Soil Processes and Current Trends in Quality Assessment

*Edited by Maria C. Hernandez Soriano*

**SOIL PROCESSES AND**

**QUALITY ASSESSMENT**

Edited by **Maria C. Hernandez Soriano**

**CURRENT TRENDS IN**


**5. Conclusion**

 The availability of a cost effective technology to ensure the economic viability of wastewater settings for domestic and industrial purposes is still limited. This chapter presented the insights in applicability of some of the cost-effective technologies in addressing the global water, energy and environmental concerns. The fate of biological systems (AD process), and some of its operating parameters essential for wastewater treatment and bioremediation to energy (biogas) were also discussed. Furthermore, limitations, applications and different configuration types of AD reactors, as well as the forthcoming of the AD process as the alternative technology for bioenergy production using wastewater as a source was discussed. The emerging membrane technologies such as Donnan membrane process are also spotted as the foreseen green and energy saving technologies for industrial and environmental applications. The future challenge for using AOPs in wastewater treatment could be addressed by developing a cost effective photocatalyst to enhance the wastewater treatment and coupled in reduction of carbon dioxide as a renewable energy source to fuels. Therefore, adapting strategies for processes integration and commercialisation of the aforementioned technologies will enhance sustainable social economy growth and development.


**1. Introduction**

#### **1.1 Leaf area index**

The green photosynthesizing leaf area of a canopy is an important characteristic of the status of the vegetation in terms of its health and production potential. At stand level, the amount of leaf area in a canopy is represented by a variable called the leaf area index (LAI), which is one of the key biophysical parameters in the global monitoring and mapping of vegetation by satellite remote sensing (Morisette et al., 2006). In this paper we adopt the, by now widely accepted, definition of LAI as the hemi-surface or half of the total surface area of all leaves or needles in the vegetation canopy divided by the horizontal ground area below the canopy. The definition is in line with the original definition of LAI, formulated for flat and (assumedly) infinitely thin leaves (Watson, 1947), as the one-sided leaf area per unit ground area. For coniferous canopies, the question arose on how to define the "one-sided" area of non-flat needles. While projected needle area formerly often has been used erroneously as a synonym to one-sided flat leaf area, it is now commonly accepted that the hemi-surface needle area represents the logical counterpart to the one-sided area of flat leaves (e.g. Chen & Black, 1992; Stenberg, 2006).

LAI controls many biological and physical processes, driving the exchange of matter and energy flow. Because LAI responds rapidly to different stress factors and changes in climatic conditions, monitoring of LAI yields a dynamic indicator of forest status and health. The link between forest productivity and LAI, in turn, lies in that LAI is the main determinant of the fraction of incoming photosynthetically active radiation absorbed by the canopy (fAPAR). The absorbed photosynthetically active radiation (APAR) quantifies the energy available for net primary production (NPP) and is thus a critical variable in NPP and carbon flux models. NPP is related to APAR by the light-use-efficiency originally introduced by Monteith (1977) for agricultural crops.

Traditionally, ground-based measurements of LAI have typically involved destructive sampling and determination of allometric relationships, e.g. between leaf area and the basal area of stem and/or branches carrying the leaves (the pipe model theory) (Shinozaki et al., 1964; Waring et al., 1982). However, such "direct methods" are quite laborious and indirect measurements of LAI using optical instruments are today the preferred choice (Welles &

Narrowband Vegetation Indices for Estimating Boreal Forest Leaf Area Index 5

of various geometric properties as the main reason for the reflectance differences between

Remote sensing of the biophysical properties, such as LAI, of a boreal coniferous forest canopy layer is further complicated by the often dominating role of the understory in the spectral signal (Rautiainen et al., 2011; Rautiainen et al., 2007; Eriksson et al., 2006; Eklundh et al., 2001; Chen & Cihlar, 1996; Spanner et al,. 1990). Coniferous forests that are regularly treated according to forest management practices tend to have relatively clumped and open canopies. Thus, the role of the understory vegetation in forming boreal forest reflectance

Canopy biophysical variables, such as LAI, can be estimated from remotely sensed data by two types of algorithms: empirical models and methods that use physically-based radiative transfer (RT) models. In empirical algorithms, the estimation is based on statistical relationships modelled between concurrent ground reference measurements and surface reflectance data. These relationships are typically expressed in the form of vegetation indices (VI). VIs include various combinations of spectral bands designed to maximize the sensitivity to vegetation characteristics while minimizing it to atmospheric conditions, background, view and solar angles (Baret & Guyot, 1991; Myneni et al., 1995). Operational LAI algorithms at global-scale typically make use of RT models, but the empirical models

The design of a VI that is optimally correlated with a particular vegetation property requires good physical understanding of the factors affecting the spectral signal reflected from vegetation. The sensitivity of a VI to a vegetation characteristic is typically maximized by including bands with high sensitivity (e.g. high absorption) to the monitored entity and bands mostly unaffected by the same entity. The simplest forms of VIs are simple differences (RB1–RB2), ratios (RB1/RB2) and normalized differences [(RB1-RB2)/(RB1+RB2)] of the reflectances of two spectral bands (RB1, RB2). (In Table 2 we give examples of common VIs used in this study.) The most apparent characteristic of the green vegetation spectrum is the pronounced difference between the red and NIR reflectances, the so called red-edge around 700 nm. For example, the normalized difference vegetation index (NDVI) utilizes this difference and has been shown to correlate with many interrelated vegetation attributes,

The most commonly used VIs were designed for broadband sensors (one spectral band spans about 50 nm or more) having red and NIR bands, such as NOAA AVHRR and Landsat MSS (e.g. Tucker, 1979). However, the basic VIs in red and NIR spectral range suffer from three well-known problems in LAI estimation: (1) they are not sensitive to LAI over its natural range but tend to saturate already at moderate levels of LAI, (2) they are sensitive to canopy background variability, and (3) the VI-LAI relationships are dependent on the vegetation type. These VIs are also sensitive to atmospheric noise and correction.

