Environ Monit Assess (2015) 187: 166 DOI 10.1007/s10661-015-4375-z

Effects of saltwater intrusion on pinewood vegetation using satellite ASTER data: the case study of Ravenna (Italy) M. Barbarella & M. De Giglio & N. Greggio

Received: 25 September 2014 / Accepted: 16 February 2015 / Published online: 7 March 2015 # Springer International Publishing Switzerland 2015

Abstract The San Vitale pinewood (Ravenna, Italy) is part of the remaining wooded areas within the southeastern Po Valley. Several studies demonstrated a widespread saltwater intrusion in the phreatic aquifer caused by natural and human factors in this area as the whole complex coastal system. Groundwater salinization affects soils and vegetation, which takes up water from the shallow aquifer. Changes in groundwater salinity induce variations of the leaf properties and vegetation cover, recognizable by satellite sensors as a response to different spectral bands. A procedure to identify stressed areas from satellite remote sensing data, reducing the expensive and time-consuming ground monitoring campaign, was developed. Multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, acquired between May 2005 and August 2005, were used to calculate Normalized Difference Vegetation Index (NDVI). Within the same

vegetation type (thermophilic deciduous forest), the areas with the higher vegetation index were taken as reference to identify the most stressed areas using a statistical approach. To confirm the findings, a comparison was conducted using contemporary groundwater salinity data. The results were coherent in the areas with highest and lowest average NDVI values. Instead, to better understand the behavior of the intermediate areas, other parameters influencing vegetation (meteorological data, water table depth, and tree density) were added for the interpretation of the results.

M. Barbarella : M. De Giglio Department of Civil, Chemical, Environmental and Materials Engineering—DICAM, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy

For more than 30 years, remote sensing data enabled large temporal and spatial scale environmental monitoring studies (Wilkie and Finn 1996; Turner et al. 2003). In reality, some ecological applications, such as the assessment of vegetation vigor or coastal water mapping, require either data from a synoptic view or measurements taken over long periods of time that cannot be collected using field-based methods (Kerr and Ostrovsky 2003). In addition, these ground surveys are often cost- and time-consuming. Instead, the use of satellite multispectral images at medium spatial resolution, such as those provided by the sensor Advanced Spaceborne Thermal Emission and Reflection

M. Barbarella e-mail: [email protected] M. De Giglio e-mail: [email protected] N. Greggio (*) Interdepartmental Centre for Environmental Science Research (CIRSA), I.G.R.G. Lab., University of Bologna, Via S. Alberto 163, 48123 Ravenna, Italy e-mail: [email protected]

Keywords NDVI . ASTER . Saltwater intrusion in coastal aquifer . Groundwater . Pinewood . Statistical evaluation

Introduction

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Radiometer (ASTER), supplies extensive low-cost information over vast areas with a revisiting time from 15 to 20 days. There are, however, limits in the case of technical and logistical problems with the sensor or due to adverse weather conditions for which the images are not always guaranteed. The association between remote sensing techniques and vegetal biophysical indicators is exploited for the diagnosis and monitoring of plant-threatened habitats, providing useful information for decision makers (Pettorelli et al. 2005; Barton 2012). In particular, spectral vegetation indexes, derived from remotely sensed observations, may be used as a biophysical indicator (Gamon et al. 1995). Vegetation indexes are mathematical combinations of several spectral bands that supply the amount or vigor of vegetation within each pixel. Normalized Difference Vegetation Index (NDVI) is the most widely used index (Tilley et al. 2007) because it correlates with vegetation productivity (Reed et al. 1994), being a measure of chlorophyll abundance and energy absorption. Therefore, NDVI has been applied with several purposes such as spatial and temporal distribution of vegetation communities (Reed et al. 1994), quantitative assessment of vegetation biomass (Foody et al. 2001), CO2 fluxes (Vourlitis et al. 2003), vegetation quality for herbivores (Griffith et al. 2002), and the extent of land degradation in various ecosystems (Holm et al. 2003). Then, on the basis of the relationship between NDVI and vegetation productivity, many studies have used the NDVI to analyze indirect effects of environmental changes (Metternicht and Zinck 2003; Aguilar et al. 2012), including those due to processes of soil and groundwater salinization (Teobaldelli et al. 2005; Zhang et al. 2011). The delineation of type and status of vegetation could provide a spatial overview of salinity distribution (Tilley et al. 2007) and support land planners to reduce the risk arising from salinization (Wiegand et al. 1994). Salinity is one of the major environmental factors limiting plant growth and productivity (Allakhverdiev et al. 2000), and its effects can be observed at the whole plant level up to the death of the plants. Increased water salinity induces stress that reduces the ability for plants to take up water, which causes a photosynthesis slowdown and decrease in stomatal conductance (DeLaune et al. 1987; Parida and Das 2005). In fact, reductions in leaf chlorophyll concentration, due to a salinity increase, are a protective mechanism to adjust the photosynthetic process (Zinnert et al. 2012) in cultivated and natural plants. These

