (moderate resolution imaging spectroradiometer) is river runoff (Yang, 1999). The river transports 4.68 108 tof a novel sensor combination in space on a single platform sediment per year into the East China Sea (Chen and Zong, providing both image and spectrum (Tank et al., 2006). MODIS 1998), with nearly half of the sediment settling in the area of has 36 visible bands from the near infrared to the far infrared, river mouth (Chen et al., 1985) and forming new mudflats of covering the electromagnetic spectrum of 0.4–14.4 nm. Of about 5 km2 per year. In order to speed up sediment accretion them, seven bands are allocated for studying vegetation and for land reclamation, Spartina alterniflora (smooth cordgrass, land surfaces. The daily global imagery is provided at spatial hereafter Spartina) was introduced to Chongming Island in 2001, resolutions of 250–1000 m with a scanning width of 2330 km. which has led to a rapid expansion of Spartina and hence a Generally, it is difficult to acquire the data with tripartite high significant growth of wetland since then (Chen et al., 2004). The resolutions (i.e., high spatial resolution, high temporal ecosystems on the island characterized by rapid succession resolution, and high spectral resolution) simultaneously, have attracted various interests of scientists. Previous studies in due to the bottlenecks in data acquisition and transmission this area have been performed on the ecophysiology of in remote sensing. MODIS overcomes these bottlenecks in dominant plants (Chen et al., 2005b), interspecific competition practice through balancing above tripartite performances in between plants (Chen et al., 2005c), topographical influence on space, time and spectrum, and provides frequent (twice a day), plant growth (Shi et al., 2007; Sun et al., 2001), mechanisms of multiple narrow-band (36 bands) coverage of the earth’s plant dispersion (Zhang et al., 2006a), and the effects of Spartina surface at a moderate spatial resolution (250–1000 m) (Xiong invasion on native biodiversity (Chen et al., 2007; Gao et al., 2006; and Barnes, 2006), which has made MODIS a powerful tool in Ma et al., 2004), and short-term nutrient cycling of ecosystems the field of earth resources observation. The data from MODIS (Mei and Zhang, 2007). Moreover, several studies investigated have improved our understanding of global dynamics and vegetation identification by remote sensing and GIS techniques processes, and are playing an important role in the develop- (Gao and Zhang, 2006; Wang et al., 2007b). However, relatively ment of validated, global, interactive earth system models to few studies have been conducted to examine the succession accurately predict global change (http://modis.gsfc.nasa.gov/ processes of plant communities (Tang and Lu, 2003). The about/). Historically, it was difficult to obtain adequate previous studies are limited to fragmented temporal and spatial remotely sensed data to perform a frequent time series scales, which are not suitable for the detection of the whole analysis, because various technical difficulties are inherent in succession process. The time series of MODIS data can provide traditional earth resources satellites. The launch of MODIS has an opportunity for monitoring vegetation succession through provided us with an opportunity to explore new methods of integrating temporal and spatial scales. remote sensing. Recently, time series MODIS datasets have The main aim of this study was to determine if a proper been widely used in the fields of earth system and environ- remote sensing-based indicator could be identified to monitor 348 ecological indicators 9 (2009) 346–356

