Dynamics of carbon storage and status of standing vegetation in temperate coniferous forest ecosystem of north western Himalaya India

Abstract

Natural ecosystems, which operate as a sink, play an important role in determining the concentration of CO₂ in the atmosphere and have a large storage capacity, assisting in mitigation of problem that has a negative impact on the human population. Forests are one of the most important carbon sinks in the terrestrial ecosystem, with the best example being the Western Himalaya, where healthy and sustainable vegetation is prized. Standard methodology was adopted for assessing the different parameters of carbon related information to enumerate the status of carbon storage and its trend in sustaining the ecosystem of the area. The current research displays the annual increment and carbon dynamics in various vegetation components and levels. Trees, shrubs, and herbs help to fix atmospheric carbon in a variety of forms, including AGC, BGC, and TC. The concentration of carbon-fixing potential was measured on an annual and seasonal basis, with herbs having the highest mean annual increment, followed by shrubs and trees. Pinus wallichiana had the largest annual carbon stock change among trees, followed by Cedrus deodara, Picea smithiana, and Abies pindrow. P. wallichiana topped the increase percentage with 60.58%, followed by C. deodara 33.35%, P. smithiana 5.61%, and A. pindrow 0.45%. Litter was also investigated as a potential source of mitigation, with the best results observed during the autumn months. Natural coniferous forests provide a regulating ecological service in the region by maintaining carbon dioxide levels in the form of biomass, according to the study.

Introduction

Global warming is a consequence of an increase in the earth's surface temperature, which is expected to continue in the following decades due to human influence. This has become a major issue, with predictions of a temperature increase of 1.0–3.5 °C in the next 50–100 years (Rustad et al. 2001). These ramifications have prompted the release of a variety of gases that have a significant impact on climate change (Li et al. 2014). Carbon sequestration is the natural process of absorbing CO₂ from the atmosphere and storing it in diverse pools such as oceans, terrestrial ecosystems, and so on (Sundquist et al. 2008; Kirschbaum 2003). Carbon sequestration is now a topic of international debate in the context of climate change, and the idea of mitigating it through forest protection and management was first proposed in 1992 and a number of countries signed the UN Framework Convention on Climate Change. The third meeting of the United Nations Framework Convention on Climate Change (UNFCCC), which later became the “Kyoto Protocol,” was held in 1997 in Kyoto, Japan, with the main goal of reducing greenhouse gas emissions by 5% or more by 2012, and was extended until 2017 in the climate conference held in 2017 in the Durban Climate Conference (Cop-17). Even in the event of a COVID-19 pandemic, underdeveloped countries worked hard to tackle the problem, paying $100 million per year from 2021 to 2025. (Cop-25). Natural forests have a high proclivity for carbon sequestration (Joshi et al. 2013), making them the most important carbon sinks that influence atmospheric carbon dioxide concentrations (Zhou et al. 2006). Forests played a significant part in the research of climate change in terms of net carbon emission and global storage (Terakunpisut et al. 2007) because they absorb carbon dioxide through photosynthesis and are thus considered carbon sinks (Valentini et al. 2000; Kuimi and Jayakumar 2012; Dhruw et al. 2009). Litter components found on forest floors have a significant part in carbon sequestration, accounting for 8% of total potential (Heath et al. 2003; Chojnacky and Amacher 2006), and changes with seasons and altitude (Sheikh et al. 2017). The alarming rise in atmospheric carbon dioxide levels over the previous few decades has prompted research into numerous areas to determine the cause because of their mitigation and control capacity, the mitigation rate and forests are rising in importance.

With a land contribution of 65% in the entire region, the Western Himalayas are temperate evergreen forests with continual litter fall throughout the year, offering a home to a variety of natural resources (Kashyap et al. 2014). The rate of carbon sequestration in temperate forests of the central Himalaya has been calculated on an annual basis (Jina et al. 2008), with all types of vegetation storing carbon and having the ability to mitigate climate change (Sharma et al. 2011). The rate of carbon sequestration is influenced by the type of vegetation as well as the location of the area (Han et al. 2010). Coniferous forests have a higher carbon sequestration potential than deciduous forests throughout time (Shorabi et al. 2016; Wani and Qaisar 2014) and contribute 14% of carbon stock (Pan et al. 2011) with a variety of services, one of which is the regulating ecosystem service (Hicks et al. 2014). The current study aims to assess the impact of coniferous forests on carbon storage on an annual and seasonal basis, with a focus on parameters related to growth rate and change in carbon stock, because the Himalayas have the greatest potential for sequestering more and more carbon due to their growing nature. The goal was to calculate the present carbon pool and the state of the southern Himalaya of Anantnag district.