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variations of the vegetation properties are recognizable by remote sensing carried out in different spectral bands (visible, near, and short infrared). By measuring the relative difference between responses of chlorophyll and cellular structure in red and near-infrared bands (Myneni et al. 1995; Asner 1998; Peñuelas 1998), the NDVI indicates the greenness of the viewed landscape. A higher NDVI indicates more vegetation productivity (Wang et al. 2003; Tilley et al. 2007). For its environmental relevance, the roman-time San Vitale pinewood (Ravenna, Italy) was selected as the study area. It represents a historical landmark for the whole Regional Po Delta Park that, with other natural features of this region (wetlands, dunes, river mouths), are classified as protected areas (EU site of importance and special area of conservation), according to the Council Directive 92/43/EEC. Several issues affect this coastal area: natural and anthropogenic land subsidence, low topography, and low natural hydraulic gradients that require an important artificial drainage system. In the same way, the destruction of coastal dunes and reduction of their protective effect, the presence of open water bodies, and the insufficient aquifer recharge have caused a widespread saltwater intrusion in phreatic aquifer (Marconi et al. 2011; Greggio et al. 2012; Mollema et al. 2013). The shallow groundwater also represents one of the most important issues affecting local coastal forest vegetation (Antonellini and Mollema 2010). During the past 20 years, many studies have pointed the attention toward a progressive degradation of these ancient pine forests and of the coastal zones (Giambastiani et al. 2007; Diani and Ferrari 2007) mainly from a hydrological point of view. Furthermore, according to the results of Giorgi and Lionello (2008), climate change will have a large influence on the water budget of Mediterranean countries, leading to an increase of the dry periods and to a subsequent increase of seawater intrusion (Church et al. 2013). The aim of this study is, therefore, to create a fast and reliable procedure to identify stressed vegetation, within a homogeneous vegetation type, using multispectral satellite data in a protected area where saltwater intrusion is a relevant issue. The reliability of the method must however be verified by a comparison with contemporary groundwater monitoring data. With the help of this procedure, it will be possible to monitor the status of these coastal pine forests, to optimize the field activities, and to address the restoration interventions to avoid further deterioration.

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Study area The study area is represented by the San Vitale pine forests (Fig. 1) located in the coastal area of Ravenna (between latitudes 44° 27′ 36″ and 44° 33′ 33″ and between longitudes 12° 12′ 36″ and 12° 14′ 15″). The pinewood is 10-km long and 1.2-km wide for a total extension of 1,200 ha (Regione Emilia Romagna 1999). It is surrounded by a drained agricultural area in the western part, an industrial area in the southern part, and the Piallassa Baiona lagoon in the eastern boundary. The lagoon is open to the sea, and artificial embankments divide it into several shallow salty or brackish basins linked by canals and locks. Mollema et al. (2013) prove the seepage between Piallasse

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surface water and pinewood groundwater. The San Vitale pine forest is separated in two different portions from the Lamone River. Inside this river, a saltwater encroachment is present and a flux estimated in 1 m3/m day into the San Vitale groundwater was assessed (Laghi et al. 2010). As with the rest of the Ravenna coastal area, the study area is artificially drained. The pumping stations are positioned in the western boundary of the San Vitale pine forest, and a dense network of drainage channels brings the water from the land and from the pinewood toward pumping machines (Giambastiani et al. 2007). As a consequence, there is groundwater everywhere below sea level, and flux vectors are all oriented from the lagoon toward the pine forest, showing that seawater

Fig. 1 San Vitale pinewood localization. Focus on main hydrological characteristics and vegetation description (Regione Emilia Romagna 1999)

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flows into the aquifer (Greggio et al. 2012). Therefore, a strong west–east salinity gradient is present, and the groundwater salinity ranges from 0 to 20 g/l with the higher values in the eastern part close to Piallassa lagoons and Lamone River. The thickness of the coastal aquifer varies from a minimum of 6 to a maximum of 22 m in correspondence with the historical pinewood (Amorosi et al. 1999). Although the pinewood lies on the paleodune, San Vitale has a very low topography, especially in the southern portion where the average elevation is 0.3 m a.s.l. Moving northwards the elevation increases up to 2 m a.s.l. (Fig. 2) (Giambastiani et al. 2007). From a micro-morphology point of view, an alternation of highs and lows (paleodunes) is present and corresponds to a different coastline stage during the Po delta evolution. As reported by Buscaroli and Zannoni (2010) in the inter-dune depressions, the salinity of the shallow groundwater can significantly affect the soil characteristics. In fact, when the groundwater suffers from salinity, the electrical conductivity (EC) of the soil profile ends up being elevated especially during summer months. The forest develops on dune ridges and slacks, and the recent anthropogenic changes (subsidence,

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hydrologic management) caused an articulated vegetation structure (Pirola 1974). Focusing our attention on higher tree species, many authors have classified the pinewood vegetation using phytosociological method, keeping Pinus pinea species out of classification because it was introduced by man (from 13th century) and not able to reproduce itself inside these natural areas. Several authors (Padula 1968; Pirola 1974) argued that this species is stressed because it is outside of its original climax. Instead, the autochthonous vegetation consists of three different orders, homogeneous for vegetation communities (Piccoli et al. 1991): Quercetalia ilicis, Quercetalia pubescentipetraeae, and Populetalia albae (Fig. 2). In the official vegetation map made by Regione Emilia Romagna (1999), the first order is called “thermophilic evergreen forest” (below TEF), and it is occasionally found inside San Vitale (Padula 1968). Whereas last two orders are grouped in the same vegetation type called Bthermophilic deciduous forest^ (below TDF) and together with TEF cover more than 80 % of the Ravenna pinewoods. Finally, three different forest habitats strongly dependent on groundwater depth were found. Respectively from shallow to deepest, they are (Fig. 2) hygrophilic,

Fig. 2 Schematic representation of the morphology and vegetation of San Vitale pinewood. The black solid line indicates a typical pinewood morphology profile from the paleodune ridge (left) to the slack (right)

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mesophlic, and xerophilic forests, based on average water availability (Pirola 1974). To understand the salinity influence on the main aforementioned tree species of the studied area, the range of tolerance with water salinity is presented in Table 1 (Lazzari et al. 2009). It is clear that vegetation status is not only dependent on groundwater salinity but also on soil properties, microclimate, morphology, vegetation community, anthropogenic pollutants, groundwater depth, etc. However, given the homogeneous geologic subsurface (sandy soils), vegetation cover, and climatic data, groundwater salinity was taken as a major factor controlling vegetation health for the Ravenna pine forests (Antonellini and Mollema 2010).