the rapid vegetation succession in estuarine wetlands. To coastal areas of the East China Sea (Sun et al., 2001), and achieve this goal, we tried to: (1) find the appropriate remote Spartina is an alien plant species intentionally introduced to sensing-based index of monitoring estuarine environment by the area in May 2001 (Chen et al., 2004). examining three widely used VIs, i.e., NDVI, EVI (enhanced With the continuous sedimentation and the increase in vegetation index) and MSAVI (modified soil adjusted vegetation elevation, the tideland protrudes out the seawater during neap index), and one water index, LSWI (land surface water index); (2) tide and forms mudflats uncolonized by higher plants. When construct a composite index of combining wetland vegetation Scirpus colonizes the mudflats, the terrain keeps being and water properties; (3) analyze the rapid vegetation succes- elevated and Scirpus is gradually replaced by Phragmites (Chen sion and estimate the successional rate and stage using the et al., 2004; Sun et al., 2001). This was the case before Spartina composite index. Additionally, we also tested if LSWI could be was introduced to Dongtan: the area with an elevation of less potentially used to measure tidal flat elevation. than 2 m was characterized by mudflats without any vegeta- tion. The tidal flats with elevations between 2.0 m and 2.9 m were dominated by Scirpus community. In the area of elevation 2. Materials and methods above 2.9 m, plant communities were dominated by Phrag- mites, with small patches of other species (He and Zuo, 2000; 2.1. Description of study area Huang and Zhang, 2007). When Spartina was introduced to the study area in 2001, it rapidly invaded the areas previously Our study area, Dongtan (1218500–1228050E, 318250–318380N), is covered by Phragmites and is currently invading the Scirpus located in the eastern shore of Chongming Island and is the zone. In the higher zones close to the dike, Phragmites tended fastest growing area of the island (Fig. 1). The local climate is a to outcompete Spartina. Therefore, the invaded in subtropical monsoon, with a mean annual temperature of the study area has a typical vegetation zonation and displays a 15.3 8C, 26 8C in summer and 3 8C in winter. The mean annual successional sequence in the following order: uncovered precipitation is 1022 mm, with 60% of rainfall occurring from mudflats ! Scirpus-dominated community ! Spartina-domi- May to September; and the mean humidity is 82%. As a nated community ! Spartina and Phragmites mixture - consequence of sedimentation of silt brought by the Yangtze ! Phragmites-dominated community. River, Dongtan is expanding eastward to the sea at a rate of With the dynamic, neonatal and natural characteristics, more than 150 m per year. Tidal fluctuations at Dongtan are and little anthropogenic physical disturbance, in terms of silt regular and semidiurnal. There are two distinct tidal periods, deposition, vegetation succession and biological invasion, ebb and flood tides, during the day. The range of tideland Dongtan provides an ideal setting for analyzing the sequence uncovered by tidewater is about 1.5 km during neap tide, of events associated with the initial plant colonization and whereas almost all tidelands are submerged under tidewater ecological succession in rapidly developing estuarine wetland during spring tide. The vegetation in the salt marshes of (Huang and Zhang, 2007). Therefore, Dongtan was selected as Dongtan at the time of study was dominated by Scirpus the model ecosystem in this paper to study the rapid mariqueter (sea-bulrush, hereafter Scirpus), Phragmites australis vegetation succession. MODIS datasets of high temporal (common reed, hereafter Phragmites) and Spartina. Among resolution, appropriate VIs, and field surveys were combined them, Scirpus is a typical pioneer of salt marsh widespread in to analyze such a rapid succession process.

Fig. 1 – Six selected MODIS pixels in our study area (the background is Landsat TM image taken in 19 July 2004). ecological indicators 9 (2009) 346–356 349

2.2. MODIS data

Though MODIS sensors on the Terra (EOS AM) and Aqua (EOS PM) satellites theoretically view the entire earth’s surface every 1–2 days, it is impossible to acquire reliable earth surface observation data at such an interval, as cloud cover may occur somewhere at the global scale. An 8-day composition was therefore done to optimize the acquisition of cloud-free images, such as the surface reflectance product MOD09A1. In so doing, several pre-treatments were performed. Firstly, atmospheric correction was performed for gases, thin cirrus clouds and aerosols (Vermote and Vermeulen, 1999). Secondly, cells with a low observational coverage were eliminated. Finally, an observation with the minimum blue band value during the 8-day period was applied and selected. Standard MODIS products were organized in a tile system with a sinusoidal projection, and each tile covered an area of 1200 km 1200 km (approximately 108 latitude 108 long- itude at equator). For a study at a small fixed location, downloading massive data for time series analysis is time- consuming and laborious. Fortunately, the University of New Hampshire developed a system which allows users to retrieve data by a given pixel geolocation and time span (http:// remotesensing.unh.edu/). The data used in this study were downloaded from the website and they were included in tile h28v05 from 2001 to 2006. More detailed about the specifica- tions of the selected MODIS pixels, see Table 1. The period of 2001–2006 was selected because continuous and intensive MODIS data were available, except for one missing observa- tion in mid-June 2001. Since the available achieve data on the website had already been systematically corrected for the effects of gaseous and aerosol scattering, any additional processing and correction were unnecessary.