Materials and methods

Study area

The study was carried out at four sites of Anantnag district viz. Daksum (Site 1), Pahalgam (Site 2), Kokernag (Site 3) Kuthar (Site 4) and with coordinates, (Site 1 Latitude 33°34′43.1 N, Longitude 75°23′17.2E), (Site 2 Latitude 33°57′08.3 N, Longitude 75°18′43.4E), (Site 3 Latitude 33°34′43.1 N Longitude 75°23′17.2E) and (Site 4 Latitude 33°34′43.1 N Longitude 75°23′17.2E). Elevation of corresponding sites was 2370 m.a.s.l. for Site 1, 2115 m.a.s.l for Site 2, 2029 m.a.s.l Site 3 and 1986 m.a.s.l Site 4 respectively (Fig. 1). The climate in the Kashmir Himalayas is variable and fluctuates greatly throughout the year. The temperature in the valley fluctuates from 15 to 20 degrees in the spring (March–May), from 20 to 35 degrees in the summer (June–August), and from 35 to 10 degrees in the autumn (September–November).

Fig. 1.

Overall all observation of the study area with GPS instrument showing satellite view and sampling plots at different sites

Sampling techniques

The study region was sampled using a random sampling method, with 32 permanent sample plots (20 m × 20 m) laid out depending on several characteristics such as anthropogenic activities, protected or open type, and altitudinal variance. Living trees with a diameter of less than 10 cm were used to determine tree density and biomass. Before converting to carbon, the diameter at breast height (DBH) was used to estimate biomass. The measurements were taken with a simple measuring tape and then translated to diameter equivalents using the formula: Diameter (cm) = (circumference cm/π). Trees with many stems were cut apart at the base and photographed separately. 32 permanent quadrat of (5 m × 5 m) were taken for shrub estimation and similar number of (1 m × 1 m) were laid down for herbs.

Estimation of biomass

For tree biomass, a non-destructive method was chosen because it was easier than destructive procedures. The study region was a natural forest, and damaging methods were not desired due to authorization and other concerns. The biomass that is attainable for temperate forests was estimated using an allometric equation. Y = 34.4703 – 8.0671D + 0.6589 D2 (Anderson and Ingram 1989) D is the diameter at breast height in cm, while Y is the above-ground biomass in kg. 50% of biomass was taken as carbon in the current study (Brown 2001; Brown and Lugo 1982, Bhat and Ravindranath 2011, Dixon et al. 1994, Cannell and Milne 1995, Terakunpisut et al. 2007). The ground biomass (BGB) was calculated by taking 15% of the above-ground biomass (Mac Dicken 1997). The overall biomass of the research area included both above and below ground biomass. Carbon stock estimation was also done using a similar process.

Herb and litter carbon

Herbs were harvested at base level and weighed using a digital scale of ± 1 g error on site and recorded the fresh weight. 50 gm subsample of each sample was taken for moisture content determination However, if the weight was less than 50 gm, the entire sample was used to estimate the weight. The samples after collection were subjected to air drying followed by oven drying at 65–70° C for at least 48 h or till the samples reached their stable weight. Total dry weight of the samples was estimated using the equation (Hairiah et al. 2001). Similarly for litter, subsamples were taken from 96 litter traps established at the study site for estimation carbon.

$$\text{Total}\text{dry}\text{weight}\left(\text{kg}{\text{m}}^2\right)=\frac{\text{Total}\text{fresh}\text{weight}\left(\text{kg}\right)\times \text{Subsample}\text{dry}\text{weight}\left(\text{g}\right)}{\text{Subsample}\text{fresh}\text{weight}\left(\text{g}\right)\times \text{sample}\text{area}\left({\text{m}}^2\right)}$$

Carbon content was found 50% by oven dry weight (Schliesinger 1991).

