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Agroforest Syst (2018) 92:1341–1352 https://doi.org/10.1007/s10457-017-0079-4(0123456789().,-volV)(0123456789().,-volV) A reliable and non-destructive method for estimating forage shrub cover and biomass in arid environments using digital vegetation charting technique . . . Mounir Louhaichi Sawsan Hassan Kathryn Clifton Douglas E. Johnson Received: 20 August 2015 / Accepted: 21 February 2017 / Published online: 27 February 2017 The Author(s) 2017. This article is published with open access at Springerlink.com Abstract Despite the importance of fodder shrubs to Aboveground shrub biomass was clipped to estimate the small ruminant diets and production in arid and semi- dry matter (DM) weight per species and to determine its arid ecosystems, they are often not considered when relationship to canopy cover. In this study, an estimate quantifying grazing land potential. This oversight is of greenness (percent green vegetation cover) was mainly due to the time consuming and costly traditional extracted by way of greenness algorithms. Simple linear techniques used to estimate shrub biomass. The shrub regressions between vegetation cover and biomass for fodder component should be measured to avoid under- 210 plots were performed. The cover of the seven estimation of the carrying capacity of rangelands. In this species differed (P\ 0.01): A. leucoclada had the study, we present a fast, reliable and non-destructive highest vegetation cover (56%) and H. aphyllum the method to estimate canopy vegetation cover to obtain lowest (7%). Vegetation cover and DM biomass were aboveground shrub biomass. The experiment was positively correlated (P\ 0.01) with R-squared ranging conducted under field conditions in northwest Syria, from 0.66 (H. aphyllum)to0.84 (S. vermiculata). Our where seedlings of seven shrub species were monitored method provided reasonable estimations of canopy for one year: Atriplex leucoclada (Moq.) Boiss., A. coverage which could predict aboveground phytomass. halimus L., A. lentiformis (Torr.) S. Watson, A. We conclude that DVCT offers a rapid, reliable and canescens (Pursh) Nutt., A. nummularia Lindl., Salsola consistent measurement of shrub cover and biomass vermiculata L. and Haloxylon aphyllum (C.A. Meyer) provided that shrubs have open architecture. This study Bunge. The experimental layout was a randomized shows the potential of digital cameras and image complete block design with five replications. We processing to determine cover/biomass in a non- destructive, timely and cost efficient way. explored the effectiveness of digital vegetation charting technique (DVCT) for estimating shrub canopy cover. Keywords Rangelands Vegetation cover Remote sensing Halophytes VegMeasure Image processing M. Louhaichi (&) S. Hassan K. Clifton Social, Economic and Policy Research Program (SEPRP), International Center for Agricultural Research in Dry Areas (ICARDA), P.O. Box 950764, Amman 11195, Jordan Introduction e-mail: m.louhaichi@cgiar.org Shrubs play a significant, worldwide role in human M. Louhaichi D. E. Johnson endeavors, and many indigenous people rely heavily Department of Animal and Rangeland Sciences, Oregon on them for their livelihoods. In many arid and semi- State University, Corvallis, OR 97331, USA 123 1342 Agroforest Syst (2018) 92:1341–1352 arid ecosystems, fodder shrubs are vital for small standing crop. In most cases, the response to rainfall ruminant production and particularly goats. Their increases positively with the total amount per season major value is that they provide protein, vitamins and (Le Houerou and Hoste 1977; Deshmukh 1984). frequently mineral elements, which are lacking in Unfortunately, this relationship works best with grassland pastures during the dry season (Le Houerou herbaceous strata and not with other life forms such 1980; Breman and De Ridder 1991; Breman and as shrubs. With a similar aim, shrub biomass equations Kessler 1995; Ahamefule et al. 2006). Despite their were developed as a function of shrub dimensions importance, they are often not considered when such as diameter and height. Equations were usually quantifying grazing land potential. This oversight is developed for particular species, and most generated mainly due to the time consuming and costly tradi- equations were for temperate coniferous forest types tional techniques used to estimate shrub biomass. The (Cannell 1984). Shrub size biomass relationships can shrub fodder component should be measured to avoid be established per species; however, shrubs have to be underestimation of the carrying capacity of destroyed and regression formulas created (Ludwig rangelands. et al. 1975; Chojnacky and Milton 2008). Once this is Researchers have developed methods for measur- completed and the relationship has been established, ing vegetation changes that are cost effective, reliable future destruction would be limited. and repeatable (Stoddard and Smith 1943; Floyd and Vegetation canopy cover is another variable used as Anderson 1987; Brady et al. 1995; Brakenhielm and a non-destructive surrogate for biomass estimates Liu 1995; Seefeldt and Booth 2006). Traditional (Montes et al. 2000; Sala and Austin 2000). In fact, methods involve using quadrats to analyze plant cover, canopy cover is an important ecological characteristic density and aboveground biomass (Cooper 1924; and is generally calculated as the percentage of ground Barbour et al. 1987; Magill 1989; Cox et al. 1990; surface covered by vegetation. Cover can be expressed Hill et al. 2005; Hoover 2008; Louhaichi et al. 2009; in absolute terms (m /ha) but is most often expressed Gayton 2014). simply as a percentage. Visual plant cover estimates Shrub biomass can be estimated using direct are often used because they are more rapid, much (destructive) or indirect (non-destructive) techniques. easier and, therefore, cheaper to measure (Sykes et al. The most accurate method for estimating biomass is 1983). In particular, the line-intercept method (Can- by destructive sampling—clipping and weighing the field 1941; Gayton 2014; Montalvo et al. 2014), which grazable portion in the field (Gibbs et al. 2007; records the length of canopy overlap along a stretched Chojnacky and Milton 2008). Costs and logistics tape laid out on the ground, has been widely used in associated with field measurements always limit the grasslands and steppes. number of destructive samples, the number of shrubs With the recent advances in geo-spatial technolo- harvested, the parts of the shrub measured and the size gies, remote sensing (RS) techniques have been used of shrubs harvested (Clark et al. 2001). In addition, at a much broader scale to estimate shrub biomass destructive techniques prevent repeated measures over (Franklin and Hiernaux 1991; Roy and Ravan 1996; time with the same plant, leading to increased Lu 2006). In fact, satellite imagery is a common tool to variability between observations. To avoid total plant generate monitoring and forage prediction maps (Al- harvest, a semi-destructive shrub biomass estimation Bakri and Taylor 2003; Frank and Karn 2003; technique was developed using representative samples Kawamura et al. 2005). However, broad-scale satellite or ‘reference units’, which reduced the need to clip the images are often too course or have inaccuracies in entire grazable biomass for each shrub. Use of semi-arid and arid areas (Smith et al. 1990). High- reference units increases the number of shrubs that resolution satellite RS imagery, as well as aerial can be sampled, allowing monitoring to capture the photography (Sivanpillai and Booth 2008) and variability of shrubland (Andrew et al. 1981). unmanned air vehicles (Rango et al. 2009; Zhang As an alternative to destructive techniques, shrub and Kovacs 2012), have been used for fine-scale biomass can be estimated using variables that are mapping but are often restricted by the higher cost and correlated with it, such as rainfall. Thus, in the 1980s security concerns (Anderson and Gaston 2013). there were several attempts to develop regressions Landsat imagery with 30 m spatial resolution has between seasonal rainfall and biomass at peak difficulty estimating vegetation cover that is less than 123 Agroforest Syst (2018) 92:1341–1352 1343 40% and has limited use in measuring variations in plant biomass (Tucker et al. 1983; Sannier et al. 2002; semi-arid and arid environments (Smith et al. 1990). Kawamura et al. 2005; Mata et al. 2007). Unfortu- Abdullah et al. (2011) reported that physical mea- nately, most algorithms and regressions relating surements of phytomass of different plant types/ remote sensing data to biomass are site and date species at ground reference sites are necessary for specific or difficult to transfer to other locations due to biomass estimation using high-resolution satellite the limitations of the model itself, and often contain imagery. substantial standard errors (Lu 2006). Therefore, it is More recently, digital cameras have been shown to important to make simultaneous ground measure- be fast, affordable and reliable for measuring biomass ments to increase the accuracy of remotely sensed and Leaf Area Index (Casadesu´s and Villegas 2014). data. There are a number of software packages capable of This study tested the measurement of shrub canopy measuring cover and bare ground from an image cover of multiple species through DVCT. We also (Lamari 2002; Louhaichi et al. 2010). Digital images investigated the relationship between canopy cover can be classified and interpolated to give overall cover and biomass production of seven shrub species maps for the site of interest. The digital vegetative commonly used for rehabilitation of degraded range- charting technique (DVCT) uses digital cameras in a lands in arid environments using simple linear regres- standardized manner to classify and