Materials Multispectral satellite data and ground monitoring data The ASTER sensor consists of three separate subsystems, operating in different spectral regions and with variable spatial resolution: visible and near-infrared (VNIR, 15 m), shortwave infrared (SWIR, 30 m), and thermal infrared (TIR, 90 m). To compute the vegetation index, only VNIR data were used (band 1, 0.52– 0.60 μm; band 2, 0.63–0.69 μm; band 3N, 0.78– 0.86 μm). Five images were downloaded from May to August 2005 (May 26, 2005, June 2, 2005, June 11, 2005, July 29, 2005, and August 30, 2005). This period was selected because of the availability of contemporary groundwater monitoring data. The non-regularity of acquisition intervals is dependent on the availability and quality of archive satellite data. Only cloud-free scenes were selected, and atmospheric correction was applied to all data (Yuan and Niu 2008).

During 2005, a monthly monitoring campaign on surface water and groundwater was performed inside the San Vitale pinewood (Giambastiani et al. 2007). Electrical conductivity, converted into salinity using UNESCO methodology (1983), and groundwater depth (referred to mean sea level) were collected from shallow piezometers, surface water bodies, and drainage channels, within and surrounding the pine forest. More than 50 monitoring points (23 piezometers and 29 surface water points) were used in the analysis (location in Fig. 1). In order to produce the monthly salinity maps, both groundwater salinity and surface water salinity were considered. For each piezometer, only the top aquifer salinity was used: firstly, because it is directly in contact with the tree’s roots and secondly because of its role in supplying water during the evaporation processes (Buscaroli and Zannoni 2010). Due to low elevation, groundwater depth was included in the discussion to better explain the findings. Climate data The climate of the Ravenna area is Mediterranean with the average temperature of 14 °C (Antolini et al. 2008). The average annual rainfall calculated on a long-term series of data (1960–2011) is more than 650 mm, and it is usually concentrated in spring and autumn months. With the aim to typify climatic conditions of the area, temperature and precipitation data were downloaded from a regional network of weather stations (RETE DEXTER ARPA). Unluckily, only one station is close enough to the study area (Fig. 1). In Fig. 3, the acquisition epochs of the satellite images are shown in addition to daily precipitations and temperature for the central months of 2005. The temperature curve for 2005 is similar to the 35-year-long trend, and small differences

Table 1 Vegetation and salinity tolerance for the main tree species involved in the investigation. With the exception of Pinus pinea, the literature reports the salt sensitivity of young plants by laboratory tests not verified in field Tree species

Water salinity range of tolerance (g/l)

Considered literature

Pinus pinea

8–10

Teobaldelli et al. (2005)

Quercus robur Quercus pubescence

0.5–3

Alaoui-Sosse et al. (1998) Sehmer et al. (1995)

Populus alba

0–1

Kotuby-Amacher et al. (1997)

Ulmus minor

0–3

Liu et al. (2012), Maas (1984)

Fraxinus oxycarpa

0–3

Maas (1984)

Salix cinerea

0–2.5

Mirck and Volk (2010)

166 Page 6 of 19 100

35

90

30

80

60

20

50

15

40

10

30

5

20

are encountered in spring and autumn. Concerning the precipitation, the total amount of rainfall during 2004 and 2005 was slightly above the historical average (Antolini et al. 2008).

Methods The study is made up of following phases: the multispectral data processing, the identification of sample areas and their characterization by statistic parameters, and the assessment of the correlation between NDVI and salinity within sample areas. In order to understand the developed procedure, a brief reference concerning the spectral behavior of vegetation and the mathematical instruments to study is given below before describing the listed phases. Spectral behavior of vegetation In general, the vegetation exhibits a spectral signature readily recognizable from other types of land cover. The reflectance is low in both the blue and red regions of the spectrum, while it has a peak in the green region. In the near-infrared range, the reflectance is much higher than that in the visible band. Besides the canopy biophysical attributes, vegetation reflectance depends on leaf and woody stem optical properties (Myneni et al. 1995), which are a function of pigments, leaf structure, water content, and nutrient concentrations.

09/27/05

09/13/05

08/30/05

08/16/05

08/02/05

07/19/05

07/05/05

06/21/05

06/07/05

05/24/05

05/10/05

04/26/05

04/12/05

-5

03/29/05

0

03/15/05

0

03/01/05

10

Temperature (°C)

25

70

Rainfall (mm)

Fig. 3 Daily precipitation (black bars) and temperature (dotted line) relative to the period from March to September 2005; the gray bars refer to acquisition date of satellite images

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Vegetation indexes are algebraic combinations of surface reflectance at different wavelengths and are designed to highlight a particular vegetation property (canopy biomass, absorbed radiation, chlorophyll content, etc.) (Gutierrez-Rodriguez et al. 2005). Between published indices (Le Maire et al. 2004), the most commonly known for analyzing vegetation is the NDVI, which is calculated on each pixel from Eq. 1 (Tucker 1979; Aguilar et al. 2012) NDVI ¼

ρ800 −ρ670 ρ800 þ ρ670

ð1Þ

where ρ800 and ρ670 are the reflectance values centered at 800 and 670 nm, respectively. The formula is based on the strong contrast across the 650–850-nm wavelength interval. The maximum chlorophyll absorption occurs at about 690 nm, while the lack of absorption in the adjacent near-infrared region happens at 850 nm. Vegetation indexes capture this contrast through the combinations of broad band red/near-infrared reflectance (Myneni et al. 1995). The NDVI value ranges from −1.0 to 1.0, where positive values indicate increasing greenness and negative values indicate non-vegetated features such as water, barren areas, ice, snow, or clouds (Wang et al. 2003; Pettorelli et al. 2005). The common range for green vegetation is 0.2 to 0.8; in particular, moderate values represent shrub and grassland (0.2 to 0.3), while high values indicate temperate and tropical forests (0.6 to 0.8) (Weier and Herring 1999).

