Keywords

1 Introduction

The morphology of watercourses, and indirectly the riparian vegetation features, can be modified after the building of check dams (Gentile et al. 1998, Bombino et al. 2014). Gentile et al. (2008) observed processes of renaturalization, upstream and downstream of numerous monitored works in several Northern Apulian streams. Moreover, Ricci et al. (2019) highlighted vegetation retreat processes where riverbed instability takes place, after the destruction of check dams. Therefore, the state of conservation of these works plays an important role in preserving their functions (Lucas-Borja et al. 2018). Monitoring the check dams in the years after their implementation is necessary to evaluate their operating status and the existing fluvial processes (Ramos-Diez et al. 2016; Piton et al. 2017).

The relationships between riverbed stability and riparian vegetation can be investigated evaluating the structure of the riparian buffers and estimating vegetation indices (VIs). To assess the VIs directly in-field could be expensive and time consuming, hence the use of remote sensing can be useful when there is a necessity to broaden the analysis at the watershed scale (Ricci et al. 2019). In this case a multiscale approach is required comparing in situ and remote sensing data related to several VIs such as the Leaf Area Index (LAI) (Nagler et al. 2001). LAI, physically the one-sided leaf area per unit ground area (Novelli et al. 2016), is an indicator of ecological plant processes (Pierce and Running 1988) and can be used to describe the main characteristics of the plant canopy (Kamal et al. 2016). Moreover, LAI can be estimated from both satellite data and in situ monitoring, thus it is one of the most used indices to compare remote sensing retrievals and field observations.

In this study, a multiscale approach, based on in situ measurements and remote sensing was applied with the following aims: (i) to assess the variability of the LAI of riparian vegetation in two Mediterranean streams characterized by the presence of intact and destroyed check-dams; (ii) to test the performances of Landsat 8 and Pleiades 1A data (30 m and 2 m pixel size, respectively) in detecting the LAI of riparian vegetation.

2 Materials and Methods

2.1 Study Area

The study area includes the Cammarota (C) and the Vallone della Madonna (VdM) streams (Fig. 1), which flow in the territory of Deliceto (northwestern Puglia, Southern Italy). Both streams are tributaries of the Carapelle torrent and are located in its upper part. The most frequent tree species are Elm (Ulmus minor Mill.) and Hornbeam (Carpinus orientalis Mill.) with a frequency of about 65% in the C and of 100% in VdM. Blackberry (Rubus ulmifolius Schott.) and Common reed (Phragmites Australis Cav.) are the most frequently occurring shrubs. Additional information about the Carapelle torrent and the two studied streams can be found in Ricci et al. (2019).

Along the monitored section of C 28 check dams are present of which 15 destroyed, 3 damaged and 10 intact. The destroyed check dams are the main cause of the active erosion processes in the stream. Hence, C was divided in four reaches (Ri), based on the conservation state of the check dams and the erosion processes, as follow (Fig. 1):

Fig. 1
figure 1

Reaches (R1–R4) of the Cammarota stream and reach (R5) of the Vallone della Madonna stream

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  • R1—Upstream reach, with three intact and one damaged check dams; without bank erosion and vegetation stratified in regular pattern.

  • R2—Incipient erosion reach, with three intact and functioning check dams, one damaged and five destroyed; eroded riverbed and presence of vegetation less dense or completely absent in the much eroded parts of the reach.

  • R3—Full erosion reach, with nine destroyed works; extremely eroded watercourse vegetation almost absent where the erosive phenomena are more relevant.

  • R4—Downstream reach, with four intact, one slightly damaged and one destroyed check dams, very low erosion and stratified riverbed vegetation.

The monitored reach of VdM (named R5, Fig. 1) is characterized by intact check dams without erosion in the riverbed or on the banks.