The saturation of NDVI occurs typically at LAI levels of 2 to 6 depending on the vegetation type and environmental conditions (e.g. Sellers, 1985; Myneni et al., 1997). In general, NDVI saturates as the fractional cover of vegetation approaches one, although LAI still increases (e.g. Carlson & Ripley, 1998). Over conifer-dominated boreal forests, NDVI varies typically

such as chlorophyll content, LAI, fractional cover, fAPAR and productivity.

broadleaved and coniferous stands.

cannot be neglected (Pisek et al., 2011).

**1.3 Vegetation indices in LAI estimation**

usually outperform them in more localized applications.

Cohen, 1996; Jonckheere et al., 2004). They provide inverse estimates of LAI based on the fraction of gaps through the canopy in different directions, which can be measured using devices such as the LAI-2000 Plant Canopy Analyzer (LI-COR, 1992) or hemispherical photography. A vast body of classical literature exists on the dependency between LAI and canopy gap fraction underlying these techniques (e.g. Wilson, 1965; Miller, 1967; Nilson, 1971; Lang, 1986). In short, the inversion methods rely upon the assumption that leaves are randomly distributed in the canopy, in which case Beer's law can be applied to plant canopies (Monsi & Saeki, 1953). However, as the organization of leaves (needles) in forest canopies is typically more aggregated ("clumped") than predicted by a purely random distribution, the technique causes underestimation of LAI, especially in coniferous stands (e.g. Smith et al., 1993; Stenberg et al., 1994). Instead of the true LAI, the inversion of gap fraction data without correction for clumping yields the quantity commonly referred to as the "effective leaf area index" (Black et al., 1991).

Monitoring LAI in a spatially continuous mode and on a regular basis is possible only using remote sensing. Estimation of LAI from optical satellite images is considered feasible because LAI is closely linked to the spectral reflectance of plant canopies in the shortwave solar radiation range (Myneni et al., 1997). The physical relationships between canopy spectral reflectances and LAI form the basis of retrieval algorithms used in current Earth observation programs (e.g. MODIS, CYCLOPES, GLOBCARBON products) for mapping LAI at global scales. They produce bi-weekly and monthly vegetation maps that are widely used by biologists, natural resources managers, and climate modelers, e.g. to track seasonal fluctuations in vegetation or changes in land use. The arrival of narrowband reflectance data (also known as hyperspectral or imaging spectroscopy data) opens up new possibilities for satellite-derived estimation/monitoring of variables connected to the status and structure of vegetation, including LAI.

#### **1.2 Spectral properties of boreal forests**

The boreal forest zone, which spreads through Fennoscandia, Russia, Canada and Alaska, is the largest unbroken forest zone in the world and accounts for approximately one fourth of the world's forests. The boreal zone is a major store of carbon and thus plays an important role in determining global albedo and climate.

The reflectance spectra of coniferous forests (even if they have the same leaf area) are very distinct from similar broadleaved forests. The reasons for the special spectral behaviour of coniferous forests are versatile, yet primarily related to their structural, not optical, properties. Firstly, a high level of within-shoot scattering of conifers was originally noted nearly four decades ago (Norman & Jarvis, 1975). More recently, Landsat ETM+ data and a forest reflectance model were used to show that the low near infrared (NIR) reflectances observed in coniferous areas can largely be explained simply by within-shoot scattering (Rautiainen & Stenberg, 2005). Secondly, absorption by coniferous needles is higher than that by broadleaved species (Roberts et al., 2004; Williams, 1991), a phenomenon which can partly contribute to the lower reflectances of conifer-dominated areas. Other explanations include, for example, that the tree crown surface of coniferous stands is more heterogeneous than in broadleaved stands (Häme, 1991; Schull et al., 2011). In other words, when surface roughness (i.e. crown-level clumping) increases, the shaded area within the canopy increases, thus leading to lower reflectances. Overall, these results highlight the importance of various geometric properties as the main reason for the reflectance differences between broadleaved and coniferous stands.

Remote sensing of the biophysical properties, such as LAI, of a boreal coniferous forest canopy layer is further complicated by the often dominating role of the understory in the spectral signal (Rautiainen et al., 2011; Rautiainen et al., 2007; Eriksson et al., 2006; Eklundh et al., 2001; Chen & Cihlar, 1996; Spanner et al,. 1990). Coniferous forests that are regularly treated according to forest management practices tend to have relatively clumped and open canopies. Thus, the role of the understory vegetation in forming boreal forest reflectance cannot be neglected (Pisek et al., 2011).