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Data analysis The satellite data processing involved three main steps: (I) atmospheric correction to adjust for influences of water vapor, aerosols, ozone, and phenomena due to the Rayleigh scattering; (II) NDVI computation; and (III) image co-registration to refer images to geographic coordinates. To process the images, the spatial resolution and the number of row and columns of all channels have to be the same. Because ASTER level 1B data are radiometrically and geometrically corrected, the preprocessing step consisted in the atmospheric correction to retrieve surface reflectance. It is performed by the MODTRAN4 module as implemented into ENVI FLAASH (FLAASH Module 2009). In this work, the rural aerosol model and 40 km of visibility were considered for all the images, while corrections due to the water vapor content values were not applied because of their unavailability. In the second step, the NDVI values were computed from Eq. (1) which represents the normalized difference, pixel to pixel, between reflectance values of 3N and 2 VNIR bands. So, in order to visualize the NDVI seasonal evolution, a spatial pattern of the NDVI values was performed from May 2005 to August 2005. Using the ENVI software, the co-registration of all NDVI raster results was carried out. Subsequently, in order to detect the spectral behaviors of vegetation at each acquisition epoch, six areas of interest (AOIs), called N, CN1, CN2, CS1, CS2, and S (Fig. 4), were identified within the pinewood. The selection of AOIs was based on the homogeneity of vegetation cover. In the CN1, CN2, CS1, CS2, and S, a TDF is present because, according to the official vegetation map, it covers most of this area, even if several pine trees are irregularly spread on whole pine forest. Unfortunately, the 15-m spatial resolution does not allow the investigation of phytosociological orders constituting the TDF vegetation type. Instead N area, mainly covered by scrubs and grassland, was only selected to compare the TDF behavior with a totally different vegetation type, and for this reason, N AOI was excluded from the following statistical analysis. For each area of every image, basic statistic of NDVI values, i.e., mean (m), standard deviation (σx), and standard deviation of the mean (σm), was calculated, and the graph of the average NDVI seasonal trends was created. It is well known that mean NDVI

Fig 4 Investigated areas of interest (AOIs) inside the San Vitale pinewood

values reflect mean productivity and biomass (Pettorelli et al. 2005), and the standard deviation represents a measure of the spatial variability in productivity. The previous statistic parameters were not able to fully explain the data distribution. Consequently, the frequency histograms of AOI NDVI values were plotted to explain the data distributions, and higher order moments, skewness, and kurtosis shape factors were obtained to evaluate a possible deviation from the Gaussian trend. The first is a measure of the asymmetry of the probability distribution of a real value, whereas kurtosis is a measure of whether the data are peaked or flattened relative to a normal distribution (NIST/SEMATECH 2003). A classification criterion to compare the vegetation health status was therefore required. Given that, the highest NDVI value represents the maximum vegetation Bgreenness,^ i.e., a measure of the best photosynthetic activity (Eidenshink 1992); in this paper, for every scene, in the AOI with highest average NDVI, the five percentile of the pixels was computed. Later, the corresponding NDVI value was used as a threshold to

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Fig. 5 The San Vitale NDVI seasonal evolution. From red to green, respectively, lower and higher NDVI values calculated for all the available images (For a better comprehension, it could be useful to show this picture in color)

quantify the percentages of pixel of the other AOIs, corresponding to stressed vegetation, that fall below this limit. Monthly groundwater salinity data, collected for the whole year 2005 from casually strewn wells in the pine forest, were used to validate the findings. An interpolated grid was created for the months corresponding to the image acquisition dates (May, June, July, and August). Starting from sparse points, the kriging algorithm with linear variogram was applied to obtain a continuous pattern (Akkala et al. 2010) using the software SURFER 11. The spacing of nodes is identical to ASTER geometric resolution. Focusing on the potential causes capable of producing damage to vegetation, the saline contour line maps were overlaid with the AOI location to highlight zones of pinewood affected by saltwater intrusion. Later from the original grid file, salinity values were extracted for each node within the AOI boundaries. After this, means, standard deviations, minimums, and maximums were computed for every area. In order to verify the relation between NDVI values and groundwater salinity, for each month, a linear functional relationship between NDVI (Y) and salinity (X) average values has been assumed and

regression coefficients b0, b1 and the mean value Ybi for a given Xi have been estimated (Eq. 2). Ybi ¼ bb0 þ bb1 X i

ð2Þ

For each image, to evaluate the goodness-of-fit of regression line, the coefficient of determination (R2) has been computed, and the confidence interval for Ybi at confidence level γ=0.8 was estimated by means of the following: Ybi ¼

∓

s b tð1þγÞ=2; n−2 Yi vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2 ffi u u1 X i −X t þ X s b ¼ sE 2 n Yi X i −X

;

s2E ¼

 X 1 þ Y i −Ybi n−2

ð3Þ where t(1+γ)/2 ;n−2 is t distributed. Considering the geographic correspondence between salinity and NDVI, the pixel-by-pixel correlation was computed for each AOI. Groundwater depth data have been processed in the same way of salinity but not directly correlated with NDVI results due to the lack of topographical

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Fig. 6 The overlap between paleodune locations and the NDVI map. Dune ridges are shown with north–south-oriented brown lines. NDVI map refers to May data

information. Since the elevated morphology variations and the absence of detailed digital elevation model of the study area, groundwater level has been used only to calculate an average vadose zone thickness starting from the well elevation data. Together with the pine tree density, this information was used to discuss the results.

Results The seasonal evolution of the NDVI values is displayed in Fig. 5, where a typical trend for the considered climatic area is visible. During growing season (May and June), the NDVI values are high, while in summer months, the NDVI values decrease for both considered vegetation type (TDF, scrubs, and grassland).