2.2 Field Measurement and Images Processing

The LAI 2200 Plant Canopy Analyzer was used to detect the LAI in-fields from May 2015 to July 2015 along transects from one bank to the other (LI-COR Inc., 2010). The width of the cross section ranged from 10 to 20 m. The transects were conducted in correspondence of a check dam or in the middle of two consecutive check dams. A total of 64 transect were measured of which 53 in C and 11 in VdM. To register the transects and the check dam positions a Garmin GPSMAP 76CSx was used.

Multispectral data were acquired from medium and high resolution imagery provided by Landsat 8 and Pleiades 1A satellites, respectively. Landsat data, with a pixel resolution of 30 m (https://landsat.usgs.gov/landsat-8), have been frequently used for land cover studies (Romano et al. 2018) while rarely for the analysis of riparian vegetation in streams with narrow cross sections (Ricci et al. 2019). Pleiades constellation provides multispectral data with a pixel resolution of 2 m in the visible bands (Vanhellemont and Ruddick 2018). The satellite images used in this work were acquired by OLI sensors on June 1 and July 3, 2015, and by Pleiades devices on 20 July, 2015. A radiometric and an atmospheric correction were performed for each image to improve the product data accuracy and better compare data sets over a multiple time period. Subsequently, following Ricci et al. (2019) the corrected Normalized Difference Vegetation Index (NDVIc) was computed using the formula suggested by Nemani et al. (1993) with the aim of reduce the near-infrared response in open areas with dense ground vegetation or highly reflective canopies and improve the correlation accuracy between the NDVI and LAI (Nemani et al. 1993). Three equations to derive LAI values from the NDVIc were used: a simple linear equation suggested by Caraux-Garson (Caraux-Garson et al. 1998; Milella et al. 2012) and two non-linear relationships proposed respectively by Lambert-Beer (Lacaze et al. 1996) and Campbell and Norman (Campbell and Norman 1998; Walthall et al. 2004). More information about the equation used can be found in Ricci et al. (2019).

3 Results and Discussion

The values of LAI in-field measurements (LAIobs) were rather different in the four reaches of C caused by the different state of conservation of the watercourse (Fig. 2). R1 and R4 showed similar values of LAIobs while a progressive decrease was observed in R2 and in R3. Generally vegetation in C is greatly fragmented due to the erosive processes in the different reaches. In the incipient erosion reach R2 and in the eroded reach R3 a decrease of vegetation was observed due to the increase of the erosive processes and the malfunctioning or the collapse of the check dams. The median value of LAIobs detected in R2, is higher than those observed in R1 and R4 reaches because of the colonization of opportunistic vegetation, such as Blackberry, during the initial stages of the erosion processes. R5 instead, showed higher values of LAIobs (Fig. 2), both in terms of median and range. Indeed, in R5, where no active erosive processes were monitored, riparian vegetation is multi-stratified with the shrubs that interpenetrates the arboreal layers. Consequently, a more marked presence of typical vegetation species of the area, such as Elm, Hornbeam and Downy oak, was detected.

Fig. 2
figure 2

Comparison between the ranges and median observed Leaf Area Index (LAIobs) values and LAI values derived from Landsat 8 images. (C), Cammarota stream including all the reaches from R1 to R4; (R5), Vallone della Madonna

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A graphical analysis was performed to compare the ranges and median LAIobs values with those retrieved from Landsat 8 in the study reaches (Fig. 2). In terms of median values, the Lambert-Beer (LB) equation resulted as the most suitable for all the reaches characterized by geomorphological stability (R1, R4 and R5) as well as for the entire C reach. The Campbell-Norman (CN) equation resulted as the more accurate formula for the eroded reach R3, while none of the equations was a good predictor in R2 because of the high variability of LAIobs caused by the incipient erosive processes.

A low descriptor was found in the case of R2 for the high variability of this reach, both in terms of vegetation and stream bank erosion. The limit, in the case of the Landsat images, is due to the spatial resolution of 30 m that makes it difficult to find a pure pixel of vegetation or bare soil to insert in the equations (Wu et al. 2004; Hu et al. 2007). LB represents the best performed equation although it needs an improving of the De Jong (1994) coefficients, which aren’t site-specific but generic for Mediterranean environments.