Estimation of shrub carbon

Representative plants were selected from a 5*5-m plot and picked at the root in three sizes: larger, medium, and smaller, with weighing in between. Following the collection of fresh weight, harvested subsamples were air dried for a few days before being oven dried for 72 h at 120 °C. The AGB of each plot was estimated as $AGB=a\times WB+b\times WM+c\times WS$ (Xu et al. 2010). Where WB, WM and WS are dry weights of the big, middle and small samples respectively and a, b and c are the counted numbers of corresponding quadrats.

Carbon variation

Carbon increment is actually the change in growth rate of plants with accumulation of carbon at particular time interval and was calculated as $(\mathrm{\Delta }C=C_2-C_1)$ where, ΔC = rate of carbon, C₁ = Carbon in initial year, C₂ = Carbon in final year.

Relative mean increment calculated was $RMI=Vt2-Ut1/Ut1$ where RMI = relative mean increment, Vt₂ = final mean increment at particular time and Ut₁ = initial mean increment at particular time. Annual carbon change was calculated by difference method (Penman et al. 2003). $\mathrm{\Delta }C=(C{t}_2-C{t}_1)/(t2-t1)$. Where, Ct₂ = Carbon in final year, Ct₁ = Carbon in initial year, t₁ and t₂ are initial and final year respectively.

Mortality and recruitment rate

Mortality rate for the study site was calculated by Sheil et al. (1995) with following equation.

$M=[1-\{(N0-m)/N0\}1/\mathrm{\Delta }t]$ where, N₀ is tree density in first year, m = number of dead/cut down/ fell down trees after final year. ΔT = difference between initial time t₁ and final time t₂ of sampling time and recruitment rate was also calculated by Sheil et al. 1996 with following equations.

$R=[(N_0+r)/N_0\}1/\mathrm{\Delta }t-1]$ where, N₀ is tree density in first year of observation, r = number of trees recruited (planted, natural regeneration) during the period of observation.

Statistical analysis

Using sigma stat 3.5 software, the statistical analysis was done to assess for significant differences between various samples using analysis of variance (ANOVA). Following the Student–Newman–Keuls range test (SNK), descriptive analysis with Normality test and equal variance test was used to assess the difference at a significance level of P < 0.05.

Results

Density and carbon stock

For the measurement of carbon in a 2-year interval, significant variation was revealed at P < 0.05 using ANOVA after passing the normality and equal variance tests. During the years 2014–2016, tree density and above ground carbon were measured, with Pinus wallichiana dominating the region with 420.5 trees per hectare, followed by Cedrus deodara 242.7, Picea smithiana 32.03, and Abies pindrow 4.68 respectively. All of the species recorded for carbon estimation on an annual basis varied significantly with respect to different components (Table 1) and growth rate was pragmatic on an increment basis, with P. wallichiana averaging 8.56 ton/ha in 2 years, followed by C. deodara 3.42 tonnes, P. Smithiana 0.71 and A. Pindrow 0.05 ton/ha. When assessing the below-ground carbon of all the species, a similar trend was seen, with P. wallichiana showing an increase of 1.25 ton/ha, followed by C. deodara 0.52 ton/ha, P. smithiana 0.11 ton/ha, and A. pindrow 0.02 ton/ha. Total carbon increment varied significantly among the species, with the largest increase of 9.81 ton/ha in P. wallichiana, followed by 3.94 ton/ha in C. deodara, 0.82 ton/ha in P. smithiana, and 0.07 ton/ha in A. pindrow (Fig. 2). Different DBH class estimations were also thoroughly investigated, with the conclusion that the 20–40 cm class sequesters the most carbon from the atmosphere (Table 2).

Table 1.