2.3. Selection of target cells

In order to monitor carbon and water flux in coastal wetlands, carbon flux towers were planned to set up in Dongtan before 2003. To find a good location for installing the micro- meteorological observation equipment, several field surveys were performed during 2001 and 2003. In August 2004, three carbon flux towers were set up within an area of 200 km2.In April 2005, a topographical survey was performed to measure the elevational variations near the towers. Meanwhile, vegetation investigations, including phenological observa- Fig. 2 – Pictures of cell 954 from an eddy flux tower in different years. (A) Taken in August 2004, the tideland was covered by Scirpus. (B) Taken in July 2005, the tideland was invaded by Spartina patches. (C) Taken in August 2006, the Table 1 – Specifications of MODIS pixels used in this tideland was fully covered by Spartina. study Central coordinates of Cell Code MODIS cells tions, plant aboveground biomass and spectral reflective Latitude Longitude characteristic measurements, were carried out every 2 weeks. 31.5178 121.9608 952, 2035 952 During the investigations conducted between 2001 and 2006, 31.5178 121.9658 953, 2035 953 we observed a rapid vegetation succession and sedimentation. 31.5178 121.9708 954, 2035 954 For example, our original intention of building the towers was 31.5178 121.9758 955, 2035 955 to partition the contributions of water and carbon fluxes in the 31.5178 121.9808 956, 2035 956 areas dominated by the monocultures of three dominant 31.5178 121.9858 957, 2035 957 species (Scirpus, Phragmites, and Spartina), so that three 350 ecological indicators 9 (2009) 346–356

relatively homogeneous monocultures were chosen as mea- canopy background adjustment factor, and C1 and C2 are surement footprints. One year after the towers were set up, coefficients of aerosol resistance, which use the blue band one site for monitoring Scirpus was greatly changed because of to correct aerosol influences in the red band. The coefficients the rapid vegetation succession and Spartina spread. It was used in this study were assumed to be the same as suggested difficult to find Scirpus monocultures in autumn 2005 (Fig. 2). by Huete et al. (2002), i.e., L =1,C1 =6,C2 = 7.5 and G = 2.5. For this reason, we selected a transect across two towers along MSAVI can be automatically adjusted by itself according to an east–west vegetation belt, and the total area of vegetation density and eliminate the effects of soil back- 3000 m 500 m, covering six MODIS pixels (952–957), was ground on VIs, and it increases the dynamic range of selected (Table 1 and Fig. 1) to study such a rapid succession vegetation sensitivity. The formula for calculating MSAVI is process. Among the cells, 952 and 954 overlapped, and covered given as follow: the footprints of the two flux towers. qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 rnir þ 1 ð2 rnir þ 1Þ 8 ðrnir rredÞ 2.4. Calculation of vegetation indices and water index MSAVI ¼ (3) 2

NDVI proposed by Deering (1978) is the most commonly used where rred and rnir remain previously defined. VI, and it is defined as To assess water content in a normalized way, LSWI was also selected in this study, which can be calculated as r r NDVI ¼ nir red (1) r þ r r r nir red LSWI ¼ nir swir (4) rnir þ rswir where rred and rnir are the reflectance values of MODIS bands 1 and 2, respectively. where rnir and rswir are the reflectance values of MODIS bands To account for residual atmospheric contamination and 2 and 5 (shortwave infrared band—SWIR), respectively. varying soil background reflectance, EVI was selected. The Since our study area is an estuarine wetland, soil moisture formula for calculating EVI is given as follows: content is high. The distribution of vegetation has great spatial heterogeneity in response to hydrological dynamics. The r r EVI ¼ G nir red (2) above factors influence the spectral reflectance of ground rnir þ C1 rred C2 rblue þ L surface. Finding VIs that are not sensitive to soil background interference and adequately monitoring the changes in water where rred, rnir and rblue are the reflectance values of MODIS and soil background moisture are a great challenge in wetland bands 1, 2 and 3, respectively, G is a given gain factor, L is a ecosystems. Among the above indices, NDVI was the most