measure vegeta- sion. Such information could contribute to the tion on the ground and requires specialized software to incorporation of biomass estimates for shrubs and be process digital images. VegMeasure is software that potentially useful for the incorporation of shrubs in the processes images that are collected in a standardized REDD ? (Reducing emissions from deforestation manner to provide classification of imagery and and forest degradation) carbon payment scheme. measure changes over time (Louhaichi et al. 2010). Variability in the estimates from DVCT was less than the variability of estimates across personnel who Materials and methods collected data using traditional methods (Booth et al. 2005). DVCT also provided greater accuracy than Study site visual estimates (Olmstead et al. 2004). The fine-scale information provided through high-resolution detailed The field experiment was conducted at the Interna- imagery at local scale when coupled with large-scale tional Center for Agricultural Research in the Dry satellite RS can be used as reference data or training Areas (ICARDA Tel Hadya station) located in Aleppo 0 00 0 00 sites to produce detailed classification maps that have in northwest Syria (361 15.61 N, 3657 20.23 E; greater detail and accuracy than could be provided 300 m above sea level). The soil at the station is from low resolution remote sensing alone. generally deep (over 1 m) and has a heavy clay texture Remote sensing techniques employ regression/cor- (fine clay, montmorillonitic, thermic Calcixerollic -1 relation of the canopy area recorded by the sensor Xerochrept). pH 8.0; CaCO 240 g kg organic -1 against the biomass measured directly or estimated matter 8.4 g kg . The soil saturated hydraulic con- indirectly on the ground. For instance, vegetation ductivity is moderate to low rate (Ryan et al. 1997). indices convert reflectance, and sometimes shrub size The climate of the site is cool semi-arid Mediterranean and roughness, to biomass based on statistical rela- with an average annual rainfall of 340 mm and the tionships established by destructive sampling (Bar growing season during October–May. During the Massada et al. 2006). By transforming remote sensing study period which lasted from the 18th of November data into index values, researchers can generate 2009 to the 28th of April 2010, the total amount of surrogate information that give a rough measure of rainfall received was 327.1 mm in 2009 and vegetation type, amount and condition on land 129.2 mm in 2010. surfaces (Jensen 2007; Lillesand et al. 2008). Remote sensing imagery, such as the Normalized Difference Target shrub species Vegetation Index (Tucker 1978; Steven et al. 2003), that show overall greenness are commonly used in A total of seven halophyte shrub species important for monitoring and to provide predictions and estimates of rangeland rehabilitation and livestock feeding in the 123 1344 Agroforest Syst (2018) 92:1341–1352 arid Mediterranean basin and Central Asia (Winter moderate drought. The roots of nummularia can go as 1981; Le Houe´rou 1992a) were tested in the current deep as 10 m reaching a deep-lying water table (Le study: Atriplex leucoclada (Moq.) Boiss., A. halimus Houerou 1992b, 2010). Medium textured soils are L., A. lentiformis (Torr.) S. Watson, A. canescens most suitable for cultivation and sandy soils should be (Pursh) Nutt., A. nummularia Lindl., Salsola vermic- avoided completely (Le Houerou 1992b). ulata L. and H. aphyllum (C.A. Meyer). More detailed Salsola vermiculata is a perennial shrub and description of these species is presented below: perhaps the most valuable browse species in arid Atriplex leucoclada, an important rangeland plant rangelands of WANA. Typical of dry soils under arid species. It grows in arid and semi-arid areas. It is climates (Rivas-Martı´nez et al. 2001). It grows in drought resistant and halophytic, tolerating soil salin- sandy or clay soils with variable salinity, above tide ity levels as high as 30 dS/m (Al-Oudat and Qadir level, and is frequent along roadsides, abandoned crop 2011). The plant exhibits high forage value qualities fields, rocky slopes, disturbed sites and maritime and provides part of animal requirements, especially in habitats (Reyes-Betancort et al. 2001). autumn and winter for all livestock classes. Atriplex Haloxylon aphyllum is an important perennial plant leucoclada has been shown to readily establish from in Asia. It tolerates arid and semi-arid conditions with seed (Le Houerou 1996) enhancing restoration efforts. less than 200 mm annual rainfall (Arabzadeh and Atriplex halimus is a perennial native shrub of the Emadian 2010). It grows in topographic depressions Mediterranean basin with an excellent tolerance to on sandy, solonchak, or gypsum soils (Gintzburger drought, salinity, and alkaline soils. It is known for its et al. 2003). The plant is widely used to rehabilitate remediation of degraded rangelands and