Iris Publishers - World Journal of Agriculture and Soil Science (WJASS)

Non-Conventional Methods as a New Alternative for the Estimation of Terrestrial Biomass and Carbon Sequestered

Authored by Salem Issa

Introduction

Carbon sequestration is becoming an essential component in the fight against global warming. Forests act as large carbon pools where CO2 in the atmosphere is converted massively to biomass in the plant by photosynthesis process. Afforestation projects and land use conversion to forest (reforestation) can be used to earn carbon credits and reduce the carbon footprint, hence providing a longterm reduction in greenhouse gases (GHGs) levels through carbon sequestration [1]. This attitude has a growing interest among policymakers and governments [2]. Plantation cropping as a land use system has the potential to contribute to carbon stocks, maintain soil biodiversity and improve soil fertility [3]. Precise carbon stock estimation is a necessary step to define carbon emission mitigation strategies and programs at the local and regional level [4]. This kind of studies is necessary for a better understanding of the long-term behavior and drivers of carbon sequestration under different global climate change scenarios [5].

The total carbon stock in any terrestrial ecosystem is the sum of carbon in living biomass, dead biomass and soil [6]. Eggleston et al. [7] has listed five terrestrial ecosystem carbon pools involving biomass: above-ground biomass (AGB), below-ground biomass (BGB), litter, woody debris and soil organic matter [7]. Of these five, AGB is the most visible, dominant, dynamic and important pool of the terrestrial ecosystem. AGB contributes to atmospheric carbon fluxes to a much greater extent due to fire, logging, land use changes, etc., and so is of much greater interest. Therefore, it is necessary to keep monitoring it continuously not only a single date mapping. However, estimation of forest biomass rises scientific challenges to identifying feasible approaches to assess carbon at national level [8].

Traditional biomass assessment methods based on field measurements are the most accurate methods; however, they are difficult and unpractical to conduct over large areas and for broad-scale assessments [9]. These difficulties make monitoring activities more costly, time consuming, and labor intensive [10]. Recently, remote sensing (RS) procedures have been applied to natural resources management and biomass assessment. RS has the ability to obtain forest information over large areas with repetitive coverages, at reasonable cost and with acceptable accuracy. Moreover, the integration of remote sensing data into GIS models will benefit from both technologies; bringing ancillary and field data into the analysis and producing more reliable estimation of the AGB and carbon sequestered.

The aim of this study is twofold: (1) to review conventional methods for estimating forest biomass and carbon sequestered including destructive and non-destructive methods, and (2) to review non-conventional methods that use RS and GIS as innovative techniques applied to biomass studies and carbon assessment.

Conventional Methods

Background

These include direct (destructive) and indirect (nondestructive) methods. The direct method which is the most precise method for determining carbon biomass by destructively harvest all plants, partition each into various constituent components (e.g. stem, branches, leaves, flowers, fruits, roots) and subsequently determine the carbon content of the various components analytically OR calculated as a fraction of measured biomass (indirect) [9]. The destructive methods of biomass estimation are limited to a small area due to the destructive nature, time, expense and labor involved and sometime illegal especially for trees. In addition, these methods ultimately rely on ground measurement and can cause severe destruction to the forests as well as a risk of environmental deterioration [10,11]. The indirect methods include the estimation based on allometric equations (§ Allometric subsection) or through non-conventional methods using RS and GIS (§ non-conventional section).