Fig. 3 – The spatiotemporal dynamics of four indices from 2001 to 2006. ecological indicators 9 (2009) 346–356 351

commonly used index in previous studies and was selected in test, the relative elevation was performed. When a linear this study for facilitating comparisons with other studies. EVI regression analysis between LSWI and elevation was per- and MSAVI were selected for determining which index is more formed (Fig. 5), the annual sum from 7 October 2004 to 30 suitable for salt marshes. LSWI was selected for detecting the November 2005 was selected, which put the survey date (April changes of soil background moisture along with the progress 2005) in the mid-point of an annual time series. of vegetation succession. To distinguish the indices of different cells, we named each 2.6. Calculation of vegetation water ratio curve by merging the initial letter of the indices and the cell number. For example, N952 stands for NDVI curve of cell 952 Vegetation cover and surface water content are the two most (Fig. 3). important indicators to characterize a wetland. In theory, a parameterintegratingthevegetationfeaturesandsurfacewater 2.5. Statistical analyses content might better reflect the succession stages of a wetland ecosystem. Therefore, we here proposed a composite index Because daily values of the indices are affected by many named VWR, which simply acquires the ratios of VIs to LSWI: uncertainties and environmental conditions, annual sum of P each index and then averaged value (mean arithmetical value) VI VWR ¼ P (5) were calculated and redrawn in Fig. 4. The process can LSWI enhance the accumulative effect at annual scale to help distinguish the inter-annual variation of the indices. where VI represents one of above mentioned VIs, i.e., NDVI, EVI In order to test if LSWI is variable in relation to tidal flat or MSAVI; SVI and SLSWI are the annual sums of VI and LSWI, elevation, a field survey of measuring the elevation was respectively. Imaginably, VWR values would increase from the performed in April 2005. The elevation was measured in three primary stage to the climax stage of a wetland sere. It is 1 different locations of each cell by using ASHTECH PRO- feasible to determine the successional stages by comparing MARK2TM. As the absolute elevation was not necessary for the the values of VWR.

Fig. 4 – The spatiotemporal dynamics of the annual sum of four indices from 2001 to 2006. 352 ecological indicators 9 (2009) 346–356

2.7. Estimation of successional stage and successional rate

When VWR is used as a criterion to determine the stages of a successional sequence, VWR in different years or different cells could be compared. The approximately equal VWR could be regarded as the similar stage. For example, the VWR value of cell 957 in 2006 approximated to that of cell 956 in 2002, and the value of cell 956 in 2006 approximated to that of cell 955 in 2002 (Fig. 6), which means that cell 957 in 2006 and cell 956 in 2002 were in the same successional stage, so were cell 956 in 2006 and cell 955 in 2002. Because the length of one cell is 500 m, both the time for accomplishing a stage change and the average successional rate could be estimated once all the cross-comparisons were performed completely.

3. Results

3.1. Spatiotemporal dynamics of VIs and of LSWI

Fig. 3 shows vegetation dynamics of cells 952–957, reflected by time series of NDVI, EVI, MSAVI and LSWI. More specifically, N952–N955 presented clear seasonal dynamics over the study period. The minimum value appeared in mid-winter and the maximum value in mid-summer. Similar seasonal patterns were observed in EVI and MSAVI. In comparison, L955–L957 showed weak seasonal dynamics, while LSWI tended to reflect the increasing influence of tides on estuarine wetlands (cell 952) at relatively higher elevation to lower lying uncolonized mudflats (cell 956–957). Thus, the patterns of L956 and L957 reflected the noise of tidal activities rather than seasonal changes that occurred in early years. Furthermore, the seasonal pattern for any of VIs presented apparent spatial heterogeneity during the whole study period, which reflected the spatial variation of vegetation distribution in a successional sequence. Fig. 3 explicitly describes the vegetation successional direction and temporal dynamics of each cell, showing that the seasonal patterns were similar across consecutive years. Fig. 4A–C illustrates spatiotemporal patterns of symmetrical vegetation succession: for each of VIs, annual mean value increased spatially from mudflat to dike; for each cell, it Fig. 6 – The spatiotemporal dynamics of vegetation water increased temporally from 2001 to 2006. ratio (VWR).

3.2. The relationship between LSWI and tidal flat elevation

Unlike VIs, the spatiotemporal dynamics of LSWI presented in Fig. 4D failed to illustrate the symmetrical vegetation succes- sion patterns. Specifically, L953 was lower than L952 in both 2003 and 2004, suggesting that this area had lower water content in that period and was probably caused by the changes of elevation. The regression analysis shows that a significantly negative linear relationship between LSWI and tidal flat elevation ( p < 0.01) (Fig. 5).