#### **1.3 Vegetation indices in LAI estimation**

4 Remote Sensing – Applications

Cohen, 1996; Jonckheere et al., 2004). They provide inverse estimates of LAI based on the fraction of gaps through the canopy in different directions, which can be measured using devices such as the LAI-2000 Plant Canopy Analyzer (LI-COR, 1992) or hemispherical photography. A vast body of classical literature exists on the dependency between LAI and canopy gap fraction underlying these techniques (e.g. Wilson, 1965; Miller, 1967; Nilson, 1971; Lang, 1986). In short, the inversion methods rely upon the assumption that leaves are randomly distributed in the canopy, in which case Beer's law can be applied to plant canopies (Monsi & Saeki, 1953). However, as the organization of leaves (needles) in forest canopies is typically more aggregated ("clumped") than predicted by a purely random distribution, the technique causes underestimation of LAI, especially in coniferous stands (e.g. Smith et al., 1993; Stenberg et al., 1994). Instead of the true LAI, the inversion of gap fraction data without correction for clumping yields the quantity commonly referred to as

Monitoring LAI in a spatially continuous mode and on a regular basis is possible only using remote sensing. Estimation of LAI from optical satellite images is considered feasible because LAI is closely linked to the spectral reflectance of plant canopies in the shortwave solar radiation range (Myneni et al., 1997). The physical relationships between canopy spectral reflectances and LAI form the basis of retrieval algorithms used in current Earth observation programs (e.g. MODIS, CYCLOPES, GLOBCARBON products) for mapping LAI at global scales. They produce bi-weekly and monthly vegetation maps that are widely used by biologists, natural resources managers, and climate modelers, e.g. to track seasonal fluctuations in vegetation or changes in land use. The arrival of narrowband reflectance data (also known as hyperspectral or imaging spectroscopy data) opens up new possibilities for satellite-derived estimation/monitoring of variables connected to the status and structure of

The boreal forest zone, which spreads through Fennoscandia, Russia, Canada and Alaska, is the largest unbroken forest zone in the world and accounts for approximately one fourth of the world's forests. The boreal zone is a major store of carbon and thus plays an important

The reflectance spectra of coniferous forests (even if they have the same leaf area) are very distinct from similar broadleaved forests. The reasons for the special spectral behaviour of coniferous forests are versatile, yet primarily related to their structural, not optical, properties. Firstly, a high level of within-shoot scattering of conifers was originally noted nearly four decades ago (Norman & Jarvis, 1975). More recently, Landsat ETM+ data and a forest reflectance model were used to show that the low near infrared (NIR) reflectances observed in coniferous areas can largely be explained simply by within-shoot scattering (Rautiainen & Stenberg, 2005). Secondly, absorption by coniferous needles is higher than that by broadleaved species (Roberts et al., 2004; Williams, 1991), a phenomenon which can partly contribute to the lower reflectances of conifer-dominated areas. Other explanations include, for example, that the tree crown surface of coniferous stands is more heterogeneous than in broadleaved stands (Häme, 1991; Schull et al., 2011). In other words, when surface roughness (i.e. crown-level clumping) increases, the shaded area within the canopy increases, thus leading to lower reflectances. Overall, these results highlight the importance

the "effective leaf area index" (Black et al., 1991).

vegetation, including LAI.

**1.2 Spectral properties of boreal forests**

role in determining global albedo and climate.

Canopy biophysical variables, such as LAI, can be estimated from remotely sensed data by two types of algorithms: empirical models and methods that use physically-based radiative transfer (RT) models. In empirical algorithms, the estimation is based on statistical relationships modelled between concurrent ground reference measurements and surface reflectance data. These relationships are typically expressed in the form of vegetation indices (VI). VIs include various combinations of spectral bands designed to maximize the sensitivity to vegetation characteristics while minimizing it to atmospheric conditions, background, view and solar angles (Baret & Guyot, 1991; Myneni et al., 1995). Operational LAI algorithms at global-scale typically make use of RT models, but the empirical models usually outperform them in more localized applications.

The design of a VI that is optimally correlated with a particular vegetation property requires good physical understanding of the factors affecting the spectral signal reflected from vegetation. The sensitivity of a VI to a vegetation characteristic is typically maximized by including bands with high sensitivity (e.g. high absorption) to the monitored entity and bands mostly unaffected by the same entity. The simplest forms of VIs are simple differences (RB1–RB2), ratios (RB1/RB2) and normalized differences [(RB1-RB2)/(RB1+RB2)] of the reflectances of two spectral bands (RB1, RB2). (In Table 2 we give examples of common VIs used in this study.) The most apparent characteristic of the green vegetation spectrum is the pronounced difference between the red and NIR reflectances, the so called red-edge around 700 nm. For example, the normalized difference vegetation index (NDVI) utilizes this difference and has been shown to correlate with many interrelated vegetation attributes, such as chlorophyll content, LAI, fractional cover, fAPAR and productivity.

The most commonly used VIs were designed for broadband sensors (one spectral band spans about 50 nm or more) having red and NIR bands, such as NOAA AVHRR and Landsat MSS (e.g. Tucker, 1979). However, the basic VIs in red and NIR spectral range suffer from three well-known problems in LAI estimation: (1) they are not sensitive to LAI over its natural range but tend to saturate already at moderate levels of LAI, (2) they are sensitive to canopy background variability, and (3) the VI-LAI relationships are dependent on the vegetation type. These VIs are also sensitive to atmospheric noise and correction.

The saturation of NDVI occurs typically at LAI levels of 2 to 6 depending on the vegetation type and environmental conditions (e.g. Sellers, 1985; Myneni et al., 1997). In general, NDVI saturates as the fractional cover of vegetation approaches one, although LAI still increases (e.g. Carlson & Ripley, 1998). Over conifer-dominated boreal forests, NDVI varies typically

Narrowband Vegetation Indices for Estimating Boreal Forest Leaf Area Index 7

biochemical composition of vegetation (water, nitrogen, cellulose and lignin), on the other hand, are reviewed by Kokaly et al. (2009). Many of the developed indices have been designed to work at leaf level and do not necessarily upscale to canopy level, because of the high sensitivity to canopy structure, background, solar and view geometry. Another approach is to find iteratively the simple combinations of bands that give the best