In particular, as can be seen from the rainfall graph of Fig. 3, during the month preceding the first satellite passage (May 26, 2005) a level of 35 mm of rain was recorded, whereas within the period included between the first and second passages (June 2, 2005), no rainfall period was recorded. On the contrary, the time between the second and third images (June 11, 2005) was characterized by considerable rainy conditions (20 mm). Before the fourth image (July 29, 2008), sporadic precipitations occurred and, between the fourth and fifth (August 30, 2005) data, a level of 65 mm of rain was recorded. Due to the time separation between first three and the other two scenes, a rapid NDVI decrease was detected. In addition, the NDVI values vary within each scene too. Lower NDVI values are located in long and narrow

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bands, mainly related to micromorphology. Looking at the overlay between NDVI and paleodune distribution (Fig. 6), this phenomenon emerges particularly in the southern part of the study area. Six AOIs, i.e., N, CN1, CN2, CS1, CS2, and S, were identified, and for each AOI, mean and standard deviation of NDVI values were computed (Table 2). Taking into account the sample power, the standard deviation ranges from 0.02 to 0.004, and the standard deviation of the mean is always equal or less than 0.001. Based on the NDVI mean values, May still results the best month (from 0.859 to 0.804), a small temporary decrease was observed at the beginning of June, while a decrease occurred in the summer (from 0.733 to 0.692, Fig. 7). The AOIs with the same vegetation are enclosed in a small NDVI interval, whereas the N area (scrubs and grassland) is separated from the other areas. Furthermore, it should be noted that S area has a different behavior: the value is high in May and it decreases more rapidly than the other AOIs during summer.

Thus, only for the AOIs covered by TDF vegetation (N is excluded), the real distributions of the NDVI values were analyzed by relative frequency histograms (Fig. 8) and skewness and kurtosis shape factors (Table 3a). Moreover, because of the extreme temporal proximity compared to the May image, June scenes were not considered. The dotted line in the histograms reflects fifth percentile for the best AOI (CS2 area), which was chosen as a threshold of unstressed vegetation for San Vitale TDF. This value decreases from 0.824 for May up to 0.690 in August, confirming the general trend found for both pinewoods. The percentage of pixels below the threshold is always around 20 % for CS1 area and moves from 43 % up to 19 % in CN2. In CN1, it goes from 68 % up to 36 %, making it the most stressed area. The S area starts with a 35 % in May, increasing up to 51 % and then back to 35 % in August (Table 3b). From the visual comparison between histograms, two different shapes appear, CS2, CS1, and CN2 show a left tail, while for S and CN1 a Gaussian

Table 2 Mean, standard deviation (SD), minimums, and maximums for NDVI, salinity, groundwater level, vadose zone thickness, and pine tree density. Based on average NDVI, the AOIs are reported in descending order AOIs No. pixel

Month

NDVI

Mean±SD CS2

CS1

CN2

S

CN1

Salinity (g/l)

Min

Max

Groundwater level (m a.s.l.) Min

Vadose zone Pine tree thickness (m) density (no./ha)

Mean±SD Min

Max

Mean±SD

Max

2,141 May June

0.859±0.020 0.731 0.906 0.47±0.32 0.50 0.827±0.019 0.686 0.868 0.45±0.31 0.50

1.35 1.57

−0.10±0.08 −0.23 0.04 0.9 −0.38±0.09 −0.51 −0.21 1.2

July

0.733±0.022 0.633 0.783 0.45±0.32 0.50

1.64

−0.56±0.09 −0.69 −0.36 1.4

August 0.742±0.032 0.613 0.826 0.57±0.39 0.50

1.87

−0.69±0.13 −0.91 −0.42 1.5

1,158 May June

0.839±0.029 0.705 0.898 5.78±2.40 2.22 0.811±0.026 0.646 0.872 4.31±1.97 1.22

11.96 −0.15±0.04 −0.26 −0.02 0.7 9.26 −0.41±0.04 −0.49 −0.30 1.0

July

0.715±0.025 0.615 0.779 3.78±1.97 0.85

8.53

August 0.723±0.035 0.599 0.824 3.34±2.01 0.50

7.76

−0.51±0.06 −0.65 −0.41 1.1

0.825±0.035 0.623 0.888 1.00±0.61 0.50 0.798±0.036 0.575 0.860 1.57±1.31 0.50

3.10 5.93

0.19±0.06 0.06 0.29 −0.07±0.07 −0.20 0.09

July

0.704±0.042 0.470 0.775 2.06±1.60 0.50

7.36

−0.32±0.06 −0.44 −0.17 1.4

August 0.721±0.041 0.521 0.817 2.30±2.06 0.50

8.78

0.9 1.1

−0.37±0.08 −0.53 −0.17 1.4

6.53 6.41

−0.34±0.03 −0.39 −0.27 1.4 −0.57±0.04 −0.65 −0.46 1.6

1,859 May June

0.835±0.031 0.716 0.899 4.04±0.87 2.48 0.802±0.034 0.669 0.873 3.61±0.98 1.44

July

0.692±0.038 0.554 0.786 3.65±0.89 1.91

6.54

−0.75±0.08 −0.84 −0.56 1.8

August 0.704±0.041 0.574 0.824 3.53±1.29 0.75

7.23

−0.70±0.11 −0.84 −0.40 1.7

0.804±0.034 0.676 0.873 6.82±2.58 1.48 0.787±0.033 0.648 0.860 6.81±2.70 1.11

12.23 0.02±0.05 −0.08 0.08 0.5 12.22 −0.24±0.06 −0.36 −0.16 0.8

0.693±0.034 0.592 0.771 8.02±3.08 1.031 3.09

−0.43±0.07 −0.57 −0.34 1.0

August 0.703±0.035 0.583 0.797 8.22±2.28 2.751 3.08

−0.36±0.08 −0.52 −0.28 0.9

July

7.6

−0.54±0.04 −0.63 −0.42 1.1

1,743 May June

1,614 May June

5.8

19.8

19.8

25.2

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Fig. 7 Average NDVI value trends for the considered AOIs in each acquisition date of satellite images. The size of confidence interval is not perceptible in the graph