salt-affected degraded rangelands (Shamsutdinov and Shamsutdi- areas. It is used as a phytoremediation plant in highly nov 2008). saline sodic clay loam soils. Atriplex Halimus grows well with high evapotranspiration and low mean Experimental design annual precipitation of 100–400 mm and 400–600 mm for arid and semi-arid areas respectively (Walker Seeds of selected shrub species were germinated in et al. 2014). Optimal soil textures for growth include pots in a greenhouse. The pots were filled with a silty, loamy, and clayey soils whereas coarse sandy mixture of 1/3 soil, 1/3 sand, and 1/3 of organic and heavy clay soils produce poor results and should manure (applied as a top layer). The resulting soil was be avoided completely (Le Houe´rou 2010). alkaline with a pH of 8.4; an organic matter content of Atriplex lentiformis grows well under saline-sodic 0.29%. Once the seedlings reached maturity stage, the soils with heavy clay textures (Le Houe´rou 2010). pots were placed outside to mimic natural field Atriplex lentiformis is an erect medium sized Atriplex conditions. Contiguous research plots (2 m 9 2m) plant with silvery-green leaves and a well-developed were prepared by cultivation. The shrub seedlings root system that can reach water tables down to 10 m were transplanted into the center of its assigned depth (Le Houe´rou 1992b). 2m 9 2 m area in the field in March using a Atriplex canescens is adapted to semiarid condi- randomized complete block design (RCBD) with five tions (Garza and Fulbright 1988). It can grow on deep, replications. A total of 210 seedlings were planted, six halophytic, sandy soils with 150 and 400 mm annual of each species in each of the five replicates. Plants rainfall. Salty soil salts improve growth of A. were then monitored for canopy cover and biomass canescens by improving organic matter production, production over the duration of the experiment. water use efficiency, and increasing the ability to extract water through osmotic adjustments (Glenn and Monitoring techniques Brown 1998). Atriplex nummularia is used as a forage crop in arid To test the efficacy of digital photography for ground and semi-arid areas due to its high palatability cover estimations, straight down images from 1.5 to compared to other Atriplex species, but is prone to 6 m above the ground were obtained using a high- over-browsing and may fail to re-grow. Atriplex resolution digital camera following the DVCT proto- nummularia grows in arid and semi-arid areas with cols (Louhaichi et al. 2010). The digital camera used 150-400 mm mean annual precipitation and tolerates in this study was a Samsung Techwin (model 123 Agroforest Syst (2018) 92:1341–1352 1345 NV100HD, VLUU NV100HD, LANDIAO NV100 images and a summary excel file that illustrate the HD, TL34HD) equipped with a 28-mm lens. The name of each image and the values (%) of the dimensions of each image were 4384 9 3288 pixels classification for each category. The total surface area and the size was about 3434 kb in JPG format. On of green vegetation from the image classification is three occasions after transplanting (at day 296, 406 calculated by summing the total area occupied by and 458) photos were taken from both 1.5 m above the pixels classified as plant. This total surface area (%) ground (Fig. 1a) for individual shrubs and from 6 m was then regressed against the DM biomass clipping (Fig. 2a) to provide a lower resolution (ground pixel weights to assess the relationship of green cover in the size of about 3 mm) assessment of cover for a relative images versus standing biomass. Once the image comparison within the subplot. At peak standing crop, processing is complete, the program computes the the aboveground shrub biomass was clipped at 5 cm accuracy assessment. This is done through computing height, dried for 24 h at 70 C and weighed to estimate the error matrix and the Kappa Index of Agreement. the dry matter (DM) weight per species. This latter is commonly used in remote sensing Estimates of greenness (% green vegetation cover) classification to assess the degree of success of a were calculated from the digital camera images using classification technique. The error matrix permits supervised classification technique in VegMeasure measurement of overall accuracy, category accuracy, software (Louhaichi et al. 2001; Johnson et al. 2009). producer’s accuracy and user’s accuracy (Congalton VegMeasure is a DVCT that measures vegetation on 1991). the ground in a non-destructive manner. The colors from the digital camera can be interpreted by the Statistical analysis software to create meaningful classes. This technique allows customization of the images. In this study we Changes in plant cover were modeled by fitting a only had two categories: plant or canopy cover and soil generalized linear model using binomial error and or bareground. After uploading the images to a specific logit link function for each of the three