Carbon Stock Estimation of Selected Subtropical Broad-Leaved Evergreen Scrub Forest

Abstract

This research estimates the carbon stock of the subtropical broad-leaved evergreen scrub forest of Lehtrar, a revenue estate of Kotli Sattian, Rawalpindi, Punjab, Pakistan. A total of six nested co-centric plots of 17.84 m² each were laid out in the forest, having two sub-plots of 5.64 m² and 1 m² each, for shrubs and litter, respectively. Stem density, tree height, diameter at breast height (DBH), total tree biomass, and total carbon stock were calculated. In each plot, parameters like latitude, longitude, aspect, slope, elevation, tree count, etc., were catalogued. The carbon value was calculated in pools such as aboveground biomass (AGB), belowground biomass (BGB), litter, shrubs, etc. The tree height was measured using Abney’s level and the diameter at breast height (DBH) with diameter tape, while factors such as volume, shrub mass, litter mass, total tree biomass, and total carbon stock were calculated by using standard formulas. Results showed Olea ferrugineae to be the most abundant tree species in the study area, followed by Acacia modesta. The total average DBH and height were calculated as 17.03 and 16.79, respectively, with the species Dalbergia sissoo having the greatest DBH value. The mean carbon stock came out to be 47.75 tons/ha, with plot number 3 having the highest value of carbon stock, owing to the greatest stem count. The results of the study were significant and reflected a rich stem density, rich biomass, and an adequate carbon stocking capacity. The scrub forests of the study area, being important carbon sinks, are prone to deforestation and forest degradation activities that need to be controlled by using proper forest management practices to keep their carbon sequestration ability intact, as suggested under various reducing emissions from deforestation and forest degradation (REDD initiatives of UNFCCC.

1. Introduction

Forests being one-third of the earth’s land surface [1] contribute far-reaching benefits to mankind as an indispensable part of the atmosphere, such as their carbon sequestration capacity [2] and other long-term ecosystem services [3]. Forests cover almost 31 percent of the global land area [4] and serve a variety of cultural, ecological, and economic roles, providing a link between the natural world and human civilization [5]. Of all the ecosystem benefits provided by forests, carbon sequestration is the most vital function of forest covers. Forest ecosystems are the world’s largest source of carbon sequestration and a cost-effective method of reducing GHG emissions. Because of their huge volume and long-lived storage, such as wood, tree trunks, roots, leaves, and the soil in which the plant exists, trees are capable of storing most of the atmospheric carbon unless they are decomposed or burned to release CO₂ back into the atmosphere [6]. Regardless of the numerous benefits supplied by forests, land-use change activities have had a significant impact on them, resulting in forest loss, soil degradation, and current ecological shifts. According to the 2022 edition of The State of the World’s Forests (SOFO), the world has lost 420 million hectares, or around 10.34% of its total forest area, in the last 30 years [7]. An alarming decline in the world’s forests has occurred owing to ever-augmenting anthropogenic activities, such as deforestation and forest degradation. Besides providing plenty of benefits, forests act as sinks as well as the source of CO₂; the former by capturing it in their biomass and the latter by releasing it in the form of greenhouse gases (GHG) due to land-use- and land-use-change activities. Deforestation leads to approximately one-fifth of yearly carbon emissions [8,9]. Forests can also play a vital role in cutting global GHG emissions by conserving the existing carbon stocks. Carbon stock is stored in carbon pools named as aboveground biomass, belowground biomass, deadwood, organic litter, and soil organic carbon, with aboveground biomass having the greatest proportion of carbon stock, i.e., 15–30% [10]. Over 650 billion tons of carbon is stored in the world’s forests, including 45 percent in the soil, 44 percent in biomass, and 11 percent in litter and dead wood [11]. Consequently, forests are essential for sustaining the global climate system, managing global carbon balance, and limiting the growth of atmospheric greenhouse gas concentrations [12]. Detailed carbon-stock inventories are therefore needed to provide accurate estimates of increase or decrease in carbon stocks as part of the REDD+ initiative. Pakistan, being a signatory to the United Nations Framework Convention on Climate Change (UNFCCC), has been working on REDD+ preparatory initiatives comprising development of the National REDD+ strategy, development of the National Forest Monitoring System, and development of the Social and Environmental Safeguard System including reviewing and adjustment of policy and legal instruments [13]. Other initiatives of Pakistan include the Green Pakistan Programme, Billion Trees Tsunami Afforestation Project, South Punjab Forest Company (SPFC), Reclamation and Development of Forest Areas in Punjab, and Social Forestry to Increase Tree Cover on Farmlands (Kissan Package) [14]. REDD+ (Reducing Emissions from Deforestation and Forest Degradation) is a set of policies and incentives designed under the United Nations Framework Convention on Climate Change to encourage forest conservation and restoration by assigning a value to forests’ carbon-sequestration capability. REDD⁺ intends to make forest restoration and conservation more profitable than deforestation and other land uses for carbon storage [15]. Boosting forest carbon storage has been a key component of the Paris Agreement’s REDD+ plan [16]. All these initiatives are considered utterly imperative for climate change-related policy formulation, decision making, and climate change mitigation initiatives [17]. Regardless of the several benefits provided by the forests, they are massively affected by land-use change activities resulting in loss of forests, degradation of soils [18], and ecological changes [19]. In Pakistan, forests occupy 4.78 million hectares, and CO₂ emissions from forests were estimated to be 10.39 million tons in 2015, with projections of 29 million tons by 2030 [14,20].