Most chlorophyll indices exploit the information in the red edge around 700 nm (Ustin et al., 2009). Imaging spectroscopy data also enables the estimation of the red edge position (REP), which is particularly sensitive to changes in chlorophyll content (e.g. Dawson & Curran, 1998). Water indices, on the other hand, utilize the water absorbing regions in the SWIR region of the spectrum (e.g. Gao, 1996; Zarco-Tejada et al., 2003). Those indices seem particularly interesting for LAI estimation considering the importance of the SWIR spectral

There is growing evidence that imaging spectroscopy data can improve LAI estimates in comparison to broadband data by reducing the saturation effects. Depending on the vegetation type and range of LAI, different types of VIs have been found useful. However, the red edge indices have been most effective in estimating LAI of crops (Wu et al., 2010), grasslands (Mutanga & Skidmore, 2004) and thicket shrubs (Brantley et al., 2011). On the other hand, indices based on NIR and SWIR bands have been successful in broadleaved (le Maire et al., 2008) and coniferous forests (Gong et al., 2003; Schlerf et al., 2005; Pu et al., 2008). The importance of the SWIR spectral region in estimating boreal forest LAI has also been emphasized by multivariate regression analysis (e.g. Lee et al., 2004). However, broadband sensors can also have advantages over narrowband sensors in LAI estimation, for example, by being less sensitive to noise due to the sensor, atmosphere and background (e.g. Broge & Leblanc, 2000). Although there are case studies from different biomes, the performance of narrowband VIs has been poorly assessed over European boreal forests.

The aim of the study is to establish the extent to which vegetation indices can be used to measure variation in LAI based on a test site in southern boreal forest in Finland. We explore different VIs in LAI estimation during full leaf development. We compare the performance of narrowband VIs to traditional broadband VIs. The objective is to identify VIs, which are least sensitive to species composition and, on the other hand, perform well in

The study area, Hyytiälä, is located in the southern boreal zone in central Finland (61° 50'N, 24°17'E) and has an annual mean temperature of 3°C and precipitation of 700 mm. Dominant tree species in the Hyytiälä forest area are Norway spruce (*Picea abies* (L.) Karst), Scots pine (*Pinus sylvestris* L.) and Silver birch (*Betula pendula* Roth). Understory vegetation, on the other hand, is composed of two layers: an upper understory layer (low dwarf shrubs

correlation with empirical data (e.g. Mutanga & Skidmore, 2004; Schlerf et al., 2005).

region in estimating LAI using broadband indices.

**2. Case study**

coniferous stands.

**2.2.1 Study area**

**2.2 Materials and methods**

**2.1 Aims**

in a narrow range and shows poor relationships with canopy LAI (Chen & Cihlar, 1996; Stenberg et al., 2004). The reason for this is the green understory, which results in a noncontrasting background in the visible part of the spectrum (Nilson & Peterson, 1994; Myneni et al., 1997).

Many modifications of basic VIs have been suggested to give better sensitivity to LAI. Typical modifications use other visible bands than red (e.g. the green vegetation index, GNDVI, Gitelson et al., 1996), try to reduce soil effects based on the soil line concept (e.g. the soil adjusted vegetation index, SAVI, Huete, 1988), or include short wave infrared (SWIR) bands. Many modifications also attempt to reduce atmospheric effects (e.g. the enhanced vegetation index, EVI, Huete et al., 2002). The soil line is based on the observation that soil reflectances fall in a line in the red-NIR spectral space (e.g. Huete, 1988). Many VIs utilize the parameterized soil line in their calculation, but these VIs have not been successful in boreal forests as bare soil is rarely visible (e.g. Chen, 1996).

The sensitivity of shortwave infrared (SWIR) reflectance to forest biophysical variables has been recognized for a long time (e.g. Butera, 1986; Horler & Ahern, 1986) and several VIs utilizing the SWIR band have been designed. Rock et al. (1986) showed that the moisture stress index (MSI), i.e. the ratio of SWIR reflectance to NIR reflectance, was an indicator of forest damage. Later, the ratio has commonly been referred to as the infrared simple ratio (ISR, Chen et al., 2002; Fernandes et al., 2003). The SWIR reflectance has also been used for adjusting NDVI (Nemani et al., 1993) and SR (Brown et al., 2000). The reduced simple ratio (RSR) has been used specifically for estimating LAI (Brown et al., 2000; Stenberg et al., 2004) and has been employed also in regional and global-scale operational algorithms (Chen et al., 2002; Deng et al., 2006). RSR seems to reduce the sensitivity to the type and amount of understory vegetation, because background reflectance varies less in SWIR than in visible and NIR (Brown et al., 2000; Chen et al., 2002). RSR has also some capability to unify coniferous and broadleaved forest types, which reduces the need for land cover type specific LAI algorithms. However, in comparison to ISR, the use of red band makes RSR sensitive to atmospheric effects (Fernandes et al., 2003). However, although inclusion of SWIR reflectance increases the sensitivity of VIs to LAI, these indices also have a tendency to saturate at high levels of LAI (e.g. Brown et al., 2000; Heiskanen et al., 2011).

Imaging spectroscopy provides much narrower spectral bands than typical multispectral sensors. Due to the more detailed sampling of the vegetation spectra, such data can detect specific absorption features of vegetation and therefore improve the estimation of vegetation biochemical properties. For example, the SPOT 5 HRG sensors capture a spectral range from 500 nm to 1750 nm with four broad bands, in comparison to Hyperion's 242 (10 nm wide) bands between 400 nm and 2500 nm. At the canopy scale, the contents of biochemical components and LAI are highly inter-related (e.g. Asner, 1998; Roberts et al., 2004). Therefore, imaging spectroscopy could potentially improve LAI estimates. Furthermore, there is potentially complementary information outside the typical spectral bands of broadband sensors.