times of plant folder, few images were selected to set the threshold cover observed. To evaluate the main effects of for each class and act as training sites for the species on plant cover, data were analyzed using a performing image processing. The pixels for each repeated measures model in randomized complete category in the image having the same value would be blocks. Biomass data were analyzed as a RCBD using displayed with a distinct color. At any time the user analysis of variance (ANOVA). Univariate regression can display results in progress by clicking on Statistics analyses were used to examine relationships between option. This step allows the user to know what biomass (as the dependent variables) and plant cover percentage falls in each class and how many pixels using the last observation date. Univariate regression remains unclassified. Once the thresholds are set we is described as follows: run the program for patch processing of all the images stored in the designated folder. This process would y ¼ a þ bx, generate an output folder containing the processed Fig. 1 Estimation of shrub canopy cover using a digital camera on the ground and c extracted image of the canopy coverage mounted on a monopod 1.5 m above the ground: a image from the digital image using image processing VegMeasure collection with the equipment, b natural image of a shrub plant program 123 1346 Agroforest Syst (2018) 92:1341–1352 Fig. 2 Estimation of shrub canopy cover using a digital camera community on the ground and c extracted image of the canopy mounted on a monopod 6 m above the ground: a image coverage of the shrub community from the digital image using collection with the equipment, b natural image of the shrub image processing VegMeasure program vegetation cover changed from 2% in 296 days after where y is dependent variable; x is independent transplanting to 17.76% in 406 days after transplant- variable; a is the intercept and b is slope of this ing (Fig. 3). Among all the seven shrub species H. function. The intercept was set to 0 in our calculated aphyllum performed the lowest plant cover changed models as it is more reasonable. Therefore, the from 0.37 to 3.61% in 296 days and 406 days after resulting model implies that biomass should be 0 transplanting respectively (Fig. 3). when plant cover is 0. When a significance value is not Data on biomass showed strong heterogeneity of given in the text, results were not significant at error variances; however, the square-root transformed P \ 0.05. This model opted to be used because it fits values supported the assumptions of consistency of the trends in our dataset better than logarithmic and error variances and normality of experimental errors. exponential regressions in terms of R-squared value. Species differences in biomass were significant While parabolic regression does not provides that (P \ 0.001, Table 1). Among the shrub species eval- significant improvement to justify using more com- uated, A. leucoclada had the greatest biomass (775 g plex models. All statistical analyses were carried out DM/shrub) followed by A. halimus (366 g DM/shrub) using Genstat software (Payne 2014). The latest and S. vermiculata (228.5 g DM/shrub), while H. version of VegMeasure has a built-in tool ‘Assess aphyllum had the lowest (25.9 g DM/shrub). How- Accuracy’ for assessing accuracy (Johnson et al. ever, there was no significant difference in biomass 2015), which allows easy computation of an error yield among A. lentiformis, A. canescens and A. matrix and a Kappa Index of Agreement for the nummularia. The study was set up in a manner that the processed pictures. effect of climate would be uniform and data is cross comparable. Therefore, climate differences were not a factor as planting and sampling were conducted on the Results same dates and the duration of the study was relatively short. As indicated earlier, the soil conditions and the Atriplex leucoclada had the highest evolution of rainfall pattern during the study period were not average vegetation cover over time followed by A. limiting factors to inhibit growth of the selected halimus, while H. aphyllum had the lowest (P \ 0. species. In general these species would withstand 01). Atriplex leucoclada plant cover increased from much drier and harsh conditions and that is why they 5.16% (296 days after transplanting) to 56% are recommended for rehabilitation of degraded (406 days after transplanting). Similarly, S. vermicu- rangelands. lata, A. lentiformis, A. canescens and A. nummularia The slopes derived from the linear regression for had the same trend of average vegetation cover over the plant cover (%) of each species were positively time. Salsola vermiculata plant cover ranged from 1.9, correlated with biomass, with R-squared (R ) ranging 3.70 and 18.23% in 296, 406 and 458 days after pots from 0.66 (H. aphyllum) to 0.84 (S. vermiculata), transplanting respectively. Likewise A. lentiformis 