Carbon (ton/ha) estimation at different time intervals in different species

Vegetation	AGC 2014	AGC 2016	BGC 2014	BGC 2016	TC 2014	TC 2016
P. wallichiana	196.24 ± 6.5	204.8 ± 8.2	29.34 ± 1.4	30.59 ± 1.6	225.58 ± 8.0	235.39 ± .4
C. deodara	61.36 ± 43.7	64.78 ± 6.2	9.17 ± 6.5	9.69 ± 6.9	70.53 ± 9.1	74.47 ± 53.1
P. smithiana	30.41 ± 7.6	31.12 ± 7.7	4.56 ± 0.9	4.67 ± 1.1	34.97 ± 8.7	35.79 ± 8.9
A. pindrow	0.47 ± 0.4	0.52 ± 0.5	0.20 ± 0.2	0.22 ± 0.2	0.67 ± 0.6	0.74 ± 0.7

The values expressed are mean standard error (p < 0.05) in comparison to different species

AGC above ground carbon, BGC below ground carbon, TC total carbon

Fig. 2.

Carbon increment and annual stock change in different components of tree species

Table 2.

Distribution of carbon in different species along with mortality and recruitment rate at different sites within different DBH classes

DBH Class	Site 1				Site 2				Site 3				Site 4
10–20 cm	PW	CD	PS	AP	PW	CD	PS	AP	PW	CD	PS	AP	PW	CD	PS	AP
Plots	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8	8
TD	269	50	31	25	3	–	–	–	3	75	–	–	122	369	–	–
AGC	9.78	3.70	0.30	0.25	0.14	–	–	–	0.06	0.30	–	–	2.09	7.21	–	–
BGC	1.47	0.56	0.04	0.04	0.02	–	–	–	0.01	0.04	–	–	0.31	1.08	–	–
TC	11.25	4.26	0.34	0.29	0.16	–	–	–	0.07	0.34	–	–	2.40	8.29	–	–
MR	0	0	0	0	0	–	–	–	0	0	–	–	0	0	–	–
RR	0	0	0	0	0	–	–	–	0	0	–	–	0	0	–	–
21–40 cm
TD	216	75	6	125	22	75	–	75	81	125	–	–	53	200	–	–
AGC	33.53	4.50	1.72	10.50	7.51	9.20	–	11.20	26.67	12.90	–	–	10.47	50.37	–	–
BGC	5.03	0.67	0.26	1.57	1.13	1.38	–	1.68	4	1.93	–	–	1.67	7.56	–	–
TC	38.56	5.17	1.98	12.07	8.63	10.58	–	12.88	30.67	14.83	–	–	12.35	57.93	–	–
MR	0	0	0	0	0	0	–	0	0	0	–	–	0	0	–	–
RR	0	0	0	0	0	0	–	0	0	0	–	–	0	0	–	–
41–60 cm
TD	31	100	44	–	337	100	–	25	275	25	–	–	50	122	–	–
AGC	18.71	10.20	23.4	–	235.54	12.50	–	9.11	157.23	6.20	–	–	31.65	74.94	–	–
BGC	2.81	1.53	3.51	–	35.39	1.87	–	1.36	23.58	0.93	–	–	4.75	11.24	–	–
TC	21.51	11.73	26.91	–	271.34	14.37	–	10.47	180.82	7.13	–	–	36.40	86.19	–	–
MR	0	0	0	–	0	0	0	0	0	0	–	–	0	0	–	–
RR	0	0	0	–	0	0	0	0	0	0	–	–	0	0	–	–
61–80 cm
TD	6	–	35	–	119	25	–	–	13	–	–	–	16	25	–	–
AGC	8.85	–	46.78	–	147.93	4.90	–	–	15.07	–	–	–	19.08	48.79	–	–
BGC	1.33	–	7.01	–	22.19	0.73	–	–	2.26	–	–	–	2.86	7.32	–	–
TC	10.18	–	53.8	–	170.12	5.63	–	–	17.34	–	–	–	21.94	56.11	–	–
MR	0	–	0	–	0	0	–	–	0	–	–	–	0	0	–	–
RR	0	–	0	–	0	0	–	–	0	–	–	–	0	0	–	–
81–100 cm
TD	6	–	19	–	6	–	–	–	6	–	–	–	6	6	–	–
AGC	12.96	–	43.83	–	12.29	–	–	–	6.58	–	–	–	16.60	27.87	–	–
BGC	1.94	–	06.57	–	1.84	–	–	–	0.99	–	–	–	2.49	4.18	–	–
TC	14.9	–	50.41	–	14.13	–	–	–	7.75	–	–	–	19.09	32.05	–	–
MR	0	–	0	–	0	–	–	–	0	–	–	–	0	0	–	–
RR	0	–	0	–	0	–	–	–	0	–	–	–	0	0	–	–