One way to utilize imaging spectroscopy data is to calculate narrow-band VIs in a similar fashion as for broadband data but using narrower bands. The aim is to improve the sensitivity of the VI to a specific vegetation biochemical property. For example, Ustin et al. (2009) give a comprehensive review on VIs used as indicators of plant pigments (chlorophyll, carotenoids and anthocyanin). The methods of estimating the non-pigment biochemical composition of vegetation (water, nitrogen, cellulose and lignin), on the other hand, are reviewed by Kokaly et al. (2009). Many of the developed indices have been designed to work at leaf level and do not necessarily upscale to canopy level, because of the high sensitivity to canopy structure, background, solar and view geometry. Another approach is to find iteratively the simple combinations of bands that give the best correlation with empirical data (e.g. Mutanga & Skidmore, 2004; Schlerf et al., 2005).

Most chlorophyll indices exploit the information in the red edge around 700 nm (Ustin et al., 2009). Imaging spectroscopy data also enables the estimation of the red edge position (REP), which is particularly sensitive to changes in chlorophyll content (e.g. Dawson & Curran, 1998). Water indices, on the other hand, utilize the water absorbing regions in the SWIR region of the spectrum (e.g. Gao, 1996; Zarco-Tejada et al., 2003). Those indices seem particularly interesting for LAI estimation considering the importance of the SWIR spectral region in estimating LAI using broadband indices.

There is growing evidence that imaging spectroscopy data can improve LAI estimates in comparison to broadband data by reducing the saturation effects. Depending on the vegetation type and range of LAI, different types of VIs have been found useful. However, the red edge indices have been most effective in estimating LAI of crops (Wu et al., 2010), grasslands (Mutanga & Skidmore, 2004) and thicket shrubs (Brantley et al., 2011). On the other hand, indices based on NIR and SWIR bands have been successful in broadleaved (le Maire et al., 2008) and coniferous forests (Gong et al., 2003; Schlerf et al., 2005; Pu et al., 2008). The importance of the SWIR spectral region in estimating boreal forest LAI has also been emphasized by multivariate regression analysis (e.g. Lee et al., 2004). However, broadband sensors can also have advantages over narrowband sensors in LAI estimation, for example, by being less sensitive to noise due to the sensor, atmosphere and background (e.g. Broge & Leblanc, 2000). Although there are case studies from different biomes, the performance of narrowband VIs has been poorly assessed over European boreal forests.


2.2.2. Quantitative RT-PCR

Total RNA samples were extracted from untreated or treated cells with an Absolutely RNA Microprep Kit (Agilent Technologies, Santa Clara, CA, USA). cDNA was generated by using the High-Capacity cDNA synthesis kit (Applied Biosystems, Foster City, CA, USA) and amplified on an Abi Prism 7000 instrument (Applied Biosystems, Foster City, CA, USA) using the Taqman Universal PCR Master Mix (Applied Biosystems). The following human TaqMan gene expression assays were used: HAS2 (Hs00193435\_m1), HAS3 (Hs00193436\_m1), NOS1 (Hs00167223\_m1), NOS2 (Hs01075529\_m1), NOS3 (Hs01574659\_m1), SYND1 (Hs00174579\_m1), SYND2 (Hs00299807\_m1), SYND3 (Hs00206320\_m1), SYND4 (Hs00161617\_m1), NDST1 (Hs00155454\_m1), EXT1 (Hs00609162\_m1), EXT2 (Hs00181158\_m1), and β-actin (Hs99999903\_m1) as the reference gene. The relative quantification of gene expression levels was determined by comparing 2ˆ-∆∆Ct [3,24] using non-treated cells, or the expression of HAS2 and Syndecan-1 as a normalizers.


**1.1 Marine seaweeds**

Three main categories of marine seaweeds are Chlorophyta (green algae), Rhodophyta (red algae) and Phaeophyta (brown algae) [13]. Each class is explained in different section in this review.


*"Everything comes down to value for money. Whichever procurement option that offers that is appropriate."*

He noted that CCs are interested in the project outcome and how they can achieve the best result with the available resources. They, therefore, skip processes in evaluating procurement preferences based on their past experiences, available resources, and project deliverables. As a result, CCs often deviate from the logical, stage-based procurement selection process and rely on their cognition in selecting procurement routes.


*Article* **Response of Chosen American** *Asparagus officinalis* **L. Cultivars to Drip Irrigation on the Sandy Soil in Central Europe: Growth, Yield, and Water Productivity**

**Roman Rolbiecki 1, Stanisław Rolbiecki 1, Anna Figas 2, Barbara Jagosz 3, Piotr Prus 4, Piotr Stachowski 5, Maciej J. Kazula 6, Małgorzata Szczepanek 7,\*, Wiesław Ptach 8, Ferenc Pal-Fam 9, Hicran A. Sadan 1 and Daniel Liberacki 5**

	- UTP University of Science and Technology in Bydgoszcz, 85-029 Bydgoszcz, Poland

**Abstract:** The aim of this study was to verify the response of 13 American asparagus cultivars cultivated for green spear on surface postharvest drip irrigation. Irrigation, used to compensate for periodic deficiencies in precipitation, allows for high- and good-quality crops for many species. The field experiment was carried out in 2006–2008 on a very light sandy soil in central Europe (Poland). Irrigation treatments were applied using the tensiometer indications. Water requirements of asparagus were calculated on the base of reference evapotranspiration and crop coefficients. The following evaluations were made: Height, diameter, and number of summer stalks, as well marketable yield, weight, and number of consumption green spears. Drip irrigation applied for 2 years (2006–2007) in the postharvest period had a positive effect on all studied traits in both summer stalks and green spears in 2007–2008. A significant increase in the height, number, and diameter of summer stalks, as well an increase in the marketable yield, weight, and number of green spears was observed for most of the cultivars. In general, postharvest drip irrigation of asparagus cultivated in very light sandy soil significantly contributes to the increase in productivity of American cultivars of this species.