distribution seems present. Skewness analysis confirms the presence of left tail for CS2 (−1.290), CS1 (−0.915), and CN2 (−0.964) and values close to zero for S and CN1 areas. Kurtosis results display peakedness in the same AOIs (3.973, 0.844, and 1.069, respectively) with left tail (Table 3a). For the other areas, the values are near to zero. These considerations are relevant for the May and July satellite data. Instead, in August, all shape factor values tend to zero, with the exception of the CN2 area. In order to understand the causes of possible vegetation stress, monthly groundwater salinity maps were produced. In Fig. 9, the groundwater salinity distribution for May is shown: for the other months, the same pattern of groundwater salinity is shown with small changes in terms of salinity values. The groundwater salinity exceeds 10 g/l in specific zones; the highest values are present in the eastern part, bordering the Piallassa Baiona and in the north-central region. In the southern portion, the salinity remains non-negligible (>3 g/l). The lower salinity is present in the western part, where the salt content becomes less than 1 g/l.

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Other areas exhibit a surface salinity around the average 4 g/l (Fig. 9). Relating to average groundwater salinity, CN1 is the most affected by this stress factor, with maximum values around 13 g/l for all analyzed months. Also, CS1 and S zones have relatively high values of salinity (>3 g/l). While the salinity is low in CN2 and close to zero in the CS2 area. From May to August, the groundwater salinity shows different trends between the AOIs. In CN1, the salinity is high and increases up to 8 g/l; in the CN2 and CS2, the salinity is constant and below 2 g/l; in the CS1 and S, it decreases and the salt content assumes a value around 4 g/l. The standard deviation ranges from 0.30 (CS2) to 3.00 (CN1) (Table 2). Considering the availability of NDVI and salinity data for each AOI, as last elaboration, their correlation was verified. The first step was to plot monthly groundwater salinity and NDVI average values of each AOI in the same graph (Fig. 10). The slope of the regression line is negative for all considered months, confirming the relation among lower NDVI and higher salinity values and vice versa. However, the estimated slope values are close to zero and are equal to −0.0043 ±0.0024 (May), −0.0043± 0.0024 (July), and −0.0045±0.0020 (August). The R2 improves from May to August, but it remains lower (from 0.3724 to 0.6391; Fig. 10). In the second step, a pixel-to-pixel spatial correlation between groundwater salinity and NDVI values was studied (Fig. 11). Because the vegetation activity of May is at a maximum, only May data were considered. Moreover, the assessment was made only for the AOIs with a range of salinity wider than 3 g/l (CN1, CS1, and S). A negative correlation between salinity and NDVI was found for CS1 and S, while for CN1, it is positive but very close to zero. In order to better explain NDVI distribution for each AOI, average vadose zone thickness and pine tree density data were taken into consideration (Table 2). In particular, average vadose zone thickness was computed as difference from well elevation and groundwater depth. As shown in Table 2, in the CS2 area, high NDVI values correspond to low salinity values and vice versa in CN1. Low pine density (5.8 N pine/ha) and vadose zone thickness (from 0.8 to 1.5 m), almost always greater than 1 m, characterize the CS2 area. Massive presence of pine trees (25.2 N pine/ha), small unsaturated zone thickness (from 0.5 to 1.0 m), and low NDVI

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Environ Monit Assess (2015) 187: 166

Fig. 8 Relative frequency histograms of NDVI values for the AOIs of the San Vitale pine forest. Based on average NDVI values, the AOIs are reported in descending order. The dotted line indicates the NDVI value corresponding to fifth percentile for the CS2 area. The June histograms were not reported because they are similar to the May graphs

values mark out the CN1 area. Based on NDVI values, CS1, CN2, and S trends are intermediate between CS2 and CN1. Groundwater salinity, vadose zone thickness, and pine tree density will be used to support the registered NDVI values in the next paragraph.

Discussion The presented methodology is based on NDVI application and its statistical assessment, considering some factors which it could interact with.

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Table 3 Results of the higher order moment analysis (skewness and kurtosis); the dark gray indicates higher and lower values, and the light gray represents the values close to zero (a). Percentage of S. Vitale AO

Shape factors

CS2

CS1

CN2

S

San Vitale AOI

May 26, 2005

July 29, 2005

August 30, 2005

Skewness

–1.290

–0.825

–0.357

CS2

Kurtosis

3.973

0.891

0.166

Skewness

–0.915

–0.490

–0.154

Kurtosis

0.844

0.323

0.027

Skewness

–0.964

–1.236

–0.891

CS1 CN2 S CN1

Kurtosis

1.069

2.585

1.534

Skewness

–0.438 –0.165

–0.094 –0.429

–0.161 –0.235

–0.404 –0.383

–0.233 –0.583

–0.306 –0.109

Kurtosis CN1

Acquisition date

pixels below the NDVI threshold determined on CS2 area; darker gray tones correspond to higher percentages (b). Based on average NDVI, the AOIs are reported in descending order

Skewness

Kurtosis

Concerning climatic data, from January to August 2005, the values and the distribution of rainfall and temperature do not differ from a 30-year-long historical dataset. Due to this, the overall NDVI trend is poorly dependent on climatic anomalies. As reported in the results, the seasonal NDVI behavior for the San Vitale vegetation has the typical pattern recognized for photosynthetic activity in deciduous forests (Reed et al. 1994). From this seasonal trend, only the third scene (June 11, 2005) exhibits a localized increase because of the precipitation felt between June 2002 and June 2011. In the same way, the August rainfall led to a slowdown in lowering for the NDVI, in almost all the AOIs. Based on the precipitation data for this study area, the NDVI seems to respond to the precipitation fallen 2 weeks before. This climatic influence was noted by many other authors. Davenport and Nicholson (1993) reported that after a major precipitation event, the response time of NDVI was typically about one biweekly period (2-week lag). Furthermore, Wang et al. (2003) found NDVI was most strongly correlated with precipitation that occurred two bi-weekly periods before (4-week lag). The study continued with NDVI analysis and then a comparison between vegetation index and groundwater salinity data. Although the NDVI seasonal evolution seems to be homogenous for almost all vegetation of the study areas, AOIs with different spectral response are recognized within the pinewood.