3.3. Vegetation water ratio (VWR)

Fig. 5 – Correlations between LSWI and relative elevation Fig. 6 presents VWR dynamics in cells 952–957 from 2001 to (the lowest elevation was defined as 0 cm). 2006. In general, NDVI-, EVI- and MSAVI-based VWRs had ecological indicators 9 (2009) 346–356 353

similar behaviors, and they could be validated with each other. promoted sedimentation. The increased terrain in cell 953 These indices reflected that the regular succession occurred in with Spartina colonization might have retained mud and sand our study area. Nevertheless, the NDVI-based VWR did not that would otherwise be flushed into cell 952, and seawater capture the changes of cell 953 between 2005 and 2006 because entered cell 952 when flood tide occurred and was retained NDVI might saturate in the areas with high vegetation cover longer, thus cell 952 maintained its elevation but became and is sensitive to soil background, while EVI- and MSAVI- moister. While the areas of cells 955–957 were still controlled based VWRs had good performance. For the same reason, cell by normal tidal activities. 952 became moister gradually after 2004. The VIs of cell 954 in 2004 were lower than those in adjacent years (i.e., 2003 and 2005) (Fig. 4A–C), which was covered by 3.4. Estimation of successional rate and stage Scirpus in early May 2003 based on our field observation. Because of natural spread and intentional introduction of By summing up all the cross-comparisons results from Fig. 6, Spartina in 2004, the terrain rose gradually, which created the the community spread rate was more than 500 m/4 years, i.e., physical environments that were unfavorable for Scirpus’s 125 m per year, which is coincident with previous studies survival and growth, leading to a decline in its density/ (Yang, 1999; Zhao et al., 2008). Fig. 6 also presents the most biomass. However, Spartina was still in its lag period with a low rapid successional process that occurred in the middle area of density/biomass. Several parts of the wetland lost vegetation the transect. Besides, we could use the VWR values to cover due to the death of Scirpus, leading to the formation of determine the successional stage. We here took EVI-based salt pans. Spartina started to invade these areas in early 2005, VWR as an example to illustrate this point. At the initial stage which was facilitated by its earlier phenological onset and of succession, VWR was less than 0.25; at the pioneer stage, taller height than Scirpus. In the area where Spartina spread, no VWR was between 0.25 and 0.5; at developing stage, VWR was Scirpus was found because the physical environments mod- between 0.5 and 2.0, and the ecosystem presents complex ified by Spartina were not longer suitable for Scirpus (Fig. 2). It community succession; when VWR is great than 2.0, the seems that Spartina of our study area does not compete ecosystem may turn into the late stage. directly with Scirpus, but excluded Scirpus through changing the physical environment. The resolution provided by MODIS is relatively coarse. A 4. Discussion and conclusions cell of 500 m 500 m contains several patches of different plant communities, tidal water, and mudflats with variable 4.1. Using MODIS to monitor the changes in coastal water contents. Arguably, a stage of vegetation succession is a wetlands fusion involving various factors such as background matrix and one or more vegetation patches. In our study area, the Vegetation succession influences surface reflectance in most rapid succession occurred not at the front of the tidal estuarine wetlands through several different mechanisms. flats, but in the middle area. After Spartina was introduced to From the viewpoint of remote sensing, lack of vegetation in our study area, it has changed the physical environments as a mudflats leads to moister soil and lower surface reflectance. In result of terrain rise, which in turn speeded up the succession a pioneer stage of vegetation succession, plant density and process. This strategy avoids direct competition with native biomass are lower than in mature stages, leading to inter- species Scirpus. Overall, the development rate of tidal flats is mediate surface reflectance. With vegetation development about 125 m per year in the study area. and community succession, the surface reflectance becomes higher. On the basis of the reflectance change of our study 4.3. The potential application of MODIS to inferring the area, the community succession could be monitored by ground elevation spectrally based remote sensing. In this work, MODIS was deemed as a suitable sensor for monitoring the changes in The tidal effects at different elevations are different because coastal wetlands. This study presented a new application to seawater tends to display a planar distribution. Lower areas the study of vegetation dynamics of estuarine environment. experience stronger tidal effects than higher areas. The Using MODIS-based VIs to determine the stages of vegetation spectral signature may change with tidal water content and succession process in wetlands is a flexible manner, since vegetation density and composition in the intertidal zone. The annual mean VIs can greatly eliminate the noise in wetland annual accumulated or average LSWI may enhance the effect matrix caused by tides. of tidal activities on elevation and reduce the interference of random noise. The significant correlation between LSWI and 4.2. The elevating effect on terrain by Spartina elevation offered us a potential application of using MODIS to introduction inverse the ground elevation.