Pakistan’s forests are also under the extreme threat of deforestation. To conserve these precious forests, REDD+ has been an effective mechanism, adopted under the UNFCCC and tested by several member states [4]. However, for adopting effective REDD+ mechanisms, robust methods for assessing carbon stocks and establishing reliable and verifiable baselines are needed for various forest types in Pakistan. Subtropical broad-leaved evergreen scrub forests keep great ecosystem vitality; however, relatively few studies have been conducted on Pakistan’s scrub forests to date, which cover 635,497 acres of the area [21]. The present study was, therefore, conducted in compartment number-47 of the scrub forest of Lehtrar, Kotli Sattian, located in the periphery of district Rawalpindi. The study aims to gauge the carbon stock of the study area, specifically to estimate the aboveground and belowground biomass, to compare the average carbon stock values in all the pools (i.e., aboveground, belowground, litter, and shrubs), and to compare the carbon stock in each pool with the factors such as elevation, aspect, slope, etc.

2. Materials and Methods

2.1. Study Area

Lehtrar, a rural community [22], is located in the district of Rawalpindi, Punjab, Pakistan and can be reached within one and half-hour’s drive from the central city [23].

Lehtrar area is located between latitude 33°42′17′′ N and longitude73°26′21′′ E [24,25,26] while, the area under study, i.e., compartment 47, is situated between latitude 33°40′13.88″ N to 33°40′00″ and longitude 73°21′57′′ E to 73°21′00″, having an elevation of 690 m above sea level and a forest area of 497 acres. The climate of the area under discussion is quite mild, with cold winters and mild summers. A satellite-generated map of the study area is given in Figure 1 that shows the adjoining neighboring areas and the geographic location of the study area.

The dominant tree species of the study area are Acacia modesta (Phulai), Olea ferrugineae (Kahu), and Dalbergia sissoo (Sheesham). Carrisa opaca Carissa opaca (Bekhal) and Dodonea viscosa (Sanatha) are the dominant shrub species [28].

The area is predominantly hilly with rich forest cover. A terrain map of the area is shown in Figure 2.

2.2. Sample Plots and Sampling Intensity

For this study, a total of six nested co-centric circular sample plots of 17.84 m² area each were laid out in the forest for carrying out the forest carbon inventory. All the trees inside the circular plots were enumerated. The plots were laid using random sampling technique. The concentric plots were chosen because no boundary could be established in them; therefore, it was easy to lay them down, and they were also prone to lesser inaccuracies [30,31].