123 Agroforest Syst (2018) 92:1341–1352 1347 296 day 406 day 458 day Atriplex Atriplex Salsola Atriplex Atriplex Atriplex Haloxylon leucoclada halimus vermiculata lenformis canescens nummularia aphyllum Species Fig. 3 Canopy cover evaluation over time (296, 406 and 458 days after seeding) of seven halophytes estimated with DVCT for monitoring shrub canopy cover Furthermore, results show that image processing to Table 1 Mean aboveground biomass (g) for single plants of different shrub species estimate vegetation cover using supervised classifica- tion provide the best overall classification accuracies Species Mean biomass at approximately 97%. This accuracy is higher than A. leucoclada 774.9 running the build in green leaf algorithm. A. halimus 366.1 S. vermiculata 228.5 A. lentiformis 155.7 Discussion A. canescens 149.8 A. nummularia 134.4 Although it is highly unlikely an estimation technique H. aphyllum 25.9 will meet every desirable trait, they should include a combination of accuracy, speed, precision, limited Numbers followed by different letters are significantly interference from environmental conditions and different (P \ 0.05); shrub biomass values were subject to square-root transformations in ANOVA topography, and if instruments are required, they should be inexpensive and easy to operate (Tucker differing significantly for all species (P \ 0.01, 1980). The use of vegetation cover as a surrogate Table 2). In our models we referred the range of variable for shrub biomass has the advantage of being validity upon the scope of plant cover observed in the non-destructive and faster compared to the harvest study for each species, for H. aphyllum this model is technique (Flombaum and Sala 2007; Tarhouni et al. correct when the plant cover is 10% or less (Table 2). 2016). A continuous need for economical and accurate Table 2 Simple linear Species b R Model Range of observed regression parameters P value plant cover (%) assuming intercept is 0 to model the relationship A. leucoclada 14.68 0.809 \0.001 15–95 between the biomass (g/ A. halimus 11.68 0.828 \0.001 5–75 shrub) and DVCT— S. vermiculata 12.89 0.843 \0.001 0–40 estimated plant cover (%) of seven halophytes A. lentiformis 9.14 0.750 \0.001 0–50 A. canescens 7.74 0.744 \0.001 0–45 A. nummularia 8.13 0.740 \0.001 5–35 H. aphyllum 6.97 0.658 \0.001 0–10 Plant cover (%) 1348 Agroforest Syst (2018) 92:1341–1352 measurements of shrub biomass that can be repeated vertical habit) may be underestimated; while shrub with precision has led to the progressive development cover of species with open or horizontal growth are, of the DVCT. Thus, biomass can be estimated over most likely, more accurate. Combining other vari- time in the same location to assess temporal response ables, such as height or vertical shape, could improve patterns. Such monitoring would assist researchers the accuracy of biomass estimates. and ecologists in future assessments to better under- For meaningful interpretation, shrub data must be stand and test hypotheses about change or stability of converted from biomass per plant to biomass per unit ecosystems. Changes in shrub density and size would area, requiring additional estimates of shrub density. be easily detected. Digital records can be kept Vertical images taken from high altitude allow permanently to observe trends over long periods. researchers to calculate both shrub density and cover. DVCT has primarily been used in rangelands to Because natural shrublands are composed of shrub monitor and measure changes over time and to species at various ages and growth stages, a series of evaluate the effectiveness of restoration (Louhaichi images taken at random locations could be used to et al. 2013) and managerial techniques. For example, estimate density, cover and aboveground biomass. DVCT was used to monitor impact of herbicide on Shrubs could also be grouped into size classes. In any native forbs in the sagebrush/bunchgrass steppe case, a set of properly collected and dated, geograph- (Louhaichi et al. 2012). DVCT applications have been ically registered, digital images contain a wealth of proven to be a successful method for agronomic information about the shrubland ecosystem. researches in particular to estimate the ground cover of The effect of rainfall on arid and semi-arid range- the monocultures (Preuss et al. 2012). This current land vegetation has been well documented showing a study confirmed that the DVCT technique is valuable positive correlation (Le Houerou and Hoste 1977). and reliable for estimating forage cover and biomass The growth habit (life form) and plant size are factors of young shrubs. DVCT method provided a reasonable that affect plant cover as well as biomass production or estimation of canopy coverage for the shrub species net primary productivity (NPP) (Flombaum and Sala that were tested in the current study. A positive 2007; Hamada et al. 2011; Hernandez et al. 