The lowest annual sum of LSWI in cell 953 corresponded to the 4.4. Evaluations of various vegetation indices highest elevation in our study area, and the measurements of elevation confirmed this. The question is why the elevation of The advantage of NDVI is that it can partly eliminate some cell 953 became so high? Spartina was introduced to the whole effects of the solar zenith angle, sensor’s observing angle and area of cell 953 and a part of cell 954 in 2001, but the growth atmosphere conditions (Kaufman and Tanre, 1992). However, burst of this species was between 2003 and 2004. The terrain in NDVI has the limitation that the index is sensitive to both cell 953 was rapidly elevated because Spartina greatly atmospheric aerosols (Holben, 1986) and soil background 354 ecological indicators 9 (2009) 346–356

(Lillesaeter, 1982). When vegetation coverage is more than changes in wetland soil background are too complicated. 80%, the NDVI will saturate (Huete, 1985). EVI directly adjusts For example, a recent comparison of four global vegetation the reflectance in the red spectral band as a function of the and wetland products with the ground-truth database of West reflectance in the blue band (Huete et al., 2002, 1997; Liu and Siberia showed that currently available land cover classifica- Huete, 1995), and it can partly overcome the shortcomings of tion products are remarkably poor indicators of vegetation NDVI. On the other hand, to minimize the effects of soil and type and water body extent (Frey and Smith, 2007). By canopy background, SAVI (soil adjusted vegetation index) was combining the groundwater level data and MODIS, Melesse introduced by Huete (1988), but the adjustment factor for SAVI et al. (2007) have evaluated the ecohydrological restoration depends on the level of vegetation cover observed, which leads activities through FVC (fractional vegetation cover) changes to the circular problem of needing to know the vegetation and latent heat flux, which is one of the few case studies of cover before the VI calculated. Therefore, Qi (1994) developed considering MODIS dataset with vegetation cover and water in MSAVI, in which the correction factor can automatically wetland environment. This study might provide a new idea for adjust itself according to vegetation density and eliminate the future perspectives in wetland monitoring. effects of soil background on VIs. To assess water content in a In conclusion, we made an attempt to construct a normalized way, the NDWI (normalized difference water composite indicator through integrating vegetation and water index) (Gao, 1996) or LSWI (Xiao et al., 2002) was proposed by factors, which offered quantitative information about the incorporating the SWIR band into the calculation. dynamics and environmental changes of estuarine wetlands through remote sensing. This study also demonstrated a 4.5. VWR as a wetland indicator potential application of MODIS datasets to monitoring eleva- tional changes in tidelands. In future studies, it is necessary to In wetland ecosystems, vegetation features and surface water determine if elevation has any tangible influence on the time content are the key factors related to succession. Generally, series of MODIS-based VIs. succession in our estuarine wetlands can be roughly divided into the following four stages: (1) the primary stage before succession: characterized by sand-covered mudflats without Acknowledgments any vegetation cover and the ecosystem is frequently submerged under seawater; (2) the pioneer stage of succes- This work was supported by the National Basic Research sion: characterized by pioneer species colonization but Program of China (No. 2006CB403305), the National Natural sporadic distribution. The tideland is frequently flooded, Science Foundation of China (No. 40471087), Shanghai exhibiting adequate water with little vegetation cover; (3) Municipal Natural Science Foundation (Nos. 06zr14015, the development stage: characterized by rapid plant growth 07DZ12038), and the Program for New Century Excellent and spread or community succession. The terrain starts to Talents in University (NCET-06-0364) through the Ministry of rise, and flooding time by tide activities is relatively short. As a Education of China. consequence, the surface water and soil moisture decrease. At this stage, the wetlands are covered by dense vegetation and seldom affected by tide activities; (4) the final stage: references characterized by more obvious terrain rise. The plant com- munities become more complex; and the vegetation cover is very high. At this stage, the wetlands are little affected by tidal Anderson, R.P., Peterson, A.T., Egbert, S.L., 2006. Vegetation- activities, so the spectral response is determined by vegetation index models predict areas vulnerable to purple loosestrife features with little soil background reflectance. It should be (Lythrum salicaria) invasion in Kansas. Southwest Nat. 51 (4), 471–480. noted that the final stage in our case did not actually exist Attrill, M.J., Rundle, S.D., 2002. 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