Two routes for achieving sequestered carbon estimation: First, estimating soil organic carbon (SOC) which is part of soil organic matter (SOM). Second, estimating vegetation biomass which can be achieved by estimating the AGB and then deriving the remaining components; BGB, Litter and Debris, from the AGB as shown in (Table 1). The most common way for estimating SOM is through soil sampling at various layers and then, the SOC is estimated using total combustion method, as explained by Walkley & Black [12]. The content of SOC included in SOM may change depending on many factors (ecosystems, type of organic residues and land management, etc.). Many studies estimate SOC from SOM using the conventional factor of 1.724 (~ 58% of SOM). This figure is widely used and has appeared in many studies and published papers in the last century; while Brady & Weil [13] concluded that this value (58% of SOM) probably applies only to highly stabilized humus [13]. After his statistical analysis of 481 studies, Pribyl [14] found that conventional factor varies from 1.35 to 7.50 with a mean value of 2.20, concluding that any single-number conversion factor, universally applied, has the potential for serious error when used to estimate the carbon content of soils [14]. However, recent studies have accepted a generic quick, simple and inexpensive coefficient of 57% for measuring SOM as percent of SOM [15]. The main objective in developing allometric equations is to avoid destructing forests when estimating their biomass and provide a cost effective and environment-friendly option since it is done without harvesting [18]. In general, allometric equation is a statistical model to estimate the biomass of the trees using their biometrical characteristics which are non-destructive and simpler to measure. Therefore, nondestructive methods through allometric relationships are increasingly used. Such equations have also been proven to be fast, inexpensive, and more suitable for largescale estimation of forest carbon stocks [6]. Allometric models are commonly used in forest inventories and ecological studies [18]. The models relate biomass of an entire tree or individual tree components (e.g., stems, branches, leaves or roots) to one or more easily tree variables and dendrometric measures (e.g. height, diameter breast height or crown size) [19]. The proportions between height and diameter, between crown height and diameter, between biomass and diameter follow rules that are common to all trees that are grown under the same conditions; and become more useful in uniform forests or plantations with similar aged stands [20]. The selection of appropriate and robust models, therefore, have considerable influence on the accuracy of estimates obtained [21]. It is worth to mention that the goal of using allometric equations is estimating biomass without the need to cut trees, but the equations must be based on destructive sampling of vegetation somewhere before they can be applied generally and they still need to be validated which requires cutting and weighting some trees components [9]. The number of trees destructively sampled to build allometric equations differ from one study to another. Currently, there is no consensus on the number of trees that should be sampled, as this is often dependent on resource availability and permission to harvest trees [9]. For example, Russell [24] and Deans et al. [25] used 15 and 14 trees, while Brown et al. [22] and Khalid et al. [23] used only 8 and 10 trees, respectively; to build their allometric equations [22–25]. In their study of oil palm plantations of Benin forests, Aholoukpè et al. [26] used 25 palms from several ages and different genetic origins to build a species specific allometric equation [26]. However, recent study showed that small sample size yield biased allometric equations [27].

Generally, there is no specific procedure to build allometric equations yet there is a recommended guideline for documenting allometric equations [28]. Jara et al. [28] recommended that researchers should only report all the details in methods section of how they build up their equations. Furthermore, sampled trees should be randomly selected, regardless of health condition or degree of damage, because sampling only trees with fully intact structural characteristics will likely result in an equation that overestimates biomass for the general case. In this respect, data outliers should not be removed simply to improve model fit metrics [9]. There are many existent allometric equations. For example, the GlobeAllomeTree database contains over 706 equations from Europe, 2843 from North America and 1058 from Africa [29]. Some are volume equations, while others are biomass equations. According to the Brown & Lugo (1992) method, the biomass can be calculated from volume of the biomass per hectare (VOB/ha) by using a generalized volume model, wood density and a biomass expansion factor [21]. One of the limitations of volume equations is that it can only be applied to stem while biomass equations cover a wide range of vegetation components [30]. Allometric models can be developed for individual species or multiple species (mixture of species) to represent a community or bioregion and can be developed to cover specific sites, regional or pan-tropical scales [9,21]. The multi-species equations built because it is practically difficult to develop allometric equations for all species present in the ecosystem [31]. For example, in their work in tropics, Chave et al. [32] has shown that one hectare of tropical forest may shelter as many as 300 different tree species [32]. So, the multi-species allometric models offer methodological efficiencies for biomass estimation compared to those developed for individual species at specific locations. However, they have the potential to misrepresent local, species- or community-specific variations and anomalies, and therefore can lead to increased error and fail to capture both variations in forest type and diversity of the natural vegetation communities [21]. Therefore, tailored equations designed for specific species are needed for more accurate biomass estimation. Such equation is still conditioned by the ecological zone where the equation had been built. Hence weakening the estimation’s accuracy of the actual forest AGB when the equation is used in another area or region [33]. Due to the different characteristics of plant species from site to site, preexisting equations developed at locations that are different from the one in consideration may have limited applicability, even if the equation is species-specific [9,10]. In their review of allometric equations in Asia, Yuen et al., (2016) concluded that applying existing allometric equations out of convenience is potentially a key source of uncertainty in above- and below-ground carbon stock estimates in many Asian landscapes [9]. The selection of allometric equations can influence local, regional and global biomass estimates, therefore, there is an importance of site-specific equations for accurate estimation of biomass as generalized equations can overestimate AGB by 50% to 65% [11]. The locally developed models are expected to provide less uncertainty than generic equations [34]. Site and species specific allometric models should logically provide a greater level of accuracy at a given location to assist the assessment of biomass carbon sequestration and that make the locally built equation a better option to produce more accurate site-specific biomass estimation [11]. Finally, since the choice of the equations is the first critical step, there has been a rapid increase in efforts to develop locally appropriate equations [29].