2011). In relationship between vegetation cover and DM fact the influence of species traits on NPP has been biomass with R of 0.658–0.843 was found. studied empirically in a number of cases (Saugier et al. Shrub size is important for rehabilitation projects 2001; Williams et al. 2005; Kerkhoff et al. 2006; because larger individuals generally produce more Byrne et al. 2011). During the study period the rainfall fodder for livestock and wildlife, more seed and was adequate and met the requirements of the studied improve the soil seed bank (Li et al. 2014). The shrub species. combination of vertical photography from a fixed Among the shrub species A. leucoclada had the height and digital image analysis allowed us to scale highest plant cover. This can be explained by the fact images so that either percent cover or horizontal that this shrub is mealy-canescent prostrate to erect surface area as m per plant could be calculated. In our stems, 30-100 cm. Furthermore, A. leucoclada had the experiment, shrubs were well spaced and had high highest biomass of the seven shrub species—it is a contrast with the soil color (Figs. 1b, c, 2b,c), so shrub-like biennial that requires only two years to confusion or overlap between species or individuals complete its full life-cycle (Sankary 1986). The other was not an issue. Our imagery provided a quick and shrubs, which are more widely spread throughout the Syrian steppe, are perennial and require more time for quantitative estimate of horizontal shrub size which could be combined with a measurement of shrub establishment and growth. A. lentiformis, A. nummu- height taken in the field with a meter stick. laria and A. canescens are erect medium sized It is worth to mention that while cover can be used Atriplex plants while S. vermiculata is a small, as a proxy to estimate biomass, it does have limitations greyish, much-branched shrub ranging in height from in terms of general accuracy especially with globular 25 to 100 cm. On the other hand, H. aphyllum is or compact shrubs. Therefore, researchers should perennial shrub or small 100–800 cm tall, many erect create regression equations for each shrub species to pendant branches. H. aphyllum had the lowest plant and each plant community. Biomass estimation for cover this could be due the slow growth in the first year shrub species with an erect architecture (upright of establishment compared to the other species. 123 Agroforest Syst (2018) 92:1341–1352 1349 Accuracy assessment of vegetation cover derived managerial decisions for six shrubs (R [ 0.85). from remote sensing data has been documented as a These equations permit aboveground biomass estima- valuable tool in evaluating the fitness of these data for tion from digital image analysis without the need for estimating biomass production (Congalton 1991). vegetative destruction, providing a quick and cost Accuracy assessment mostly generates one single efficient way to estimate carrying capacity for live- measure such as proportion of pixels correctly clas- stock and wildlife as well as to monitor shrubland sified. The contrast encountered between the color of condition and trend on a larger scale. soil and vegetation cover was so distinct. This factor Acknowledgements The authors acknowledge ICARDA, the facilitated the image processing which led to a high CGIAR Research Program on Livestock Agri-Food Systems overall accuracy. Other factors which should be (CRP Livestock) and Oregon State University (OSU), considered for reaching remarkable results include Department of Animal and Rangeland Sciences for their resolution of the digital camera. In this regards the support and funding. Special thanks go to Dr. Jay Angerer (Texas A & M University) for his critical review of this camera should be set on the finest resolution possible. manuscript. Timing during the day also may interfere with the quality of the output. In general, it is recommended to Open Access This article is distributed under the terms of the avoid early and late afternoon. Also make sure there is Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrest- no shadow in the ground where the image will be taken ricted use, distribution, and reproduction in any medium, pro- (Johnson et al. 2015). vided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Conclusion Various techniques have been developed to take indirect measurements of biomass, including modify- References ing previous methods, developing different measure- ments and creating new tools for measurements. It is Abdullah HM, Akiyama T, Shibayama M, Awaya Y (2011) difficult to explicitly declare one method superior to Estimation and validation of biomass of a mountainous another, because each was developed for a specific agroecosystem by means of sampling, spectral data and QuickBird satellite image. 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Published: Oct 1, 2018
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