2.3. Size and Shape of Sample Plots

To find the carbon stock, each of the co-centric nested plots was further divided into three sub-units, having sizes of 17.84 m, 5.64 m² and 1 m², respectively. The shape of these plots are shown as in Figure 3.

The details of each sample plot, explaining the factors such as geographical coordinates, elevation, aspect, slope, and elevation, have been cataloged in

Table 1. Detailed information of all sampling plots of the study site.

Plot Nos.	Latitude (X)	Longitude (Y)	Aspect (Degrees)	Slope (Degrees)	Elevation (Meters above Sea Level)
1	33°40′00″	73°21′00″	NW/312.28	1.7	687
2	33°43′52″	73°24′00″	NW/309.36	1.9	690
3	33°56′59.87″	73°36′00″	NE/47.28	2.6	691.76
4	33°59′00″	73°43′00″	NE/46.32	0.97	695
5	33°41′55.8″	73°22.8′00″	N/1.28	2.36	695.8
6	33°54′00″	73°35.8′00″	NW/326.25	3.45	697

.

2.4. Trees Identification

Identification of the tree species was made with the help of the local people and forest guards affiliated with the forest department. The species density and total dominance of tree species in each plot was also found.

2.5. Basic Wood Density (BWD)

For this research study, the default basic wood density values for all the species, as given in

Table 2. Common and botanical names of prominent trees species of the study area along with their BWDs [32].

S.No	Name of Species	BWD (kg/m3)
01	Acacia modesta (phulai)	0.96
02	Olea ferruginaea/Olea Cuspidata (kahu)	1.125
03	Dalbergia sissoo (sheesham)	0.93
04	Prinsepia utilis (bekhal)	0.40

were applied.

2.6. Measurements in the Sample Plot

In order to conduct the carbon inventory of each respective pool, field measurements were carried out as tabulated in

Table 3. Various procedures and calculations adopted for conducting the carbon stock inventory in the forest field.

Methods and Calculations for Measuring the AGB as Well as Average Carbon Stock
AGB—Trees Biomass calculation of trees involved the measurement of tree height, diameter at breast height (DBH) and tree volume (V).	Height:	In order to reach the height measurements of the trees present in each plot the instrument called Abney’s level was used.
	DBH:	In order to carry out the carbon stock measurements of the trees, the diameter at breast height (DBH) was measured using a diameter tape.
	Volume:	The volume of trees was measured using the following formula: V(m3) = (∏/4 × d2) × h × f Here: h = height of tree, d2 = square of the DBH, and f = form factor (Phillips, 1994) [31].
Shrubs	The biomass and carbon stock estimates of the shrubs were carried out by undergoing destructive sampling, i.e., all the shrubs present in the plot of 5.64 m were cut. These shrubs were sent to the laboratory for dry weight measurements after passing them through oven-drying. The dry weight was considered as the biomass of these shrubs in grams (g) (Oliver et al., 2009) [33].
Litter	This was done by collecting all the twigs, fallen leaves, etc., found in the smaller plot of 1 m2. These collected samples were put in bags and sent for dry weight measurements.
Total AGB/ total tree biomass:	The stem biomass was obtained after multiplying the stem volume with specific wood density of particular tree species: Stem biomass = SV × WD While the total crown biomass or total tree biomass was obtained by multiplying the resultant estimate with BEF. The following formula was used: Total tree biomass = SV × WD × BEF where SV= stem volume (m3) and WD= basic wood density (kg/m3). WD values were taken from the available literature (Haripriya, 2000 [32]).
Total Carbon Stock	It is generally considered that about half of the dry biomass consists of carbon. As per the Kyoto Protocol of the United Nations Framework Convention on Climate Change, to reduce greenhouse gas emissions, it is important to take into account the biomass sinks (Thenkabail et al., 2004 [34]). These sinks determine the hotspots for carbon dioxide storage which can help in conducting CO2 emissions from forests inventory. Moreover, the threat of forest fires can also be monitored. Thus, the dry biomass can be converted to carbon stock by multiplying it with the carbon content conversion factor having a default value of 0.47. In the current study, the carbon stock was calculated by assuming that the carbon content is 47 % of total biomass, as described by IPCC (IPCC, 2003 [35]). Total carbon stocks (tons/ha) = biomass (tons/ha) × 0.47 Belowground biomass was estimated by multiplying the aboveground biomass with the carbon content conversion factor having a default value of 0.26. Thus, it supports the study of Thenkabail et al., 2004 [34] and Aseefa et al., 2013 [9].