Results were estimated on average basis (ton/ha) for carbon estimation and density trees per ha

TD = tree density, AGC = above ground carbon, BGC below ground carbon, TC total carbon, MR mortality rate (%/year), RR recruitment rate (%/year). PW Pinus wallichiana, CD Cedrus deodara, PS Picea smithiana, AP Abies pindrow

Carbon stock in shrub vegetation

Among the many sites in the area, the study site noticed little undergrowth vegetation at Site 1 and Site 2. Between 2014 and 2016, the data revealed an increase in above ground carbon of 0.69 ton/ha, below ground carbon of 0.11 ton/ha, and an increase in total carbon of 0.80 ton/ha. (Table 3, Fig. 3).

Table 3.

Carbon content in shrub vegetation (ton/ha) (mean, ± within SD)

Vegetation	AGC 2014	AGC 2016	BGC 2014	BGC 2016	TC 2014	TC 2016
Shrub	2.56 ± 1.6	3.25 ± 2.1	0.38 ± 0.2	0.49 ± 0.3	2.94 ± 1.8	3.74 ± 2.4

Fig.3.

Carbon increment and stock change in different components of shrub vegetation

Carbon stock in forest floor vegetation

During the assessment of seasonal and annual carbon stocks in forest floor standing vegetation in the western Himalayas, significant observations at P < 0.05 were observed with normality and equal variance tests passing. The above-ground carbon stock was measured during different seasons of the year, including Autumn, spring, and summer, as well as seasonal and annual variations. Summer had the highest annual carbon increase of above ground standing forest floor (0.115 ton/ha), followed by autumn (0.032 ton/ha) and spring (0.012 ton/ha). Below ground, carbon increment followed the same trend. Summer had the highest increase in total carbon in herb plants, with 0.132 ton/ha (Table 4, Fig. 4).

Table 4.

Carbon Stock in forest floral (Herb) vegetation (ton/ha) (mean, ± within SD)

Components	Autumn		Spring		Summer
Year	2014	2015	2015	2016	2015	2016
AGC	0.095 ± 0.04	0.127 ± 0.05	0.190 ± 0.01	0.202 ± 0.02	0.132 ± 0.02	0.247 ± 0.02
BGC	0.014 ± 0.01	0.019 ± 0.02	0.028 ± 0.01	0.030 ± 0.01	0.020 ± 0.01	0.037 ± 0.01
TC	0.109 ± 0.02	0.149 ± 0.03	0.218 ± 0.02	0.232 ± 0.03	0.152 ± 0.01	0.284 ± 0.01

The values expressed are mean standard error (P < 0.005) in comparison to different seasons

Fig. 4.

Annual carbon increment and stock change in different components of herb vegetation

Annual carbon stock change

Carbon change is the potential for carbon fixation that contributes to the ecosystem services given by natural forests, and it was shown to differ considerably (P < 0.05) at the research location. Carbon stock change was evaluated on an annual basis among different components of tree species, with the highest value being 4.32 ton/ha/year for P. wallichiana, followed by 1.71 ton/ha/year, C. deodara, 0.35 ton/ha/year, P. smithiana, 0.35 ton/ha/year, P. smithiana and A. pindrow 0.025 ton/ha/year respectively. P. wallichiana had the largest carbon stock change of 0.65 ton/ha/year, followed by C. deodara 0.26 ton/ha/year, P. smithiana 0.05 ton/ha/year, and A. pindrow 0.004 ton/ha/year, respectively. During the 2-year period, similar findings were seen in annual carbon change of total carbon, with P. wallichiana dominating the area with 4.97 ton/ha/year, followed by C. deodara 1.97 ton/ha/year, P. smithiana 0.40 ton/ha/year, and A. pindrow 0.029 ton/ha/year, respectively (Fig. 2). The total carbon 0.397 ton/ha/year, which is the sum of the above ground and below ground carbon, was used to estimate the annual carbon stock change in shrub vegetation (Fig. 3). Summer had the biggest above-ground carbon stock change of herbs with 0.057 ton/ha/year, followed by autumn 0.016 ton/ha/year, and spring 0.006 ton/ha/year, respectively. Similarly, the change in below-ground and total carbon stocks was consistent with the findings (Fig. 4).