The analysis of Fig. 2 highlights a good correspondence for Landsat data, comparing the median values of LAIobs and LAI retrieved in all the reaches, except for R2. It also indicates that the ranges of LAI values derived from remote sensing is narrower than those observed. It can be ascribed to the ability of the canopy cover analyzer to better detect the variability of the vegetation cover compared to satellite images. Landsat images, because of their spatial resolution, could include in one “mixed pixel” (Lu and Weng 2004) several land cover types such as, as in the case of the study area, riparian vegetation, surrounding wheat fields and orchards, which negatively affect the images accuracy. In order to limit this problem, pixels with less than 75% of riparian vegetation were excluded from the analysis according to Chen et al. (2005).

To improve the performance of satellite images in retrieving LAI values in the reaches characterized by higher variability (R2 and R3), a satellite image with higher resolution (Pleiades) was used. In Table 1 LAIobs are compared with LAI retrieved from Landsat 8 and from Pleiades. Regarding the latter, for R2 the most suitable equation was LB while for R3 was the Caraux-Garson (CG). The values obtained were comparable with those retrieved from the Landsat 8.

Table 1 Comparison between the observed LAI values and LAI values retrieved from satellite data
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In R2 both the Landsat 8 and the Pleiades showed not good performances in terms of statistical indices. In this reach R2, NSE and PBIAS are respectively 0.04, −0.43 and 24.12 for the Landsat 8 and respectively 0.31, −0.03 and 22.34 for the Pleiades. This shows that a good descriptor wasn’t found in the case of R2 caused by the high variability of this reach, both in terms of vegetation and stream bank erosion, which wasn’t detected by satellite imagery neither with medium or high pixel resolution.

In sum, it can be observed that the evaluation of the characteristics of riparian vegetation of streams with narrow cross sections is possible with medium resolution satellite images. Moreover, Landsat images are open access and allow the users to carry out historical analyses to obtain information about the areas where detailed in-field studies should be conducted. The methodology adopted in this study could be useful when considering a comprehensive analysis of check dams’ efficiency (Piton et al. 2017). The analysis of high resolution images (Pleiades in this study) shows that there isn’t an improvement of the estimation performance in the reaches characterized by strong erosion processes and high variable vegetation, where the medium resolution images show low performances.

4 Conclusions

This study analyzed the effect of check dams on variability of riparian vegetation in a Mediterranean stream by means of in-field measurements of the LAI and its retrieval from Landsat 8 and Pleiades satellite data. The study was carried out in the Cammarota stream, with active erosion processes, and the results were compared with those found in the Vallone della Madonna stream, presenting steady geomorphological conditions.

Highly variable LAI values were observed in the Cammarota riparian environment, whereas the eroded reach R3 showed low LAI values because of the presence of destroyed check dams and scarce vegetation. Conversely, high LAI values were detected in reaches R1 and R4, with functioning check dams. It could be concluded that the intact check dams in the studied streams had positive effects on riparian vegetation, making it possible the progressive introduction of broadleaved heliophiles (Hornbeam, Manna ash, Downy oak) in the Vallone della Madonna stream.

The comparison between LAI values detected in-field and those retrieved from remote sensing images highlights the limitations of extracting reliable information on vegetation indices in the analyzed riparian context, as a consequence of the resolution of the considered images and the narrow stream cross sections. However, Landsat data can be useful to conduct preliminary studies on the possible long-term effects of the check dams in such territorial context before further in-field measurements. A limited effect in improving the performance of satellite images using higher resolution products like Pleiades was found in the reaches characterized by intense erosive processes. This result should be better analyzed taking into account also different types of images such as Sentinel 2, characterized by a resolution between that of Landsat 8 and that of Pleiades.