Threshold 5% % below threshold

Acquision date May 26, 2005

July 29, 2005

0.824

0.692

August 30, 2005 0.690

24.7

18.8

17.8

43.6

31.7

19.1

35.4

51.4

35.0

68.8

46.7

36.1

Considering only TDF vegetation, based on average NDVI values, San Vitale AOIs are separated and their NDVI behavior is the same for the whole period. An exception is evident for the S area. The area with the healthiest vegetation is CS2. Its relative frequency histogram shows the 95 % of the pixels distributed between the high NDVI values 0.824 and 0.901. While the most stressed area is CN1 and its relative frequency histogram shows almost 69 % of the pixels, below the threshold established in CS2. For the AOIs with intermediate NDVI, shape and distribution analyses do not help in the differentiation of the AOIs. In effect, for May and July, left tail is present for these San Vitale AOIs, proving however the presence of larger amounts of stressed vegetation. Moreover, in August 30, 2005 scene, all relative frequency histograms do not highlight tails. Further proof is provided by higher order moment results. A probable explanation for the absence of tail could be that the NDVI values decrease close to the lower limit for the forests, making it harder to recognize the stressed vegetation (Weier and Herring 1999). The discrimination of coastal vegetation areas with different spectral behavior through the developed NDVI analysis is also suggested to achieve the zonation of plant communities based on physical characteristics (Naumann et al. 2009). Anyhow, whatever the ultimate goal, to explain the real physical phenomena, the NDVI may not be enough and ground validation data is

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Environ Monit Assess (2015) 187: 166

May

July

N D V I

August

Fig. 9 Overlay between May groundwater isohaline curves (g/l) and AOI locations for San Vitale pine forest

needed, as other authors reported (Metternicht and Zinck 2003; Tilley et al. 2007). If the inverse relationship between average groundwater salinity and average NDVI is evident for CS2 (healthiest AOI) and CN1 (most stressed AOI), for the other AOIs, this relation is less clear. Excluding environmental factors, the only reason could be a different vegetation species present in TDF, but further analysis is needed using high-resolution satellite data and field botanical surveys. Moreover, due to general low elevation, the micromorphology for the whole pine forest is emphasized. This is also confirmed by the overlap between

Salinity (g/l) Fig. 10 Correlation between average groundwater salinity and average NDVI values. The June correlations were not reported because similar to May correlation

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y = -0.0025x + 0.844 R² = 0.0047

0.950 0.900

S

CN1

CS2

NDVI

0.850 0.800 0.750 0.700

y = -0.0023x + 0.8596 R² = 0.0018

0.650

y = 0.0007x + 0.7988 R² = 0.0033

0.600 0

1

2

3

4

5

6 7 Salinity g/l

8

9

10

0.950

11

CN2

0.900

12

13

CS1

NDVI

0.850 0.800 0.750

y = -0.0035x + 0.8583 R² = 0.0837

0.700

y = 0.0183x + 0.8064 R² = 0.1008

0.650 0.600 0

1

2

3

4

5

6 7 Salinity g/l

8

9

10

11

12

13

Fig. 11 Pixel-to-pixel spatial correlation between groundwater salinity and NDVI values for each AOI. This graph refers to May data, because the vegetation activity is at a maximum

paleodunes and NDVI maps (Fig. 6). This aspect complicates the understanding of the San Vitale condition. At this low resolution, it is difficult to distinguish between the natural vegetation distribution related to dune ridges and slacks and the component caused by environmental stresses. However, from the correlation between average groundwater salinity and NDVI values, a negative correlation resulted, and from the pixel-by-pixel analysis, a smaller negative correlation was found. An explanation for this could be given by a timing in salinization processes. Given that the San Vitale forest is affected by historical saltwater encroachment from the Piallassa lagoon (Antonellini and Mollema 2010), its vegetation could have had time to adapt to the new environmental condition. Instead, in case of natural disasters, during which the sudden and relevant changes in groundwater characteristics occur, the vegetation damage is more evident. For example, in zones where phenomenon such

as flooding, tsunamis (Goto et al. 2015), and hurricanes (Steyer 2008; Rodgers III et al. 2009) increased groundwater salinity, considerable NDVI reductions were identified a few months after the event. In our study, groundwater salinity as the only terrain data was not enough to explain the complicated situation of the San Vitale pinewood. Average vadose zone thickness and pine tree density were considered. It is well documented that, with groundwater salinity, groundwater depth is one of the most important factors controlling the vegetation pattern in lowland environments (Antonellini and Mollema 2010; Mata-González et al. 2012). In this study, because of the scarce number of groundwater depth measurements (23 piezometers), the vadose zone thickness is used only to support some consideration about AOI behavior, and it was not directly correlated with NDVI results. Because of the low elevation in the San Vitale forest, the shallow groundwater reduces the unsaturated zone and the tree species