The mathematical model commonly used for modeling aboveground biomass is based on the power function [9]. This was founded on the basis that the growth of a plant is characterized by the relation of proportionality between its total biomass and its size [35]. Biometric variables measured in plant species were considered as independent variables (diameter breast height, total height, crown variables, stem height, etc) and incorporated into a power function model [36]. The allometry based on power model have good reliability as indicated by high coefficient of determination indices [37]. Researchers involved in the development and application of biomass allometric equations are faced with many challenges. One of them is the choice between simple bivariate power-law (typical allometric) functions and models with multiple predictors [29]. Different variables (structural and non-structural) were considered when building biomass allometric equations. Most equations for above-ground biomass, or biomass of any component (stem, branch, leaves, other) use equations with diameter and/or height as independent variables. Other variables such as girth, basal area and crown dimensions have been used even less frequently— usually in special cases [9]. Using wood density, when it is available, as a predictor is considered as significantly improving the biomass prediction equation when dealing with multispecies dataset [32]. In their study to investigate the allometric equations in China, Cheng et al. [30] found that the most frequently used predictive variable in single-variable models is diameter at breast height (DBH), and in two-variable models are DBH and tree height while wood density and crown diameter are presented in more complicated models [30]. They found that diameter variables have a dominant proportion of 87.4% of the surveyed equations. However, DBH showed a weak correlation with biomass quantity in specific species, like palm for example [38,39]. Age as a predictor used in estimating the biomass in many studies as there is a linear correlation between age and biomass accumulation [1,40–42]. Many studies have highlighted the importance of tree height as predictor variable in the aboveground biomass equation [3,10,19,35]. Crown variables as indicators for biomass estimation became more interesting as a result of improving RS technologies. Furthermore, more than one allometric equation can be developed for each plant species. The reasons behind that can be: (1) difference in ecoregion sites that these equations developed for (Tropical or Amazonian forests ..etc), (2) the decision of the developers of the allometric equations and choosing of the suitable variable/s (height, DBH, trunk height, etc.) to work as input (independent variable) to the model, (3) the use of the allometric equations to cover either specific parts of the plant (AGB, crown biomass, trunk biomass, etc.) or specific age (young, mature, mixed, etc.), and (4) the selection of the mathematical equation form (power, linear, algorithmic, etc.). Finally, more recently, allometric equations have been used, coupled with remote sensing (RS) and field-based structural variables measurements [35,43]. For example, Cheng et al. [30] recommended to develop more equations with different field structural variables that can be linked to RS predictors [30]. Likewise, Jucker et al. [44] suggested in their review of allometric equations to develop a new generation of allometric equations that estimate biomass based on attributes which can be remotely sensed [44].

Non-Conventional Methods

Background

Non-conventional methods that used RS and related technologies such as GIS have proved to be practical and cost/time effective. During the preparation of this review, 156 articles related to AGB estimation by non-conventional methods, were covered (Figure 1). Three quarters of these used optical sensors (with different spatial resolutions) and the remaining quarter used active sensors (almost equally between RADAR and LiDAR sensors). For optical sensors, half of these studies used coarse spatial resolution (>100 meter) like MODIS and SPOT VEG sensors. Around one third of studies that used optical sensors estimated the biomass by moderate spatial resolution (~10-100 meter) like Landsat, IRS, and SPOT sensors while around 20% of the studies used fine spatial resolution data (submeter to 5 meter) like IKONOS, Quickbird and World View sensors. RS can provide data over large areas at a fraction of the cost associated with extensive field works and enables access to inaccessible places. Data from RS satellites are available at various scales, from local to global, and from several different platforms. There are also different types of data both Passive, such as optical and thermal remote sensing sensors, or Active, such as Radar and LiDAR sensors, with each has its own advantages and disadvantages [45]. On the other hand, GIS is a platform hosting spatial databases capable of assembling and integrating geographically referenced data, running spatial analysis, integrating various types and formats of spatial data, building spatial models enabling the prediction of future scenarios, and allowing for good management of forests. The estimation and modelling of carbon sequestered using RS and GIS methods is receiving an increasing attention and usage due to the multiple benefits they offer to scientists. To improve the accuracy of estimating AGB, integration of more than one sensor is becoming a trend as well as the integration with GIS-based approaches. More than 46 articles were reviewed that integrated both approaches. The trend is increasing in order to improve the accuracy of AGB estimates in plant species levels, instead of forests in general or mixed species.

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