, in detail:

3. Results and Discussion

Characteristics such as the number, mean height, diameters, etc., of the studied sites are part of every forest inventory study, revealing the stem density in all six of the studied plots as 54, 49, 57, 48, 51, and 53, respectively. This stem density depicted satisfactory results and showed that owing to an adequate number of trees per plot, this forest compartment is well stocked. Following are the results discussed in detail:

3.1. Stem Density and Abundance of Trees

The main tree species found in the area are Acacia modesta, Olea ferrugineae (Olea cuspidata) i.e., Kahu), Dalbergia sissoo (Sheesham), and Prinsepia utilis (Bekhal), alongside species such as Adhatoda vasica (Baikar) and Camellia hance, which are also found in rare numbers, while the most common shrub species is Dodenaea viscosa (Snatha).

The stem density of the total study area ranged from an average of 52 trees per plot, with the highest tree count recorded in plot number 3 comprising 57 trees in total. The respective stem densities are presented in

Table 4. The stem density or the number of trees per plot in the study area.

Plot Number	Number of Trees
1	54
2	49
3	57
4	48
5	51
6	53

.

The abundance of different tree species found in the study area is graphically represented in Figure 4 to show the relative number of each species, and to depict the species found in the maximum and the minimum numbers, in light of the field observations carried out. The species found in the highest quantity were Olea ferrugineae (Kahu) followed by Acacia modesta (Phulai).

3.2. Mean Height and Mean Diameter at Breast Height (DBH)

The mean heights and diameters of the trees in each plot were computed to have an idea of the size and age of the trees (

Table 5. The mean height and mean DBH of the trees found in all six of the sampling points.

Plot Nos.	Mean Heights (m)	Mean DBHs (cm)
1	21.7	25.4
2	18.7	26.3
3	18.315	10.0
4	16.3	12.3
5	15.8	13.1
6	9.93588	15.1
Mean	16.79	17.03

) The data thus obtained revealed that the average height and average diameter of trees found in all of the six plots was 16.79 m and 17.03 cm, respectively, while species-wise DBH was recorded for every individual prominent species. The mean DBH of Acacia modesta, Olea ferruginaea, Carissa opaca, Adhatoda vasica and Dalbergia sissoo was 19, 17.25, 7.69, 15.2, and 20.5, respectively. The results indicated that the Dalbergia sissoo (Sheesham) had the highest DBH, while Adhatoda vasica (Baikar) had the lowest DBH.

3.3. Biomass and Carbon Stocks

The average carbon stock of the whole studied forest compartment was 47.75 tons/ha.

3.4. Pool-Wise Carbon Stock

Average carbon in every carbon pool was compared. The graphical representation in Figure 5 depicts that the carbon content was highest in the AGB followed by shrubs and the BGB, while in the majority of the plots the value was the lowest in the litter. This is owing to a relatively small number of dead wood and leaf litter found in each plot. The highest value of total average carbon stock density was found in plot number 3. This is because it had the highest number of trees and greater stem sizes as compared to other plots (Figure 5).

3.5. Statistical Analysis

Pearson’s correlation coefficient was used to find out any correlation between carbon stock in each pool and the features such as elevation and slope (

Table 6. Correlation among all the studied variables.