Assessment on proportion basis

In the research area, the density of different species was calculated as a percentage, with P. wallichiana dominating 60.07%, followed by C. deodara 34.67%, P. smithiana 4.57%, and A. pindrow 0.66%. P. wallichiana was found to have 66.23% AGC, 49.6% BGC, and 60.58% TC when the increment percentages of several tree characteristics were calculated. C. deodara, on the other hand, had 27.66% AGC, 20.63% BGC and 33.35% TC. P. smithiana demonstrated 5.77% increase in AGC, 28.96% increase in BGC, and 5.61% increase in TC. 0.32% AGC, 0.79% BGC, and 0.45% TC were found in A. pindrow. For carbon estimation, shrubs were evaluated and expressed as a percentage. Similarly, over the research period, the percentage of forest floor increment was taken into account. Summer is the most popular season (72.32%) across all components, followed by autumn (20.12%) and spring (7.54%).

Carbon variation of litter

Annual variation was detected in numerous components such as needles, branches, bark, and cones, and across all increments among different sites, Site 2 was determined to be the most variable, followed by Site 4, Site 1, and Site 3. All of the components were estimated separately, and the findings were collected on a seasonal basis, with autumn having the highest litter fall and anually Site 4 has the largest cone increase, followed by Site 2, Site 1, and Site 3. Needle carbon increment was highest at Site 2 and lowest at Site 3. Similarly, yearly branch and bark increase was highest at Site 2 and lowest at Site 3. (Fig. 5). Similar results were estimated in increments during the spring season, with no significant fluctuation at P < 0.05 (r² = 0.34). Among different sites of the study area, Site 1 showed highest value followed by Site 2, Site 3 and Site 4 respectively. On annual basis, the component wise increment for branch was highest at Site 1 and lowest at Sites 3 and 4. At Site 2, the needle have the highest, while the cone and bark both contributed the most (Fig. 6). During the summer, the combined carbon increment contribution of all components was found to be highest at Sites 2 and 4, followed by Sites 1 and 3. Among the various components, Site 2 had the most contribution for needle increment, similarly cone had the highest contribution at Site 1 and Site 2, branch had the biggest contribution at Site 4 and Site 2, and bark increment was only discovered at Site 4 with no increment at the other sites (Fig. 7).

Fig. 5.

Annual carbon increment in different components of litter during autumn season

Fig. 6.

Annual carbon increment in different components of litter during spring season

Fig. 7 .

Annual carbon increment in different components of litter during summer  season

Carbon pool, mortality and recruitment rate

The total yearly carbon pool in all components was evaluated, and the aggregate total of all the respective features was computed (Fig. 8) with trees contributing the most, followed by litter, shrubs, and herbs, in that order. Although the soil carbon pool adds to the total pool, the study only looked at standing vegetation, therefore SOC participation was overlooked. For the abovementioned criteria, parameters such as mortality and recruitment rate were taken into account for observing the present situation of the area by keeping a continuous record of density regardless of nature, size, girth, and so on. During the study period, both regeneration and plantation, as well as fall down due to natural calamity or artificial force, were examined, and neither a rise in the number of trees nor a decrease in the number of trees were seen (Table 2).

Fig. 8.

Total annual carbon pool of standing vegetation along with litter

Relative mean increment

When assessing relative mean increment of different vegetation types in the research area, significant variance p < 0.05 (r² = 0.99) was detected. Herbs on the forest floor are the most abundant, followed by shrubs and trees. Herbs were found to be the fastest growing vegetation in the research region, followed by shrubs and trees (Fig. 9).

Fig. 9.