**Keywords:** *Asparagus officinalis* L.; cultivars; spears yield; sandy soil; water requirements; IWUE


**1. Introduction**

Electricity monitoring of power lines based on current sensing devices is of great significance to improve the security and reliability of the electric power systems. The traditional current sensing devices, such as Hall sensors [1], magnetoresistance sensors [2,3], and current transformers [4], have been investigated. But there are some limitations for these devices. The Hall sensors require external power and place high demands on signal conditioners due to the weak Hall voltage. Magnetoresistance sensors also need external power supply and exhibit large thermal drifts. Current transformers have the disadvantage of magnetic saturation and relay maloperation might be induced. Magnetoelectric (ME) structures based on magnetostrictive materials and piezoelectric materials have been reported for current measuring [5–8], but the devices need to encircle the wire in operation, which may limit their real applications. Furthermore, most of the previously proposed ME structures require DC bias magnetic field, and the demand for DC bias magnetic field greatly increases the occupied space. Cantilever-based non-invasive piezoelectric current sensors are proposed [9–11]. The devices are designed to resonate at the power frequency (50 Hz or 60 Hz) to obtain maximal responses. However, due to the nonlinearity of the piezoelectric materials [12], it is difficult to maintain the resonant state of the devices for varying current amplitudes, and non-resonant structures are more suitable for current sensing. Recently, non-resonant piezoelectric current sensing devices were developed for a single wire in electric power grids [13,14], which operate non-invasively and exhibit high linearity, but the proposed structures are not suited for a two-wire power cord carrying identical currents in opposite directions.

In the past several years, the shear effect of the piezoelectric materials has attracted great attention due to the high piezoelectric constant and electromechanical coupling coefficient [15–21]. Ren et al. [15] presented a shear-mode piezoelectric vibration energy harvesting device with a maximum

output power of 4.16 mW. Carrera et al. [16,17] analyzed multilayered piezoelectric structures in shear-mode using finite element models (e.g., LW models with di fferent theory approximation orders). Liu et al. [21] theoretically investigated the ME e ffect of a magnetoelectric laminated composite working in shear–shear (S–S) mode, which exhibits stronger ME coupling coe fficients. In this paper, a shear-mode non-resonant piezoelectric heterostructure for current sensing of two-wire power cords is designed. The brass cap, the shear-mode piezoelectric materials, and the permanent magnet constitute a cymbal structure, which results in force amplification and potential enhancement of the response to the currents. Theoretical study was conducted, and a prototype was fabricated to investigate the output characteristics of the device. The least squares method is used to analyze the linearity, and the accuracy is investigated for a given electric current. The prototype exhibits high linearity and sensitivity, which are favorable for current sensing in electric power grids. The experimental results verify the theoretical model and the feasibility of the proposed heterostructure. Meanwhile, a conversion factor of 0.77 V/A is obtained for the current-to-voltage conversion experiment, which indicates the latent advantages of the proposed device in practical applications compared with the magnetoelectric laminated composites using DC bias magnetic field [21–24].

#### **2. Structure and Analysis**

Figure 1 shows the schematic diagram and photograph of the proposed piezoelectric heterostructure. The device comprises a cymbal structure and a permalloy plate. The cymbal structure is constructed from a magnet, two shear-mode piezoelectric plates, and a brass cap. The piezoelectric ceramic Pb(Zr,Ti)O3 (PZT5H) is chosen as the material of the piezoelectric plates. The dimension of each PZT5H plate is 1 mm (*dp*) × 6 mm ( *wp*) × 3 mm (*lp*). The material of the permanent magnet is NdFeB (N35), and the size of the magnet is 5 mm (*dm*) × 6 mm ( *wm*) × 22 mm (*lm*). The magnet also acts as a retaining plate for the cymbal structure. Under the action of the AC magnetic field produced by the two-wire power cord, the magnet is acted upon by an AC magnetic force due to the nonuniform AC magnetic field acting on the magnet. The magnetic force results in amplified shear stress on the piezoelectric plates. Then, a voltage is produced, due to the piezoelectric e ffect of the piezoelectric material.

**Figure 1.** Schematic diagram and photograph of the proposed piezoelectric heterostructure.

The magnetic force on the magnet *Fm* can be expressed as

$$F\_m = \sigma\_m \iint\_{S\_W} \Delta H dS\_{m\prime} \tag{1}$$

$$
\Delta H = H\_{1a} - H\_{1b\prime} \tag{2}
$$

(3)

where σ*m* is the magnetic charge density, σ*m* = *Br* [25], *Br* is the remnant flux density, *Sm* is the surface area of one pole of the magnet, and *H*1*a* and *H*1*b* are the magnetic fields on the bottom and top surfaces of the magnet, respectively. *Fm* can be expressed as a power series of the electric current *I*, and the coefficients of the power series can be determined by fitting curve. Assuming that the vertical force (in 1-direction) acting on one piezoelectric plate by the magnet is *Fm*/2, the vertical force acting on the magnet by one piezoelectric plate is the same in amplitude and opposite in direction (−*Fm*/2).