	Slope	Elevation	AGB	Litter	Shrubs	Total AGB	Total BGB	Total Biomass	Total AGC	Total BGC
Elevation	0.39
AGB	0.16	0.24
Litter	0.44	0.41	−0.40
Shrubs	−0.15	−0.71	−0.34	−0.55
Total AGB	0.05	−0.72	0.27	−0.52	0.68
Total BGB	−0.28	0.25	−0.43	0.51	−0.21	−0.36
Total biomass	0.78	0.61	0.38	0.00	−0.07	−0.04	−0.33
Total AGC	0.67	0.56	0.44	−0.18	−0.01	−0.01	−0.42	0.98
Total BGC	0.39	−0.08	−0.33	0.78	−0.13	0.11	0.49	−0.17	−0.34
Total MCS	−0.03	−0.79	−0.08	−0.62	0.86	0.74	−0.64	−0.05	0.04	−0.24

Key: AGB—aboveground biomass; BGB—belowground biomass; AGC—aboveground carbon; BGC—belowground carbon; MCS—mean carbon stock.

). A strong positive correlation was found between total biomass and total above ground carbon, total mean carbon stock and shrubs, and total biomass and elevation, showing that as we go towards the upper altitude, the carbon stocks in each carbon pool keep on increasing. The reason is the presence of lesser human disturbances as we move higher up. Our results contradict the findings of Sheikh et al. (2009) [28], according to whom a negative correlation exists between the two. However, the results of the present study are in conformation with the findings of the studies Singh et al., 2011 [36]; Sims et al., 1986 [37]; and Tate 1992 [38], respectively.

No significant correlation was found between carbon stock and aspect and carbon stock and slope. This judgment is against the results concluded by Sharma et al. (2011) [39], who stressed a positive correlation between carbon stock and aspect, while assessing carbon in seven different forests which were mostly temperate.

The total average carbon stock of the study area. i.e., the subtropical broad-leaved evergreen scrub forest of Lehtrar, came out to be 47.75 tons/ha as compared with the findings of the study conducted by Nizami (2012) [40] in an unmanaged sub-tropical broad-leaved evergreen forests of Kherimurat and Sohawa. The mean carbon stock was calculated to be 31.18 ± 1.73 tons/ha in Kherimurat, while it was 24.36 ± 1.59 tons/ha in Sohawa (Figure 6). The value obtained in the present study area was significantly higher as compared to that in Kherimurat and Sohawa, whose total average carbon stock was determined by Nizami 2012 [40]. This depicts the great carbon sequestration potential of the present study area.

The reason for this significantly high amount of carbon stock is the smaller area of study, smaller plots, and thus lesser disturbances recorded in the study area; if the study was on a larger scale, greater interruptions might have been witnessed. This is also mainly because of the higher number of trees, good canopy cover, less grazing pressure in the area, limited human access, fewer anthropogenic activities, and proper regulations and checks on part of the local forest authorities. The good canopy cover could also be due to the proper rainfall and availability of nutrients in the study area.

The greater value of carbon content found in this study shows that the scrub forest has great potential for carbon sequestration and storage, and thus can be considered an important forest type to be conserved and improved as part of the climate change mitigation efforts in Pakistan. Lehtrar sub-tropical scrub forest is of paramount significance in this regard, as it stores a suitable amount of carbon, and also seems to be uninterrupted by almost any sort of human-caused interruptions, as the canopy cover or canopy density was visually observed to be nearly 85–90%.

4. Conclusions

Lehtrar region (Rawalpindi) displays a great carbon storage capacity with an average carbon stock of 47.75 tons/ha in all the carbon pools except soil carbon. The high carbon stock presents an enormous potential of these scrub forests for REDD+ interventions in Pakistan. The study found good canopy cover and tree density, coupled with good management and conservation efforts, resulting in the higher carbon content of the scrub forests. Moreover, improved conservation efforts and the reduction in anthropogenic pressures may result in reducing the drivers of deforestation and forest degradation, and can contribute to the enhancement of carbon stock in these forests, along with improvements of other ecological and environmental benefits. Further studies covering larger areas and including soil carbon pools need to be carried out to assess the exact amount of carbon stock being stored in these forests. Moreover, the impact of different management practices on carbon stock also needs to be investigated.