Relative mean increment of different standing  vegetation types of the area

Discussion

Forests have produced biomass for a long time, but disturbance by anthropogenic activity has resulted in a dramatic decrease over time (Naburaas and Schelaas 2002). Through the Kyoto Protocol (UNFCCC 1997), the dynamics of these forests in carbon dioxide variation from the atmosphere compelled management of carbon sequestration in natural forests with sustained yield of various goods along carbon credit route from forest ecosystems (Karjalainen 1996; Thornley and Cannell 2000). Tree density is influenced by the dominance of a certain species in a given area as a result of geographic location, climatic circumstances, nutrient concentration, soil type, and altitudinal variation. Due to its dominant nature, Pinus wallichiana had the highest tree density and relationship with carbon mitigation potential in the western Himalayas (Bhat and Ravindranath 2011). Because of the short time frame to predict variation in tree density, there was no variation in tree density over the 2 years, but carbon change was extremely appealing (Aryal et al. 2014) over a 2-year period. Aubinet et al. 2002 conducted a 5-year study of carbon dioxide flux in mixed forests, which validates the current research. Ullah and Al-Amin (2012) conducted a similar investigation in which they calculated AGC, BGC, and TC and found that the results followed the same pattern as the current study. Pant and Tewari (2013), Jina et al. (2008), and Wani et al. (2015) all found a similar trend in natural forest carbon sequestration and mitigation potential. Wani et al. 2014 calculated yearly carbon increases in C. deodara species, which supported the current findings when monitored on an annual basis for the same species. Forest increment (Xia et al. 2009) directly indicates the growth rate with a positive link among all vegetation types in the forest ecosystem, demonstrating carbon fixation through biomass creation. The carbon capture potential was projected in different ranges using DBH classes (Piyaphongkul et al. 2011), which guides the relationship between age and carbon dynamics (Aryal et al. 2014). The carbon estimation within different DBH classes demonstrated the maximum fixation potential in 21–40 cm and 41–60 cm, and less contribution in 81–100 cm, with the rationale that the smaller number of species within such diameter classes contribute less to carbon fixation as time goes on (Aryal et al. 2014). As a result, the current observation predicts that young forests are key carbon sinks that can aid in carbon sequestration over a long period of time (Pan et al. 2011; Le Quere 2013).

The contribution of shrubs to carbon sequestration revealed an important role and can be considered an active pool in carbon mitigation, as demonstrated by Labata et al. 2012, who estimated the results from a 3-year study on a kg/ha basis, which were more or less similar to the results from a 2-year study when converted to ton/ha. Rawat and Giri (2013) investigated shrub carbon estimation as well, with results that were nearly identical to those of the current study. Zhao et al. (2012) investigated the above-ground biomass estimation of shrubs and found that biomass and carbon estimation of shrubs have an impact on ecosystems, which ties in with the current research. When compared to deciduous forests, it was discovered that deciduous forest shrubs store more carbon than temperate forest shrubs (Salunkhe et al. 2014). The explanation for the lack of undergrowth vegetation is likely due to the allelopathic influence of tree species growing next to shrub vegetation, which causes stunted growth. Various biological and anthropogenic factors are also to blame for the reduced amount of shrub vegetation in the study area. Carbon mitigation was directly connected with growth rate and other relevant parameters, while shrub vegetation gave less information about its contribution to carbon fixation. Due to anthropogenic demand during peak summer months for diverse uses such as fuel wood, grazing, tourism, and so on, shrub contribution was minimal.

Anthropogenic pressure was also applied to the herbs in the form of grazing and trash removal, which indirectly affects the growth of floral plants by supplying nutrients to the soil (Chen 2015; Marble et al. 2011). The herb estimation results of (Rawat and Giri 2013) are consistent with the current study, and mitigating potential varying with different seasons was a strong signal for grazing lands where only small plants are growing to participate in carbon sequestration (Ghosh and Mahanta 2014). Because the grass growing in the forest is neither grazed by animals nor taken out by locals, the maximum herb carbon was discovered in the autumn. Rawat (2013) came to the conclusion that forest floor vegetation has a large carbon sequestration potential. Ullah and Al-Amin (2012) Amin's research of all plant carbon components yielded similar results when compared to the current study. The development pattern of forest floor vegetation is also affected by environmental flux, with different seasons having varying environmental circumstances such as temperature, precipitation, soil moisture, nutrient availability, and so on. Herb carbon stock change was assessed on an annual and seasonal basis, with yearly carbon stock change varying significantly.