Figure 2 shows the force *Ft* exerted by the brass cap on one piezoelectric plate. Based on the decomposition of the force *Ft*, the following equation is obtained

**Figure 2.** The force *Ft* exerted by the brass cap on a piezoelectric plate.

The shear force *Fs* (in 3-direction) can be expressed as

$$F\_s = -\frac{F\_v}{t\mathcal{g}\rho'}\tag{4}$$

where *tg*ϕ is determined by

$$t \,\mathrm{tg}\,\eta = \frac{2\left(h - d\_p\right)}{l\_{\mathrm{m}} - 2l\_p - l\_t},\tag{5}$$

where *h* is the distance between the magnet (N pole) and the top part (inner surface) of the brass cap, *dp* and *lp* are respectively the thickness (in 1-direction) and length (in 3-direction) of one piezoelectric plate, *lm* is the length of the magnet (*wm* and *dm* are respectively the width and the thickness of the magnet), and *lt* is the length of the top part of the brass cap. Then, the shear stress acting on one piezoelectric plate is

$$T\_s = \frac{F\_s}{w\_p l\_p} = -\frac{F\_v}{w\_p l\_p t g \rho^{\prime}} \tag{6}$$

where *wp* is the width of one piezoelectric plate. Based on piezoelectric constitutive equations in shear-mode [26], the electric field in 1-direction of the piezoelectric material is given by

$$E\_1 = -h\_{15} \\ S\_5 = h\_{15} \frac{F\_v}{w\_p l\_p t g \rho c c\_{55}^D} \\ \tag{7}$$

where *h*15 is the shear stiffness constant, and *cD*55 is the elastic stiffness coefficient (at constant *D*1) in shear-mode. In open-circuit condition, the electric displacement *D*1 = 0. Thus, the output voltage of one piezoelectric plate is obtained as

$$V\_1 = E\_1 d\_p = -\frac{F\_m h\_{15} d\_p}{2w\_p l\_p t \, g \rho c\_{55}^D} \tag{8}$$

Based on Equation (8), it is obvious that the voltage is proportional to the magnetic force *Fm* for determined material and geometric parameters of the device, which depends on the magnetic field magnetic field gradient Δ*H* on the NdFeB magnet. For current-to-voltage conversion application, the conversion factor can be defined as

$$\gamma = \frac{V\_1}{I\_{\rm coil}} = -\frac{F\_m l \eta\_{15} d\_p}{2w\_p l\_p I\_{\rm coil} t g \eta c\_{55}^D} \tag{9}$$

If the output voltage exhibits a linear response to the input current of the coil, the conversion factor λ will show a flat response to the current.

#### **3. Results and Discussions**

A prototype was fabricated to study the current sensing performances of the device. The fabrication process is as follows. (1) The brass cap, the piezoelectric plates (PZT5H), the permalloy plate, and the permanent magnet (NdFeB) were dipped in propanone to clean. (2) The brass cap, the piezoelectric plates, and the NdFeB magnet were bonded to constitute the cymbal structure using insulate epoxy adhesive, and the cymbal structure was naturally dried in the air. (3) The permalloy plate was bonded with the cymbal structure. The prototype was then used to current sensing for a two-wire power cord. The configuration and the experimental set-up for the presented device are illustrated in Figure 3 (the power cord carries opposite currents). The electric currents of the two-wire power cord were generated by a current generator, and the output voltages were monitored by a lock-in amplifier. The current sensing device was placed above the two-wire power cord. The permalloy plate concentrates the magnetic field produced by the two wires of the power cord to the NdFeB magnet, which can potentially enhance the response of the device to electric currents. In Figure 3, the parameter *d* represents the distance between the bottom surface of the permalloy plate and the top surface of the power cord.

**Figure 3.** Configuration and experimental set-up for the proposed device.

*Micromachines* **2019**, *10*, 421

Figure 4 shows the output peak voltage versus the current in the power cord (*f* = 50 Hz). It can be seen from Figure 4 that the experimental voltage increases from 13.91 mV to 128.59 mV when the current is increased from 1 A to 10 A for *d* = 0 mm, and the average sensitivity is 12.74 mV/A in the given current range (1–10 A). For *d* = 3 mm, there is an obvious drop for the induced voltages, and the voltage varies from 7.23 mV to 59.48 mV with an average sensitivity of 5.81 mV/A. The theoretical voltages obtained from Equation (8) for 4 A, 6 A, and 8 A at *d* = 0 mm were plotted in Figure 4 (respectively 60.5 mV, 88.1 mV, and 114.2 mV), which validate the developed model. In order to analyze the linearity of the proposed device, the least squares method was adopted. The equation of the fitting curve can be expressed as

$$V = aI + b\_{\prime} \tag{10}$$

where *a* is the slope and *b* is the intercept of the equation. Using the experimental data in Figure 4, the slope of the equation *a* = 12.82077 for *d* = 0 mm and *a* = 5.785861 for *d* = 3 mm. Correspondingly, the intercept *b* = 1.546273 for *d* = 0 mm and *b* = 1.548193 for *d* = 3 mm. The correlation coefficients are 0.999897 and 0.999945 for *d* = 0 mm and *d* = 3 mm, respectively. After plotting the fitting curves in Figure 4, the linearity of the proposed device is calculated by

$$
\delta = \frac{\Delta V\_{\text{max}}}{V\_{\text{max}}} \times 100\% \tag{11}
$$

where Δ*V*max is maximal deviation between the experimental results and the fitting results, and *V*max represents the full-scale output of the device. The corresponding results are 0.9% and 0.67% for *d* = 0 mm and *d* = 3 mm, respectively. Compared with the resonant structures, the high linearity makes the presented device very suitable for current sensing in electric power systems.