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Environ Monit Assess (2015) 187: 166

typical of the xerophilic habitats become stressed. On the contrary, when the groundwater is deeper, tree species, typical of meso-hygrophilic habitats, become stressed from the drought (Diani and Ferrari 2007). Concerning the number of the pines, a high pine tree density is considered a negative factor because in these environments, it is a stressed species (Piccoli et al. 1991; Pirola 1974; Padula 1968). For each San Vitale AOI, an expected vegetation status was suggested by the combination of each known factor effect. An extreme summary is presented in Table 4 where a comparison between satellite results and expected status was made. The expected vegetation status and NDVI indication are in accordance only with the extreme situations (CS2 and CN1). The low salinity registered in the CS2 and CN2 areas is strongly related to the adjacent Punte Alberete hygrophilic forest, which is constantly kept flooded by the nearby Lamone River. In CS2, a good vegetation status is also explained by a low pine density (Pirola 1974). Finally, a big vadose zone is guaranteed by the proximity of a pumping station. On the contrary, in the CN1 area, high salinity values, shallow water table, and high density of pine trees spell out the poorer performance of NDVI. The first two factors are related to the nearby Pialasse, while the large density of pine trees is due to the recent reforestation (Diani and Ferrari

2007). The status of CS2 and CN1 could be predicted from the overlap between salinity maps and San Vitale AOI locations. The same prediction could not be done for CS1, CN2, and S, by only looking at the groundwater salinity map. In fact, the intermediate salinity value found in the CS1 area suggests a medium-poor expected vegetation status, but from the spectral response, a highintermediate health status came out. This result could be attributed to the low number of pine trees which is most similar to CS2 area, meaning that excluding pine species, the rest of the adult vegetation could be able to adapt to these salinity values (4–6 g/l) which are below the tolerance threshold (3 g/l) determined for an adjacent area by Antonellini and Mollema (2010). In the same way, in the CN2 area, compared to low groundwater salinity values, the high pine density could justify the NDVI intermediate results. The major complexity was encountered for the S area, where the greater NDVI variation is present during studied period. In spite of the west location, the low elevation makes it more exposed to saltwater encroachment and the salinity reaches intermediate values. Because of the vicinity to the pumping station, the groundwater is deeper and the vadose zone is broadest. A medium-poor evaluation status is expected, but a strange NDVI behavior was obtained by spectral

Table 4 Comparison between expected vegetation status and NDVI results for the San Vitale AOIs. The following classes were defined: salinity (g/l): 6 high; pine density (no./ha): 18 high; variability vadose zone (m): 0.0–1.0 small, 0.7–1.1 intermediate, 0.9–1.8 big;

NDVI: assessment derived from the occupied position in the NDVI trend graph. The light gray color cells represent a positive property, the dark gray is for negative properties, and the medium gray indicates the intermediate situations

San Vitale AOIs

Salinity

Pine density

Vadose zone thickness

Expected vegetaon status

NDVI

CS2

Low

Low

Big

Good

Higher

CS1

Intermediate

Low

Intermediate

Medium-poor

Highintermediate

CN2

Low

High

Big

Medium

Intermediate

S

Intermediate

Intermediate

Big

Medium-poor

Variable

CN1

High

High

Small

Bad

Lower

Environ Monit Assess (2015) 187: 166

analysis. Currently, with the ground information and the spatial resolution available, it was not possible to clearly explain the NDVI trend of the S area. Finally, since the salinization process affecting the San Vitale pinewood is widely diffused even in the surrounding farmland, this tested procedure can also be applied on crops. In fact, the salinization problem involves a large part of the Mediterranean basin, characterized by an increasing soil and groundwater salinization. Some studies were already conducted based on reflectance indices and leaf parameters in response to the effects of salinity on agricultural crops (Tilley et al. 2007; Hernández et al. 2014).

Conclusions The forested systems are often overlooked and will be dramatically altered with climate change. In particular, the sea-level rise is threatening many coastal areas with saltwater intrusion. The San Vitale pinewood is already affected by an ancient salinization of groundwater. The procedure developed in this paper enables to discriminate the health status between areas with homogeneous vegetation and to classify them with respect to the area with the higher average NDVI value. In particular, in this work, the zones that present the best and the worst vegetation conditions are influenced, respectively, by lower and higher groundwater salinity values registered in the ground monitoring campaign. However, the complexity of the study area, where other stress elements are present in addition to salinity, leads to classification uncertainties of the areas with NDVI intermediate values. In the studied pine forest, which grows in a complex hydrological context (low elevation, bordered with brackish lagoon, widely drained), an assessment of other environmental variables was needed. Just considering the vadose zone thickness and pine tree density as environmental factors in support of salinity, an explanation of the NDVI was given for all the San Vitale AOIs with exception to the S area. In general, due to the high seasonality of the NDVI and its dependence on short-term climatic events, it is important to apply this proposed procedure for the spring months and to constantly consider precipitation and temperature parameters. Finally, the revealed problems could be partially overcome by the recent development of multispectral satellite sensors with higher spatial resolution (about

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2.5 m). They could allow the identification of a singletree species, reducing the interference related to the large number of species present with different groundwater salinity tolerances. In the same way, a better geometric detail could enhance the comprehension of the effects for both micro-morphology and interaction between surface water and groundwater, which ended up being the main problem for vegetation in the considered pine forests. Consequently, expensive and time-consuming field monitoring campaigns, rescue operations, and defensive work could be reduced and focused only on the impacted areas. There is currently an interesting development of this study being carried out along these lines. Acknowledgments We would like to acknowledge the Land Processes Distributed Active Archive Center (LP DAAC), located at the US Geological Survey’s EROS Data Center, for the ASTER data availability. We would like to acknowledge the very helpful comments of two anonymous reviewers that greatly improved the quality of the manuscript. Special thanks to Prof. Giovanni Gabbianelli for the constant supervision to the work and for the fundamental discussions during the writing of the paper. Lastly, this research would not have been possible without the field data collected by Beatrice Giambastiani (CIRSA) during her PhD project.

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