For the explanation of ecological variables and to counterbalance the climatic circumstances of the area, carbon stock change on an annual basis demonstrated higher fixation potential of standing vegetation in distinct components with greatest density of P. wallichiana. The projected annual carbon stock change of surviving trees (Bhat and Ravindranath 2011) is consistent with the findings of the current study. The proportional assessment gives an indication of the relevance of particular species in the research region, which reflects the same species' potential to regulate environmental circumstances (Baishya and Barik 2011; Bramyrd 1979). Due to its density and adaptability, P. wallichiana dominated with the highest percentage obtained. The largest annual increment was detected in young plants with a diameter at breast height of up to 30 cm, indicating that young aged plants thriving in the area possessed dominance power (Liu et al. 2003). The mean annual increment reflected the growth rate of the study site (Pandey et al. 2017), with relative mean increment indicating the species' ability to grow quickly. The results showed that herbs are the fastest growing vegetation, with the highest relative mean increment value, while shrubs and trees have the lowest, possibly due to physiological processes and the nature of vegetation. Litter variation was directly related to environmental and anthropogenic activity (Kavvadias et al. 2001; Pedersen and Hansen 1999). Different components of litter were calculated on a seasonal basis (Sheikh et al. 2017) and were found to be related to vegetation, growth rate, and seasons (Ogunyebi et al. 2012), with the highest levels occurring during the autumn season. Litter not only serves to reduce carbon dioxide emissions, but it also decomposes and aids in the regeneration of biodiversity. The lowest carbon content observed during the spring season is due to the growth of new components on trees that replace the existing old ones. As a result, it takes time for various components to mature and fall down, which happens in the summer and autumn. (Ogunyebi et al. 2012) analysed seasonal variation and found similar results, with the highest litter fall occurring during the autumn season. According to Rawat (2012), summer has the biggest litter fall, followed by spring and winter, which contradicts the current study. The reason for this could be the nature of the vegetation, geographical position, altitudinal variance, and climate of the area.

The parameters of mortality and recruitment rates are indicators of the area's diversity indices, with which the estimation of various parameters has a direct relationship (Bhat and Ravindranath 2011). The study area's mortality and recruitment rates were both neutral, with no increase or decrease in the number of species counted for the density in the first year of sampling. These variables have a significant impact on carbon mitigation with a positive relationship (Phillips 1996), as well as the impact of internal climatic conditions on the habitat of numerous organisms (Nascimento et al. 1999). Various workers have assigned rates to various girth classes of plants (Turner 2001), some of whom are opposed to the viewpoints (Swaine 1987). Maritenz-Ramos and Alvarez-Buylla (1998) predicted a high mortality rate in more diameter classes due to susceptibility to natural environmental calamities. and others linked less diameter classed plants with physico-chemical parameters and argued their relationship with mortality (Lutz and Harplen 2006). The current findings can be discussed and linked to species adaptability and availability of nutrients for long-term growth, in addition to effective management, which not only protects the old ones but also allows young ones to regenerate and contribute to density, resulting in an impact on carbon levels.

Conclusion

Because of its young and dense vegetation, the Western Himalaya had a strong potential to store atmospheric carbon and showed signs of moderating environmental carbon dioxide. The forests had negligible mortality rate and a significant annual mean carbon addition, indicating long-term carbon storing potential. All of the components' annual stock changes were easily contributing to carbon dioxide cleanup in the environment. Shrubs and herbs also have a positive impact on overall carbon sequestration potential. Continuous litter fall throughout the year not only serves to maintain the soil ecosystem through decomposition, but it also aids in carbon dioxide mitigation. Only anthropogenic disturbance is a future issue. Local encroachment and tree felling by official or unauthorised means should not be permitted. The forest service should take a great interest in the management of these natural resources so that future generations can benefit from this ecosystem-regulating service.