**Figure 4.** Induced voltage versus the electric current for *d* = 0 mm and *d* = 3 mm.

The current remains unchanged at 1.5 A (*f* = 50 Hz). Figure 5a plots the induced experimental voltage at different times. As can be seen from Figure 5a, the voltage changes with time. The average voltage is 20.4 mA (measurement number *n* = 120) in the given time range (0–1200 s). The histogram of the voltages is shown in Figure 5b. It can be seen from Figure 5b that the voltages approximately obey normal distribution. Therefore, the accuracy of the device for current sensing can be calculated by

$$A = v \pm \frac{4\sigma}{\sqrt{n}},\tag{12}$$

where ν is the systematic error, σ is the standard deviation, and ±4σ/ √*n* represents the uncertainty. If we do not take into account the systematic error, the accuracy of the device is calculated to be ±0.0177 mV.

**Figure 5.** (**a**) Induced voltage versus time at 50 Hz for *d* = 0 mm and (**b**) Histogram for the voltage at 1.5 A.

A small current step (Δ*I* = 0.04 A, *f* = 50 Hz) was applied in the two-wire power cord. Figure 6 shows the induced experimental voltage versus time for *d* = 0 mm. As can be seen from Figure 6, by adjusting the current amplitudes within 140 s, the output voltage exhibits a step change. It is clear that a current change (Δ*I*) of 0.04 A can be distinguished. We predict further resolution improvement could be achieved by replacing the PZT5H with shear-mode PMN-PT single crystal, which has a higher piezoelectric coefficient *d*15.

**Figure 6.** Resolution of the device to the power-frequency current step of 0.04 A at *d* = 0 mm.

A copper wire coil was wound around the permalloy plate of the prototype, and a current of 40 mA–400 mA (*I*in) was applied in the coil for current-to-voltage conversion. The experimental output peak voltage increases from 33.18 mV to 270.85 mV with approximately linear response to the current at the low-frequency of 1 kHz, as shown in Figure 7. It also can be seen from the inset of Figure 7 that the factor λ exhibits an approximate flat response. It varies in the range of 0.68 V/A to 0.83 V/A, with an average value of 0.77 V/A. The results show that the heterostructure has the potential to produce large magnetoelectric effect without using magnetostrictive materials and bias magnetic field [27,28].

**Figure 7.** Output open-circuit voltage as a function of the input electric current in the coil at 1 kHz. The inset indicates the conversion factor in the current range of 40 mA to 400 mA.


UK Public Health Rapid Support Team: What Does It Take to Be Ready to Deploy within 48 Hours?

#### Pre-departure, in the field, and post deployment processes

## **EmpowermentRemote: A Pilot Project on Video-Based Group Psychotherapy for Refugees**

**Michael Strupf1, Maren Wiechers1, Andreas Hoell2, Johanna Schneider1, Max Burger1, Peter Falkai1, Malek Bajbouj3, Aline Übleis1, and Frank Padberg1**

**1** Department of Psychiatry and Psychotherapy, University Hospital LMU Munich, Munich, Germany 2 Department of Psychiatry and Psychotherapy, Central Institute for Mental Health, Mannheim, Germany

3 Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin, Campus Benjamin Franklin, Berlin, Germany

Refugees commonly report high levels of mental distress upon arrival in Western countries of asylum. The urgent need for psychosocial care in refugee populations remains widely uncovered due to the scarcity of culture-sensitive treatment, multiple barriers to accessing healthcare services, and, most recently, the COVID-19 pandemic. Western healthcare systems are in charge of providing low- threshold accessible mental health interventions that are specifically tailored toward the needs of refugees in the post-migration setting. Here, we present an innovative video-based group therapy for refugees with affective disorders named *EmpowermentRemote*. The intervention relies on the evidence- based therapy manual Empowerment, which has been shown to be effective and cost-efficient in a multicenter trial. In *EmpowermentRemote* groups, we aim to apply the intervention in a video-assisted setting by reaching out to refugee patients via tablet or smartphone. Refugees are equipped with the knowledge and skills to cope with depressive symptoms and stressors of the post-migration environment. To ensure culture-sensitive treatment, *EmpowermentRemote* groups are supported by professional language mediators, and Western concepts of psychotherapy are culturally adapted.

Video-based group psychotherapy bears great potential as we can treat a larger number of refugees at lower costs and independent of location or service hours, involve translators of diverse languages, and reduce the risk of SARS-CoV-2 infections. Preliminary data indicated that *EmpowermentRemote* is feasible. We aim to investigate the effectiveness of *EmpowermentRemote* in a multicenter trial and implement the intervention in our routine healthcare structure to provide culture-sensitive treatment for refugees in times of the COVID-19 pandemic.

*Figure 1. Main migration routes across the globe. Source: UNHCR. Retrieved from https://news.nationalgeographic.com*

#### Methods and Results

*Figure 2. Care model of the EmpowermentRemote group therapy vs. Treatment-as-usual Figure 3. Content overview of the EmpowermentRemote intervention*

#### Discussion

#### References

Blackmore R, Boyle JA, Fazel M, Ranasinha S, Gray KM, Fitzgerald G, et al. (2020) The prevalence of mental illness in refugees and asylum seekers: A systematic review and meta-analysis. PLoS Med 17(9): e1003337

Satinsky, E., Fuhr, D. C., Woodward, A., Sondorp, E., & Roberts, B. (2019). Mental health care utilization and access among refugees and asylum seekers in Europe: A systematic review. *Health Policy, 123*, 851-863.

Wiechers, M., Übleis, A., & Padberg, F. (2019). *Empowerment für Menschen mit affektiven Erkrankungen und Migrationserfahrungen. Therapiemanual für Einzel- und Gruppensettings*. Stuttgart: Schattauer.

#### Acknowledgements

The *Empowerment* intervention was developed within the Mental Health in Refugees and Asylum Seekers (MEHIRA) project and funded by the Innovationsfond of G-BA (Gemeinsamer Bundesausschuss). The authors declare no conflicts of interest.